计算机类 | SCI期刊专刊截稿信息4条

计算机体系结构,并行与分布式计算

Computer

Curricular Foundations of Cybersecurity–CALL FOR PAPERS

全文截稿: 2018-08-01

影响因子: 1.755

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：硬件 - 3区

  • 小类 : 计算机：软件工程 - 3区

网址: https://www.computer.org/computer-magazine/

Because of rapidly increasing workforce demand, cybersecurity has become a very popular topic for professional training. It is considerably less pervasive within the mainstream of the undergraduate university, where the primary objective is education rather than training. It is the compelling need for educated cybersecurity professionals that drives universities to offer educational programs in computing, business, and criminal justice that include cybersecurity content. These programs are of two types: either involving integration of cybersecurity content into existing programs or offering new degree programs entitled “cybersecurity” or something similar.

Both types of cybersecurity education are valid and have the potential to positively address the workforce demand. Regarding the integration of cybersecurity content within existing degree programs, the DHS/NSA Centers for Academic Excellence (CAE) have already had significant impact for approximately 20 years. Furthermore, Cybersecurity Curricula 2017 (CSEC2017) was recently released, providing additional guidance in integrating cybersecurity content into existing programs. Regarding the offering of new cybersecurity programs, it is unclear to what extent CAE and CSEC2017 really help in defining parameters for undergraduate programs that are simply identified as “cybersecurity,” as their focus is on the original program.

If higher education is to assume a role in educating cybersecurity professionals, well-understood curricular foundations are needed for institutions to use as models for designing and assessing programs. This includes models for integrating cybersecurity into existing disciplines and for viewing cybersecurity as its own discipline. These models must also accommodate the broad landscape that cybersecurity covers, and as such a one-size-fits-all approach is insufficient. Degree program variants of cybersecurity must be defined on overall educational objectives, and ultimately, a standard nomenclature of degree types and names needs to be developed—much like the NICE framework that provides a nomenclature of job categories on the workforce side of the equation. These program variants would share a common core, with different specializations based on discipline.

In this special issue of Computer, the guest editors seek to cover different approaches to creating effective curricular foundations for cybersecurity. Specific topics include but are not limited to the following:
- Assessment of current practices in cybersecurity undergraduate education;
- Historical surveys and evaluative comparisons among different curriculum models, as well as proposals to unify or standardize existing terminology and/or curriculum models;
- Guidance for developing standalone cybersecurity programs, or for developing programs that conform to CSEC2017 or CAE;
- Non-computing cybersecurity curricular foundations within programs; and
- Differentiating between cybersecurity curricular foundations for the high school, 2- and 4-year college, and graduate levels.

计算机体系结构,并行与分布式计算

Future Generation Computer Systems

Special Issue on Advanced Artificial Intelligence Technologies for Multimedia Communications and Processing in Internet of Vehicles

全文截稿: 2018-08-01

影响因子: 3.997

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：理论方法 - 2区

网址: http://www.journals.elsevier.com/future-generation-computer-systems/

Internet of Vehicles (IoVs) is expected to analyze and utilize the various information, especially multimedia inside and outside vehicles itself through wireless communication techniques. Specifically, through the vehicle-to-vehicle (V2V), infrastructure-to-vehicle (I2V) and vehicular-to-infrastructure (V2I) communications, which are the foundation and key support technologies determining the overall performance of vehicular networks, road safety and traffic efficiency are significantly improved assisted advanced artificial intelligence (AI).

To address the advanced demands which cannot be met by the traditional multimedia processing and communication technologies, such as high throughput, high mobility, low latency, heterogeneity and scalability, etc., innovative AI-based multimedia technologies have been applied to IoVs for raising the user experience through developing high-performance multimedia communications system and providing intelligent services based on multimedia processing and analytics. Especially, novel vehicular networks assisted by cognitive computing, data mining, machine learning and other advanced techniques are available to provide entertainment, navigation, location-based services, etc., and even significantly improve the driving experience and effectively guarantee traffic safety. For example, through advanced multimedia technologies, it is available to monitor the driver’s physiological and psychological state for avoiding traffic accidents caused by fatigued driving and mood swings.

This special issue focuses on the crossroads among scientists, industry practitioners, and researchers from the domains in the multimedia communications, mobile computing, artificial intelligence, multimedia processing, decision making, etc. We envision to provide a platform for researchers to further explore innovative AI technologies combined with the domain knowledge of multimedia IoVs from both theoretical and practical perspectives. We invite researchers from academia, industry, and government to discuss challenging ideas, novel research contributions, demonstration results, and standardization efforts on multimedia communications and processing in IoVs.

The interest of this SI includes the following topics, but is not limited to:

• Smart multimedia sensors and sensing technologies for IoVs

• Intra/Inter-vehicle multimedia communications in IoVs

• Fog/Edge computing for multimedia processing in IoVs

• Big multimedia data analysis/mining for driver assistance and autonomous driving

• QoS and QoE of systems, applications, and services based on multimedia in IoVs

• Cognitive computing, affective computing, machine learning and other novel tools, services, technologies, algorithms and methods for multimedia analysis in IoVs

• Innovative architecture, infrastructure, techniques and testbeds for multimedia communications and processing in IoVs

人工智能

Cognitive Systems Research

Machine Learning for Emerging Cognitive Internet of Things

全文截稿: 2018-08-31

影响因子: 1.182

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 4区

  • 小类 : 计算机：人工智能 - 4区

  • 小类 : 神经科学 - 4区

网址: http://www.journals.elsevier.com/cognitive-systems-research/

The Internet of Things (IoT) is of our future world. There has been immense outgrowth in recent years in the development of IoT. IoT interconnects various physical devices, objects, and people. A vital role of IoT is to collect and share information between connected physical objects, such as mobile devices, sensors, vehicles and manufacturing machines. IoT has been widely applied in many areas of our lives like smart city development, smart home development, continuous patient monitoring and environmental monitoring systems.

All these brings the necessity to handle big data with their continuous monitoring. The cognitive internet of things enables these IoT devices to intelligently interact with other devices. This brings in importance of machine learning for cognitive internet of things. The IoT will be just a raw data without intelligent processing via various AI and machine learning methods. In recent years, many machine learning algorithms have been developed exclusively to process the massive amounts of IoT computing data. This special issue focuses on innovations in various machine learning algorithms and AI computing methods for cognitive IoT. The key research topics that include:

- Machine learning algorithms for IoT

- Decision support systems for IoT

- Nature inspired algorithms for IoT

- Cognitive computation for IoT

- ANN and fuzzy systems for IoT

- Cognitive aspects of artificial intelligence

- Knowledge-based techniques for IoT

- Optimization methods for IoT

- Non-linear programming methods for IoT

- Automated reasoning in IoT

计算机科学与技术

Computers in Industry

Special Issue on “Deep Learning for Diagnosis and Prognosis in Manufacturing”

全文截稿: 2018-08-31

影响因子: 2.691

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：跨学科应用 - 3区

网址: https://www.journals.elsevier.com/computers-in-industry

With increased complexity of modern manufacturing systems, exponential growth of data has been seen in manufacturing industry. Efficient utilization of those big data would provide intelligence to infer the health conditions of manufacturing machines, for improved fault detection, diagnosis, prognosis, health management, and maintenance scheduling. Machine learning, as one of the prevailing data analytics methods, has been widely used to devise complex models and algorithms that lend themselves to derive knowledge from the data. As a branch of machine learning, deep learning attempts to model high level representations behind data and classify (predict) patterns via stacking multiple layers of information processing modules in hierarchical architectures, which has shown great potential for machine health condition inference and performance degradation prediction, especially in the big data era.

The aim of this special issue is to solicit high quality papers that report recent findings and emerging research developments of Deep Learning for Diagnosis and Prognosis in manufacturing applications. Potential authors are invited to submit original contributions and reviews to this special issue.

Topics:

- Manufacturing machine/process condition monitoring

- Manufacturing machine/process fault detection and diagnosis

- Remaining useful life prediction and life assessment

- Reliability evaluation and health management

- System-level diagnostic and prognostic modeling

- Unsupervised and supervised learning for fault feature extraction

- Model fusion and big data analytics

- Explainable deep neural network

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