Calls for Papers for Journals

The IEEE Computer Society Transactions publish archive-quality research papers on a variety of topics related to computer science and technology. If you are interested in publishing with us, please view our list of on-going calls for papers to determine which journal best suits your area of expertise.



CALIEEE Computer Architecture Letters

Ongoing Call-For-Papers

IEEE Computer Architecture Letters (CAL), a bi-annual forum for fast publication of new, high-quality ideas in the form of short, critically refereed, technical papers, is seeking submissions on any topic in computer architecture.

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TACIEEE Transactions on Affective Computing

Ongoing Call-For-Papers

The IEEE Transactions on Affective Computing (TAC), a new bi-annual online-only publication, is seeking submissions of original research on the principles and theories explaining why and how affective factors condition interaction between humans and technology, on how affective sensing and simulation techniques can inform our understanding of human affective processes, and on the design, implementation, and evaluation of systems that carefully consider affect among the factors that influence their usability. Surveys of existing work will be considered for publication when they propose a new viewpoint on the history and the perspective on this domain.

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TBDIEEE Transactions on Big Data

Special Issue on Wireless Big Data

Biomedical imaging is an essential component in various fields of biomedical research and clinical practice. Biologists quantitatively study cell behavior and generate high-throughput microscopy data sets. Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans. Virologists generate 3D reconstructions of viruses from micrographs, and radiologists identify and quantify tumors from MRI and computed tomography (CT) scans. Advanced imaging equipment and diverse applications have driven the generation of biomedical big data. The main challenge and bottleneck for the related research is the conversion of “biomedical big data” into interpretable information and hence discoveries. Computer vision theory has a huge potential in many aspects for automated understanding of biomedical data and has been used successfully to speed up and improve applications such as large-scale cell image analysis (image preconditioning, cell segmentation and detection, cell tracking, and cell behavior identification), image reconstruction and registration, organ segmentation and disease classification. Moreover, when it comes to the new era of machine learning, deep learning has revolutionized multiple fields of computer vision, significantly pushing the state of arts of computer vision systems in a broad array of high-level tasks.

This special issue serves as a forum to bring together active researchers all over the world to share their recent advances in this exciting area. We solicit original contributions in three-fold: (1) present state-of-the-art theories and novel application scenarios related to biomedical big data analytics; (2) survey the recent progress in this area; and (3) build benchmark datasets.

Submission Deadline: August 31, 2017. View PDF

Special Issue on Big Data in Ubiquitous Computing

With the continuous expansion of ubiquitous sensors, devices, networks and Internet of Things, all kinds of data become widely available and large in amount. Generation of huge amounts of data, called big data, reflects the dynamics of physical world and can be the basis for ubiquitous intelligence. Big data in ubiquitous intelligence scenarios exhibit some specific characteristics, like multi-source, heterogeneous, large-scale, real-time streaming, continuous, ever-expanding and spatial-temporal. Traditional ubiquitous computing approaches or systems began to show their limitations. It is difficult to manage and utilize all kinds of big data to accelerate ubiquitous intelligence in real-world. We believe that we need a new way for ubiquitous intelligence and computing where big data is immensely involved, especially for the data trace collected from ambient sensors, wearable, social media and so on. Intensive research is required on the collaboration between big data and ubiquitous computing. This special issue, as a dedicated forum, aims for the scientific and industrial community to present their novel models, methodologies, techniques and solutions which can address theoretical and practical issues.

Submission Deadline: September 1, 2017. View PDF

Special Issue on Big Data from Space

Big Data from Space refers to the massive spatio-temporal Earth and Space observation data collected by space-borne sensors, and their use in synergy with data coming from other aerial or ground based sensors or sources. This domain is currently facing sharp development with numerous new initiatives and breakthroughs ranging from computational sensors to space sensor web, covering almost the entire electromagnetic spectrum from Gamma-rays to radiowaves, or from gravitational to quantum principles. The analysis of these data largely contributes to the broad scientific effort to understand the Universe and to enhance life on Earth. The recent multiplication of open access initiatives to Big Data from Space is giving momentum to the field by widening substantially the spectrum of scientific communities and users as well as awareness among the public while offering new benefits at all levels from individual citizens to the whole society.

In this Special Issue, we solicit high-quality scientific research articles, in areas such as, but not limited to, Earth Observation, planetary sciences, Space and Security, deep space exploration, astrophysics, satellite telecommunication, navigation and positioning systems, addressing key challenges and innovative solutions on how Big Data paradigms can improve the space sciences, technologies, and applications.

Submission Deadline: January 31, 2018. View PDF

Special Issue on Edge Analytics in the Internet of Things

The cloud-based Internet of Things (IoT) that connects a wide variety of things including sensors, mobile devices, vehicles, manufacturing machines, and industrial equipments, etc. is changing the way we live. IDC forecasts that the IoT will grow to 50 billion connected devices by 2020, and will generate an unprecedented volume and variety of data. However, moving this big volume of data from the network edge to a central data center for processing and analysis not only adds latency but also consumes network bandwidth. Therefore, the cloud-based IoT with a centralized data center may not be able to enable smart environments, such as cities, homes, schools, etc., or smart systems, such as automated vehicles, traffic controls, factories, etc., whose data need to be analyzed and acted on quickly. This is especially true in scenarios such as health monitoring or autopilot, where milliseconds can have fatal consequences. Such demand indicates that data processing and analysis has to be performed where the data are collected or generated instead of waiting for the data to be sent back to the centralized data center. Also, often these smart environments or systems need to be capable of self-monitoring, self-diagnosing, self-healing, and self-directing, and thus the task of edge-based data analytics may need to incorporate the technology of machine learning. Thus, there is a need to find a way to push intelligence from the central data center to the edge of the network. Indeed, IDC also predicts that up to 40% of IoT data will need edge-based analytics for applications that need real-time action. To solve this issue, fog computing, in which a set of interconnected micro data centers, called fog nodes, are deployed in between the things and the cloud data center, has been adopted as a bridge linking IoT devices and their remote data center. Since a fog node can run IoT-enabled applications for real-time data analytics with millisecond response time, fog computing enables application services of the IoT to be performed close to their consumers, and has created an emerging technology { edge analytics. Meanwhile, some IoT things are getting more capable and more powerful, making edge-based analytics possible. On the other hand, for the moment, most of the IoT things still do not have the computing and storage resources to perform intelligent analytics directly. For such IoT things, a nearby fog node or cloudlet may perform the tasks on their behalf. Furthermore, since data sources are widely distributed, some analytics tasks may need to be collaboratively performed by a set of fog nodes working together with some IoT things. As such, orchestrating fog nodes by means of topology control and network function virtualization may leverage the edge analytics performance.

Though edge analytics is in its nascent stage, it is getting more and more popular. The goal of this special issue is to provide a forum for researchers working on IoT and fog computing to present their recent research results in edge analytics.

Submission Deadline: February 1, 2018. View PDF

Ongoing Call-For-Papers

The IEEE Transactions on Big Data (TBD) publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications. Research areas for big data include, but are not restricted to, big data analytics, big data visualization, big data curation and management, big data semantics, big data infrastructure, big data standards, big data performance analyses, intelligence from big data, scientific discovery from big data security, privacy, and legal issues specific to big data. Applications of big data in the fields of endeavor where massive data is generated are of particular interest.

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TCIEEE Transactions on Computers

Ongoing Call-For-Papers

IEEE Transactions on Computers (TC), a monthly archival publication, is seeking submissions of papers, brief contributions, and comments on research in areas that include, but are not limited to, computer organizations and architectures; operating systems, software systems, and communication protocols; real-time systems and embedded systems; digital devices, computer components, and interconnection networks; and new and important applications and trends.

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TCCIEEE Transactions on Cloud Computing

Ongoing Call-For-Papers

IEEE Transactions on Cloud Computing (TCC), will publish peer-reviewed articles that provide innovative research ideas and applications results in all areas relating to cloud computing. Topics relating to novel theory, algorithms, performance analyses and applications of techniques relating to all areas of cloud computing will be considered for the transactions. The transactions will consider submissions specifically in the areas of cloud security, tradeoffs between privacy and utility of cloud, cloud standards, the architecture of cloud computing, cloud development tools, cloud software, cloud backup and recovery, cloud interoperability, cloud applications management, cloud data analytics, cloud communications protocols, mobile cloud, liability issues for data loss on clouds, data integration on clouds, big data on clouds, cloud education, cloud skill sets, cloud energy consumption, cloud applications in commerce, education and industry. This title will also consider submissions on Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), and Business Process as a Service (BPaaS).

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TDSCIEEE Transactions on Dependable and Secure Computing

Special Issue on Security in Emerging Networking Technologies

Network infrastructure is undergoing a major shift away from ossified hardware-based networks to programmable software-based networks. One compelling example of this paradigm shift is the advent of Software-Defined Networking (SDN). A traditional network mixes control and traffic processing logic in single hardware devices, making the network more complex and harder to manage. SDN has addressed this issue by decoupling the control plane in network devices from the data plane to simplify production networks. On the other hand, enterprise networks are populated with a large number of proprietary and expensive hardware-based middleboxes, such as firewall, IDS/IPS, and load balancing. Hardware-based middleboxes present significant drawbacks such as high costs, management complexity, slow time to market, and unscalability. Network Function Virtualization (NFV) was proposed as another new network paradigm to address those drawbacks by replacing hardware-based network functions with virtualized software systems running on generic and inexpensive commodity hardware. Given their benefits, SDN and NFV have recently attracted significant attention from both academia and industry.

SDN and NFV introduce significant granularity, visibility, flexibility, and elasticity to networking, but at the same time bring forth new security challenges. For example, decoupling the data plane and the control plane in SDN essentially opens a door to attackers for exploiting the vulnerabilities of SDN controllers, APIs, applications, and protocols, and further break their trust relations. Meanwhile, both SDN and NFV could be leveraged to strengthen network defense. The aim of this special issue is to encompass research advances in all areas of security in emerging networking technologies. The special issue intends to provide a venue for interested researchers and practitioners to share their novel research ideas and results.

Submission Deadline: September 31, 2017. View PDF

Ongoing Call-For-Papers

IEEE Transactions on Dependable and Secure Computing (TDSC), a bimonthly archival publication, is seeking submissions of papers that focus on research into foundations, methodologies, and mechanisms that support the achievement—through design, modeling, and evaluation—of systems and networks that are dependable and secure to the desired degree without compromising performance. The focus also includes measurement, modeling, and simulation techniques, and foundations for jointly evaluating, verifying, and designing for performance, security, and dependability constraints.

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TETCIEEE Transactions on Emerging Topics in Computing

Technical Tracks

IEEE Transactions on Emerging Topics in Computing (TETC) seeks original manuscripts for submission under Technical Tracks. In a track the technical contents of a submitted manuscript must be of an emerging nature and fall within the scope and competencies of the Computer Society. Manuscripts not abiding by these specifications will be administratively rejected. The topics of interest for the Technical Tracks are as follows:

  • Enterprise Computing Systems
  • Computational Networks
  • Hardware and Embedded System Security
  • Educational Computing
  • High Performance Computing
  • Next Generation Wireless Computing Systems
  • Computer System Security
  • Emerging Hardware for Computing

Submitted articles must describe original research which is not published or currently under review by other journals or conferences. Extended conference papers should be identified in the submission process and have considerable novel technical content; all submitted manuscripts will be screened using a similarity checker tool. As an author, you are responsible for understanding and adhering to our submission guidelines. You can access them at the IEEE Computer Society web site, Please thoroughly read these before submitting your manuscript.

Please submit your paper to Manuscript Central at and select the "Technical Track" option in the drop-down menu for "Manuscript Type".

Please address all other correspondence regarding this Call For Papers to Fabrizio Lombardi, EIC of IEEE TETC,

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Special Issue on Advanced Command, Control and On-Board Data Processing for Space Avionic Systems

IEEE Transaction on Emerging Topics in Computing (TETC) seeks original manuscripts for a Special Issue/Section on Command, control and on board data processing for space avionic systems scheduled to appear in the third issue of 2018.The domain of space avionic systems is changing extremely rapidly, compared to other technical domains in space-faring industry, under the pressure of an intense competition, the continuous emergence of new markets and players, the need for cost reduction, as well as an increased obsolescence rate of components and processes.

This rapidly changing landscape is as well opening a large amount of opportunities for the space avionic systems: the new high-performance processors architectures and silicon processes, which offer the possibility to integrate different functions until now implemented on several boards either in a single chip (SoC), or in application-specific standard products (ASSP) or in new large FPGAs are allowing multi-fold gains in performances and miniaturization for electronic systems.

Reliability and availability constraints remain the main driving requirements for established space hardware manufacturers. In this context, the emergence of space systems based on Commercial-Off-The-Shelf (COTS) only and aggressive commercial platforms adds further uncertainties and possibilities to an already very dynamic landscape. New creative and technically sound solutions are needed to provide a valid and attractive alternative to the tempting shortcut of cutting costs by waving the rigorous test and quality assurance processes applied to bigger satellite.

Submission Deadline: September 1, 2017. View PDF

Special Issue on Green Computing in Internet of Things

As an emerging cross-disciplinary research area, Green Computing is attracting worldwide attention. The emergence of the Green Computing will significantly change the way we see the world. All aspects of Information Technology are under investigation, from energy saving design of individual devices, to strategies that consider the entire energy consumption in the design, planning, and management phases, to new paradigms for long term sustainability that includes reformed attitudes of users’ as well as smart energy harvesting techniques. The above considerations motivated us to promote this special issue. This special issue will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to Green Computing in Internet of Things. Only papers that are focused on computing will be considered.

Submission Deadline: September 1, 2017. View PDF

Special Issue on eGovernment Development and Applications (SIEGDA)

The SIEDGA addresses the main issues of public administration and electronic democracy with an academic and practical perspective. It covers technical and non-technical aspects of, but not limited to the following areas: eGovernment, eDemocracy, eParticipation, eScociety, eHealth, and eGovernance.

Extended Submission Deadline: October 1, 2017. View PDF

Special Issue on Scholarly Big Data

Recent years have witnessed the rapid growth of scholarly information due to advancements in information and communication technologies. Scholarly big data is the vast quantity of research output, which can be acquired from digital libraries, such as journal articles, conference proceedings, theses, books, patents, experimental data, etc. It also encompasses various scholarly related data, such as author demography, academic social networks, and academic activity. The abundance of scholarly data sources enables researchers to study the academic society from a big data perspective. The dynamic and diverse nature of scholarly big data requires different data management techniques and advanced data analysis methods. Today’s researchers realize that new scholarly-big-data specific platform/management/techniques/ are needed. Therefore, a set of emerging topics such as scholarly big data acquisition, storage, management and processing are important issues for the research community. Manuscripts submitted to TETC should be computing focused.

This special issue focuses on covering the most recent research results in scholarly big data management and computing. The issue welcomes both theoretical and applied research (e.g. platforms and applications). It will encourage the effort to share data, advocate gold-standard evaluation among shared data, and promote the exploration of new directions.

Submission Deadline: December 1, 2017. View PDF

Special Issue on Design of Reversible Computing Systems

IEEE Transaction on Emerging Topics in Computing (TETC) seeks original manuscripts for a Special Issue/Section on Design of Reversible Computing Systems scheduled to appear in the first issue of 2019.

Over the coming decade, the historical trend of exponentially-increasing computer performance for systems at a given cost level is expected to slow, as conventional digital technology approaches practical limits to its computational energy efficiency, which in turn limits system performance within any given power and cooling constraints. In the long term, due to fundamental connections between thermodynamics and information theory, the only possible way to continue improving the energy-efficiency and affordable performance of computing systems indefinitely is if their designs increasingly thoroughly apply reversible computing principles. However, the question of exactly how to design practical, cost-competitive reversible computing systems is an extremely challenging engineering problem, which today still remains far from being fully solved. To overhaul the existing industrial infrastructure of manufacturing processes, design tools and software in all of the ways that will likely be needed to fully realize the potential of this unconventional but essential new computing paradigm will arguably require a multi-billion-dollar sustained investment in associated research and development activities. We cannot assume this investment will be made until the research community builds a sufficiently solid case showing that workable implementation approaches exist and are economically feasible. It is the goal of this special issue to solicit high-quality contributions across all levels of computing that pointedly address the crucial issues in the theory, design, and engineering analysis of reversible computing systems, so as to eliminate all of the remaining conceptual roadblocks that impede investment, and establish that the reversible computing paradigm indeed provides a viable path forwards, towards an unbounded new future for computing.

Submission Deadline: March 1, 2018. View PDF

Ongoing Call-For-Papers

IEEE Transactions on Emerging Topics in Computing is an open access journal that publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT. TETC aggressively seeks proposals for Special Sections and Issues focusing on emerging topics. Prospective Guest Editors should contact the TETC EIC Fabrizio Lombardi at for further details.

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ToHIEEE Transactions on Haptics

Ongoing Call-For-Papers

The IEEE Transactions on Haptics (ToH), a quarterly archival publication, is seeking submissions that address the science, technology, and applications associated with information acquisition and object manipulation through touch.

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TKDEIEEE Transactions on Knowledge & Data Engineering

Ongoing Call-For-Papers

IEEE Transactions on Knowledge and Data Engineering (TKDE), a monthly archival publication, is seeking submissions that present well-defined theoretical results and empirical studies that have a potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services. We welcome treatments of the role of knowledge and data in the development and use of information systems and in the simplification of software and hardware development and maintenance.

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TLTIEEE Transactions on Learning Technologies

Ongoing Call-For-Papers

IEEE Transactions on Learning Technologies (TLT), a quarterly archival online-only publication using a delayed open access publication model, is seeking submissions about all advances in learning technologies, such as innovative online learning systems, personalized and adaptive learning systems, and learning with mobile devices

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TMCIEEE Transactions on Mobile Computing

Ongoing Call-For-Papers

IEEE Transactions on Mobile Computing (TMC), a monthly archival publication, is seeking submissions of mature works of research, typically those that have appeared in part in conferences, and that focus on the key technical issues related to, but not limited to, architectures, support services, algorithm/protocol design and analysis, mobile environments, mobile communication systems, and emerging technologies.

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TMSCSIEEE Transactions on Multi-Scale Computing Systems

Ongoing Call-For-Papers

The IEEE Transactions on Multi-Scale Computing Systems (TMSCS) is a peer-reviewed publication devoted to computing systems that exploit multi-scale and multi-functionality. These systems consist of computational modules that utilize diverse implementation scales (from micro down to the nano scale) and heterogeneous hardware and software functionalities; moreover, these modules can be based on operating principles and models that are valid within but not necessarily across their respective scales and computational domains. Contributions to TMSCS must address computation of information and data at higher system-levels for processing by digital and emerging domains. These computing systems can also rely on diverse frameworks based on paradigms at molecular, quantum and other physical, chemical and biological levels. Innovative techniques such as inexact computing, management/optimization of smart infrastructures and neuromorphic modules are also considered within scope.

This publication covers pure research and applications within novel topics related to high performance computing, computational sustainability, storage organization and efficient algorithmic information distribution/processing; articles dealing with hardware/software implementations (functional units, architectures and algorithms), multi-scale modeling and simulation, mathematical models and designs across multiple scaling domains and functions are encouraged. Novel solutions based on digital and non-traditional emerging paradigms are sought for improving performance and efficiency in computation. Contributions on related topics would also be considered for publication.

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TNSEIEEE Transactions on Network Science and Engineering

Special Issue on Scalability and Privacy in Social Networks

The growing popularity of Online Social Networks and their emerging applications attracted much attention from both academia and industry. Due to their nature, social networks are considered as sources of Big Data containing large amounts of privacy-sensitive information. A social network is frequently abstracted using a mathematical model such as a graph, which is usually very large, that can later be used as an input to other algorithms for further processing. Recent reports show that if the abstractions of social networks are not properly designed, a large amount of private information can be extracted from them. As the area of Data Science and related technologies are getting more mature, it is highly possible that what is considered a safe abstraction of social networks today, becomes unsafe tomorrow. Unfortunately, the problem of designing privacy-aware social network abstractions is very challenging. Generally speaking, this is because a change in input data forces a change in the structure of the algorithms which will process the input data. Such change can also affect the output of the algorithm. Certainly, the emerging Big Data analytic techniques, such as differential analysis, will bring more complexity to this already-conundrum-like problem. Most importantly, any solution to this problem has to be scalable. This special issue aims to provide a prime venue for researchers from both academia and industry to discuss about this impelling, but not well-understood, problem.

Extended Submission Deadline: September 1, 2017. View PDF

Ongoing Call-For-Papers

IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.

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TPDSIEEE Transactions on Parallel & Distributed Systems

Ongoing Call-For-Papers

IEEE Transactions on Parallel and Distributed Systems (TPDS), a monthly archival publication, is seeking submissions that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest in parallel systems include, but are not limited to, architectures, software, and algorithms and applications. Particular areas of interest in distributed systems include, but are not limited to, algorithms and foundation, distributed operating systems, and Internet computing and distributed applications.

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TPAMIIEEE Transactions on Pattern Analysis & Machine Intelligence

Ongoing Call-For-Papers

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), a monthly archival publication, is seeking submissions that discuss the most important research results in all traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Other areas of interest are machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, face and gesture recognition, and relevant specialized hardware and/or software architectures.

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TSCIEEE Transactions on Services Computing

Special Issue on Fog Computing and Services

The emerging Internet of Things (IoT) and rich cloud services have helped create the need for fog computing (also known as edge computing), in which data processing occurs in part at the network edge or anywhere along the cloud-to-endpoint continuum that can best meet user requirements, rather than completely in a relatively small number of massive clouds. Fog computing could address latency concerns, devices’ limited processing and storage capabilities and battery life, network bandwidth constraints and costs, and many security and privacy concerns that arise from the emerging IoT.

The new Fog/Edge computing paradigm will enable the resources and services of computing, storage, networking, and control to be distributed closer to the users. Software distributions for various applications can now be hosted by fog servers, e.g., image processing packages for preprocessing images in video surveillance applications. Operating systems and the associated services can be offered through nearby fog servers to reduce round trip latency. Equipment outsourcing, such as storage, hardware, servers, and networking components can also be provisioned through fog servers. Fog computing is an extension to cloud services – it complements the clouds to enable computing where it makes the most sense.

Many new problems arise in enabling fog computing and services, creating a fertile ground for research and innovation. We are prompted to design new algorithmic, mathematical, statistical and computational methods to solve services computing problems on this new architecture. Service creation, development, and management, web services, business processes, and so on, need to be carefully redesigned. In addition, the new fog computing architecture can further provide new solutions to hard problems in the existing architectural framework, e.g., IoT services, security and privacy.

Submission Deadline: October 31, 2017. View PDF

Ongoing Call-For-Papers

IEEE Transactions on Services Computing (TSC), is a quarterly archival online-only publication, is seeking submissions that emphasize the algorithmic, mathematical, statistical and computational methods that are central in services computing: the emerging field of service-oriented architecture, Web services, business process integration, solution performance management, services operations, and management.

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TSEIEEE Transactions on Software Engineering

Ongoing Call-For-Papers

IEEE Transactions on Software Engineering (TSE), a bimonthly archival publication, is seeking submissions of well-defined theoretical results and empirical studies that have a potential impact on the construction, analysis, or management of software. The scope of this Transactions ranges from the mechanisms through the development of principles to the application of those principles to specific environments. Since the journal is archival, it is assumed that the ideas presented are important, have been well analyzed, and/or empirically validated, and are of value to the software engineering research or practitioner community.

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TSUSCIEEE Transactions on Sustainable Computing

Special Issue on Cryptography and Data Security in Sustainable Computing

With the proliferation of several kinds of attacks towards ICT infrastructures and the relative effects caused by a successful compromise of them, data security is of pivotal importance in our current society. As a practical example, health-related data are rapidly being digitalised passing from paper-based communications among patients and physicians to computer-based ones. However, the occurrence of data leakage is increasing, with the consequence of stealing sensitive personal information from the leaked health-related data. To protect the ICT infrastructures from these attacks, several solutions have been proposed, where cryptography plays a key role. Despite being able to provide a suitable degree of security and privacy, such solutions have not been designed by taking care of their energy consumption and resource usage. Therefore, they are not optimal in the case of resource-constrained systems, such as sensor networks, and are under radical rethinking in order to be effectively adopted in such context. Moreover, the recent increasing attention to climate changes and environmental issues are leading a considerable debate on how changing the current computing technologies so as to have less severe effects on the global warming and resource usage. Such a debate involves also the current cryptosystems and the other widely-accepted solutions to provide data security, so as to modify them by considering their sustainability.

The aim of the special issue is to solicit novel contributions to the current debate of realizing sustainable solutions to support data security and to realize cryptosystems to protect the data at rest and in motion within the current ICT infrastructures, by also seeking practical experiences in using these solutions in concrete use cases of Green Computing and Resource-Constrained Systems.

Extended Submission Deadline: September 1, 2017. View PDF

Special Issue on Smart Data and Deep Learning in Sustainable Computing

We are living in a data-driven era in which numerous infrastructure can be connected and the interconnected systems can perform “smart” when the large pool of the data are well utilized. Finding the way of well utilizing the large volume of data has an urgent demand in multiple realms, including academics, industries, and education. The force behind the data can be pushed out from a variety of data-driven techniques, such as machine learning and deep learning, which is a great potential for generating successful model, framework, and method for achieving sustainable computing. Therefore, gathering recent achievements in smart data and deep learning in sustainable computing is meaningful and valuable for powering the capability of data-driven domain and the various applications, implementations, and innovations in different disciplines and fields.

This special issue focuses on two aspects considering the perspective of sustainable computing, which include smart data and deep learning. The smart data covers all dimensions of data usage lifecycles, such as data selections and collections, data preprocessing, data mining, and data analytics, in various application scenarios. The other aspect, deep learning, emphasizes the intelligent performance of applying data-driven techniques in practices and research explorations. Thus, this special issue aims at collecting updated outstanding papers that illustrate the latest achievements and development updates concerning the smart data and deep learning solutions, issues, applications, trends, and implementations in sustainable computing.

Submission Deadline: September 1, 2017. View PDF

Special Issue on Sustainable Cyber Forensics and Threat Intelligence

Increasing societal reliance on interconnected digital systems, including smart grids and Internet of Things (IoT), made sustainable detection and investigation of threat actors among highest priorities of any society. Scale and attack surface of modern networks mandate optimized deployment of limited cyber forensics and threat intelligence resources to detect and remove malicious actors in a timely manner. However, timely dealing with such a huge number of attacks is not possible without employment of artificial intelligence and machine learning techniques. When a significant amount of data is collected from or generated by different security monitoring solutions; intelligent big-data analytical techniques are necessary to mine, interpret and extract knowledge out of those data. The emerging field of cyber threat intelligence is investigating applications of artificial intelligence and machine learning techniques to perceive, reason, learn and act intelligently against advanced cyber attacks.

Submission Deadline: September 1, 2017. View PDF

Ongoing Call-For-Papers

IEEE Transactions on Sustainable Computing (TSUSC) is a peer-reviewed journal devoted to publishing high-quality papers that explore the different aspects of sustainable computing, over a wide range of problem domains and technologies from software and hardware designs to applications. Sustainability includes energy efficiency, natural resources preservation, and use of multiple energy sources as needed in computing devices and infrastructure.

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TVCGIEEE Transactions on Visualization & Computer Graphics

Ongoing Call-For-Papers

IEEE Transactions on Visualization and Computer Graphics (TVCG), a monthly archival publication, is seeking submissions that present important research results and state-of-the-art seminal papers related to computer graphics and visualization techniques, systems, software, hardware, and user interface issues. Specific topics in computer graphics and visualization include, but are not limited, algorithms, techniques and methodologies; systems and software; user studies and evaluation; rendering techniques and methodologies, including real-time rendering, graphics hardware, point-based rendering, and image-based rendering; and animation and simulation, including character animation, facial animation, motion-capture, physics-based simulation and animation.

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TCBBIEEE/ACM Transactions on Computational Biology and Bioinformatics

Special Issue on Advanced Machine Learning Techniques for Bioinformatics

“Prediction” is the main task in machine learning research, and is becoming more and more popular in biomedicine and bioinformatics, especially after Obama proposed the precision medicine project. We have witnessed the great development of advanced machine learning techniques boomed in the computer science community and related conferences such as ICML, AAAI, NIPS, etc. Among them, deep learning and other deep-based representative learning algorithms have been applied successfully in imagine understanding, speech recognition, and text classification, etc. Besides, Semi-supervised Learning, Learning from Positive and Unlabeled Example (PU learning), Multi-view Learning, Transfer Learning, Probabilistic Graphical Model, etc. are also rapidly developed. Nevertheless, the latest prediction techniques were not well applied in the bioinformatics or biomedicine community. Therefore, the aim of this special issue for TCBB is to bridge the advanced machine learning approaches and the biological applications. We hope that methodology implementation and data from real-world applications could both be covered in this issue. The issue will hopefully provide novel guidance for the machine learning researchers and broaden the perspectives of medicine and bioinformatics researchers.

Submission Deadline: September 1, 2017. View PDF

Ongoing Call-For-Papers

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), a bimonthly archival publication, is seeking submissions that discuss research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology. This includes, but is not limited to, the development and testing of effective computer programs in bioinformatics; the development and optimization of biological databases; and important biological results that are obtained from the use of these methods, programs, and databases.

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