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.
- IEEE Computer Architecture Letters
- IEEE Transactions on Affective Computing
- IEEE Transactions on Big Data
- IEEE Transactions on Computers
- IEEE Transactions on Cloud Computing
- IEEE Transactions on Dependable and Secure Computing
- IEEE Transactions on Emerging Topics in Computing
- IEEE Transactions on Haptics
- IEEE Transactions on Knowledge & Data Engineering
- IEEE Transactions on Learning Technologies
- IEEE Transactions on Mobile Computing
- IEEE Transactions on Multi-Scale Computing Systems
- IEEE Transactions on Network Science and Engineering
- IEEE Transactions on Parallel & Distributed Systems
- IEEE Transactions on Pattern Analysis & Machine Intelligence
- IEEE Transactions on Services Computing
- IEEE Transactions on Software Engineering
- IEEE Transactions on Visualization & Computer Graphics
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
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.
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.
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.
Joint Special Section on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
With increasing defect rates in highly scaled CMOS and emergence of alternative nanotechnology devices, defect and fault tolerance in VLSI and nanotechnology systems is of growing importance. The IEEE Transactions on Computers and IEEE Transaction on Nanotechnology seek original manuscripts for a Special Section on Defect and Fault Tolerance in VLSI Systems scheduled to appear in the issue of March 2016.
Special Issue on Selected Topics in Smart City Computing
Developing smart city is the key to the next generation urbanization process for improving the efficiency, reliability, and security of a traditional city. The concept of smart city includes various aspects such as environmental sustainability, social sustainability, regional competitiveness, natural resources management, cybersecurity, and quality of life improvement. With the massive deployment of networked smart devices/sensors, unprecedentedly large amount of sensory data can be collected and processed by advanced computing paradigms which are the enabling techniques for smart city. For example, given historical environmental, population and economic information, salient modeling and analytics are needed to simulate the impact of potential city planning strategies, which will be critical for intelligent decision making. Analytics are also indispensable for discovering the underlying structure from retrieved data in order to design the optimal policies for real time automatic control in the cyberphysical smart city system. Furthermore, uncertainties and security concerns in the data collected from heterogeneous resources aggravate the problem, which makes smart city planning, operation, monitoring and control highly challenging.
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.
Special Issue on Big Data Computing on Clouds
Big data is an emerging paradigm applied to datasets whose size or complexity is beyond the ability of commonly used computer software and hardware tools. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). More importantly, big data has to be of high value (Value) and establish trust in it for business decision making (Veracity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. As estimated by IDC, by 2020, about 40% data globally would be touched with Cloud Computing. Besides, Cloud Computing provides strong storage, computation and distributed capability in support of Big Data processing. Therefore, there is a strong demand to investigate various challenges about how to support Big Data processing by facilitating Cloud Computing potential. This special issue will focus on this challenging topic.
Special Issue on Many-Task Computing in the Cloud
The Special Issue on Many-Task Computing (MTC) in the Cloud will provide the scientific community a dedicated forum, within the prestigious IEEE Transactions on Cloud Computing journal, for presenting new research, development, and deployment efforts of loosely coupled large scale applications on Cloud Computing infrastructure. MTC, the theme of this special issue, encompasses loosely coupled applications, which are generally composed of many tasks to achieve some larger application goal. This special issue will cover challenges that can hamper efficiency and utilization in running applications on large-scale systems, such as local resource manager scalability and granularity, efficient utilization of raw hardware, parallel file-system contention and scalability, data management, I/O management, reliability at scale, and application scalability. We welcome paper submissions in theoretical, simulations, and systems topics with special consideration to papers addressing the intersection of petascale/exascale challenges with large-scale cloud computing. We seek submission of papers that present new, original and innovative ideas for the "first" time in TCC (Transactions on Cloud Computing). That means, submission of "extended versions" of already published works (e.g., conference/workshop papers) is not encouraged unless they contain significant number of "new and original" ideas/contributions along with more than 49% brand "new" material. For more information on this special issue, please see http://datasys.cs.iit.edu/events/TCC-MTC15/.
Special Issue on Mobile Clouds
Mobile cloud computing represents one of the latest developments in cloud computing advancement. In particular, mobile cloud computing extends cloud computing services to the mobile domain by enabling mobile applications to access external computing and storage resources available in the cloud. Not only mobile applications are no longer limited by the computing and data storage limitations within mobile devices, nevertheless adequate offloading of computation intensive processes also has the potential to prolong the battery life.
Besides, there is also an incentive for mobile devices to host foreign processes. This represents a new type of mobile cloud computing services. Ad-hoc mobile cloud is one instance that mobile users sharing common interest in a particular task such as image processing of a local happening can seek collaborative effort to share processing and outcomes. Vehicular cloud computing is another instance of mobile cloud computing that exploits local sensing data and processing of vehicles to enhance Intelligent Transportation Systems.
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).
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.
Special Issue on Circuit and System Design Methodologies for Emerging Technologies
The demand for ever smaller, portable, low-power and high-performance electronic systems has been the primary driver for CMOS technology scaling. As CMOS scaling approaches physical limits, it has been fraught with challenges that required introduction of newer processes and materials. High-κ oxide and metal-gate stack was introduced to mitigate oxide leakage. Thin body, undoped channels were introduced mitigate subthreshold leakage. 3D transistors such as FinFET and trigates were introduced to improve ON current while maintaining layout efficiency. While these incremental adjustments have allowed CMOS technology to scale, a number of alternative devices have been proposed to replace CMOS transistors such as graphene transistors (GFET), tunnel transistors, graphene nanoribbon tunnel transistors, quantum-dots and single-electron devices (SET). Newer memory technologies such as resistive RAMs, memristors, STT-RAMs similarly promise to revolutionize the design landscape. However for these alternative technologies to become practical, design methodologies that allow efficient modeling, design space exploration, and trade-off analysis is crucial. This is the driving motivation for this special issue.
Special Issue on Emerging Security Trends for Deeply-Embedded Computing Systems
Unlike traditional embedded systems, nowadays, emerging computing systems are embedded in every aspect of human lives. These deeply-embedded computing systems often perform extremely sensitive tasks, and in some cases, such as health-care IT, these are life-saving. Thus, in addition to the security threats to traditional embedded systems, emerging deeply-embedded computing systems exhibit a larger attack surface, prone to more serious or life-threatening malicious attacks. These call for revisiting traditional security mechanisms not only because of the new facets of threats and more adverse effects of breaches, but also due to the resource limitations of these often-battery-powered and extremely-constrained computing systems. As such, new trends for providing security for deeply embedded systems are emerging; many of which abandoning use of cryptographic computations or make use of lightweight crypto-systems, feasible for these computing platforms. Indeed, there exists paramount potential for applying these emerging security approaches to sensitive applications such as health-care IT for implantable medical devices, big data analytics and machine learning in deeply embedded systems, smart buildings, and smart fabrics. The focus of this special issue will be on novel security methods for deeply-embedded computing systems, emerging cryptographic solutions applicable to extremely-constrained applications such as green cryptography, and advancements in feasible security measures for evolving interdisciplinary research trends such as computing for: health-care IT, cyber-physical embedded systems, big data, and smart buildings/fabrics.
Special Issue on Advances in Mobile Cloud Computing
There is a phenomenal burst of research activities in mobile cloud computing, which extends cloud computing functions, services, and results to the world of future mobile communications applications, and the paradigm of cloud computing and virtualization to mobile networks. Mobile applications demand greater resources and improved interactivity for better user experience. Resources in cloud computing platforms such as Amazon, Google AppEngine and Microsoft Azure are a natural fit to remedy the lack of local resources in mobile devices. The availability of cloud computing resources on a pay-as-you-go basis, the advances in network virtualization, software defined networks, and the emergence of advanced wireless networks such as cloud-based radio access networks (C-RANs) create a new space of rich research problems. The objective of this special section is to cover the most recent research and development on the technologies for mobile cloud computing. This special section is to offer a venue for industry and academia to show case their recent progresses and potential research directions on the mobile cloud computing technologies.
Special Issue on Emerging Trends in Education
Technological advancements, such as those seen in cloud computing, mobile devices, and big, open and linked data, to name just a few, bring with them great opportunities for broadening the reach of, and enriching, the educational experience. For instance, virtual learning environments are becoming commonplace in the communication between students and teachers, who can use a plethora of web-based tools and applications to publish assignments and submit them for grading, perform automatic assessment, etc. At the same time, mobile computing is contributing at expanding the reach of learning content and frameworks, which are becoming accessible in an always-on and ready-to-go fashion. By leveraging on smartphones and tablets, new pedagogical tools are being implemented which exploit their innovative interaction capabilities and the rich set of sensors to create immersive and interactive experiences not previously possible. Furthermore, the massive volume of information produced in these tools and environments opens greater possibilities including the sharing, analysis, and visualization of education data patterns and their trends. Although there are many different visions for education in the future, great efforts will be needed to reach a profound integration between the technologies that are already well-established and those that are considered as emerging. By building on a solid scientific and methodological foundation where theory and practice converge, this special issue aims to present both the current trends that characterize the learning and teaching domains of today as well the expected evolution that will shape the education of tomorrow.
Special Issue on Big Data Benchmarks, Performance Optimization, and Emerging Hardware
Big data are emerging as a strategic property of nations and organizations. There are driving needs to generate values from big data. However, the sheer volume of big data requires significant storage capacity, transmission bandwidth, computation, and power consumption. It is expected that systems with unprecedented scales can resolve the problems caused by varieties of big data with daunting volumes. Nevertheless, without big data benchmarks, it is very difficult for big data owners to make a decision on which system is best for meeting with their specific requirements. They also face challenges on how to optimize the systems for specific or even comprehensive workloads. Meanwhile, researchers are also working on innovative data management systems, hardware architectures, and operating systems to improve performance in dealing with big data.
Special Issue on Methods and Techniques for Processing Streaming Big Data in Datacentre Clouds
Internet of Things (IoT) is a part of Future Internet and comprises many billions of Internet connected Objects (ICOs) or ‘things' where things can sense, communicate, compute and potentially actuate as well as have intelligence, multi-modal interfaces, physical/ virtual identities and attributes. ICOs can include sensors, RFIDs, social media, actuators (such as machines/equipments fitted with sensors) as well as lab instruments (e.g., high energy physics synchrotron), and smart consumer appliances (smart TV, smart phone, etc.). The IoT vision has recently given rise to IoT big data applications that are capable of producing billions of data stream and tens of years of historical data to support timely decision making. Some of the emerging IoT big data applications, e.g. smart energy grids, syndromic bio-surveillance, environmental monitoring, emergency situation awareness, digital agriculture, and smart manufacturing, need to process and manage massive, streaming, and multi-dimensional (from multiple sources) data from geographically distributed data sources.
Despite recent technological advances of the data-intensive computing paradigms (e.g. the MapReduce paradigm, workflow technologies, stream processing engines, distributed machine learning frameworks) and datacentre clouds, large-scale reliable system-level software for IoT big data applications are yet to become commonplace. As new diverse IoT applications begin to emerge, there is a need for optimized techniques to distribute processing of the streaming data produced by such applications across multiple datacentres that combine multiple, independent, and geographically distributed software and hardware resources. However, the capability of existing data-intensive computing paradigms is limited in many important aspects such as: (i) they can only process data on compute and storage resources within a centralised local area network, e.g., a single cluster within a datacentre. This leads to unsatisfied Quality of Service (QoS) in terms of timeliness of decision making, resource availability, data availability, etc. as application demands increase; (ii) they do not provide mechanisms to seamlessly integrate data spread across multiple distributed heterogeneous data sources (ICOs); (iii) lack support for rapid formulation of intuitive queries over streaming data based on general purpose concepts, vocabularies and data discovery; and (iv) they do not provide any decision making support for selecting optimal data mining and machine algorithms, data application programming frameworks, and NoSQL database systems based on nature of the big data (volume, variety, and velocity). Furthermore, adoption of existing datacentre cloud platform for hosting IoT applications is yet to be realised due to lack of techniques and software frameworks that can guarantee QoS under uncertain big data application behaviours (data arrival rate, number of data sources, decision making urgency, etc.), unpredictable datacentre resource conditions (failures, availability, malfunction, etc.) and capacity demands (bandwidth, memory, storage, and CPU cycles). It is clear that existing data intensive computing paradigms and related datacentre cloud resource provisioning techniques fall short of the IoT big data challenge or do not exist.
Special Issue on Approximate and Stochastic Computing Circuits, Systems and Algorithms
The last decade has seen renewed interest in non-traditional computing paradigms. Several (re-)emerging paradigms are aimed at leveraging the error resiliency of many systems by releasing the strict requirement of exactness in computing. This special issue of TETC focuses on two specific lines of research, known as approximate and stochastic computing.
Approximate computing is driven by considerations of energy efficiency. Applications such as multimedia, recognition, and data mining are inherently error-tolerant and do not require perfect accuracy in computation. The results of signal processing algorithms used in image and video processing are ultimately left to human perception. Therefore, strict exactness may not be required and an imprecise result may suffice. In these applications, approximate circuits aim to improve energy-efficiency by maximally exploiting the tolerable loss of accuracy and trading it for energy and area savings.
Stochastic computing is a paradigm that achieves fault-tolerance and area savings through randomness. Information is represented by random binary bit streams, where the signal value is encoded by the probability of obtaining a one versus a zero. The approach is applicable for data intensive applications such as signal processing where small fluctuations can be tolerated but large errors are catastrophic. In such contexts, it offers savings in computational resources and provides tolerance to errors. This fault tolerance scales gracefully to high error rates. The focus of this special issue will be on the novel design and analysis of approximate and stochastic computing circuits, systems, algorithms and applications.
Special Issue/Section on Low-Power Image Recognition
Digital images have become integral parts of everyday life. It is estimated that 10 million images are uploaded to social networks each hour and 100 hours of video uploaded for sharing each minute. Sophisticated image / video processing has fundamentally changed how people interact. For example, automatic classification or tagging can mediate how photographs are disseminated to friends. Many of today's images are captured using smartphones, and cameras in smartphones can be used for a wide range of imaging applications, from high-fidelity location estimation to posture analysis. Image processing is computationally intense and can consume significant amounts of energy on mobile systems. This special issue focuses on the intersection of image recognition and energy conservation. Papers should describe energy efficient systems that perform object detection and recognition in images.
This special issue aims to establish milestones of the state of the art. Thus, all papers are required to include results on a common core of datasets using the same metrics. Training images will be sampled from the data for ILSVRC 2014 (ImageNet Large Scale Visual Recognition Challenge). These images are already annotated with objects. The objects are classified into approximately 200 categories that are typically used in everyday lives, for example, flower, car, and dog. Authors will submit results on a separate test dataset set to a centralized server in order to evaluate detection accuracy.
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 email@example.com for further details.
Special Issue on Special Issue on Active Touch Sensing in Robots, Humans and Other Animals
This special issue addresses the challenges posed by active sensing and interacting with the world through the sense of touch, whether the latter is implemented through a technological or biological system. Active touch sensing is recovering information about the world by ‘touching' rather than ‘being touched' – by interpreting signals from sensors whose motion is deliberately controlled to facilitate information gain.
The scope of this issue includes both biological and technological systems for active touch sensing, and implications for haptics. This issue will consider electronic systems for active touch sensing that are biologically inspired systems, in addition to other inherently active approaches to touch sensing.
Biological systems for active touch sensing are highly capable, and, by comparison, the field of robotic touch sensing is in its infancy. The former demonstrate many valuable concepts for active touch sensing that are being intensively investigated. They have also illustrated ways that active touch sensing is enabled through specialized sensory transduction channels, biomechanics, structural morphology, behavioral, and control strategies that are implemented by biological systems, and through other advantages that they achieve, including robustness, adaptability, and power efficiency. Similar challenges must be overcome if technological systems are to one day achieve comparable levels of sensorimotor performance to biological systems.
Submission Deadline: March 1, 2015. View PDF
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.
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.
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
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.
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.
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.
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.
Special Issue on Multimodal Human Pose Recovery and Behavior Analysis - M2HuPBA
Analyzing human poses and behaviors from visual and non-visual sensor data is one of the most challenging topics in Computer Vision, Pattern Analysis and Machine Learning. Recently driven by the need of user friendly interfaces, the next challenge is to integrate and analyze sensor data coming from different modalities, including RGB cameras, 3D range sensors, infrared cameras, audio signals, or Inertial Measurement Unit data, among others.
Human Pose Recovery and Behavior Analysis (HuPBA) has been used for posterior analysis of gestures and context in both still images and image sequences. However, HuPBA requires dealing with the articulated nature of the human body, changes in appearance because of clothes, and the inherent difficulties of clutter scenes, such as background artifacts, occlusions and illumination changes. Given the inherent difficulties of human pose estimation and the requirement for accurate estimations in order to perform posterior human behavior analysis, alternative visual modalities from different input sensors have drawn a lot of attention. This includes Time-of-Flight (ToF) cameras, other active or passive 3D range images (e.g. Infrared-based Kinect© Microsoft device), camera networks, light field cameras, multispectral sensors, underwater vision, and other non-conventional visual sensors as the new generation of low cost Thermal wavelengths cameras. These and other visual modalities have shown to offer complementary information, so data fusion increases the accuracy of computer vision approaches.
In addition to the use of different visual modalities for HuPBA, behavior analysis can be potentially benefited from the use of other complementary sources of sensor data, such as audio signals, Inertial Measurement Unit data, Electrothermal activity responses, and Electroglottograph signals, among others. In this sense, some challenges that arise from the use of different modalities for behavior analysis essentially includes feature extraction, synchronization of data coming from different sensors, data fusion, and temporal series analysis.
Several areas have emerged that require accurate multi-modal HuPBA technologies, such as Affective Computing or Social Signal Processing. Moreover, the efforts involved in these fields of research will be compensated by its potential applications, including leisure (gaming, intelligent retrieval of video data, augmented reality, Human Computer Interaction, etc.), security (security surveillance and ambient intelligence), health care (greater autonomy for those suffering diseases, advanced assisted living, inpatient monitoring, supported diagnosis, etc.) and energy (smart rooms, buildings and cities), to name just a few. In addition to this broad range applications, some novel approaches are being explicitly designed to be implemented in graphical processor units, smart phones, and game consoles.
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.
Special Issue on Cloud Services Meet Big Data
The concept of cloud services represents a prime facility and feature of cloud computing services made available to users on demand, when and where needed. Because cloud computing provides flexibility in scaling IT resources up and down according to dynamically changing business requirements, it makes cloud services so valuable to attract the gaze of people from both academia and industry. Consumers, providers, operators, etc. are generating a large amount of data on such services per minute on the Internet, which increasingly comes to show all typical properties of big data. New methodologies and techniques are therefore required for designing, validating, developing, testing and deploying cloud services on demand in this scenario, as well as for being effectively adaptive to business dynamics and users' explicit and implicit requirements.
The objective of this special issue is to identify the most promising research fields, report the main recent innovative results and promote the visibility and relevance of this noteworthy direction in the interdisciplinary field between services computing and other emerging disciplines. It will cover all the aspects about cloud services in the era of Big Data.
Special Issue on In Search of a New Alignment in Service Research
It is high time for engineering and business scholars to join forces to advance service research. The editors of two of the top journals in the service area, one from IEEE's services computing community and one from INFORMS' service science community, invite you to break out of your disciplinary comfort zone. Parallel special issues have been commissioned to intentionally challenge and disrupt the status quo. This dual special issue is intended to reconcile existing multi-pronged orientations and approaches to service research.
Trans-disciplinary service research will leapfrog the current landscape to improve services computing formalisms while concomitantly designing and delivering the highest quality business service systems that can profitably delight customers and clients. In many areas, modern research advances have benefited from multiple perspectives. Trans-disciplinary research means reaching out to scholars from other backgrounds. It means that vocabulary, research methods and historical foundations need to be shared, taught, challenged and reconciled. It's going to be hard work. It will take time. But it's what is needed. This dual-journal call-for-papers is intended to catalyze trans-disciplinary and risk-taking research, and the review policies and procedures for both special issues will be tuned to this end.
What's missing has been a research agenda to reconcile business service systems and systems of systems ideals with services computing's formal methods, standards, best practices and repeatable processes. New research is essential to address what has become an important inflection point: the business services and services computing research agendas need to be viewed from a common, global, societal lens. At stake is the continued viability of both research streams.
Some science and engineering scholars view business services research as a soft discipline, but there is significant respect for formal business process and workflow methods that can deliver model-driven development. Business researchers often view the engineering and computer science perspectives as remiss in addressing the essential and dynamic elements of customer co-production, B2B contracting and pricing, but there is significant respect for services computing's role in distributing, cost-effective scaling and personalization of computing service capability. Both areas are in transition, given the increasing value of big data methods and associated possibilities. Yes, there will be dueling methodologies, ranging from empirical to execution benchmarks to proof, and there will be dueling problems, ranging from NP-Complete to novel business strategy frameworks. Potential authors will need to leave their comfort zones behind and work together across disciplinary lines.
Special Issue on Security and Dependability of Cloud Systems and Services
Service-based cloud systems are being used in business-, mission- and safety-critical scenarios to achieve operational goals. Their characteristics of complexity, heterogeneity, and fast-changing dynamics bring difficult challenges to the research and industry communities. Among them, security and dependability (Sec & Dep) have been widely identified as increasingly relevant issues. Crucial aspects to be addressed include: metrics, techniques and tools for assessing Sec & Dep; modeling and evaluation of the impact of accidental and malicious threats; failure and recovery analysis; Sec & Dep testing, testbeds, benchmarks; infrastructure interdependencies, interoperability in presence of Sec & Dep guarantees.
The objective of this Special Issue is to bring together sound original contributions from researchers and practitioners on methodologies, techniques and tools to assess or improve the security and dependability of cloud systems and services.
Special Issue on Service-Oriented Collaborative Computing and Applications
Industries and societies today require new technologies to address increasingly complex design issues for products, processes, systems, and services while meeting the high expectation of customers. Service-oriented collaborative computing provides technological supports to meet this requirement. This special issue intends for researchers and practitioners involved in different but related fields to confront research challenges, issues, as well as research results and solutions in the related areas. The scope of this special issue includes the research and development of service-oriented collaborative computing technologies and their applications to the design of products, processes, systems, and services in industries and societies.
Special Issue on Software Engineering and Applications for Cloud-based Mobile Systems
With the global trend in making software systems both mobile and cloud-based, many such systems are being designed and offer situation-aware runtime services to their end users. At the same time, both latest and historic data, in massive amount, generated or accepted by these systems can be kept, processed, used, derived, and shared. Mobile components of such systems can be installed and upgraded through app markets either automatically or manually. Moreover, the changes in features of cloud components of such systems can be chosen by service providers at any time. This emerging type of software systems both enables and requires novel methods in conducting software requirement, design, verification and validation, deployment, operation, and maintenance activities. It also finds major applications in solving technical issues in the domains of smart and connected health, software analytics, wearable computing, internet-of-things, cyber-physical systems, creative computing, and smart planet, to name a few. Nonetheless, significant new challenges must be addressed. For instance, how to scale up and downsize mobile or cloud-based components of such systems at run time, how to collaboratively discover, manage and harvest lifecycle data and social information when conducting development activities and in operation , how to achieve data-driven coordination among the cyber-physical entities, how to manage the informationflow and decision making options in such systems are pressing issues to be investigated.
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.
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.
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.
Special Section on Semantic-Based Approaches for Analysis of Biological Data
The integration of biological (e.g. omics) data with biological knowledge is a recent trend in Bioinformatics. A lot of biological information is available and is spread on different sources and encoded in different ontologies (e.g. Gene Ontology, as well as in many others hosted by the Open Biomedical Ontologies Foundry).
Biological information is associated with biological concept in a process known as annotation. Annotating existing protein data with biological information may enable the use (and the development) of algorithms that use biological ontologies as a framework to mine annotated data.
Recently many methodologies and algorithms that use ontologies to extract knowledge from data, as well as to analyse ontologies themselves have been proposed and applied to other fields. Conversely, the use of such annotations for the analysis of protein data is a relatively novel research area that is currently becoming more and more important in research. As shown in literature there is a positive trend in the use of biological information in the analysis of protein data.
Proposed approaches span from the definition of the similarity among genes and proteins on the basis of the annotating terms, to the definition of novel algorithms that use such similarities for mining protein data on a system-level scale.
Special Issue on Big Data Processing in Computational Biology and Bioinformatics
Big data has emerged as an important application field which has shown its huge impact in different scientific research domains. In particular, the big data bioinformatics applications such as DNA sequence analysis have posed significant challenges to the state-of-the-art processing and computing systems. With the growing explosive data scale, the collection, storage, retrieval, processing, scheduling and visualization are key big data issues to be tackled.
This journal Special Issue on "Big Data Processing in Computational Biology and Bioinformatics" of IEEE/ACM Transactions on Computational Biology and Bioinformatics will provide a dedicated forum for discussing new research, development, and deployment efforts in big data processing paradigms in computational biology and bioinformatics. In this special issue, we invite authors to submit original, high-quality research articles, clearly focused on aspects of the design and implementation of processing methodologies to for big data bioinformatics and computational biology problems.
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.