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.
Special Issue on Toward Commercial Applications of Affective Computing
Emotional cues generated subconsciously by the human body have always been a crucial part of affective computing. Machines can learn much about a person's affective state by analyzing measurements such as autonomic nervous system responses, brain measurements, facial expressions and eye behavior. They can then act on the inferred information, adapting their own behavior, response or service based on the user's affective state. For example, physiological responses are used to quantify the stress and workload of critical personnel such as astronauts or pilots, then adapt the level of support provided by the machine, thereby keeping the user in a desired workload state. Numerous other applications have been proposed, from health and driver monitoring to exercise and game adaptation.
However, although great advances have been made and many applications have been proposed, few robust implementations have been presented or validated, and affective technology remains largely a curiosity. This weak adoption of affective technology is not due to lack of consumer interest, but rather due to a number of unsolved challenges. One crucial issue has been how to actually respond to the recognized affective states. Current research has focused mainly on affect recognition, and machines that do respond to recognized states mainly use very simple rules. Little work has been done on how the machine can bring the user to a desired affective state or how it can show empathy toward the user given many possible actions, user-specific preferences and potentially unreliable affect recognition. Furthermore, how can we measure whether the desired state has been actually achieved and whether it could have been achieved more efficiently? Finally, how can we do this in noisy and unpredictable situations where any rules defined in the laboratory may no longer be valid? Without this knowledge, it is nearly impossible to efficiently close the affective 'loop' and demonstrate a measurable, commercially attractive benefit of affective computing.
This special issue focuses on the next step toward commercial success of affective computing: systems that can both recognize affective states and efficiently respond to them outside the laboratory. In order to create this bidirectional link between the human and the machine, we hope to involve not only the affective computing community, but also other scientists and engineers who work on related topics such as artificial intelligence and user modeling. We particularly encourage submissions about commercial or commercializable applications.
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.
Special Issue on Special Issue on Big Data Analytics and the Web
Last few years have seen the rapid increase of sheer amount of data produced and communicated over the Web. Such Big Data are generated from all kinds of sources and applications such as social network services, cloud services, knowledge bases, and intelligent terminals, and often in a wide variety of formats such as unstructured, semi-structured, and structured. A particular recent trend around the Web is to connect and communicate between billions of physical objects (also called “Things”), i.e., Web of Things (WoT). WoT offers the capability of integrating both physical and virtual worlds and massive volumes of real-time data are expected to be produced by these connected things and their associated sensors.
While it is widely believed that Big Data holds the potential to revolutionize many aspects of our modern society (e.g., smart cities), many technical challenges need to be addressed before this potential can be realized. Indeed, Big Data requires a revisit of data analysis systems in fundamental ways at all stages from data acquisition and storage to data transformation and interpretation. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of Big Data. This special issue aims at presenting the latest developments, trends, and research solutions of Big Data analytics on the Web.
Special Issue on Media Data: Understanding, Search, and Mining
The explosion of images, videos and other media data in the Internet, mobile devices, and desktops has attracted more and more interest in the Big Media research area. Big media opens great unprecedented opportunities to address many challenging computing problems, offering a promising possibility for in-depth media understanding, as well as exploring the very big scale media data to bridge the well-known semantic gap between high-level semantic and low-level features. Big media provides richer information, ranging from social relations to context information associated to rich media data of diverse modalities. It also provides us the opportunity to mine reliable and helpful knowledge from Big media for a wide variety of applications.
Big media is big in terms of various aspects, such as the number of media items, the dimension of the representation, and the number of concepts, and thus entails a lot of research challenges and opportunities. For example, how does the traditional machine learning algorithms, which have been proven efficient and effective in thousands of data points, scale up to the web-scale big media data with millions and even billions of items? Seeking the answer motivates us to design parallel and distributed machine learning platforms, exploiting GPUs as well as developing practical algorithms to fit in restricted storage limits and accelerate the algorithms with the ever-growing size of the database and the dimension. Moreover, how is the big media data organized and how can it be managed to enable efficient browsing and retrieval? The research interests in this direction produced many hashing, indexing and clustering algorithms for high-dimensional data. Besides, it is also important to construct benchmark data to facilitate and validate the newly-developed big-media algorithms.
This special issue targets the researchers and practitioners from both the industry and the academia, and provides a forum to publish recent state-of-the-art achievements in the Big Media research area.
Special Issue on Big Scholar Data Discovery and Collaboration
Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, and course materials. The abundance of data sources enables researchers to study scholarly collaboration at a very large scale. The ever increasing diversity of disciplines and complexity of research problems, particularly multi-disciplinary research, requires collaboration. Besides the traditional venues of collaboration where scholars typically meet annually at conferences or meetings, the Internet provides a wide range of platforms for scholars to engage with other scholars. These new platforms include academic search-oriented Web engines such as Google Scholar, social media sites such as Academia.edu, ResearchGate and Mendeley, more interactive social sites such as Twitter and Facebook, and Wiki-style virtual collaboration sites. These services allow scholars to share academic resources, exchange opinions, follow each other’s research, keep up with current research trends, and build their professional networks. Researchers increasingly realize that scholarly achievements should not merely be the final published articles. The datasets used in study and many other intermediary results are equally important for supporting research. Therefore, a set of rapidly developing research topics, research data management, data curation/stewardship, data sharing policy, etc. are becoming important issues for research communities. This special issues aims at bringing together researchers with diverse interdisciplinary backgrounds interested in scholarly big data.
Special Issue on nalytics with Big Medical Data
In the past decade, we have witnessed the greying of society and the escalating costs of medical managements, which have been the number one concern of most governments. This has heightened the need for preventive healthcare practices that helps to anticipate and prevent the onset of illnesses. On the other hand, with the help of advanced medical devices and social networking services, medical data is more convenient to be acquired, shared, and delivered. As such, medical filed is entering a big data era. When being applied to big medical data applications, lots of the existing tools and systems for big medical data analytics would become questionable. However, the big medical data itself in turn has provided unique opportunity for better wellbeing.
On the other hand, there is a realization that an essential part of long-term healthcare is in adopting a good life style that involves proper exercises and diets. Many companies marketing wearable health sensor products therefore also offer mobile health apps that provide first-order analytics to monitor and track personal life styles. However, the sensing data and the low-level analytics are typically used in isolation without integration to medical knowledge or environmental data, such as weather and pollution. In addition, there are strong links between personalized health sensor data to knowledge of critical illnesses such as Diabetes, Depression or Arthritis, as the long-term cares of these illnesses are related to proper activities and diets. The integration of these sources would usher in a new era of personalized wellness that enables the system and users to work collaboratively towards better wellness and lifestyles.
This special issue aims to link big medical data to sensor and environmental data to support better personalized health and user mobility, especially with respect to critical illnesses. Originality and impact on society, in combination with the innovative technical aspects of the proposed solutions will be the major evaluation criteria.
Special Issue on Urban Computing
Urbanization’s rapid progress has modernized people’s lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges can seem nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data contain rich knowledge about a city and can help tackle these challenges when used correctly.
Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion . Urban computing connects unobtrusive and ubiquitous sensing technol-ogies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. Urban computing also helps us understand the nature of urban phenomena and even predict the future of cities.
Special Issue on Big Data Infrastructure
Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand.
In this special issue, we invite articles on innovative research to address challenges of Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures, Hadoop or Spark with emphasis on addressing real-time requirements imposed by emerging Big Data applications such as sensing data, e-commerce data, business transactions and web logs, and etc.
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.
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 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.
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
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, www.computer.org. Please thoroughly read these before submitting your manuscript.
Please submit your paper to Manuscript Central at https://mc.manuscriptcentral.com/tetc-cs 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, firstname.lastname@example.org
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.
Special Issue on Security of Beyond CMOS Devices: Issues and Opportunities
Continuous scaling of CMOS in the quest of smaller and faster transistors has brought us into the sub 50-nm technology era with the end of CMOS scaling in sight. Number of alternative nanoscale devices – both silicon and non-silicon – with interesting switching characteristics have come onto the horizon with the promise to replace CMOS as both computing and/or information carrier devices. These nanoscale devices are poised to profoundly impact the design of secure information processing systems by ushering in new attack modalities, and by enabling new approaches to secure design that leverage their unique characteristics. Continuing the scaling of integrated systems into the deep nanoscale era will require unprecedented innovation in device technology, including materials, device structures, and possibly entirely new state variables (e.g., mechanical state, electron spin) to represent information. These major shifts will impact the design of secure systems in profound and unanticipated ways. First, the properties of emerging nanoscale devices (e.g., switching behavior, non-volatility) could lead to new vulnerabilities. Second, new defense mechanisms (e.g., new design of security primitives or more powerful cryptographic solution) may be enabled by the unique characteristics of nanoscale devices. With these observations in mind, this special issue aims at comprehensively covering security issues with beyond-CMOS devices and emerging security solutions for systems built with these devices.
Special Issue on Defect and Fault Tolerance in VLSI and Nanotechnology Systems
The continuous scaling of CMOS devices as well as the increased interest in the use of emerging technologies make more and more important the topics related to defect and fault tolerance in VLSI and nanotechnology systems. All aspects of design, manufacturing, test, reliability, and availability that are affected by defects during manufacturing and by faults during system operation, are of interest. The IEEE Transaction on Emerging Topics in Computing (TETC) seeks original manuscripts for a Special Section on Defect and Fault Tolerance in VLSI Systems scheduled to appear in the December issue of 2016.
Special Issue on Emerging Computational Paradigms and Architectures for Multicore Platforms
Multicore and many core embedded architectures are emerging as computational platforms in many application domains ranging for high performance computing to deeply embedded systems. The new generations of parallel systems, both homogeneous and heterogeneous that are developed on top of these architectures represent what is called the emerging computing continuum paradigm. A successful evolution of this paradigm is however imposing various challenges from both an architectural and a programming point of view. The design of embedded multicores/manycores requires innovative hardware specification and modeling strategies, as well as low power simulation, analysis and testing. New synthesis approaches, possibly including reliability and variability compensation, are key issues in the coming technology nodes. Furthermore, thermal aware design is mandatory to manage power density issues. The design of effective interconnection networks is a key enabling technology in a manycore paradigm. New solutions such as photonics and RF NoCs architectures are emerging solutions on this regard. At the same time, these new interconnection systems have to be compliant with innovative 3D VLSI packaging technologies involving vertical interconnections in 3D and stacked ICs. These design solutions enable the integration of more and more IPs, resulting in heterogeneous platform where reconfigurable components, multi-DSP engines and GPUs collaborate to provide the target performance and energy requirements. Along with design and architectural innovations, many challenges have to be faced to enable an effective programming environment to many core systems. These challenges call from innovative solutions at the various levels of the programming toolchain, including compilers, programming models, runtime management and operating systems aspects. Holistic and cross-layer programming approaches have to be targeted considering not only performance, but also energy, dependability and real-time requirements. Finally, on the application side, multicore/manycore embedded systems are pushing developments in various domains such as biomedical, health care, internet of things, smart mobility, and aviation.
This special issue/section asks for emerging computation technology aspects related, but not limited to the mentioned topics. Contributions must be original and highlight emerging computation technologies in design, testing and programming multicore and manycore systems.
Special Issue on New Paradigms in Ad Hoc, Sensor and Mesh Networks, From Theory to Practice
Ad hoc, sensor and mesh networks have attracted significant attention by academia and industry in the past decade. In recent years however new paradigms have emerged due to the large increase in number and processing power of smart phones and other portable devices. Furthermore, new applications and emerging technologies have created new research challenges for ad hoc networks. The emergence of new operational paradigms such as Smart Home and Smart City, Body Area Networks and E-Health, Device-to-Device Communications, Machine-to-Machine Communications, Software Defined Networks, the Internet of Things, RFID, and Small Cells require substantial changes in traditional ad hoc networking. The focus of this special issue is on novel applications, protocols and architectures, non-traditional measurement, modeling, analysis and evaluation, prototype systems, and experiments in ad hoc, sensor and mesh networks.
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: June 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.
Special Issue on Wearable Technologies and the Internet of Things in Education and Training
The Internet of Things (IoT) is being touted as "the next technological revolution" and one that will be "the most potentially disruptive" we will see in our lifetime, surpassed only by the World Wide Web and universal mobile connectivity [1, p. 24]. It involves real-world, physical objects with embedded computational and networking capabilities communicating and interacting with one another, with other computing devices, as well as with users on the global Internet. With the advent and growth of the IoT, homes, workplaces, and educational institutions – even entire cities and countries – are becoming increasingly "smart" and interconnected, which promises to substantially enhance or change the ways in which we live, play, work, and learn.
Amid the rise of the IoT, we have also been witnessing advances in wearable computing and electronic technologies that have made possible the creation of the "Internet of Me" . Such technologies have now entered the mainstream  and products powered by them are becoming increasingly available on the mass market, with consumer-level devices like smart glasses (e.g., Google Glass, Microsoft HoloLens), smart watches (e.g., Apple Watch), smart clothes, fitness bands/activity trackers (e.g., Fitbit, Nike+ FuelBand), and headmounted cameras (e.g., GoPro) regularly dominating the technology news headlines of late. These technologies and devices along with others still being developed are able to augment human cognition, behavior, and interactions in powerful ways that were previously inconceivable.
It is clear that wearable technologies and the IoT hold much potential for and have many possible applications in education and training , . While they have garnered considerable attention and interest in this sector –, however, there continues to be a dearth of real scholarship surrounding their use for learning, teaching, and assessment, the majority of published work to date consisting largely of anecdotal reports or being focused primarily on the technology. This themed special issue of IEEE Transactions on Learning Technologies will seek to address this gap by publishing a combination of theoretical/conceptual and empirical articles that contribute to the building of a rigorous evidence base aimed at guiding and supporting practice in addition to inspiring and informing future research and development in this rapidly emerging and evolving area. Submissions that go beyond technical descriptions or "show and tell" to engage deeply with pertinent questions and issues relating to pedagogical and learning design as well as those that systematically examine the efficacy of tools, methods, and approaches in improving learning are especially encouraged. Multidisciplinary studies are particularly welcome.
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.
Special Issue on Emerging Memory Technologies - Modeling, Design, and Applications for MultiScale Computing
Emerging memory technologies have demonstrated a great potential on improving many aspects of present-day and future memory hierarchy, offering high integration density, large capacity, close-to-zero standby power, and good resilience to soft errors. The recent technology progress of various emerging memories, such as phase change memory, spintronic memory, resistive memory (memristor) and ferroelectric memory etc., is due to attracted tremendous investments from both academia and industry. Besides developing robust and scalable devices, the unique characteristics of these emerging memories, such as read-write asymmetry, stochastic programming behavior, and the tradeoffs among performance, power, and data retention, etc., introduce plenty of opportunities and challenges for novel circuit designs, architectures, system organizations, and management strategies. There is an urgent need of modeling, analysis, design and application of emerging memory technologies across multiple technology scales to accelerate their technology development and adoption.
Special Issue on Hardware/Software Cross-Layer Technologies for Trustworthy and Secure Computing
The increasing complexity of networked computing systems makes modern network systems vulnerable to various attacks against their resources, infrastructure, and operability. While the reasons for such attacks may be tied to complex sociological issues, the cause of our inadequate defense solutions lies in the single-layered approach used to address computer systems security. Current security approaches separate defense strategies into distinct realms, either hardware or software. Accordingly, cross-layer approaches for secure computing and circuit systems are entirely lacking. In addition, the wide usage of third-party IP cores and outsourcing fabrication/packaging services make it possible for malicious hardware modules to enter the design flow and, therefore, complicates the problem of trusted system design and verification. While hardware security has been under investigation for years, systematically understanding the security threats to hardware infrastructure from a cross-layer perspective is an emerging research topic. Therefore, this special issue intends to serve as a forum to present state-of-the-art security solutions crossing software and hardware layers towards trustworthy computing system development.
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.
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 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 Services and Software Engineering towards Internetware
Services computing provides a foundation to build software systems and applications over the Internet, and emerging hybrid networked platforms motivated by it. Due to the open, dynamic, and evolving nature of the Internet, new features were born with these Internet-scale and service-based software systems. Such systems should be situation-aware, adaptable, and able to evolve to effectively deal with rapid changes of user requirements and runtime contexts. These emerging software systems both enable and require novel methods in conducting software requirement, design, deployment, operation, and maintenance beyond existing services computing technologies. New programming and lifecycle paradigms accommodating such Internet-scale and service-based software systems, referred to as Internetware, are inevitable.
This special issue intends for researchers and practitioners involved in different but related fields to confront research challenges and innovative results and solutions and lead various pathways towards Internet-scale and service-based software.
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.
Special Issue on Mobile Big Data Management and Innovative Applications
With the rapid growth of smart phones and the development of wireless technology, mobile applications in the world are continuously expanded, while the proliferation of mobile devices and their enhanced onboard sensing capabilities are playing increasingly important role to drive the explosion of mobile data. Furthermore, advances in social networking and cyber-physical systems are making mobile data “big” and consequently bring challenges for management and processing. All these factors contribute to a possible new service paradigm: mobile big data driven service computing, which includes various unique aspects, e.g., mobile big data collection and sensing; novel technologies for mobile big data transmissions, such as software-defined data transmissions and processing; mobile big data mining, such as mobility and demographic tracing based data mining. Under the new service paradigm, mobile big data management techniques and innovative applications need to be extensively investigated to gain the great potentials brought by the mobile big data.
In this special issue, we are particularly interested in high quality contributions and innovations in this interdisciplinary area of mobile big data technologies, systems, and services. We are especially interested in mobile big data management and innovative applications.
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.
Emerging Security Trends for Biomedical Computations, Devices, and Infrastructures
Biomedical deeply-embedded systems (deployed in human body, with computer programs sending and receiving medical data and performing data mining for the decisions) are currently getting developed with rapid rate and tremendous success. Moreover, the security/privacy issues in every aspect of biomedical devices including confidentiality/integrity/ availability/privacy of implantable and wearable medical devices, secure and private big data analytics, acquisition, and storage, privacy-preserving data mining for biomedicine, secure machine-learning of bioinformatics, and security of hardware and software systems used for biological databases are emerging given their unique constraints and usage model. Many of the systems for such computations will need to be transparently integrated into sensitive environments – the consequent size and energy constraints imposed on any security solutions are extreme. Thus, unique challenges arise due to the sensitivity of computation processing, need for security in implementations, and assurance "gaps."
The focus of this special issue will be on novel security methods for biomedical computations, devices, and infrastructures, emerging cryptographic solutions applicable to extremely-constrained medical devices (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 used for therapeutic and diagnosis purposes, and bioinformatics big data.
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.