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.
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 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 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 Cloud Security Engineering
As the use of cloud computing grows throughout society in general, it is essential that cloud service providers and cloud service users ensure that security and privacy safeguards are in place. There is, however, no perfect security and when a cybersecurity incident occurs, digital investigation will require the identification, preservation and analysis of evidential data.
This special issue is dedicated to the identification of techniques that enable security mechanisms to be engineered and implemented in Cloud-based systems. A key focus will be on the integration of theoretical foundations with practical deployment of security strategies that make Cloud systems more secure for both end users and providers - enabling end users to increase the level of trust they have in Cloud providers - and conversely for Cloud service providers to provide greater guarantees to end users about the security of their services and data. Significant effort has been invested in performance engineering of Cloud-based systems, with a variety of research-based and commercial tools that enable autoscaling of Cloud systems, mechanisms for supporting Service Level Agreement-based provisioning and adaptation and more recently for supporting energy management of large scale data centres. This special issue will be devoted to understanding whether a similar engineering philosophy can be extended to support security mechanisms, and more importantly, whether experience from the performance engineering community (who often need to carry out analysis on large log files) can be carried over into the security domain.
We encourage authors to be exploratory in their papers - reporting on novel use of performance engineering tools that could be repurposed for supporting security management and vice versa.
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 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.
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 Wearables, Implants, and Internet of Things
Recent years have seen an explosion of deeply embedded, smart, and highly connected computing devices in diverse form factors. In particular, wearable and implants technologies, and Internet of Things (IoT) have made significant forays into nearly all aspects of our life. With advances in technology, design of new and advanced sensors, pervasive connectivity, and the trend in business towards cloud-driven data-centric solutions, the future is projected to see an even higher proliferation of systems comprising of such devices that coordinate through cloud to solve complex, distributed tasks. Commensurate with computing capability, the applications have also scaled in complexity by several factors, e.g., from smart phones to smart cities. This special issue targets comprehensive coverage of research issues related to wearables, implants, and IoT.
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.
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 Processes Meet Big Data
The aim of process mining is to discover, monitor and improve business processes by extracting knowledge from event logs readily available in today’s information systems. Process monitoring and analysis has enjoyed a tremendous growth and a rapid development at both conceptual and algorithmic levels. In particular, there have been successful realizations of process monitoring systems in many application areas, including manufacturing, e-health and e- government. Today, the current trend toward large-scale collaborative processes featuring thousands of elementary activities per minute is generating a number of new research issues. When large-scale processes are executed on (cloud- based) service-oriented environments or even on the global Net, elementary activities can be mapped to fine or coarse- grained protocol events and process logs increasingly come to show all typical properties of “big data”: wide physical distribution, diversity of formats, non-standard data models, heterogeneous semantics. Computing metrics over such “big logs” also requires to handle security and privacy concerns of many participants, and even to deal with non- uniform trustworthiness of log entries. New techniques are therefore required for designing, validating and deploying process metrics in this scenario, as well as for effectively dash-boarding the processes’ performance indicators.
This special issue of IEEE Transaction on Service-Oriented Computing is intended to create an international forum for presenting innovative developments of process monitoring and analysis over service-oriented architectures, aimed at handling “big logs” and use them effectively for discovery, dash-boarding and mining. The ultimate objective is to identify the promising research avenues, report the main results and promote the visibility and relevance of this area.
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.
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.