Journal Information
IEEE Transactions on Emerging Topics in Computing (TETC)
Impact Factor:
Call For Papers
EEE Transactions on Emerging Topics in Computing 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.
Last updated by Dou Sun in 2021-07-25
Special Issues
Special Issue on Advances in Emerging Privacy-Preserving Computing
Submission Date: 2022-06-15

Machine learning and cloud computing have dramatically increased the utility of data. These technologies facilitate our life and provide smart and intelligent services. Notably, machine learning algorithms need to learn from massive training data to improve accuracy. Hence, data is the core component of machine learning and plays an important role. Cloud computing is a new computing model that provides on-demand services, such as data storage, computing power, and infrastructure. Data owners are allowed to outsource their data to cloud servers, but will lose direct control of their data. The rising trend in data breach shows that privacy and security have been major issues in machine learning and cloud computing. Computation over unencrypted sensitive data may compromise the confidentiality of data and suffer various security attacks, such as identity theft and fraud. To regulate data collecting and releasing, the European Union (EU) announced the strict privacy protection policy called General Data Protection Regulation (GDPR), which was put into effect in 2018 and applied to enhance individuals’ control and rights over their personal data. Traditional cryptographic methods (such as DES, AES, and hash) can be applied to protect data confidentiality, but do not support computation over encrypted data. Therefore, these methods are not suitable for machine learning and cloud computing. To address the requirements for computation in machine learning and cloud computing, new computation techniques must be developed. Privacy-preserving computation techniques were proposed to provide protection on sensitive data and support secure computation over encrypted data. Since data remains encrypted or opaqued during computation, it is immune to being collected and abused. Some methods have been proposed to implement privacy-preserving computation, such as homomorphic encryption and secure multi-party computation, but these methods are computationally costly. Therefore, it is urgent to develop new privacy-preserving computation techniques to protect data confidentiality and support efficient computation over encrypted data. Relevant topics of interest to this special section include (but are not limited to): - Foundations and applications of privacy-preserving computing: secure multi-party computation, zero-knowledge, oblivious transfer, security models, etc. - Real-world applied differential privacy, such as e-health, image sharing, and location-based services - Homomorphic encryption (HE): novel applications of HE, implementations in hardware and software of HE, etc. - Functional encryption (FE): novel applications of FE, efficient constructions for concrete functions, implementations in hardware and software of FE, etc. - Privacy-preserving computing and machine learning in real-world computing applications - Design and implementation of trusted platform modules (TPMs): TPM-based anonymous authentication, signature, encryption, identity management, etc. - Applied trusted execution environments (TEEs): TEE-based privacy techniques, vulnerability and countermeasures of TEE, distributed TEE, decentralized TEE, etc.
Last updated by Dou Sun in 2022-04-30
Special Issue on Emerging Trends and Advances in Graph-Based Methods and Applications
Submission Date: 2022-06-30

Recently, graph structures have attracted a lot of attention in many domains. Indeed, there is an increasing number of applications where data are cogently represented by well-structured and flexible graph models, mainly due to their ability to encode both topological and semantic information, thus going beyond the classical and simple Euclidean domain. For example, in e-commerce, a graph-based learning system can exploit the interactions between users and products to make highly accurate and customized recommendations. In chemistry, molecules are modelled as graphs, and identifying their bioactivity leads to drug discovery. In a citation network, papers are linked to each other via citationships, and they need to be categorized into groups. Beyond these examples, data can be represented by graphs in many other applications: scene graph generation and understanding, object tracking, point clouds classification, and action recognition in computer vision; proteinomic and genomic data, text classification, relationships among documents or words for inferring document labels in natural language processing; forecasting traffic speed, congestion, and anomalies in transportation networks; and many more. Remarkably, in most of the above scenarios, the adoption of Graph Neural Network (GNN) models has been proven to be particularly effective. Despite their success and wide applicability, some important issues, such as scalability, computing adaptability, and effectiveness of the solutions, still remain open: Scalability – It is the main bottleneck in using graphs for real applications; graphs have been used from the beginnings in computer science domains such as operational research, pattern recognition, programming and computing, but most of the proposed methods and algorithms could handle sparse graphs only. More recently, many diverse solutions have been proposed to address this problem, such as heuristic methods, approximate solutions, and the exploitation of massively parallel and distributed architectures. Nonetheless, scalability issues still remain an open problem. Computing adaptability – Recently, in many research domains such as pattern recognition, databases, and knowledge reasoning, we are witnessing a significant shift from Euclidean space representations towards graph-based ones. Nonetheless, this re-design poses problems of adaptability that deserve to be tackled. Moreover, there exist many other aspects to take into account when dealing with graphs, such as the memory utilization (e.g., wasting allocated memory with very large sparse matrices representing graphs), or the lack of locality principle in memory accesses, both limiting the performances of most graph algorithms. Effectiveness of the solutions – In some domains, algorithms dealing with vector data are still far better performing than their graph-based version, in terms of accuracy, error rate, and other metrics. This poses the question whether such inefficiency is due to the graph-based representation itself or to the inadequacy of the algorithmic model designed to handle such data structures. To this end, novel algorithms and models adopting Deep Learning on Graphs may represent a promising direction requiring further investigation. The purpose of this special section is to provide a forum for all novel aspects of graph-based methods over wide application and research domains, as well as to foster a thorough discussion about state-of-the-art techniques and results, achieved goals, and open challenges. Topics of interest to this special section include: - Advances in computing and learning on graphs Graph Neural Networks (GNNs) Graph data fusion methods and graph embedding techniques Efficient, parallel, and distributed processing frameworks for big graphs Novel dynamic, spatial, and temporal graphs for recognition and learning Emerging graph-based methods in computer vision Interactivity, explainability, and trust in graph-based learning methods - Applications of GNNs Human behavior and scene understanding using graphs Benchmarks for GNNs Graph signal processing Application of graph data processing in biology, healthcare, transportation, natural language processing, social networks, etc.
Last updated by Dou Sun in 2022-04-30
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