Chunming Rong

University of Stavanger

Background Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slides...

Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensit...

Background: Histopathology is a gold standard for cancer diagnosis. It involves extracting tissue specimens from suspicious areas to prepare a glass slide for a microscopic examination. However, histological tissue processing procedures result in the introduction of artifacts, which are ultimately transferred to the digitized version of glass slide...

Mobile edge computing (MEC) relieves the latency and energy consumption of mobile applications by offloading computation-intensive tasks to nearby edges. In wireless metropolitan area networks (WMANs), edges can better provide computing services via advanced communication technologies. For improving the Quality-of-Service (QoS), edges need to be co...

The total volume of data in the subsurface is tremendous and grows exponentially each year. Sufficiently and effectively utilizing the subsurface data from multiple sites will advance the knowledge required for innovations that power the future growth of the energy industry. This research article explores the increasing needs and challenges for cro...

Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural ar...

Digitized histopathology glass slides, known as Whole Slide Images (WSIs), are often several gigapixels large and contain sensitive metadata information, which makes distributed processing unfeasible. Moreover, artifacts in WSIs may result in unreliable predictions when directly applied by Deep Learning (DL) algorithms. Therefore, preprocessing WSI...

Statistical heterogeneity, especially feature distribution skewness, among the distributed data is a common phenomenon in practice, which is a challenging problem in federated learning that can lead to a degradation in the performance of the aggregated global model. In this paper, we introduce pFedV, a novel approach that leverages a variational in...

The aim of dataset distillation is to encode the rich features of an original dataset into a tiny dataset. It is a promising approach to accelerate neural network training and related studies. Different approaches have been proposed to improve the informativeness and generalization performance of distilled images. However, no work has comprehensive...

Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural ar...

Background Approximately 3-8% of all newborns do not breathe spontaneously at birth, and require time critical resuscitation. Resuscitation guidelines are mostly based on best practice, and more research on newborn resucitation is highly sought for. Methods The NewbornTime project will develop artificial intelligence (AI) based solutions for activ...

Nonlinear Conservation Laws of Partial Differential Equations (PDEs) are widely used in different domains. Solving these types of equations is a significant and challenging task. Graph Neural Networks (GNNs) have recently been established as fast and accurate alternatives for principled solvers when applied to standard equations with regular soluti...

Gradient inversion attacks can reconstruct the victim's private data once they have access to the victim's model and gradient. However, existing research is still immature, and many attacks are conducted in ideal conditions. It is unclear how damaging such attacks really are and how they can be effectively defended. In this paper, we first summariz...

The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g ., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradie...

In federated learning, multiple parties may use their data to cooperatively train a model without exchanging raw data. Federated learning protects the privacy of users to a certain extent. However, model parameters may still expose private information. Moreover, existing encrypted federated learning systems need a trusted third party to generate an...

Modern information systems are built fron a complex composition of networks, infrastructure, devices, services, and applications, interconnected by data flows that are often private and financially sensitive. The 5G networks, which can create hyperlocalized services, have highlighted many of the deficiencies of current practices in use today to cre...

The demand for electricity is increasing exponentially day by day especially with the arrival of electric vehicles. In smart community neighborhood project, it demands electricity should be produced at the household or community level and sell or buy according to the demands. Since the actors can produce, sell and also buy according to the demands,...

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Se...

Many existing searchable encryption schemes are inflexible in retrieval patterns. The data usage authorization is almost permanent valid as long as the user is not revoked. This "all-or-nothing" authorization mode is not compatible with the "pay-as-you-use" commercial billing model. In this paper, we propose a new notion called time-controlled expr...

As the workloads and service requests in cloud computing environments change constantly, cloud-based software services need to adaptively allocate resources for ensuring the Quality-of-Service (QoS) while reducing resource costs. However, it is very challenging to achieve adaptive resource allocation for cloud-based software services with complex a...

Modern information systems are built fron a complex composition of networks, infrastructure, devices, services, and applications, interconnected by data flows that are often private and financially sensitive. The 5G networks, which can create hyperlocalized services, have highlighted many of the deficiencies of current practices in use today to cre...

Unlike traditional central training, federated learning (FL) improves the performance of the global model by sharing and aggregating local models rather than local data to protect the users' privacy. Although this training approach appears secure, some research has demonstrated that an attacker can still recover private data based on the shared gra...

Trusted third parties (TTPs) are frequently used for serving as an authority to issue and verify transactions in applications. Although the TTP-based paradigm provides customers with convenience, it causes a whole set of inevitable problems such as security threats, privacy vulnerabilities, and censorship. The TTP-based paradigm is not suitable for...

Byzantine Fault Tolerant (BFT) state machine replication protocols are used to achieve agreement among replicated servers with arbitrary faults. Most existing BFT protocols perform well in fault-free cases, but usually suffer from serious performance degradation when faults occur. In this paper, we present DBFT, a BFT protocol that realizes gracefu...

In this article, we propose d ual t raceable d istributed a ttribute b ased e ncryption with s ubset k eyword s earch system (DT-DABE-SKS, abbreviated as $\mathcal {DT}$ ) to simultaneously realize data source trace (secure provenance) and user trace (traitor trace) and flexible subset keyword search from polynomial interpolatio...

A redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-tradi...

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use exc...

Renewable energy microgeneration is rising leading to creation of prosumer communities making it possible to extract value from surplus energy and usage flexibility. Such a peer-to-peer energy trading community requires a decentralized, immutable and access-controlled transaction system for tokenized energy assets. In this study we present a unifie...

We have entered the era of big data, and it is considered to be the "fuel" for the flourishing of artificial intelligence applications. The enactment of the EU General Data Protection Regulation (GDPR) raises concerns about individuals' privacy in big data. Federated learning (FL) emerges as a functional solution that can help build high-performanc...

This paper addresses the estimation of household communities’ overall energy usage and solar energy production, considering different prediction horizons. Forecasting the electricity demand and energy generation of communities can help enrich the information available to energy grid operators to better plan their short-term supply. Moreover, househ...

Due to privacy protection, the conventional machine learning approaches, which upload all data to a central location, has become less feasible. Federated learning, a privacy-preserving distributed machine learning paradigm, has been proposed as a solution to comply with privacy requirements. By enabling multiple clients collaboratively to learn a s...

Traditional searchable encryption schemes based on the Term Frequency-Inverse Document Frequency (TF-IDF) model adopt the presence of keywords to measure the relevance of documents to queries, which ignores the latent semantic meanings that are concealed in the context. Latent Dirichlet Allocation (LDA) topic model can be utilized for modeling the...

TOTEM: Token for controlled computation is a newly proposed framework that integrates blockchain with big data systems. This framework allows users to send the computational code towards the data and analyse instead of the conventional method of data analysis which required data to be sent across the network. The framework provides a TOTEM defined...

Cloud Computing provides on-demand computing services like software, networking, storage, analytics, and intelligence over the Internet (“the cloud”). But it is facing challenges because of the explosion of the Internet of Things (IoT) devices and the volume, variety, veracity and velocity of the data generated by these devices. There is a need for...

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address th...

Large scale technological, economical and environmental changes led to increased energy consumption all over the world. Electrical energy became an indispensable daily factor due to the large automation and emergence of smart devices and equipments. Therefore, efficient resource management is a major goal of the energy market. In order to adapt ele...

Decision-making in the energy field, especially in recent years, is highly data-driven. The crucial information extracted from historical data tells a lot about future behaviour and expected events. The extraction of this information is traditionally performed by statistical analysis and predicting future trends. However, in recent years, the amoun...

With the maturation of technologies such as communication, sensors and networks, very large amounts of data are generated that are available for processing and analysis. With the popularizing process of the Internet of Things (IoT), available data resources will become even more plentiful and diversified in the near future. To provide feasible solu...

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent medical model, which includes illness states, treatment methods...

As bitcoin has drawn a lot of attention, people have developed various cryptocurrencies based on blockchain framework. The decentralization feature of blockchain makes the transaction information public in the cryptocurrency. It is possible to expose the user’s privacy. Therefore, it is necessary to design an efficient and secure scheme to hide tra...

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment...

Internet of Things (IoT) has become a critical infrastructure in the smart city services. Unlike traditional network nodes, most of the current IoT devices are constrained with limited capabilities. Moreover, frequent changes in network status (e.g., nodes turns into sleep mode to save battery) make it even more difficult to set up a stable, secure...

With time-varying workloads and service requests, cloud-based software services necessitate adaptive resource allocation for guaranteeing Quality-of-Service (QoS) and reducing resource costs. However, due to the ever-changing system states, resource allocation for cloud-based software services faces huge challenges in dynamics and complexity. The t...

Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the curren...

Cities are becoming smarter by incorporating hardware technology, software systems, and network infrastructure that provide Information Technology (IT) systems with real-time awareness of the real world. What makes a “smart city” functional is the combined use of advanced infrastructure technologies to deliver its core services to the public in a r...

Blockchain, as an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, has attracted intensive attention in both research and applications recently. Blockchain, especially powered by chain-coded smart contracts, has the full potential of revolutionizing increasingly centralized cyber-...

The emerging multi-cloud environments (MCEs) empower the execution of large-scale scientific workflows (SWs) with sufficient resource provisioning. However, due to complex task dependencies in SWs and various cost-performance of cloud resources, the SW scheduling in MCEs faces huge challenges. To address these challenges, we propose an Online Workf...

Nowadays, the importance of energy management and optimization by means of smart devices has arisen as an important issue. On the other hand, the intelligent application of smart devices stands as a key element in establishing smart cities, which have been suggested as the solution to complicated future urbanization difficulties in coming years. Co...

Data analytics is rapidly growing field in both academia and industry dealing with processing and interpreting large and complex data sets. It has got already many successful applications via advancing Machine (ML) and Deep (DL) Learning techniques, starting to evolve in the upstream petroleum industry as well. The industry operates now with huge a...

As an innovated and revolutionized technology, blockchain has been applied in many fields, such as cryptocurrency, food traceability, identity management, or even market prediction. To discover its great potential, both the industry and academia have paid great attention to it and numerous researches have been conducted. Based on the literatures an...

Internet of Things (IoT), coming with billions of connected devices, could potentially transform our daily life but could also create a serious security headache. It brings greater complications in securely accessing these devices with privacy protection guaranteed, and several research issues need to be investigated in detail, e.g., access control...

Current proof-of-work blockchains are not sustainable in terms of energy needed to run them. In this paper we propose a new scheme that avoids wasted proof-of-work by a dynamic probabilistic method, where the consensus algorithm can be adjusted according to the parties' required assurance levels.

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy mi...