Lianyong Qi’s research while affiliated with Qufu Normal University and other places

Smart environments are now an undeniable part of our life. A day-by-day increase in the use of new technologies based on Artificial Intelligence (AI), Internet of Things (IoT), communication and information systems, human-machine interactions, multimedia sensors, and bio-sensing devices is happening. The good research practice in these areas is absolutely the main factor of advancing our knowledge in this regard. Smart environments contain a variety of applications in different industries including entertainment industry, manufactures, healthcare systems, and Information Technology (IT). From the viewpoint of social systems, the main large-scale applications (not personal) of Cyber-Physical Systems (CPS) to make a smart environment are mainly smart homes and cities, health systems, intelligent transportation systems, green energy systems, and environmental protection and monitoring systems (including remote sensing). The personal use of such technologies is mostly around entertainment including IPTVs, virtual reality, games, other multimedia services, as well as personal healthcare services.

Deep reinforcement learning (DRL) has shown promising results in modeling dynamic user preferences in RS in recent literature. However, training a DRL agent in the sparse RS environment poses a significant challenge. This is because the agent must balance between exploring informative user-item interaction trajectories and using existing trajectories for policy learning, a known exploration and exploitation trade-off. This trade-off greatly affects the recommendation performance when the environment is sparse. In DRL-based RS, balancing exploration and exploitation is even more challenging as the agent needs to deeply explore informative trajectories and efficiently exploit them in the context of RS. To address this issue, we propose a novel intrinsically motivated reinforcement learning (IMRL) method that enhances the agent’s capability to explore informative interaction trajectories in the sparse environment. We further enrich these trajectories via an adaptive counterfactual augmentation strategy with a customised threshold to improve their efficiency in exploitation. Our approach is evaluated on six offline datasets and three online simulation platforms, demonstrating its superiority over existing state-of-the-art methods. The extensive experiments show that our IMRL method outperforms other methods in terms of recommendation performance in the sparse RS environment.

Meteorological reports are one of the most important means of recording the weather conditions of a place over a period of time, and the existence of a large number of meteorological reports creates a huge demand for text processing and information extraction. However, valuable data and information are still buried deep in the mountain of meteorological reports, and there is an urgent need for an automated information extraction technique to help people integrate data from multiple meteorological reports and perform data analysis for a more comprehensive understanding of a specific meteorological topic or domain. Named entity recognition (NER) technique can extract useful entity information from meteorological reports. By analyzing the characteristics of nested entities in meteorological reports, this paper further proposes to introduce Multi-Conditional Random Fields (Multi-CRF), which uses each layer of CRF to output the recognition results of each type of entities, which helps to solve the problem of identifying nested entities in meteorological reports. The experimental results show that our model achieves state-of-the-art results. The final recognition results provide effective data support for automatic text verification recognition in the meteorological domain and provide important practical value for the construction of knowledge graphs of related meteorological reports.

In the present era of the pandemic, vaccination is necessary to prevent severe infectious diseases, i.e., COVID-19. Specifically, vaccine safety is strongly linked to global health and security. However, the main concerns regarding vaccine record forgery and counterfeiting of vaccines are still common in the traditional vaccine supply chains. The conventional vaccine supply chains do not have proper authentication among all supply chain entities. Blockchain technology is an excellent contender to resolve the issues mentioned above. Although, blockchain based vaccine supply chains can potentially satisfy the objectives and functions of the next-generation supply chain model. However, its integration with the supply chain model is still constrained by substantial scalability and security issues. So, the current blockchain technology with traditional Proof-of-Work (PoW) consensus is incompatible with the next-generation vaccine supply chain framework. This paper introduces a model named "VaccineChain" - a novel checkpoint-assisted scalable blockchain based secure vaccine supply chain. VaccineChain guarantees the complete integrity and immutability of vaccine supply records to combat counterfeited vaccines over the supply chain. The dynamic consensus algorithm with various validating difficulty levels supports the efficient scalability of VaccineChain. Moreover, VaccineChain includes anonymous authentication among entities to provide selective revocation. This work also consists of a use case example of a secure vaccine supply chain using checkpoint assisted scalable blockchain with customized transaction generation-rule and smart contracts to demonstrate the application of VaccineChain. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of VaccineChain. Further, the detailed performance analysis with test simulations shows the practicability of VaccineChain.

Unsupervised cluster detection in social network analysis involves grouping social actors into distinct groups, each distinct from the others. Users in the clusters are semantically very similar to those in the same cluster and dissimilar to those in different clusters. Social network clustering reveals a wide range of useful information about users and has many applications in daily life. Various approaches are developed to find social network users’ clusters, using only links or attributes and links. This work proposes a method for detecting social network users’ clusters based solely on their attributes. In this case, users’ attributes are considered categorical values. The most popular clustering algorithm used for categorical data is the K-mode algorithm. However, it may suffer from local optimum due to its random initialization of centroids. To overcome this issue, this manuscript proposes a methodology named the Quantum PSO approach based on user similarity maximization. In the proposed approach, firstly, dimensionality reduction is conducted by performing the relevant attribute set selection followed by redundant attribute removal. Secondly, the QPSO technique is used to maximize the similarity score between users to get clusters. Three different similarity measures are used separately to perform the dimensionality reduction and similarity maximization processes. Experiments are conducted on two popular social network datasets; ego-Twitter, and ego-Facebook. The results show that the proposed approach performs better clustering results in terms of three different performance metrics than K-Mode and K-Mean algorithms.

With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models’ training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices, which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing. Compared with existing papers, this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing. Considering the practicalities, commonly used lightweight models in a distributed system are introduced as well. As the key technique, the parallel strategy will be described in detail. Then some typical applications of distributed processing will be analyzed. Finally, the challenges of distributed processing with edge computing will be described.