A Holistic Review of Cyber-Physical-Social Systems: New Directions and Opportunities.

A Cyber-Physical-Social System (CPSS) is an evolving subset of Cyber-Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media's influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work.

Schiff bases as linker in the development of quinoline-sulfonamide hybrids as selective cancer-associated carbonic anhydrase isoforms IX/XII inhibitors: A new regioisomerism tactic.

A novel set of quinoline tailored with the sulfonamide as zinc-binding group (ZBG) has been rationalized and synthesized as carbonic anhydrase (CA, EC 4.2.1.1) inhibitors. Such hybrids were decorated by a novel elongated imine linker with/without ethylene spacer with variable hydrophobic and lipophilic pockets. Therefore, a regioisomeric tactic has been established, most of which act as efficient inhibitors of the tumor-associated CA isoforms IX and XII. Interestingly, one hybrid 10b displayed an appreciable activity in MCF-7 cell line under normoxic condition (IC50 of 8.42 µM) in comparison to the standard staurosporine (IC50 = 5.34 µM) and excellent activity under hypoxic conditions (IC50 = 1.56 µM) in comparison to staurosporine (IC50 = 4.45 µM). Furthermore, hybrids 8a and 10b encouraged MCF-7 and MDA-MB-231 cell apoptosis alongside promising Bax/Bcl expression ratio change. Docking studies were also, performed and agreed with the biological results. Our SAR study suggested that our regiosiomerization tactic for the quinoline based-sulfonamide molecules led to effective inhibition of tumuor-relevant hCAs IX/XII.

Trustworthy Deep Neural Network for Inferring Anticancer Synergistic Combinations.

The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations warrants the consideration of different synergy metrics to develop efficient predictive models. Furthermore, neglecting combination sensitivity may lead to biased synergistic combinations, which are ineffective in cancer treatment. In this paper, we propose a deep learning-based model, SynPredict, which effectively predicts synergy in five synergy metrics together with the combination sensitivity score. SynPredict assesses the impact of multimodal fusion architectures of the input data, including the gene expression data of cancer cells, along with the representative chemical features of drugs in pairwise combinations. Both ONEIL and ALMANAC anticancer combination datasets are employed comparatively. The impact of the training datasets was more significant and consistent across most synergy models than input data fusion architectures. Synpredict outperforms the state-of-the-art predictive models, including DeepSynergy, AuDNN synergy, TranSynergy and DrugComb, with up to 74% decline in the mean square error. We highlight the pivotal need to consider a multiplex of synergy metrics and the combined sensitivity in the predictive models.

Edge Intelligence: Federated Learning-Based Privacy Protection Framework for Smart Healthcare Systems.

Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to malicious attacks and could be breached by privacy attacks such as inference and free-riding, leading to inefficient training models. Besides, uploaded analysing parameters by patients can reveal private information and the threat of direct manipulation by the central server. To address these issues, we present a three-fold Federated Edge Aggregator, the so-called Edge Intelligence, a federated learning-based privacy protection framework for safeguarding Smart Healthcare Systems at the edge against such privacy attacks. We employ an iteration-based Conventional Neural Network (CNN) model and artificial noise functions to balance privacy protection and model performance. A theoretical convergence bound of Edge Intelligence on the trained federated learning model's loss function is also introduced here. We evaluate and compare the proposed framework with the recently established methods using model performance and privacy budget on popular and recent datasets: MNIST, CIFAR10, STL10, and COVID19 chest x-ray. Finally, the proposed framework achieves 90% accuracy and a high privacy rate demonstrating better performance than the baseline technique.

Progressive ShallowNet for large scale dynamic and spontaneous facial behaviour analysis in children.

COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to death by increasing the chance of heart disease. Not only adults, children's are equally impacted where the contribution of emotional competence to social competence has long term implications. Early identification skill for facial behaviour emotions, deficits, and expression may help to prevent the low social functioning. Deficits in young children's ability to differentiate human emotions can leads to social functioning impairment. However, the existing work focus on adult emotions recognition mostly and ignores emotion recognition in children. By considering the working of pyramidal cells in the cerebral cortex, in this paper, we present progressive lightweight shallow learning for the classification by efficiently utilizing the skip-connection for spontaneous facial behaviour recognition in children. Unlike earlier deep neural networks, we limit the alternative path for the gradient at the earlier part of the network by increase gradually with the depth of the network. Progressive ShallowNet is not only able to explore more feature space but also resolve the over-fitting issue for smaller data, due to limiting the residual path locally, making the network vulnerable to perturbations. We have conducted extensive experiments on benchmark facial behaviour analysis in children that showed significant performance gain comparatively.

AI-Driven Synthetic Biology for Non-Small Cell Lung Cancer Drug Effectiveness-Cost Analysis in Intelligent Assisted Medical Systems.

According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer (International Agency for Research on Cancer, 2018), (Bray et al., 2018). Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of alung cancer patients. The 21st century is an era of life medicine, big data, and information technology. Synthetic biology is known as the driving force of natural product innovation and research in this era. Based on the research of NSCLC targeted drugs, through the cross-fusion of synthetic biology and artificial intelligence, using the idea of bioengineering, we construct an artificial intelligence assisted medical system and propose a drug selection framework for the personalized selection of NSCLC patients. Under the premise of ensuring the efficacy, considering the economic cost of targeted drugs as an auxiliary decision-making factor, the system predicts the drug effectiveness-cost then. The experiment shows that our method can rely on the provided clinical data to screen drug treatment programs suitable for the patient's conditions and assist doctors in making an efficient diagnosis.

Two-Stage Deep Learning Framework for Discrimination between COVID-19 and Community-Acquired Pneumonia from Chest CT scans.

COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia on lung CT scans. Thus, this study introduces a novel two-stage DL framework for discriminating COVID-19 from community-acquired pneumonia (CAP) depending on the detected infection region within CT slices. Firstly, a novel U-shaped network is presented to segment the lung area where the infection appears. Then, the concept of transfer learning is applied to the feature extraction network to empower the network capabilities in learning the disease patterns. After that, multi-scale information is captured and pooled via an attention mechanism for powerful classification performance. Thirdly, we propose an infection prediction module that use the infection location to guide the classification decision and hence provides interpretable classification decision. Finally, the proposed model was evaluated on public datasets and achieved great segmentation and classification performance outperforming the cutting-edge studies.

A threat intelligence framework for protecting smart satellite-based healthcare networks.

Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices to end-users in smart satellite-based healthcare IIoT networks (SmartSat-IIoHT). However, such protocols are extensively deployed without appropriate security configurations, making attackers' mission easier for abusing these protocols to launch advanced cyber threats. This paper, therefore, presents a new threat intelligence framework to examine and model CoAP protocol's attacks in these systems. We present a ransom denial of service (RDoS) as a new threat that would exploit this protocol's vulnerabilities. We propose many RDoS attack's techniques to understand the attack indicators and analyse their behaviour on systems. Moreover, we present a real-time discovery of attacks' network behaviours using deep learning. The experiment results demonstrate that this proposed discovery model obtains a better performance in revealing RDoS than other conventional machine learning algorithms and accomplishing high fidelity of protecting SmartSat-IIoHT networks.