### Recent Submissions

• #### Experimental validation of fuel cell, battery and supercapacitor energy conversion system for electric vehicle applications

Due to the increasing air pollution and growing demand for green energy, the most of research is focused on renewable and sustainable energy. In this work, the PEM fuel cell is proposed as a solution to reduce the impact of the internal combustion engines on air pollution. In this paper a PEM fuel cell, battery and supercapacitor energy conversion system is proposed to ensure the energy demand for an electric vehicle is achieved. The storage system consisting of a battery and supercapacitor offers good performance in terms of autonomy and power availability. In this paper, an energy management of the PEM fuel cell electric vehicle has been first simulated in Matlab/Simulink environment and the results are discussed. Second, a Realtime experimental set up is used to test the performance of the proposed PEM fuel cell electric vehicle system. Experimental results have shown that the proposed system is able to satisfy the energy demand of the electric vehicle.
• #### Implementation of fuel cell and photovoltaic panels based DC micro grid prototype for electric vehicles charging station

Today, electric vehicle (EV) appears as an evident solution for the future automotive market. The introduction of EV will lead to the reduction of greenhouse gas emissions and decrease the travelling cost. However, electric vehicle is truly an ecological solution only if the production of electricity necessary for its operation is produced from sustainable energy sources. In this paper, an Electric Vehicle Charging Station (EVCS) through sustainable energy sources via a DC micro-grid system has been proposed. The proposed system includes a fuel cell (FC), photovoltaic (PV) panels, storage battery and possibility of a connection to the grid. In this work a low power prototype of a micro-grid based EVCS has been first validated using a numerical simulation under Matlab/Simulink using variable irradiance and number of recharging vehicles. In the second part of this paper, an EVCS prototype has been realized in the laboratory. The tests are realized using an emulator of the PEM fuel cell with the concept of the hardware-in-the-loop (HIL). The objective of this emulation is to evaluate the performances of the whole system without the need for a real fuel cell. The whole system is implemented on the dSPACE 1103 platform and the results of the tests are discussed.
• #### An experimental online judge system based on docker container for learning and teaching assistance

Programming Languages are core courses in computer science and computing-related disciplines, and there are increasing demands on improving the experiences, motivation and efficiency of programming language teaching and learning. In recent decades, online judge (OJ) systems have been popularly adopted in educational environments to support real-time learning feedback, and provide interactive programming practice. However, most existing OJ systems rely on significant resource for hardware virtualization, and suffer from long development cycles and extensive maintenances. This paper proposed a course-oriented OJ system based on the Docker Container techniques to significantly reduce the cost and maintenance of deployment of existing OJ system. The proposed approach is designed in the form of an experimental system, which is effective in stimulating the students to learn programming language and assist the instant assessment of coursework. Meanwhile, our proposed system can be integrated with major massive open online course (MOOC) systems for assessment. The efficiency and performance of our proposed system has been evaluated and tested by 1460 students in Nanyang Institute of Technology during term time between 2018 and 2019.
• #### Proton exchange membrane fuel cell modules for ship applications

In this article, we proposed a more reliable architecture composed of five fuel cell modules (FC), a storage system composed of battery and supercapacitor was also proposed to support the operation of the fuel cell. The main objective of this work is to study the feasibility of using the global system for small marine applications. In this paper, the global system was modeled and then simulated using Matlab/Simulink. The fuel cell is used as the main power source; each fuel cell is connected with a DC bus via a DC–DC boost converter. The Energy Storage System (HESS) is controlled as a fast-bidirectional auxiliary power source, it contains a battery and supercapacitors and each source is connected to the DC bus via a bidirectional buck-boost DC–DC converter (BBDCC). In order to optimize the HESS, the supercapacitors and the batteries are designed to allow high-efficiency operation and minimal weight. The entire system’s energy management algorithm (PMA) is developed to satisfy the energy demand of the boat. Finally, simulation tests are presented in Matlab/Simulink and discussed, where the effectiveness of the proposed system with its control is confirmed.
• #### Performances analysis of a micro-grid connected multi-renewable energy sources system associated with hydrogen storage

This work highlights the modelling and simulation of a micro-grid connected renewable energy system. It comprises of wind turbine (WT) based on doubly fed induction generator (DFIG), photovoltaic generator (PV), fuel cell (FC) generator, a Hydrogen tank, a water electrolyser used for long-term storage, and a battery bank energy storage system (BBESS) utilized for short-term storage. In this paper, a global control strategy and an energy management strategy are proposed for the overall system. This strategy consists in charging the BBESS and producing hydrogen from the water electrolyser in case of power excess provided from WT-DFIG and photovoltaic generators. Therefore, the FC and the BBESS will be used as a backup generator to supply the demand required power, when the WT-DFIGs and the PV energy are deficient. The effectiveness of this contribution is verified through computer simulations under Matlab/Simulink, where very satisfactory results are obtained.
• #### Z-boson production in p-Pb collisions at $$\sqrt{s_{\mathrm{NN}}}$$ = 8.16 TeV and Pb-Pb collisions at $$\sqrt{s_{\mathrm{NN}}}$$ = 5.02 TeV

Measurement of Z-boson production in p-Pb collisions at √sNN = 8.16 TeV and Pb-Pb collisions at √sNN = 5.02 TeV is reported. It is performed in the dimuon decay channel, through the detection of muons with pseudorapidity −4 < ημ < −2.5 and transverse momentum pTμ > 20 GeV/c in the laboratory frame. The invariant yield and nuclear modification factor are measured for opposite-sign dimuons with invariant mass 60 < mμμ < 120 GeV/c2 and rapidity 2.5 < ycmsμμ< 4. They are presented as a function of rapidity and, for the Pb-Pb collisions, of centrality as well. The results are compared with theoretical calculations, both with and without nuclear modifications to the Parton Distribution Functions (PDFs). In p-Pb collisions the center-of-mass frame is boosted with respect to the laboratory frame, and the measurements cover the backward (−4.46 < ycmsμμ < −2.96) and forward (2.03 < ycmsμμ < 3.53) rapidity regions. For the p-Pb collisions, the results are consistent within experimental and theoretical uncertainties with calculations that include both free-nucleon and nuclear-modified PDFs. For the Pb-Pb collisions, a 3.4σ deviation is seen in the integrated yield between the data and calculations based on the free-nucleon PDFs, while good agreement is found once nuclear modifications are considered.
• #### Exploring network embedding for efficient message routing in opportunistic mobile social networks

With the advancement in communication technologies and the widespread availability of mobile devices, the opportunistic mobile social networks (OMSNs) are gaining momentum in supporting spontaneous communication and interaction among end-users who opportunistically contact each other. However, existing research on message routing in OMSNs face major challenges on achieving a high routing efficiency and low latency for social information request. This paper proposes a personalised message routing (PMR) framework that leverages an inductive network embedding model and an attention-based mechanism to facilitate efficient message routing in opportunistic networks. Specifically, the network embedding model encompasses a higher-order proximity profiling algorithm in order to embed both the content-based and structure-based network features beyond immediate friends into low dimensional representations. Further, we present an attentional neural network model to learn user-friend preferences, for the purpose of capturing the diversified interests among connected users and to determine the most informative friends during the message dissemination process. The performance of our proposed framework is evaluated through simulations on three real-world mobile network trace datasets and the experimental results show that the proposed PMR framework considerably and consistently outperforms the state-of-the-art message routing methods.
• #### A GRU-based prediction framework for intelligent resource management at cloud data centres in the age of 5G

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back-end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio-economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU-SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state-of-art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real-world Cloud trace logs demonstrate that the proposed IGRU-SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.
• #### A privacy-preserved probabilistic routing index model for decentralised online social networks

Despite the tremendous success of online social networks (OSNs), centrally controlled OSNs have inherent issues related to lack of user privacy and single point of failure. These limitations have motivated the research community to shift the computing paradigm from a centralised architecture to decentralised alternatives. Existing research works mainly focused on the routing mechanisms using social information in decentralised OSNs, without considering the user's privacy. This paper proposes a self-organised decentralised architecture (SDA) that leverages privacy-preserved routing methods to facilitate query routing in decentralised social networks. This architecture encompasses a hash-based profiling model to characterise semantic features of the user's content with low dimensionality and privacy-aware mechanisms to organise similarity users into semantic communities. Furthermore, a probabilistic routing method is proposed to support efficient information dissemination and service discovery. The correctness and efficiency of our proposed approach are evaluated through simulations on real-world datasets. The experimental results demonstrated that our approach achieved a better topological structure with high routing efficiency.
• #### Artificial neural networks training acceleration through network science strategies

The development of deep learning has led to a dramatic increase in the number of applications of artificial intelligence. However, the training of deeper neural networks for stable and accurate models translates into artificial neural networks (ANNs) that become unmanageable as the number of features increases. This work extends our earlier study where we explored the acceleration effects obtained by enforcing, in turn, scale freeness, small worldness, and sparsity during the ANN training process. The efficiency of that approach was confirmed by recent studies (conducted independently) where a million-node ANN was trained on non-specialized laptops. Encouraged by those results, our study is now focused on some tunable parameters, to pursue a further acceleration effect. We show that, although optimal parameter tuning is unfeasible, due to the high non-linearity of ANN problems, we can actually come up with a set of useful guidelines that lead to speed-ups in practical cases. We find that significant reductions in execution time can generally be achieved by setting the revised fraction parameter (ζ) to relatively low values.
• #### Stability of discrete-time fractional-order time-delayed neural networks in complex field

Dynamics of discrete‐time neural networks have not been well documented yet in fractional‐order cases, which is the first time documented in this manuscript. This manuscript is mainly considered on the stability criterion of discrete‐time fractional‐order complex‐valued neural networks with time delays. When the fractional‐order β holds 1 < β < 2, sufficient criteria based on a discrete version of generalized Gronwall inequality and rising function property are established for ensuring the finite stability of addressing fractional‐order discrete‐time‐delayed complex‐valued neural networks (FODCVNNs). In the meanwhile, when the fractional‐order β holds 0 < β < 1, a global Mittag–Leffler stability criterion of a class of FODCVNNs is demonstrated with two classes of neuron activation function by means of two different new inequalities, fractional‐order discrete‐time Lyapunov method, discrete version Laplace transforms as well as a discrete version of Mittag–Leffler function. Finally, computer simulations of two numerical examples are illustrated to the correctness and effectiveness of the presented stability results.
• #### An inductive content-augmented network embedding model for edge artificial intelligence

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effective resource discovery services in edge environments, by achieving a proximity-preserving representation of the network resources. Most of the existing techniques of network embedding fail to capture accurate proximity information among the network nodes and further lack exploiting information beyond the second-order neighbourhood. This paper leverages artificial intelligence for network representation and proposes a deep learning model, named inductive content augmented network embedding (ICANE), which integrates the network structure and resource content attributes into a feature vector. Secondly, a hierarchical aggregation approach is introduced to explicitly learn the network representation through sampling the nodes and aggregating features from the higher-order neighbourhood. A semantic proximity search model is then designed to generate the top-k ranking of relevant nodes using the learned network representation. Experiments conducted on real-world datasets demonstrate the superiority of the proposed model over the existing popular methods in terms of resource discovery and the query resolving performance.
• #### Vehicular sensor networks: Applications, advances and challenges

Vehicular sensor networks (VSN) provide a new paradigm for transportation technology and demonstrate massive potential to improve the transportation environment due to the unlimited power supply of the vehicles and resulting minimum energy constraints. This special issue is focused on the recent developments within the vehicular networks and vehicular sensor networks domain. The papers included in this Special Issue (SI) provide useful insights to the implementation, modelling, and integration of novel technologies, including blockchain, named data networking, and 5G, to name a few, within vehicular networks and VSN.
• #### Ubiquitous health profile (UHPr): a big data curation platform for supporting health data interoperability

The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed.
• #### Acquiring Guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

Background and Objective: Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts.Methods: This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification.Results: ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs.Conclusion: ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.
• #### Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks.
• #### CRT-BIoV: A cognitive radio technique for blockchain-enabled internet of vehicles

Cognitive Radio Network (CRN) is considered as a viable solution on Internet of Vehicle (IoV) where objects equipped with cognition make decisions intelligently through the understanding of both social and physical worlds. However, the spectrum availability and data sharing/transferring among vehicles are critical improving services and driving safety metrics where the presence of Malicious Devices (MD) further degrade the network performance. Recently, a blockchain technique in CRN-based IoV has been introduced to prevent data alteration from these MD and allowing the vehicles to track both legal and illegal activities in the network. In this paper, we provide the security to IoV during spectrum sensing and information transmission using CRN by sensing the channels through a decision-making technique known as \textit{Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS)}, a technique that evokes the trust of its Cognitive Users (CU) by analyzing certain predefined attributes. Further, blockchain is maintained in the network to trace every activity of stored information. The proposed mechanism is validated rigorously against several security metrics using various spectrum sensing and security parameters against a baseline solution in IoV. Extensive simulations suggest that our proposed mechanism is approximately 70% more efficient in terms of malicious nodes identification and DoS threat against the baseline mechanism.
• #### A multi-objective optimized service level agreement approach applied on a cloud computing ecosystem

The cloud ecosystem provides transformative advantages that allow elastically offering ondemand services. However, it is not always possible to provide adequate services to all customers and thus to fulfill service level agreements (SLA). To enable compliance with these agreements, service providers leave the customer responsible for determining the service settings and expect that the client knows what to do. Some studies address SLA compliance, but the existing works do not adequately address the problem of resource allocation according to clients’ needs since they consider a limited set of objectives to be analyzed and fulfilled. In previous work, we have already addressed the problem considering a single-objective approach. In that work, we identified that the problem has a multi-objective characteristic since several attributes simultaneously influence the SLA agreement, which can lead to conflicts. This paper proposes a multi-objective combinatorial optimization approach for computational resources provisioning, seeking to optimize the efficient use of the infrastructure and provide the client with greater flexibility in contract closure.
• #### Privacy-preserving crowd-sensed trust aggregation in the user-centeric internet of people networks

Today we are relying on the Internet technologies for various types of services ranging from personal communication to the entertainment. The online social networks (Facebook, twitter, youtube) has seen an increase in subscribers in recent years developing a social network among people termed as the Internet of People. In such a network, subscribers use the content disseminated by other subscribers. The malicious users can also utilize such platforms for spreading the malicious and fake content that would bring catastrophic consequences to a social network if not identified on time. People crowd-sensing on the Internet of people system has seen a prospective solution for the large scale data collection by leveraging the feedback collections from the people of the internet that would not only help in identifying malicious subscribers of the network but would also help in defining better services. However, the human involvement in crowd-sensing would have challenges of privacy-preservation, intentional spread of false high score about certain user/content undermining the services, and assigning different trust scores to the peoples of the network without disclosing their trust weights. Therefore, having a privacy-preserving system for computing trust of people and their content in the network would play a crucial role in collecting high-quality data from the people. In this paper, a novel trust model is proposed for evaluating the trust of the people in the social network without compromising the privacy of the participating people. The proposed systems have inherent properties of the trust weight assignment to a different class of user i.e. it can assign different weights to different users of the network, has decentralized setup, and ensures privacy properties under the malicious and honest but curious adversarial model. We evaluated the performance of the system by developing a prototype and applying it to different online social network dataset.
• #### Designing privacy-aware internet of things applications

Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software engineering processes when designing IoT applications. With the advent of behaviour driven security mechanisms, failing to address privacy concerns in the design of IoT applications can also have security implications. In this paper, we explore how a Privacy-by-Design (PbD) framework, formulated as a set of guidelines, can help software engineers integrate data privacy considerations into the design of IoT applications. We studied the utility of this PbD framework by studying how software engineers use it to design IoT applications. We also explore the challenges in using the set of guidelines to influence the IoT applications design process. In addition to highlighting the benefits of having a PbD framework to make privacy features explicit during the design of IoT applications, our studies also surfaced a number of challenges associated with the approach. A key finding of our research is that the PbD framework significantly increases both novice and expert software engineers’ ability to design privacy into IoT applications.