Biblio

To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution.

We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent-space while training the generator. Enforcing sparsity naturally leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the sparsity driven latent space sampling approach improves the accuracy and aids in faster recovery of the signal in GMCS.

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.

Nowadays, children, teens, and almost everyone around us tend to receive abundant and frequent advice regarding the usefulness of syllabification. Not only does it improve pronunciation, but it also makes it easier for us to read unfamiliar words in chunks of syllables rather than reading them all at once. Within this paper, we have designed, implemented, and presented a Turing machine-based syllable splitter. A Turing machine forms the theoretical basis for all modern computers and can be used to solve universal problems. On the other hand, a syllable splitter is used to hyphenate words into their corresponding syllables. We have proposed our work by illustrating the various states of the Turing machine, along with the rules it abides by, its machine specifications, and transition function. In addition to this, we have implemented a Graphical User Interface to stimulate our Turing machine to analyze our results better.

Evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the “survival of the fittest” principle in natural selection. However, in a real-world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RCF) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RCF-based privacy-preserving PSO and DE can solve the PPOP without performance loss.

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm’s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.

Numerical simulation of fluid flow is a significant research concern during the design process of a machine component that experiences fluid-structure interaction (FSI). State-of-the-art in traditional computational fluid dynamics (CFD) has made CFD reach a relative perfection level during the last couple of decades. However, the accuracy of CFD is highly dependent on mesh size; therefore, the computational cost depends on resolving the minor feature. The computational complexity grows even further when there are multiple physics and scales involved making the approach time-consuming. In contrast, machine learning (ML) has shown a highly encouraging capacity to forecast solutions for partial differential equations. A trained neural network has offered to make accurate approximations instantaneously compared with conventional simulation procedures. This study presents transient fluid flow prediction past a fully immersed body as an integral part of the ML-CFD project. MLCFD is a hybrid approach that involves initialising the CFD simulation domain with a solution forecasted by an ML model to achieve fast convergence in traditional CDF. Initial results are highly encouraging, and the entire time-based series of fluid patterns past the immersed structure is forecasted using a deep learning algorithm. Prepared results show a strong agreement compared with fluid flow simulation performed utilising CFD.

Application of computational intelligence methods in data analysis and optimization problems can allow feasible and optimal solutions of complicated engineering problems. This study demonstrates an intelligent analysis scheme for determination of optimal operating condition of an internal combustion engine. For this purpose, an artificial neural network learning model is used to represent engine behavior based on engine data, and a metaheuristic optimization method is implemented to figure out optimal operating states of the engine according to the neural network learning model. This data analysis scheme is used for adjustment of optimal engine speed and fuel rate parameters to provide a maximum torque under Nitrous oxide emission constraint. Harris hawks optimization method is implemented to solve the proposed optimization problem. The solution of this optimization problem addresses eco-friendly enhancement of vehicle performance. Results indicate that this computational intelligent analysis scheme can find optimal operating regimes of an engine.

Earlier around in year 1880’s, Industry 2.0 marked as change to the society caused by the invention of electricity. In today’s era, artificial intelligence plays a crucial role in defining the period of Industry 4.0. In this research study, we have presented Computational Intelligence based Machine Condition Monitoring system architecture for determination of developing faults in industrial machines. The goal is to increase efficiency of machines and reduce the cost. The architecture is fusion of machine sensitive sensors, cloud computing, artificial intelligence and databases, to develop an autonomous fault diagnostic system. To explain CI-MCMs, we have used neural networks on sensor data obtained from hydraulic system. The results obtained by neural network were compared with those obtained from traditional methods.

Pooling together samples and testing the resulting mixture is gaining considerable interest as a potential method to markedly increase the rate of testing for SARS-CoV-2, given the resource limited conditions. Such pooling can also be employed for carrying out large scale diagnostic testing of other infectious diseases, especially when the available resources are limited. Therefore, it has become important to design a user-friendly tool to assist clinicians and policy makers, to determine optimal testing pool and sub-pool sizes for their specific scenarios. We have developed such a tool; the calculator web application is available at https://riteshsingh.github.io/poolsize/. The algorithms employed are described and analyzed in this paper, and their application to other scientific fields is also discussed. We find that pooling always reduces the expected number of tests in all the conditions, at the cost of test sensitivity. The No sub-pooling optimal pool size calculator will be the most widely applicable one, because limitations of sample quantity will restrict sub-pooling in most conditions.

Intelligent design has been an emerging important area in the design. Existing works related to intelligent design use objective indicators to measure the quality of AI design by comparing the differences between AI-generated data and real data. However, the level of quality and efficiency of intelligent design compared to human designers remains unclear. We conducted user experiments to compare the design quality and efficiency of advanced design methods with that of junior designers. The conclusion is advanced intelligent design methods are comparable with junior designers on painting. Besides, intelligent design uses only 10% of the time spent by the junior designer in the tasks of layout design, color matching, and video editing.

Human Resource (HR) Analytics has been increasingly attracted attention for a past decade. This is because the study field is adopted data-driven approaches to be processed and interpreted for meaningful insights in human resources. The field is involved in HR decision making helping to understand why people, organization, or other business performance behaved the way they do. Embracing the available tools for decision making and learning in the field of computational intelligence (CI) and Artificial Intelligence (AI) to the field of HR, this creates tremendous opportunities for HR Analytics in practical aspects. However, there are still inadequate applications in this area. This paper serves as a survey of using the tools and their applications in HR involving recruitment, retention, reward and retirement. An example of using CI and AI for career development and training in the era of disruption is conceptually proposed.

In this fifth generation of vehicular communication, the security against various malicious attacks are achieved by using malicious vehicles identification and trust management (MAT) algorithm. Basically, the proposed MAT algorithm performs in two dimensions, they are (i) Node trust and (ii) information trust accompanied with a digital signature and hash chain concept. In node trust, the MAT algorithm introduces the special form of key exchanging algorithm to every members of public group key, and later the vehicles with same target location are formed into cluster. The public group key is common for each participant but everyone maintain their own private key to produce the secret key. The proposed MAT algorithm, convert the secrete key into some unique form that allows the CMs (cluster members) to decipher that secrete key by utilizing their own private key. This key exchanging algorithm is useful to prevent the various attacks, like impersonate attack, man in middle attack, etc. In information trust, the MAT algorithm assigns some special nodes (it has common distance from both vehicles) for monitoring the message forwarding activities as well as routing behavior at particular time. This scheme is useful to predict an exact intruder and after time out the special node has dropped all the information. The proposed MAT algorithm accurately evaluates the trustworthiness of each node as well as information to control different attacks and become efficient for improving a group lifetime, stability of cluster, and vehicles that are located on their target place at correct time.

With the increasing application of micro-nano satellite network, it is extremely vulnerable to the influence of internal malicious nodes in the practical application process. However, currently micro-nano satellite network still lacks effective means of routing security protection. In order to solve this problem, combining with the characteristics of limited energy and computing capacity of micro-nano satellite nodes, this research proposes a secure routing algorithm based on trust value. First, the trust value of the computing node is synthesized, and then the routing path is generated by combining the trust value of the node with the AODV routing algorithm. Simulation results show that the proposed MNS-AODV routing algorithm can effectively resist the influence of internal malicious nodes on data transmission, and it can reduce the packet loss rate and average energy consumption.

Wireless Sensor Network (WSN)is a predominant technology that is widely used in many applications such as industrial sectors, defense, environment, habitat monitoring, medical fields etc., These applications are habitually delegated for observing sensitive and confidential raw data such as adversary position, movement in the battle field, location of personnel in a building, changes in environmental condition, regular medical updates from patient side to doctors or hospital control rooms etc., Security becomes inevitable in WSN and providing security is being truly intricate because of in-built nature of WSN which is assailable to attacks easily. Node involved in WSN need to route the data to the neighboring nodes wherein any attack in the node could lead to fiasco. Of late trust mechanisms have been considered to be an ideal solution that can mitigate security problems in WSN. This paper aims to investigate various existing trust-based Secure Routing (SR) protocols and mechanisms available for the wireless sensing connection. The concept of the present trust mechanism is also analyzed with respect to methodology, trust metric, pros, cons, and complexity involved. Finally, the security resiliency of various trust models against the attacks is also analyzed.

Transportation of people and goods is important and crucial in the context of smart cities. The trend in regard of people's mobility is moving from privately owned vehicles towards shared mobility. This trend is even stronger in urban areas, where space for parking is limited, and the mobility is supported by the public transport system, which lowers the need for private vehicles. Several challenges and barriers of currently available solutions retard a massive growth of this mobility option, such as the trust problem, data monopolism, or intermediary costs. Decentralizing mobility management is a promising approach to solve the current problems of the mobility market, allowing to move towards a more usable internet of mobility and smart transportation. Leveraging blockchain technology allows to cut intermediary costs, by utilizing smart contracts. Important in this ecosystem is the proof of identity of participants in the blockchain network. To proof the possession of the claimed identity, the private key corresponding to the wallet address is utilized, and therefore essential to protect. In this paper, a blockchain-based shared mobility platform is proposed and a proof-of-concept is shown. First, current problems and state-of-the-art systems are analyzed. Then, a decentralized concept is built based on ERC-721 tokens, implemented in a smart contract, and augmented with a Hardware Security Module (HSM) to protect the confidential key material. Finally, the system is evaluated and compared against state-of-the-art solutions.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. What is “hardware” security? The network designer relies on the security of the router box. The software developer relies on the TPM (Trusted Platform Module). The circuit designer worries about side-channel attacks. At the same time, electronics shrink: sensor nodes, IOT devices, smart devices are becoming more and more available. Adding security and cryptography to these often very resource constraint devices is a challenge. This presentation will focus on Physically Unclonable Functions and True Random Number Generators, two roots of trust, and their security testing.

The digitalization of safety-critical railroad infrastructure enables new types of attacks. This increases the need to integrate Information Technology (IT) security measures into railroad systems. For that purpose, we rely on a security architecture for a railway object controller which controls field elements that we developed in previous work. Our architecture enables the integration of security mechanisms into a safety-certified railway system. In this paper, we demonstrate the practical feasibility of our architecture by using a Trusted Platform Module (TPM) 2.0 and a Multiple Independent Levels of Safety and Security (MILS) Separation Kernel (SK) for our implementation. Our evaluation includes a test bed and shows how certification and homologation can be achieved.

The Internet of Things (IoT) paradigm has been proposed in the last few years with the goal of addressing technical problems in fields such as home and industrial automation, smart lighting systems and traffic monitoring. However, due to the very nature of the IoT devices (generally low-powered and often lacking strong security functionalities), typical deployments pose a great risk in terms of security and privacy. In this respect, the utilization of both a Trusted Execution Environment (TEE) and a Trusted Platform Module (TPM) can serve as a countermeasure against typical attacks. Furthermore, these functional blocks can serve as safe key storage services and provide a robust secure boot implementation and a firmware update mechanism, thus ensuring run-time authentication and integrity. The Common Criteria for Information Technology Security Evaluation allows to determine the degree of attainment of precise security properties in a product. The main objective of this work is to identify, propose and compose bricks of protection profile (PP), as defined by Common Criteria, that are applicable to secure IoT architectures. Moreover, it aims at giving some guiding rules and facilitate future certifications of components and/or their composition. Finally, it also provides a structure for a future methodology of assessment for IoT devices.

Trusted Platform Modules (TPM) have grown to be crucial safeguards from the number of software-based strikes. By giving a restricted range of cryptographic providers by way of a well-defined user interface, divided as a result of the program itself, TPM and Infrastructure as a service (IaaS) can function as a root of loyalty so when a foundation aimed at advanced equal protection methods. This information studies the works aimed at uses on TPM within the cloud computing atmosphere, by journal times composed somewhere among 2013 as well as 2020. It identifies the present fashion as well as goals from these technologies within the cloud, as well as the kind of risks that it mitigates. The primary investigation is being focused on the TPM's association to the IaaS security based on the authorization and the enabling schema for integrity. Since integrity measurement is among the key uses of TPM and IaaS, particular focus is given towards the evaluation of operating period phases as well as S/W levels it's put on to. Finally, the deep survey on recent schemes can be applied on Cloud Environment.

To develop the next generation of Internet of Things, Edge devices and systems which leverage progress in enabling technologies such as 5G, distributed computing and artificial intelligence (AI), several requirements need to be developed and put in place to make the devices smarter. A major requirement for all the above applications is the long-term security and trust computing infrastructure. Trusted Computing requires the introduction inside of the platform of a Trusted Platform Module (TPM). Traditionally, a TPM was a discrete and dedicated module plugged into the platform to give TPM capabilities. Recently, processors manufacturers started integrating trusted computing features into their processors. A significant drawback of this approach is the need for a permanent modification of the processor microarchitecture. In this context, we suggest an analysis and a design of a software-only TPM for RISC-V processors based on seL4 microkernel and OP-TEE.

Recently, Mobile Edge Cloud computing (MEC) has attracted attention both from academia and industry. The idea of moving a part of cloud resources closer to users and data sources can bring many advantages in terms of speed, data traffic, security and context-aware services. The MEC infrastructure does not only host and serves applications next to the end-users, but services can be dynamically migrated and reallocated as mobile users move in order to guarantee latency and performance constraints. This specific requirement calls for the involvement and collaboration of multiple MEC providers, which raises a major issue related to trustworthiness. Two main challenges need to be addressed: i) trustworthiness needs to be handled in a manner that does not affect latency or performance, ii) trustworthiness is considered in different dimensions - not only security metrics but also performance and quality metrics in general. In this paper, we propose a trust layer for public MEC infrastructure that handles establishing and updating trust relations among all MEC entities, making the interaction withing a MEC network transparent. First, we define trust attributes affecting the trusted quality of the entire infrastructure and then a methodology with a computation model that combines these trust attribute values. Our experiments showed that the trust model allows us to reduce latency by removing the burden from a single MEC node, while at the same time increase the network trustworthiness.

5G improves previous generations not only in terms of radio access but the whole infrastructure and services paradigm. Automation, dynamism and orchestration are now key features that allow modifying network behaviour, such as Virtual Network Functions (VNFs), and resource allocation reactively and on demand. However, such dynamic ecosystem must pay special attention to security while ensuring that the system actions are trustworthy and reliable. To this aim, this paper introduces the integration of the Manufacturer Usage Description (MUD) standard alongside a Trust and Reputation Manager (TRM) into the INSPIRE-5GPlus framework, enforcing security properties defined by MUD files while the whole infrastructure, virtual and physical, as well as security metrics are continuously audited to compute trust and reputation values. These values are later fed to enhance trustworthiness on the zero-touch decision making such as the ones orchestrating end-to-end security in a closed-loop.

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.

Machine learning algorithms are becoming more and more widespread in industrial as well as in societal settings. This diffusion is starting to become a critical aspect of new software-intensive applications due to the need of fast reactions to changes, even if temporary, in data. This paper investigates on the improvement of reliability in the Machine Learning based classification by extending Random Forests with Bayesian Network models. Such models, combined with a mechanism able to adjust the reputation level of single learners, may improve the overall classification trustworthiness. A small example taken from the healthcare domain is presented to demonstrate the proposed approach.