Abstract: Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

Abstract: This paper discusses how the state-of-the-art techniques in cyber-physical systems facilitate building smart warehouses to achieve the promising vision of industry 4.0. We focus on four significant issues when applying CPS techniques in smart warehouses. First, efficient CPS data collection: when limited communication bandwidth meets numerous CPS devices, we need to make more effort to study efficient wireless communication scheduling strategies. Second, accurate and robust localization: localization is the basis for many fundamental operations in smart warehouses, but still needs to be improved from various aspects like accuracy and robustness. Third, human activity recognition: human activity recognition can be applied in human–computer interaction for remote machine operations. Fourth, multi-robot collaboration: smart robots will take the place of humans to accomplish most tasks particularly in a harsh environment, and smart and fully-distributed robot collaborating algorithms should be investigated. Finally, we point out some challenging issues in the future CPS-based smart warehouse, which could open some new research directions.

Abstract: Tracking the 6DoF pose of arbitrary 3D objects is a fundamental topic in Augmented Reality (AR) research, having received a large amount of interest in the last decades. The necessity of accurate and computationally efficient object tracking is evident for a broad base of today’s AR applications. In this work we present a fully comprehensive pipeline for 6DoF Object Tracking based on 3D scans of objects, covering object registration, initialization and frame to frame tracking, implemented to optimize the user experience and to perform well in all typical challenging conditions such as fast motion, occlusions and illumination changes. Furthermore, we present the deployment of our tracking system in a Remote Live Support AR application with 3D object-aware registration of annotations and remote execution for delay and performance optimization. Experimental results demonstrate the tracking quality, real-time capability and the advantages of remote execution for computationally less powerful mobile devices.

Abstract: Cellular beams are an attractive option for the steel construction industry due to their versatility in terms of strength, size, and weight. Further benefits are the integration of services thereby reducing ceiling-to-floor depth (thus, building’s height), which has a great economic impact. Moreover, the complex localized and global failures characterizing those members have led several scientists to focus their research on the development of more efficient design guidelines. This paper aims to propose an artificial neural network (ANN)-based formula to precisely compute the critical elastic buckling load of simply supported cellular beams under uniformly distributed vertical loads. The 3645-point dataset used in ANN design was obtained from an extensive parametric finite element analysis performed in ABAQUS. The independent variables adopted as ANN inputs are the following: beam’s length, opening diameter, web-post width, cross-section height, web thickness, flange width, flange thickness, and the distance between the last opening edge and the end support. The proposed model shows a strong potential as an effective design tool. The maximum and average relative errors among the 3645 data points were found to be 3.7% and 0.4%, respectively, whereas the average computing time per data point is smaller than a millisecond for any current personal computer.

Abstract: Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

Abstract: Healthcare sectors have been at the forefront of the adoption and use of IoT technologies for efficient healthcare diagnosis and treatment. Because healthcare IoT sensor technology obtains health-related data from patients, it needs to be integrated with the electronic healthcare records (EHR) system. Most EHR systems have not been designed for integration with IoT technology; they have been designed to be more patient-centric management systems. The use of the IoT in EHR remains a long-term goal. Configuring IoT in EHR can enhance patient healthcare, enabling health providers to monitor their patients outside of the clinic. To assist physicians to access data resources efficiently, a data model that is semantic and flexible is needed to connect EHR data and IoT data that may help to provide true interoperability and integration. This research proposes a semantic middleware that exploits ontology to support the semantic integration and functional collaborations between IoT healthcare Information Systems and EHR systems.

Abstract: Biometric verification systems have become prevalent in the modern world with the wide usage of smartphones. These systems heavily rely on storing the sensitive biometric data on the cloud. Due to the fact that biometric data like fingerprint and iris cannot be changed, storing them on the cloud creates vulnerability and can potentially have catastrophic consequences if these data are leaked. In the recent years, in order to preserve the privacy of the users, homomorphic encryption has been used to enable computation on the encrypted data and to eliminate the need for decryption. This work presents DeepZeroID: a privacy-preserving cloud-based and multiple-party biometric verification system that uses homomorphic encryption. Via transfer learning, training on sensitive biometric data is eliminated and one pre-trained deep neural network is used as feature extractor. By developing an exhaustive search algorithm, this feature extractor is applied on the tasks of biometric verification and liveness detection. By eliminating the need for training on and decrypting the sensitive biometric data, this system preserves privacy, requires zero knowledge of the sensitive data distribution, and is highly scalable. Our experimental results show that DeepZeroID can deliver 95.47% F1 score in the verification of combined iris and fingerprint feature vectors with zero true positives and with a 100% accuracy in liveness detection.

Abstract: In this paper, the study and modelling of a lithium-ion battery cell is presented. To test the considered cell, a battery testing system was built using two programmable power units: an electronic load and a power supply. To communicate with them, a software/hardware interface was implemented within the National Instruments (NI) LabVIEW environment. This dedicated laboratory equipment can be used to apply charging/discharging cycles according to user defined load profiles. The battery modelling and the parameters identification procedure are described. The model was used to estimate the State Of Charge (SOC) under dynamic loading conditions. The most spread techniques used in the field of battery modelling and SOC estimation are implemented and compared.

Abstract: Biometrics play an avid role in today’s mobile security realm. Security experts have attempted to implement different forms of biometrics, from finger, hand, and signature to voice, retina, and iris. Recently, facial biometrics has been added to this list and has introduced another method for a more secure form of authentication. Various organizations believe that by using something you are, like biometrics, their system would be strongly secured. As this may be true, applications used for facial biometrics have lost their credibility when easily defeated. This may be through printed photographs, electronic images, and even look alike. This paper explores the scientific background as to why facial biometrics have become a trusted form of authentication, a user-friendly method, and explores the security of mobile device applications available for Android and iOS systems. We test several applications with our developed methods and discuss the results.

Abstract: Due to their large sizes and/or dimensions, the classification of Big Data is a challenging task using traditional machine learning, particularly if it is carried out using the well-known K-nearest neighbors classifier (KNN) classifier, which is a slow and lazy classifier by its nature. In this paper, we propose a new approach to Big Data classification using the KNN classifier, which is based on inserting the training examples into a binary search tree to be used later for speeding up the searching process for test examples. For this purpose, we used two methods to sort the training examples. The first calculates the minimum/maximum scaled norm and rounds it to 0 or 1 for each example. Examples with 0-norms are sorted in the left-child of a node, and those with 1-norms are sorted in the right child of the same node; this process continues recursively until we obtain one example or a small number of examples with the same norm in a leaf node. The second proposed method inserts each example into the binary search tree based on its similarity to the examples of the minimum and maximum Euclidean norms. The experimental results of classifying several machine learning big datasets show that both methods are much faster than most of the state-of-the-art methods compared, with competing accuracy rates obtained by the second method, which shows great potential for further enhancements of both methods to be used in practice.

Abstract: Petri nets are a useful tool for the modeling and performance evaluation of discrete event systems. Literature reveals that the Petri Net models of real-world discrete event systems are most frequently event graphs (a subclass of Petri nets). Literature also reveals that there are some simple methods for the performance evaluation of event graphs. The general-purpose Petri Net simulator (GPenSIM) is a new simulator that runs on the MATLAB platform. GPenSIM provides a Petri net language, with which Petri net classes and extensions can be developed. GPenSIM also provides functions for performance analysis. Since real-world discrete event systems usually possess a large number of resources, the Petri net models of these systems tend to become huge. Activity-Oriented Petri Nets (AOPN) is an approach that reduces the size of the Petri nets. In addition to the simulator functions, GPenSIM also realizes the AOPN approach on the MATLAB platform. Thus, AOPN is an integral part of GPenSIM. As a running example, a flexible manufacturing system is firstly modeled as an event graph, and then the size of the model is reduced with the AOPN approach. The advantages of GPenSIM and AOPN are discussed in this paper.

Abstract: We live in a heavily technologized global society. It is therefore not surprising that efforts are being made to integrate current information technology into the treatment of diabetes mellitus. This paper is dedicated to improving the treatment of this disease through the use of well-designed mobile applications. Our analysis of relevant literature sources and existing solutions has revealed that the current state of mobile applications for diabetics is unsatisfactory. These limitations relate both to the content and the Graphical User Interface (GUI) of existing applications. Following the analysis of relevant studies, there are four key elements that a diabetes mobile application should contain. These elements are: (1) blood glucose levels monitoring; (2) effective treatment; (3) proper eating habits; and (4) physical activity. As the next step in this study, three prototypes of new mobile applications were designed. Each of the prototypes represents one group of applications according to a set of given rules. The most optimal solution based on the users’ preferences was determined by using a questionnaire survey conducted with a sample of 30 respondents participating in a questionnaire after providing their informed consent. The age of participants was from 15 until 30 years old, where gender was split to 13 males and 17 females. As a result of this study, the specifications of the proposed application were identified, which aims to respond to the findings discovered in the analytical part of the study, and to eliminate the limitations of the current solutions. All of the respondents expressed preference for an application that includes not only the key functions, but a number of additional functions, namely synchronization with one of the external devices for measuring blood glucose levels, while five-sixths of them found suggested additional functions as being sufficient.

Abstract: Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study.

Abstract: In this paper, a Proportional–Integral–Derivative (PID) controller is fine-tuned through the use of artificial neural networks and evolutionary algorithms. In particular, PID’s coefficients are adjusted on line using a multi-layer. In this paper, we used a feed forward multi-layer perceptron. There was one hidden layer, activation functions were sigmoid functions and weights of network were optimized using a genetic algorithm. The data for validation was derived from a desired results of system. In this paper, we used genetic algorithm, which is one type of evolutionary algorithm. The proposed methodology was evaluated against other well-known techniques of PID parameter tuning.

Abstract: Lightweight block ciphers are an important topic in the Internet of Things (IoT) since they provide moderate security while requiring fewer resources than the Advanced Encryption Standard (AES). Ongoing cryptographic contests and standardization efforts evaluate lightweight block ciphers on their resistance to power analysis side channel attack (SCA), and the ability to apply countermeasures. While some ciphers have been individually evaluated, a large-scale comparison of resistance to side channel attack and the formulation of absolute and relative costs of implementing countermeasures is difficult, since researchers typically use varied architectures, optimization strategies, technologies, and evaluation techniques. In this research, we leverage the Test Vector Leakage Assessment (TVLA) methodology and the FOBOS SCA framework to compare FPGA implementations of AES, SIMON, SPECK, PRESENT, LED, and TWINE, using a choice of architecture targeted to optimize throughput-to-area (TP/A) ratio and suitable for introducing countermeasures to Differential Power Analysis (DPA). We then apply an equivalent level of protection to the above ciphers using 3-share threshold implementations (TI) and verify the improved resistance to DPA. We find that SIMON has the highest absolute TP/A ratio of protected versions, as well as the lowest relative cost of protection in terms of TP/A ratio. Additionally, PRESENT uses the least energy per bit (E/bit) of all protected implementations, while AES has the lowest relative cost of protection in terms of increased E/bit.

Abstract: Currently, it is possible to combine Mobile-Rendered Head-Mounted Displays (MR HMDs) with smartphones to have Augmented Reality platforms. The differences between these types of platforms can affect the user’s experiences and satisfaction. This paper presents a study that analyses the user’s perception when using the same Augmented Reality app with two MR HMD (low-end and high-end). Our study evaluates the user’s experience taking into account several factors (control, sensory, distraction, ergonomics and realism). An Augmalpha-lowerented Reality app was developed to carry out the comparison for two MR HMDs. The application had exactly the same visual appearance and functionality for both devices. Forty adults participated in our study. From the results, there were no statistically significant differences for the users’ experience for the different factors when using the two MR HMDs, except for the ergonomic factors in favour of the high-end MR HMD. Even though the scores for the high-end MR HMD were higher in nearly all of the questions, both MR HMDs provided a very satisfying viewing experience with very high scores. The results were independent of gender and age. The participants rated the high-end MR HMD as the best one. Nevertheless, when they were asked which MR HMD they would buy, the participants chose the low-end MR HMD taking into account its price.

Abstract: Over the past decades, many systems composed of arrays of microphones have been developed to satisfy the quality demanded by acoustic applications. Such microphone arrays are sound acquisition systems composed of multiple microphones used to sample the sound field with spatial diversity. The relatively recent adoption of Field-Programmable Gate Arrays (FPGAs) to manage the audio data samples and to perform the signal processing operations such as filtering or beamforming has lead to customizable architectures able to satisfy the most demanding computational, power or performance acoustic applications. The presented work provides an overview of the current FPGA-based architectures and how FPGAs are exploited for different acoustic applications. Current trends on the use of this technology, pending challenges and open research opportunities on the use of FPGAs for acoustic applications using microphone arrays are presented and discussed.

Abstract: In this paper we introduce a new histogram computation method to be used within the histogram of oriented gradients (HOG) algorithm. The new method replaces the arctangent with the slope computation and the classical magnitude allocation based on interpolation with a simpler algorithm. The new method allows a more efficient implementation of HOG in general, and particularly in field-programmable gate arrays (FPGAs), by considerably reducing the area (thus increasing the level of parallelism), while maintaining very close classification accuracy compared to the original algorithm. Thus, the new method is attractive for many applications, including car detection and classification.

Abstract: Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comic strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations.

Abstract: Providing Internet of Things (IoT) environments with service level guarantee is a challenging task for improving IoT application usage experience. We specify in this paper an IoT architecture enabling an IoT Service Level Agreement (iSLA) achievement between an IoT Service Provider (IoT-SP) and an IoT Client (IoT-C). In order to guarantee the IoT applications’ requirements, Quality of Service (QoS) mechanisms should be implemented within all the layers of the IoT architecture. Thus, we propose a specific mechanism for the lowest layer of our service level based IoT architecture (i.e., sensing layer). It is an adaptation of the IEEE 802.15.4 slotted CSMA/CA mechanism enabling to take into consideration the requirements of real-time IoT services. Our access method called QBAIoT (QoS based Access for IoT) extends IEEE 802.15.4 systems by creating a new contention access period for each specified traffic class in the iSLA. Furthermore, due to the huge number of IoT connected devices, self-configuring capability provisioning is necessary for limiting human intervention and total cost of ownership (TCO). Thus, we integrate a self-configuring capability to the QBAIoT access method by implementing the MAPE-K closed control loop within the IoT High Level Gateway (HL-Gw) of our proposed QoS based IoT architecture.

Abstract: This paper presents single- and dual-loop fuzzy control schemes to precisely control the piezo-driven nanopositioner in the x- and y-axis directions. Various issues are associated with this control problem, such as low stability margin due to the sharp resonant peak, nonlinear dynamics, parameter uncertainty, etc. As such, damping controllers are often utilised to damp the mechanical resonance of the nanopositioners. The Integral Resonant Controller (IRC) is used in this paper as a damping controller to damp the mechanical resonance. A further inherent problem is the hysteresis phenomenon (disturbance), which leads to degrading the positioning performance (accuracy) of the piezo-driven stage. The common approach to treat this disturbance is to invoke tracking controllers in a closed-loop feedback scheme in conjunction with the damping controllers. The traditional approach uses the Integral Controller (I) or Proportional Integral (PI) as a tracking controller, whereas this paper introduces the Proportional and Integral (PI)-like Fuzzy Logic Controller (FLC) as a tracking controller. The effectiveness of the proposed control schemes over conventional schemes is confirmed through comparative simulation studies, and results are presented. The stability boundaries of the proposed control schemes are determined in the same way as with a conventional controller. Robustness against variations in the resonant frequency of the proposed control schemes is verified.

Abstract: Distributed systems, such as the Internet of Things (IoT) and cloud computing, are becoming popular. This requires modeling that reflects the natural characteristics of such systems: the locality of independent components, the autonomy of their decisions, and asynchronous communication. Automated verification of deadlocks and distributed termination supports rapid development. Existing techniques do not reflect some features of distribution. Most formalisms are synchronous and/or use some kind of global state, both of which are unrealistic. No model supports the communication duality that allows the integration of a remote procedure call and client-server paradigm into a single, uniform model. The majority of model checkers refer to total deadlocks. Usually, they do not distinguish between communication deadlocks from resource deadlocks and deadlocks from distributed termination. Some verification mechanisms check partial deadlocks at the expense of restricting the structure of the system being verified. The paper presents an original formalism for the modeling and verification of distributed systems. The Integrated Model of Distributed Systems (IMDS) defines a distributed system as two sets: states and messages, and the relationship of the “actions” between these sets. Communication duality provides projections on servers and on traveling agents, but the uniform specification of the verified system is preserved. General temporal formulas over IMDS, independent of the structure of the verified system, allow automated verification. These formulas distinguish between deadlocks and distributed termination, and between communication deadlocks and resource deadlocks. Partial deadlocks and partial termination can be checked. The Dedan tool was developed using IMDS formalism.

Abstract: This work proposes the first scaler designed specifically for the three-moduli set M 1 = { 2 n + 1 − 1 , 2 n , 2 n − 1 } . Hence, there is no other functionally similar scaler to compare the proposed scaler with. However, when compared with the latest published scalers for a different moduli set, M 2 = { 2 n + 1 , 2 n , 2 n − 1 } , the proposed scaler has a better area and power performance, while it requires a longer time delay. As demonstrated in earlier publications, replacing the ( 2 n + 1 ) channel in the M 2 moduli set by the ( 2 n + 1 − 1 ) channel, to form the M 1 moduli set, considerably improves the overall time performance of residue-based multiply–accumulate arithmetic units.

Abstract: An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason.

Abstract: Electronic visual display enabled by touchscreen technologies evolves as one of the universal multimedia output methods and a popular input intermediate with touch–interaction. As a result, we can always gain access of an intelligent machine by obtaining control of its display contents. Since remote screen sharing systems are also increasingly prevalent, we propose a cross-platform middleware infrastructure which supports remote monitoring and control functionalities based on remote streaming for networked intelligent devices such as smart phone, computer and smart watch, etc. and home appliances such as smart refrigerator, smart air-conditioner and smart TV, etc. We aim to connect all these devices with display screens, so as to make possible remote monitoring and controlling a certain device by whichever one (usually the nearest one) of display screens among the network. The system is a distributed network consisting of multiple modular nodes of server and client, and is compatible to prevalent operating systems such as Windows, macOS, Unix-like/Linux and Android, etc.

Abstract: In 2010, Yeh et al. proposed two robust remote user authentication schemes using smart cards; their claims were such that their schemes defended against ID-theft attacks, reply attacks, undetectable on-line password guessing attacks, off-line password guessing attacks, user impersonation attack, server counterfeit attack and man-in-the-middle attack. In this paper, we show that Yeh et al.’s schemes are still vulnerable to ID-theft attack, off-line password guessing attacks, undetectable on-line password guessing attacks and user impersonation attack. Notably, problems remain in situations where the user lost a smart card or the malicious legal user. To remedy these flaws, this paper proposes an improvement on Yeh et al.’s remote user authentication schemes using smart cards.

Abstract: This paper analyzes the most relevant spatial-temporal stochastic properties of benchmark synthetic mobility models. Each pattern suffers from various mobility flaws, as will be shown by the models’ validation. A set of metrics is used to describe mobility features, such as the speed decay problem, the density wave phenomenon, the spatial node distribution, and the average neighbor percentage. These metrics have already been validated for the random waypoint mobility model (RWPMM), but they have not yet been verified for other mobility patterns that are most frequently used. For this reason, this investigation attempts to deeply validate those metrics for other mobility models, namely the Manhattan Grid mobility, the Reference Point Group mobility, the Nomadic Community mobility, the Self-Similar Least Action Walk, and SMOOTH models. Moreover, we propose a novel mobility metric named the “node neighbors range”. The relevance of this new metric is that it proves at once the set of outcomes of previous metrics. It offers a global view of the overall range of mobile neighbors during the experimental time. The current research aims to more rigorously understand mobility features in order to conduct a precise assessment of each mobility flaw, given that this fact further impacts the performance of the whole network. These validations aim to summarize several parameters into 18,126 different scenarios with an average of 486 validated files. An exhaustive analysis with details like those found in this paper leads to a good understanding of the accurate behaviors of mobility models by displaying the ability of every pattern to deal with certain topology changes, as well as to ensure network performances. Validation results confirm the effectiveness and robustness of our novel metric.

Abstract: This paper proposes a novel framework for solving the portfolio selection problem. This framework is excogitated using two newly parameters obtained from an existing basic mean variance model. The scheme can prove entirely advantageous for decision-making while using computed values of these significant parameters. The framework combines effectiveness of the mean-variance model and another significant parameter called Conditional-Value-at-Risk (CVaR). It focuses on extracting two newly parameters viz. αnew and βnew, which are demarcated from results obtained from mean-variance model and the value of CVaR. The method intends to minimize the overall cost, which is computed in the framework using quadratic equations involving these newly parameters. The new structure of ANFIS is designed by changing existing structure of ANFIS and this new structure contains six layers instead of existing five-layered structure. Fuzzy sets are harnessed for the design of the second layer of this new ANFIS structure. The output parameter acquired from the sixth layer of the new ANFIS structure serves as an important index for an investor in the decision-making. The numerical results acquired from the framework and the new six-layered structure is presented and these results are assimilated and compared with the results of the existing ANFIS structure.

Abstract: In this work, we compare the exergaming experience of young and old individuals under four difficulty adjustment methods. Physical inactivity is a leading cause of numerous health conditions including heart diseases, diabetes, cancer, and reduced life expectancy. Committing to regular physical exercise is a simple non-pharmaceutical preventive measure for maintaining good health and sustaining quality of life. Incorporating exercise into games, studies frequently used exergames as an intervention tool over the last decades to improve physical functions and to increase adherence to exercise. While task difficulty optimization is crucial to exergame design, researchers consistently overlooked age as an element which can significantly influence the nature of end results. We use the Flow State Scale to analyze the mental state of young and old individuals to compare constant difficulty with ramping, performance-based, and biofeedback-based difficulty adjustments. Our results indicate that old individuals are less likely to experience flow compared to young under the same difficulty adjustment methods. Further investigation revealed that old individuals are likely to experience flow under ramping and biofeedback-based difficulty adjustments whereas performance-based adjustments were only feasible for young.