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Composing music with bio-technology: an intelligent algorithmic composition system using Physarum polycephalum-based memristorsComputer-assisted and automated composition systems often harness Artificial Intelligence models such as Markov chains, Neural Networks, and Genetic Algorithms to generate musical material. The field of Unconventional Computing (UC) explores non-digital ways of data storage, processing, input, and output. UC paradigms such as Biocomputing and Quantum Computing delve into domains beyond the binary bit to handle complex nonlinear functions. In this chapter, we explore harnessing the biological computing substrate Physarum polycephalum as a memristor to process and generate musical material within an algorithmic composition system. It details the journey and process of creating a piece of popular music using this novel technology, which we originally showcased at the New Interfaces for Musical Expression (NIME) conference in 2020. In this piece, entitled Creep into my Lawn, the Physarum polycephalum-based memristors act as creative collaborators in the composition process. Our work aims to demonstrate the potential of UC paradigms in creative applications and to disseminate this technology to non-experts and musicians so that they can incorporate it into their creative processes.
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SSVEP-based brain–computer interface for music using a low-density EEG systemIn this paper, we present a bespoke brain–computer interface (BCI), which was developed for a person with severe motor-impairments, who was previously a Violinist, to allow performing and composing music at home. It uses steady-state visually evoked potential (SSVEP) and adopts a dry, low-density, and wireless electroencephalogram (EEG) headset. In this study, we investigated two parameters: (1) placement of the EEG headset and (2) inter-stimulus distance and found that the former significantly improved the information transfer rate (ITR). To analyze EEG, we adopted canonical correlation analysis (CCA) without weight-calibration. The BCI for musical performance realized a high ITR of 37.59 ± 9.86 bits min−1 and a mean accuracy of 88.89 ± 10.09%. The BCI for musical composition obtained an ITR of 14.91 ± 2.87 bits min−1 and a mean accuracy of 95.83 ± 6.97%. The BCI was successfully deployed to the person with severe motor-impairments. She regularly uses it for musical composition at home, demonstrating how BCIs can be translated from laboratories to real-world scenarios.
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On growing computers from living biological cellsThe technology behind the computers, and all sorts of data processing devices pervading our daily lives, are underpinned by paradigms such as the Turing machine, the von Neumann architecture, the Harvard architecture, and so on, which were invented in the 1930 and 1940s (Rojas and Hashagen in The First Computers – History and Architectures, The MIT Press, Cambridge, MA [29]; Soare in Turing Computability, Springer, Cham, Switzerland [33]). These paradigms are so successful that they still prevail in the design of today’s digital computers. We are interested in harnessing biological systems to build new kinds of processors for Artificial Intelligence, music and creativity. Our ambition is to develop electronic components, data processors and eventually full-fledged computers, with living organisms, such as bacteria and slime mould. This chapter focuses on the work that is being developed with slime mould at the University of Plymouth’s Interdisciplinary Centre for Computer Music Research (ICCMR). It tells the story a wild musical idea, born in 2009, and which resulted in the development of a biological processor that is capable of improvising music and doing Boolean logics.
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Detection for user impersonation attacks in mobile social networks based on high-order Markov chainsIn security defense of MSN (MSN), attackers often impersonate themselves as other users, making it difficult to detect network user attacks based on user behavior. Multi-order Markov chains can consider the front-to-back correlation of user behavior, thereby more accurately identifying disguised users. Therefore, this paper proposes a user impersonation attack detection method based on multi-order Markov chains. First, the relevance coefficient method is used to determine the order of the multi-order Markov chain, and by defining appropriate multi-order Markov chain states to capture key features in user behavior, a multi-order Markov chain is established. Then, through the multi-order Markov chain combined with Shell commands, the normal behavior profile of legitimate users is established, and based on this, the probability of occurrence of the state sequence is calculated to complete the detection of userimpersonation attacks. The experimental results show that the similarity between the results of the proposed method and the actual situation in detecting impersonation attacks is more than 97%, indicating that this method can detect MSN user impersonation attacks with high accuracy.
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'Colorblind game' can enhances awareness of color blindnessThis study investigates whether awareness of color blindness can be enhanced through a virtual colorblind gaming experience. We conducted two user studies—one with undergraduates and one with working adults—using ‘colorblind’ color schemes in a digital game to explore whether such an experience enhances their assessment of their own knowledge about color blindness and awareness of its associated disadvantages in society, workplaces, and private life. The results with undergraduates showed increases in general, but not much in workplace disadvantages. In contrast, the results with working adults showed a significant improvement in the assessment of knowledge but not in other aspects. Thus, a virtual colorblind gaming experience can enhance awareness of color blindness, yet the interpretation of the experience may vary significantly depending on the players’ backgrounds.
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An integrated system framework for preventing crime in retail supermarketRetail supermarkets have been investing billions of poundsto prevent and reduce crime in their stores, but the rate of crime keep increasing. Retail shrinkage monitoring as far back as 1995 showed that the retail stores were losing the equivalent of 0.3 per cent of their gross revenues which have taken up to 20 to 30 percent of their profit. Also recently, the British Retail Crime Report (2023)showed a significant increase from the 2019 report in retail crime and subsequent loss to retailers. In 2021/2022, the retail staff incidents of violence stood at 850 per day, and the cost of retail crime was £1.76b. There were eight million incidents of theft over the year and a total of £715 million was spent on crime prevention. As crime keeps increasing, examining the three security solutions (Cyber, Physical and System) that are used in retail supermarkets becomes paramount. This article will look into if the lack of interconnectedness is the cause of continuous porosity in criminality in stores using Aldi and Sainsbury in the United Kingdom as a case study. A combination of mix method approach has been used in this study which allows a combination of quantitative data gathering through questionnaires and qualitative data through interviews. Accessing the current effectiveness of the three security solution (Cyber, System and Physical), it becomes important to identify the strategic gap between actual and potential performance so that steps can be taken to identify the shortfall in the Security solutions. The Ishikawa fishbone model is used as a theoretical tool to examine the cause and effect of retail crime. This will identify other causes that affect the effectiveness of security solutions. From the findings, a Hierarchical Taxonomy of Crime Prevention Framework in line with the Ishikawa fishbone theoretical tool was developed to help supermarkets reduce and prevent crimes. For many years supermarkets have been investing lots of money on security solutions but the rate of crimes keep increasing.
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A trustworthy multiparty authentication architecture for IIoT leveraging IOTA integrationThe rapid adoption of developing technologies, particularly the Industrial Internet of Things (IIoT), has raised significant security and privacy concerns. IIoT seeks to enhance industrial operations through the integration of specialised devices, primarily sensors, within the industrial environment. These sensors continuously monitor multiple processes while providing essential data to maintain proper functionality. However, safeguarding IIoT systems is especially complex because of the variety of devices, makers, and networks; the need for collaboration across several security domains; and the constraints of resource- constrained devices. Conventional security systems struggle to adapt, especially when users and services interact dynamically across several IoT networks. To address these issues, this research introduces a novel multi-party authentication architecture that enables secure and dynamic communication among participants from different security groups. The design enables the secure exchange of shared secrets for session authentication while protecting security credentials during resource access. One significant advance is the integration of IOTA distributed ledger technology (DLT) alongside IoT systems to enable multi-party authentication. Specifically, the IOTA Streams protocol is used to enable structured, secure, and scalable data sharing while maintaining data integrity, privacy, and authenticated access. The NuSMV model checker is employed to verify the effectiveness as well as security of the proposed authentication approach. It ensures fulfilment of security requirements and operational functionality.
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Robust sensor fault detection in wireless sensor networks using a hybrid conditional generative adversarial networks and convolutional autoencoderIn the rapidly growing realm of the Internet of Things (IoT), reliance on sensor-generated data has become crucial for the operation of multiple services and systems. As essential components of these systems, wireless sensor networks (WSNs) are installed in a wide range of diverse and often harsh environments. However, these networks are highly prone to a range of faults, including software bugs, communication failures, and hardware malfunctions. Such issues can lead data to data being transmitted incorrectly, endangering the security, reliability, and economic stability of the systems they support. Addressing the challenge of sensor fault detection, we propose a novel hybrid technique to enhance the classification of sensor fault data in WSNs. Our method leverages a publicly available dataset of temperature sensor readings to generate synthetic data by using a conditional generative adversarial networks. These synthetic samples closely resemble common temperature sensor data despite the introduction of artificial sensor faults in WSNs, including hardover, drift, spike, erratic, and stuck faults. In order to capture the temporal dependencies in time-series data, we transform the sensor readings into Gramian Angular Field images (GAF), retaining the temporal structure. These GAF images are then processed using a convolutional autoencoder to extract rich feature representations, followed by a three-layer artificial neural network for the multi-class classification of sensor faults. Our proposed method not only addresses the challenges of data scarcity and imbalance but also enhances accuracy in sensor fault detection. The proposed method demonstrates high accuracy, F1-score, recall, and sensitivity, achieving 95.93%, 95.84%, 95.88%, and 95.88% respectively.
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A formal framework for software development using Publish/Subscribe architectureHigh flexibility of publish/Subscribe architecture that is a common architectural style for component based systems make it to be capable of developing and supporting large software systems. But difficult aspect of Publish/Subscribe systems is their validation. Existing efforts for developing formal foundation for specifying and reasoning about these systems are hard to use by practitioners who are not familiar with formal methods. To face this challenge this paper proposes a formal framework for software development using Publish/Subscribe architecture. Modeling components by Abstract State Machines (ASMs), presentation new characteristics for parametric dispatcher and the use of modelbased testing for validation are the key features of this framework. © 2010 IEEE.
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Using ASMs and Spec# to formal modeling and analysis publish/subscribe architecturesThe Publish/Subscribe architecture has been proposed as a suitable architecture to develop highly dynamic systems. Although the structure of this architecture is easy to understand, unfortunately modeling and validating the whole system is complicated. In this paper, we present a formal approach based on Abstract State Machines (ASM) to model systems using this architecture. Then, to validate the designed models we use model-based testing. To do so, we propose a transformation from ASMs to Spec# language. The key feature of the proposed approach are new parametric dispatcher and the use of model-based testing for validation. © 2010, INSInet Publication.
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Monte-Carlo study of some robust estimators: The simple linear regression caseIn this study, Least Trimmed Squares (LTS), Theil’s Pair-wise Median (Theil) and Bayesian estimation methods (BAYES) are compared relative to the OLSE via Monte-Carlo Simulation. Variance, Bias, Mean Square Error (MSE) and Relative Mean Square Error (RMSE) were calculated to evaluate the estimators’ performance. The Simple Linear Regression model is explored for the conditions in which the error term is assumed to be drawn from three error distributions: unit normal, lognormal and Cauchy. Theil’s non-parametric estimation procedure was found to have the strongest and most reliable performance. The subsequent-best results are acquired from LTS approach Though it was observed that the Bayesian estimators are affected by deviation of the dataset from normality, yet it is established from the results that the Bayesian estimators performed optimally more than all other competitors, even under non normal situations (especially under the standard lognormal distribution) in some cases, except whenever the error is drawn from a heavy tail distribution (Lognormal and Cauchy)..OLSE is most effective reliable as long as the normality assumptions preserve
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Applying software engineering solutions to law firm management, Nigeria as a case studyLegal technology has changed the way law firms are managed worldwide. Substantial research has been undertaken on the role of legal technology in law firm management especially in developed countries. Though, most studies have only focused on the benefits and challenges, and have failed to analyse law firm management areas requiring software solutions. The principal objective of this paper was to investigate the level of technology adoption among Nigerian law firms, as well as to develop a software solution to automate work processes in identified areas. This investigation was done using systematic literature review to gather relevant data on the subject area and identify knowledge gaps. Findings from the research indicated a need for further analysis of the various areas in law practice that could require software solutions. The findings also discussed the implementation of a property management module which is an important contribution to the management of law firms in Nigeria. A speech-to-text transcription feature was also implemented to eliminate the need for lengthy typing
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A conditional GAN and dual-channel hybrid deep feature framework for robust sensor fault detection in WSNsSensor-generated data is vital to the operation of numerous systems and services in the rapidly growing field of the Internet of Things. Wireless Sensor Networks, as an essential setup for these systems, are frequently deployed in large, diverse, and often harsh environments. However, these networks are highly vulnerable to various faults, potentially leading to improper data transmission, reliability, and financial stability of the systems. To address these challenges, we propose a hybrid model for sensor fault detection that integrates a machine learning classifier with the deep learning (DL) model, specifically VGG-16 and ResNet-50. Synthetic samples are generated using a Conditional Generative Adversarial Network and common sensor faults, such as hardover, drift, spike, erratic, and stuck fault are introduced by leveraging a publicly available temperature sensor dataset. Time-series data is transformed into Gramian Angular Field images, from which deep features are extracted using VGG-16 and ResNet-50. These extracted features are then fused to form a hybrid feature pool. Our framework effectively addresses problems related to data imbalance and enhances accuracy. The proposed model outperforms the individual feature sets, VGG-16 (89.22%) and ResNet-50 (84.21%), achieving notable accuracy of 92.55% with the fused feature set, underscoring its potential for robust sensor fault detection.
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Roles of e-service in cconomic development: case study of Nigeria, a lower-middle income countryE-Government activities are still very low in Nigeria, a lower middle-income country, and this is hindering E-Service adoption. E-Service is inextricably linked to E-Government and they will not develop separately, but as one progresses the other moves forward. Having a new technology like E-service opens new opportunities for government, private and public sectors. Despite the fact that the new technology will not be without a hindrance, the overall benefits of using outweigh its lapses. Nigeria has overtaken South Africa as top Africa economy. There is still more to be done in increasing the revenue of the country, reducing the huge external debt owing the World Bank. Furthermore, there is a need to sustain the new status as top economy in Africa. There are many unresolved problems like corruption. This leads to a slow movement of files in offices, embezzlement, election irregularities, and port congestions among others. Adoption of E-Service will help to reduce these problems and increase the revenue base of the country. This study will identify e-Service roles in economic development in Nigeria, a lower middle-income country. The study is based on literature review methodology and recent online survey that shows the level of E-Service awareness and roles. We shall also examine previous conference papers related to this study and necessary recommendations will be suggested and offered to the authority in Nigeria on how best the e-service adoption will add more success to the economic development.
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E-service security: taking proactive measures to guide against theft, case study of developing countriesThe rapid diffusion of internet couple with the digitalization of most economies in the world today which have given rise to e-Government services adoption to promote good governance capability and accountability of public organisations. There are still concerns about e-Service security in developing countries as consumers and online users have become the victims of co-ordinated cybercrimes despite the fact that e-Government services are helping to boost government revenue through very fast transactions, reduce corruption through the use of modern technology and transparent operations. It is imperative to state that the security issues in the E-Service domain have been gaining more attention over past two decades. This paper discusses the general overview through the use of on-going online survey and literature review of related works in e-Service security as existing approach to combat the issues still has many setbacks. The findings show a deep understanding of what proactive measures need to be taken against e-Service theft in developing countries. A model also emerged that capture the e-Service technology security issues in developing countries and the proactive measures to reduce and eradicate these threats.
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Splitting frequency behavior of wireless power transfer for eddy current testing applicationsThis paper presents a novel approach for Non-destructive Testing and Evaluation (NDT&E) of cracks in metallic structures using Eddy Current Testing (ECT) integrated with the resonance Wireless Power Transfer (WPT) concept. The proposed method enhances ECT for efficient power transfer between transmitter-receiver (Tx-Rx) coils and employs Gaussian Random Projection (GRP) for feature reduction, enabling real-time data processing. Experimental results on two aluminium material samples demonstrate the effectiveness of the proposed approach in localising and characterising slots, with an R2-value/RMSE of 99.86%/0.06 mm for width and 99.38%/0.25 mm for depth slot parameters. The findings highlight the potential of this method for improving NDT&E of metallic structures.
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User identification across online social networks in practice: pitfalls and solutionsTo take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.
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A simulated annealing algorithm for multi-manned assembly line balancing problemAssembly line balancing problems with multi-manned workstations usually occur in plants producing high volume products (e.g. automotive industry) in which the size of the product is reasonably large to utilize the multi-manned assembly line configuration. In these kinds of assembly lines, usually there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. However, owing to the high computational complexity, it is quite difficult to achieve an optimal solution to the balancing problem of multi-manned assembly lines with traditional optimization approaches. In this study, a simulated annealing heuristic is proposed for solving assembly line balancing problems with multi-manned workstations. The line efficiency, line length and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with a numerical example problem, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. Results show that the proposed algorithm performs well.
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Following people's behavior across social mediaTo face the new challenge of giving an all-around picture of people's online behavior, in this paper we perform a multidimensional analysis of users across multiple social media sites. Our study relies on a new rich dataset collecting information about how users post their favorite contents and about their centrality on different social media. Specifically posting activities and social sites usage have been gathered from the social media aggregator Alternion. The analysis of social media usage shows that Alternion data capture the typical trend of today's users. However the novelty is the multidimensional and longitudinal nature of the dataset. In fact by performing a rank correlation analysis on the degree in the different social sites, we find that the degrees of a given user are scarcely correlated. This is suggesting that the individuals' importance changes from medium to medium.We also investigate the posting activities finding a slightly positive correlation on how often users publish on different social media. Finally we show that users tend to use similar usernames to keep their identifiability across social sites.
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User identification across online social networks in practice: pitfalls and solutionsTo take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.
