Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework

Cybersecurity of smart grids have been a topic of much interest in recent years. As this critical infrastructure operation increases dependency on automated processes and controls, exposure to cyber-physical threats become inevitable. Considering cyber-physical security of the grid, much focus of attention has been made towards smart grids real-time monitoring solutions, including the state estimation process. Analyzing the relevant literature, one can note though that seldom research has been done on cyber-physical security of smart grids protection systems. Protection systems have intangible value towards grid reliability. This paper presents a cybersecurity framework for smart grids protection systems. A physics-based inspired machine learning solution is at the core process of the framework. Processed relay inputs and outputs are used by a deep predictive coding network. A formal mathematical model, a quasi-static state estimator, provides an oracle when low confidence decision is reached. Evolving knowledge is derived through reinforcement learning. Implementation aspects considering the Pacific Northwest National Laboratory Electricity Infrastructure Operations Center are presented. Built as an extra control layer to protection systems, without hard-to-derive parameters, highlights potential aspects towards real-life applications.
Relation between Edge Computing and the Internet of Things: A Systematic Literature Review

The Internet of Things (IoT) is an emerging computing paradigm providing new approaches to collect and analyze environmental data. However, as specific challenges arose, the paradigm of Edge Computing with its potential solution capabilities came into place. The combination of both paradigms is currently highly discussed in industry and research. This paper aims to contribute to this field by conducting a systematic literature review to examine the differences and relation between IoT and Edge Computing on a meta-level. It first investigates conceptual backgrounds, use cases, and implementation types. After that, the differences between the paradigms are highlighted. It becomes clear that the significant distinction is in the architectural composition. However, the scientific consensus reveals that both paradigms have a common historical background, and Edge Computing is perceived as the next step in the evolution of IoT. Furthermore, Edge Computing-based systems can address common IoT challenges identified in the two paradigms’ problem-solution space. Ultimately, there is a need for further research in security, edge intelligence, and standardization, with Edge Computing frameworks able to address these in practice.
Critical Success Factors Affecting the Successful Implementation of Industry 4.0 in The Sri Lankan Apparel Manufacturing Industry

The Sri Lankan apparel manufacturing business, a major contributor to the country’s export revenue, has been attempting to adopt industry 4.0. Only a few developing nations have been able to capture the maximum benefits of the fourth industrial revolution. The purpose of this study is to identify the critical factors that must be considered for the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector. Throughout the research, a quantitative approach was used. Initially, the six most significant critical factors and two moderating variables were determined by a review of prior research and the opinions of industry professionals. Partial Least Square – Structural Equation Modelling (PLS-SEM) was used to analyze the relationship between the factors. Greater financial investments, organizational strategy, workforce, a dynamic organizational culture, the involvement of top management, and the availability of IT infrastructure have a significant positive impact on the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector, as determined by the final findings of the data analysis. In addition, the availability and accessibility of support services have a significant positive moderating effect on financial investments, when successfully implementing industry 4.0 in the Sri Lankan apparel industry. In addition, the advancement of digital technologies has a significant positive moderating effect on financial investments and, a significant negative effect on organizational strategy and the involvement of top management when successfully implementing industry 4.0 in the Sri Lankan apparel industry. The outcomes of this study assist the managers of the Sri Lankan clothing manufacturing sector in comprehending the critical factors that must be considered when successfully implementing industry 4.0 technologies.
Intrusion Detection for Internet of Things Networks using Attention Mechanism and BiGRU

The rapid growth of Internet of Things (IoT) devices has led to increased vulnerability to serious security threats within IoT networks. As such, it is crucial to employ network intrusion detection techniques to monitor these networks. This paper presents an intrusion detection model, SEW-MBiGD, which integrates data processing and fusion neural networks to address data imbalance and insufficient feature learning in existing models. Firstly, to balance the dataset and mitigate the influence of edge data, the model employs Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) algorithms for data preprocessing, while also utilizing Wasserstein Generative Adversarial Networks (WGAN) to generate minority class data. The proposed intrusion detection model is based on Bidirectional Gated Recurrent Unit (BiGRU) and multi-head self-attention (MHSA) mechanisms, which effectively manage long sequence data and capture correlations between data and global features. Experimental results demonstrate the efficacy of the proposed SEW-MBiGD model outperforming baseline models in achieving a balanced dataset and classification tasks.
Quantile-based Reliability Measures and Some Associated Stochastic Orderings

There are several statistical models which have explicit quantile functions, but do not have manageable cumulative distribution functions. For example, Govindarajulu, various forms of lambda, and power-Pareto distributions. Thus, to study the reliability measures for such kind of distributions, a quantile-based tool is essentially required. In this article, we consider quantile version of some well- known reliability measures in the reversed time scale. We study stochastic orders based on the reversed hazard quantile function and the mean inactivity quantile time function. Further, we discuss relative reversed hazard quantile function order, likelihood quantile ratio order, and elasticity quantile order. Connections between the newly proposed orders and the existing stochastic orders are established. AMS 2010 Subject Classification: 60E15, 62E10
Data-driven FDI Attacks: A Stealthy Approach to Subvert SVM Detectors in Power System

False Data Injection (FDI) attacks can mislead the grid operator’s state estimation without being detected by the Chi-2 detector in bad data detection. While machine learning methods have been used to detect FDI attacks, existing FDI construction methods only consider the Chi-2 detector and not machine learning (ML) detectors. This paper investigates the limitation of a support vector machine (SVM) detector in detecting FDI attacks. The study proposes a novel data-driven FDI (DFDI) attack that aims to remain undetected by the SVM detector and have a significant impact on the power system operation. The DFDI attack uses the minimum-area ellipse enclosing (MEE) method to obtain the boundary of the historical measurements’ cluster, which is used as a constraint to ensure that the compromised measurements are projected into the cluster. Numerical results on the IEEE 14-bus system demonstrate the limitations of the SVM detector and drawbacks of traditional FDI (TFDI) attacks, and further verify the stealthiness of the proposed attacks against the SVM detector.
Network Fuzzing: State of the art

As fuzzing has seen increased interest not only in academia but also in industry settings, algorithmic advancements have become increasingly difficult to obtain. Moreover, the computational requirements for finding new bugs in well known Open Source projects have also experienced an upturn. These factors have prompted a shift in focus towards a previously neglected but critical type of stateful system: network applications. In this paper we present an overview of recent network fuzzing advancements, implementation techniques used in state-of-the-art fuzzers, and benchmarking frameworks used in fuzzer evaluation and scale testing. Our goal is to provide a better understanding of current day challenges and research directions with respect to network fuzzing.
Functional Dynamics of Innovation for Inclusive Development Projects: Event History Analysis of the Stock Visibility System (SVS) in South Africa

Innovation is an essential factor in stimulating economic growth. However, despite the positive effect of innovation, it is often focused on high-income groups, excluding marginalized individuals and groups, possibly worsening inequality as a consequence. In response, the Innovation for Inclusive Development (I4ID) model has been introduced, drawing on the theory of the conventional Innovation Systems (IS) perspective to help the marginalized benefit from innovation. In this article, we use the Event History Analysis (EHA) research method and causal loop diagrams (CLDs) as an analytical approach to explore the dynamics of the Stock Visibility System, which was implemented in 3300 clinics in South Africa. The study’s findings present the analysis of the core dynamics and motors of innovation that led to the project’s success.
Can Enterprise Resource Planning Improve Balance Scorecard Reporting?

The purpose of this study is to examine whether Enterprise Resource Planning (ERP) system can enhance Balance Scorecard (BSC) reporting in order to enable senior management to take informed decisions that advances the company’s strategy. The case study focuses on a metallurgical mining company which produces calcium fluoride. An ERP system is a software used by a mining company to manage and integrate its daily business activities. The system is able to automate and integrate Core Operational Functions (CoF) which enables synchronization of process data. BSC on the other hand is a method used to assess the mine’s strategic objectives and goals. The method drives and communicates the mine’s strategy across CoF. The BSC comprises of four perspectives. These perspectives assist in selecting relevant Key Performance Indicators (KPIs) to measure in order to understand the performance state of each of the mine’s CoF. To determine whether ERP system is able to improve BSC reporting, a quantitative desktop research is followed. Performance of each CoF is measured and analysed to obtain finds. A Model is then developed to understand the impact ERP system has on BSC reporting. The results obtained shows that indeed ERP system is able to improve BSC reporting.
Inertial Navigation for Motion Tracking in Android

As the interest in IoT increases, the need for an accurate IPS is more and prominent. An accurate system, specifically designed for location retrieval system is the GNSS. Unfortunately, due to the the signal hindering properties of the building walls and roofs, GPS signals systems are rendered as almost useless. For this specific reason, the indoor positioning raises quite a challenge. Nevertheless, there are solutions which take advantage of existing infrastructures. For example, the current implementation of the Airdocs [1] middleware uses wireless technologies to aproximate location indoors. Even if the WiFi location retrieval technologies are highly useful, their disadvantage is the accuracy as most of the times they depend on the structure of the environment. To cater for this downside, extra systems may be included in the Airdocs IPS, such as inertial measuring units. This paper aims to improve the accuracy of current solutions such as Airdocs. Being already present on the android phones, the accelerometer, gyroscope and magnetometer may offer promising results for IMU construction and integration withing the existing solutions.
Grid-Supportive Load Frequency Control using Deep Reinforcement Learning

System frequency is one of the main essential variables along with voltage to ensure reliability, stability, and security of power system operation. Currently, maintaining system frequency becomes a concern because of: (i) the integration of renewable resources (i.e., solar, wind) in mixed energy, (ii) the requirement of real-time techniques to tune classical controller’s gain, and (iii) the requirement of knowing the complex model of new participants. Therefore, the need for a model-free scheme for frequency control is the primary driver to propose a frequency controller based on deep reinforcement learning. In this paper, primary and secondary frequency controllers are investigated. An infinite state-action agent, namely deep deterministic policy gradient (DDPG), is proposed for secondary control along with considering demand response (DR) for primary control. The proposed framework considers DR to enhance primary response in terms of response speed and frequency nadir. The impact of two typical grid-supportive loads is analyzed. The results demonstrate that the proposed framework can effectively respond to various system conditions. The DR can support the grid and allow more renewable energy resources (RES) integration.
It ain't what you do, it's the way that you do it: a qualitative study of advice for young cannabis users.

INTRODUCTION Advice is a widely recommended and practised intervention with young drug users. Study of precisely how advice is given and received in any setting has, however, been limited. DESIGN AND METHODS We qualitatively analysed 106 audio-recordings of advice sessions on cannabis use for young people within a randomised trial. Inductive data analysis was guided by a focus on practitioner behaviour which served to engage the active participation of the young drug user in the session. RESULTS A cluster of 'Information Management' activities was identified together with an 'Interactive Orientation' evident in a series of specific behaviours. Participants were most successfully engaged when both were combined, understood here as 'Personalised Advice-giving'. DISCUSSION AND CONCLUSIONS These components identified in this exploratory study might assist further research in rectifying the absence of a solid empirical basis for effective practice in advice giving with young drug users and more widely.
Characterisation of a turbulent separation over a ramp : entrainment and scaling laws

Massive turbulent separations are common phenomena that can cause sizeable aerodynamical losses and detrimental effects in industrial flows, for example on airplane wings. This work contributes to their understanding with a phenomenological analysis of a canonical turbulent separation, representative of a large number of real flows. The first objective is to identify the scaling laws of turbulent separations, in particular with respect to their dependencies on the characteristics of the flow upstream of the ramp. A second objective is the analysis, both at large and small scale, of the transfert mechanisms that drive the functioning of separated flows. To this end, a new approach is proposed, centered on the experimental and analytical description of the separated shear layer and of the turbulent interfaces that bound it. Our results suggest that the scaling laws of the separated flow vary in a complex way, in function of the interaction of the incoming boundary layer, the separated shear layer and the free-stream. The size of the separation is related to the intensity of turbulent mass entrainment within the shear layer, which in turn depends on the turbulence in the incoming boundary layer, well upstream of the separation point. This dependency might apply over the entire range of turbulent length scales that are responsible for mass transfer. These observations clearly show the role of the shear layer in the functioning of massive separation. They also suggest the feasibility of new control strategies, both of feedback and feed-forward type, based on turbulent entrainment.
Automated Neuron Activity Classiﬁcation in C. elegans

Caenorhabditis elegans is commonly used to study neural activity due to 1) its well-studied genome and connectome with only 302 neurons, 2) its transparency, which makes it possible to capture detailed neural activity, and 3) its ability to self-fertilize, which allows for the maintaining of a genetically identical C. elegans population. Analyzing images of C. elegans has the exciting potential to link the activity of particular neurons to behavior or environmental stimuli. However, generating such insights has been limited by the diﬃculty of naming speciﬁc neurons in these images. Current approaches rely on manual classiﬁcation, which is both time-consuming and error-prone. Neuron tracking systems such as ZephIR, can be leveraged to help perform labeling. However, using a tracking system in this way requires the additional step of 45 minutes of manual labeling per 11 minutes of captured imagery in order to track a speciﬁc neuron. In this study, we develop a neural-network based classiﬁer that automatically labels sensory neurons in C. elegans with 91.61% accuracy. This is achieved through the use of an iterative, landmark-based neuron identiﬁcation process, designed to mimic the manual annotation procedure.
Alapana Generation using Finite State Machines and Generative Adversarial Networks

The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation.
Statistical analyses of motion-corrupted MRI relaxometry data

Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2*, which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2*, and MTsat, which are computed from multiple sets of images. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. In sum, QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.
Ecotourism Landscapes: An Editorial

This special issue of American Journal of Tourism Management is about the significance of ecotourism landscapes as important components of the urban and sub-urban grain in developing nations. Identifying this fact, the papers document the characteristics, tourists’ experiences and means of continual conservation of the landscapes studied towards enhancing their tourism values. While the papers presented centres on the potentials of these landscapes, they have a common aim at ensuring that tourists and potential tourists are focused as being central to the successful management of tourism industry. Essentially, the landscapes studied are either parks or gardens or cultural/historical sites in different contexts. This editorial sets out the overall scope of the special issue and considers the relationship between ecotourism landscapes and tourists satisfaction literature. It then outlines and summarizes some key points as well as commenting on each paper. It concludes by asserting that ecotourism landscapes are germane to the overall success of the ecological conservation movement and upholding the indigenous value-system of the local communities, with numerous social, economic and cultural benefits.
Comparison of Decadal Trends among Total Solar Irradiance Composites of Satellite Observations

We present a new analysis of the two-decade-old controversy over interpretation of satellite observations of total solar irradiance (TSI) since 1978 and the implications of our findings for TSI as a driver of climate change. Our approach compares the methods of constructing the two most commonly referenced TSI composites (ACRIM and PMOD) that relate successive observational databases and two others recently constructed using a novel statistical approach. Our primary focus is on the disparate decadal trending results of the ACRIM and PMOD TSI composite time series, namely, whether they indicate an increasing trend from 1980 to 2000 and a decreasing trend thereafter (ACRIM) or a continuously decreasing trend since 1980 (PMOD). Construction of the four-decade observational TSI composites from 1978 to the present requires the use of results from two less precise Earth Radiation Budget experiments (Nimbus7/ERB and ERBS/ERBE) during the so-called ACRIM-Gap (1989.5–1991.8), between the end of the ACRIM1 and the beginning of the ACRIM2 experiments. The ACRIM and PMOD composites used the ERB and ERBE results, respectively, to bridge the gap. The well-established paradigm of positive correlation between Solar Magnetic Field Strength (SMFS) and TSI supports the validity of the upward trend in the ERB results and the corresponding decadal upward trend of the ACRIM composite during solar cycles 21 and 22. The ERBE results have a sensor degradation caused downward gap trend, contrary to the SMFS/TSI paradigm, that biased the PMOD composite decadal trend downward during solar cycles 21 and 22. The different choice of gap bridging data is clearly the cause of the ACRIM and PMOD TSI trending difference, agreeing closely in both magnitude and direction. We also analyze two recently proposed statistical TSI composites. Unfortunately their methodology cannot account for the gap degradation of the ERBE experiment and their resulting uncertainties are too large to uniquely distinguish between the trending of the ACRIM and PMOD composites. Our analysis supports the ACRIM TSI increasing trend during the 1980 to 2000 period, followed by a long-term decreasing trend since.