• Targeted ensemble machine classification approach for supporting IOT enabled skin disease detection

      Yu, Hong Qing; Reiff-Marganiec, Stephan; University of Derby (IEEE, 2021-03-26)
      The fast development of the Internet of Things (IoT) changes our life in many areas, especially in the health domain. For example, remote disease diagnosis can be achieved more efficiently with advanced IoT-technologies which not only include hardware but also smart IoT data processing and learning algorithms, e.g. image-based disease classification. In this paper, we work in a specific area of skin condition classification. This research work aims to provide an implementable solution for IoT-led remote skin disease diagnosis applications. The research output can be concluded into three folders. The first folder is about dynamic AI model configuration supported IoT-Fog-Cloud remote diagnosis architecture with hardware examples. The second folder is the evaluation survey regarding the performances of machine learning models for skin disease detection. The evaluation contains a variety of data processing methods and their aggregations. The evaluation takes account of both training-testing and cross-testing validations on all seven conditions and individual condition. In addition, the HAM10000 dataset is picked for the evaluation process according to the suitability comparisons to other relevant datasets. In the evaluation, we discuss the earlier work of ANN, SVM and KNN models, but the evaluation process mainly focuses on six widely applied Deep Learning models of VGG16, Inception, Xception, MobileNet, ResNet50 and DenseNet161. The result shows that each of the top four models for the major seven skin conditions has better performance for the specific condition than others. Based on the evaluation discovery, the last folder proposes a novel classification approach of the Targeted Ensemble Machine Classify Model (TEMCM) to enable dynamically combining a suitable model in a two-phase detection process. The final evaluation result shows the proposed model can archive better performance.
    • Pseudoprimality related to the generalized Lucas sequences

      Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University, Cluj-Napoca, Romania; University of Derby (Elsevier BV, 2021-03-13)
      Some arithmetic properties and new pseudoprimality results concerning generalized Lucas sequences are presented. The findings are connected to the classical Fibonacci, Lucas, Pell, and Pell–Lucas pseudoprimality. During the process new integer sequences are found and some conjectures are formulated.
    • An LMI Approach-Based Mathematical Model to Control Aedes aegypti Mosquitoes Population via Biological Control

      Dianavinnarasi, J.; Raja, R.; Alzabut, J.; Niezabitowski, M.; Selvam, G.; Bagdasar, O.; Alagappa University, Karaikudi 630 004, India; Prince Sultan University, Riyadh 12435, Saudi Arabia; Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland; Vinayaka Missions University, Salem 636308, India; et al. (Hindawi Limited, 2021-03-09)
      In this paper, a novel age-structured delayed mathematical model to control Aedes aegypti mosquitoes via Wolbachia-infected mosquitoes is introduced. To eliminate the deadly mosquito-borne diseases such as dengue, chikungunya, yellow fever, and Zika virus, the Wolbachia infection is introduced into the wild mosquito population at every stage. This method is one of the promising biological control strategies. To predict the optimal amount of Wolbachia release, the time varying delay is considered. Firstly, the positiveness of the solution and existence of both Wolbachia present and Wolbachia free equilibrium were discussed. Through linearization, construction of suitable Lyapunov–Krasovskii functional, and linear matrix inequality theory (LMI), the exponential stability is also analyzed. Finally, the simulation results are presented for the real-world data collected from the existing literature to show the effectiveness of the proposed model.
    • Controlling Wolbachia transmission and invasion dynamics among aedes aegypti population via impulsive control strategy

      Dianavinnarasi, Joseph; Raja, Ramachandran; Alzabut, Jehad; Niezabitowski, Michał; Bagdasar, Ovidiu; Alagappa University, Karaikudi, India; Prince Sultan University, Riyadh, Saudi Arabia; Silesian University of Technology, Akademicka 16, Gliwice, Poland; University of Derby (MDPI AG, 2021-03-08)
      This work is devoted to analyzing an impulsive control synthesis to maintain the self-sustainability of Wolbachia among Aedes Aegypti mosquitoes. The present paper provides a fractional order Wolbachia invasive model. Through fixed point theory, this work derives the existence and uniqueness results for the proposed model. Also, we performed a global Mittag-Leffler stability analysis via Linear Matrix Inequality theory and Lyapunov theory. As a result of this controller synthesis, the sustainability of Wolbachia is preserved and non-Wolbachia mosquitoes are eradicated. Finally, a numerical simulation is established for the published data to analyze the nature of the proposed Wolbachia invasive model.
    • Graph and Network Theory for the Analysis of Criminal Networks

      Cavallaro, Lucia; Bagdasar, Ovidiu; De Meo, Pasquale; Fumara, Giacomo; Liotta, Antonio; University of Derby; University of Messina, Italy; Free University of Bozen-Bolzano, Italy (Springer, Cham, 2021-02-19)
      Social Network Analysis is the use of Network and Graph Theory to study social phenomena, which was found to be highly relevant in areas like Criminology. This chapter provides an overview of key methods and tools that may be used for the analysis of criminal networks, which are presented in a real-world case study. Starting from available juridical acts, we have extracted data on the interactions among suspects within two Sicilian Mafia clans, obtaining two weighted undirected graphs. Then, we have investigated the roles of these weights on the criminal networks properties, focusing on two key features: weight distribution and shortest path length. We also present an experiment that aims to construct an artificial network which mirrors criminal behaviours. To this end, we have conducted a comparative degree distribution analysis between the real criminal networks, using some of the most popular artificial network models: Watts-Strogats, Erdős-Rényi, and Barabási-Albert, with some topology variations. This chapter will be a valuable tool for researchers who wish to employ social network analysis within their own area of interest.
    • Application of caputo–fabrizio operator to suppress the aedes aegypti mosquitoes via wolbachia: an LMI approach

      Dianavinnarasi, J.; Raja, R.; Alzabut, J.; Cao, J.; Niezabitowski, M.; Bagdasar, O.; Alagappa University, Karaikudi, India; Prince Sultan University, Riyadh 12435, Saudi Arabia; Southeast University, Nanjing, China; Yonsei University, Seoul, South Korea; et al. (Elsevier BV, 2021-02-11)
      The aim of this paper is to establish the stability results based on the approach of Linear Matrix Inequality (LMI) for the addressed mathematical model using Caputo–Fabrizio operator (CF operator). Firstly, we extend some existing results of Caputo fractional derivative in the literature to a new fractional order operator without using singular kernel which was introduced by Caputo and Fabrizio. Secondly, we have created a mathematical model to increase Cytoplasmic Incompatibility (CI) in Aedes Aegypti mosquitoes by releasing Wolbachia infected mosquitoes. By this, we can suppress the population density of A.Aegypti mosquitoes and can control most common mosquito-borne diseases such as Dengue, Zika fever, Chikungunya, Yellow fever and so on. Our main aim in this paper is to examine the behaviours of Caputo–Fabrizio operator over the logistic growth equation of a population system then, prove the existence and uniqueness of the solution for the considered mathematical model using CF operator. Also, we check the alpha-exponential stability results for the system via linear matrix inequality technique. Finally a numerical example is provided to check the behaviour of the CF operator on the population system by incorporating the real world data available in the known literature.
    • Blessing of dimensionality at the edge and geometry of few-shot learning

      Tyukin, Ivan Y.; Gorban, Alexander N.; McEwan, Alistair A.; Meshkinfamfard, Sepehr; Tang, Lixin; University of Leicester; Lobachevsky University, Russia; St Petersburg State Electrotechnical University, Russia; University College London; Northeastern University, China; et al. (Elsevier BV, 2021-02-03)
      In this paper we present theory and algorithms enabling classes of Artificial Intelligence (AI) systems to continuously and incrementally improve with a priori quantifiable guarantees – or more specifically remove classification errors – over time. This is distinct from state-of-the-art machine learning, AI, and software approaches. The theory enables building few-shot AI correction algorithms and provides conditions justifying their successful application. Another feature of this approach is that, in the supervised setting, the computational complexity of training is linear in the number of training samples. At the time of classification, the computational complexity is bounded by few inner product calculations. Moreover, the implementation is shown to be very scalable. This makes it viable for deployment in applications where computational power and memory are limited, such as embedded environments. It enables the possibility for fast on-line optimisation using improved training samples. The approach is based on the concentration of measure effects and stochastic separation theorems and is illustrated with an example on the identification faulty processes in Computer Numerical Control (CNC) milling and with a case study on adaptive removal of false positives in an industrial video surveillance and analytics system.
    • Energy-aware scheduling of streaming applications on edge-devices in IoT based healthcare

      Tariq, Umair Ullah; Ali, Haider; Liu, Lu; Hardy, James; Kazim, Muhammad; Ahmed, Waqar; Central Queensland University, Sydney, Australia.; University of Derby; University of Leicester; De Montfort University; et al. (Institute of Electrical and Electronics Engineers (IEEE), 2021-02-02)
      The reliance on Network-on-Chip (NoC) based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this paper we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG) based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.
    • Centrality dependence of J/ψ and ψ(2S) production and nuclear modification in p-Pb collisions at √sNN = 8.16 TeV

      Acharya, S.; Adamová, D.; Adler, A.; Adolfsson, J.; Aggarwal, M. M.; Agha, S.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; et al. (Springer Science and Business Media LLC, 2021-02-01)
      The inclusive production of the J/ψ and ψ(2S) charmonium states is studied as a function of centrality in p-Pb collisions at a centre-of-mass energy per nucleon pair √sNN = 8.16 TeV at the LHC. The measurement is performed in the dimuon decay channel with the ALICE apparatus in the centre-of-mass rapidity intervals −4.46 < ycms < −2.96 (Pb-going direction) and 2.03 < ycms < 3.53 (p-going direction), down to zero transverse momentum (pT). The J/ψ and ψ(2S) production cross sections are evaluated as a function of the collision centrality, estimated through the energy deposited in the zero degree calorimeter located in the Pb-going direction. The pT-differential J/ψ production cross section is measured at backward and forward rapidity for several centrality classes, together with the corresponding average ⟨pT⟩ and ⟨pT^2⟩ values. The nuclear effects affecting the production of both charmonium states are studied using the nuclear modification factor. In the p-going direction, a suppression of the production of both charmonium states is observed, which seems to increase from peripheral to central collisions. In the Pb-going direction, however, the centrality dependence is different for the two states: the nuclear modification factor of the J/ψ increases from below unity in peripheral collisions to above unity in central collisions, while for the ψ(2S) it stays below or consistent with unity for all centralities with no significant centrality dependence. The results are compared with measurements in p-Pb collisions at √sNN = 5.02 TeV and no significant dependence on the energy of the collision is observed. Finally, the results are compared with theoretical models implementing various nuclear matter effects.
    • Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm

      Almomani, Ammar; Nawasrah, Ahmad Al; Alauthman, Mohammad; Betar, Mohammed Azmi Al; Meziane, Farid; Al-Balqa Applied University, Irbid, Jordan; Taibah University, Median, Saudia Arabia; Zarqa University, Jordan; University of Derby (Inderscience, 2021-01-28)
      A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders for increasing the lifetime of malicious servers through changing the IP addresses of the domain name quickly. In the present research, we aimed to propose a new system. This system is named fast flux botnet catcher system (FFBCS). This system can detect FF-domains in an online mode using an adaptive dynamic evolving spiking neural network algorithm. Comparing with two other related approaches the proposed system shows a high level of detection accuracy, low false positive and negative rates, respectively. It shows a high performance. The algorithm's proposed adaptation increased the accuracy of the detection. For instance, this accuracy reached (98.76%) approximately.
    • Development of an ambisonic guitar system GASP: Guitars with ambisonic spatial performance

      Werner, Duncan; Wiggins, Bruce; Fitzmaurice, Emma; University of Derby (CRC Press/ Routledge, 2021-01-22)
      Ambisonics, pioneered by Michael Gerzon (1977,1985), is a kernel-based 3D surround sound system. The encoding (recording or panning) of the audio is separated from the decoding (or rendering) of the audio to speaker feeds or, more recently, head tracked headphones (by binaurally decoding the Ambisonic sound field). Audio encoded in this way can be rendered to any number of speakers in almost any position in 3D space, as long as the positions of the speakers are known. Moreover, Ambisonics is a system optimised around a number of psycho-acoustic criteria which, when implemented, reduce the variability of audio no matter what speaker arrangement is used for reproduction. This allows for a `mix once' system where subsequent remixing is not necessary when replayed over different loudspeaker systems or headphones and allows for full 3D reproduction. The Ambisonics system is finally gaining some traction due to its use in Virtual Reality audio, using the ambiX standard (Nachbar et al. 2011) but few instruments exist that make use of this 3D spatial audio format, although previous studies into some aspects of the relationship between instruments, performance and spatialisation is available, for example, see Pysiewica and Weinzierl (2017), Graham and Bridges (2017), Bates (2010), Pukette (2007) and Graham (2012). The system combines custom and off-the-shelf hardware/software to create both a live performance Ambisonic guitar system, and virtual reality (VR) ready, binaural performance instrument. The system comprises of two aspects: firstly as an innovative audio project, fusing the musical with the technical, combining individual string timbralisation with Ambisonic surround sound. And secondly as an artistic musical project, providing alternative experimental surround sound production ideas for the guitarist and music producer, with potential applications in the Sound Arts world as well as commercial musical applications. This paper explores the possibilities of the Guitar as a spatial instrument detailing the technical and artistic processes involved in the production and live performance of the instrument. Key features of the described system include: Multichannel hexaphonic guitar pickups facilitate the guitar system to process individual strings independently for both timbre and spatial location. Guitar production timbre and effects are achieved with Line 6 Helix commercial sound processing software for individual string timbralisation. Ambisonic surround-sound performance: spatial positioning is achieved using our own bespoke WigWare algorithms and can be heard over either an array of circular (2D) or spherical (3D) loudspeakers, alternatively the user can listen to the output with headphones using binaural implementation. Rhythmic gate-switching of individual strings, such that either simple or complex polyrhythms can be programmed or performed live across individual strings (producing similar results to a keyboard controlled arpeggiator). ‘Auditory Scenes’ have been developed for presenting combinations of individual string timbres, spatial, and rhythmic arpeggiator parameters. The system can be applied to post-production sound manipulation, or as a real-time live Ambisonic performance instrument within a concert environment. These two categories can yield differing production possibilities. We have also identified potential applications for guitar training and education.
    • On some new arithmetic properties of the generalized Lucas sequences

      Andrica, Dorin; Bagdasar, Ovidiu; Babeş-Bolyai University of Cluj-Napoca, Cluj-Napoca, Romania; University of Derby (Springer Science and Business Media LLC, 2021-01-21)
      Some arithmetic properties of the generalized Lucas sequences are studied, extending a number of recent results obtained for Fibonacci, Lucas, Pell, and Pell–Lucas sequences. These properties are then applied to investigate certain notions of Fibonacci, Lucas, Pell, and Pell–Lucas pseudoprimality, for which we formulate some conjectures.
    • Diagnostic model for the society safety under COVID-19 pandemic conditions

      Varotsos, Costas A.; Krapivin, Vladimir F.; Xue, Yong; University of Athens, Greece; Kotelnikov’s Institute of Radioengineering and Electronics, Russian Academy of Sciences; University of Mining and Technology, Xuzhou, China; University of Derby (Elsevier BV, 2021-01-11)
      The aim of this paper is to develop an information-modeling method for assessing and predicting the consequences of the COVID-19 pandemic. To this end, a detailed analysis of official statistical information provided by global and national organizations is carried out. The developed method is based on the algorithm of multi-channel big data processing considering the demographic and socio-economic information. COVID-19 data are analyzed using an instability indicator and a system of differential equations that describe the dynamics of four groups of people: susceptible, infected, recovered and dead. Indicators of the global sustainable development in various sectors are considered to analyze COVID-19 data. Stochastic processes induced by COVID-19 are assessed with the instability indicator showing the level of stability of official data and the reduction of the level of uncertainty. It turns out that the number of deaths is rising with the Human Development Index. It is revealed that COVID-19 divides the global population into three groups according to the relationship between Gross Domestic Product and the number of infected people. The prognosis for the number of infected people in December 2020 and January-February 2021 shows negative events which will decrease slowly.
    • Simulation and experimental investigation into a photovoltaic and fuel cell hybrid integration power system for a typical small house application

      Djoudi, H; Benyahia, N; Badji, A; Bousbaine, Amar; Moualek, R; Aissou, S; Benamrouche, N; University of Tizi-Ouzou, Tizi-Ouzou, Algeria; French Naval Academy, Brest, France; Haute Alsace University, Mulhouse, France; et al. (Taylor & Francis, 2021-01-08)
      The paper addresses the simulation of a novel real-time implementation of a photovoltaic (PV) and fuel cell (FC) hybrid integration power system. The hybrid system has the potential of reducing the dependency on batteries, leading to reduced cost and increased life span of the whole system using the Proton Exchange Membrane (PEM) fuel cell. The interface structure of the hybrid system has been explored incorporating the Maximum Power Point Technique (MPPT) for maximum power extraction. The simulation of the hybrid system including fuel cell, PhotoVoltaic panels (PVs) and battery has been carried out using SimPowerSystems. An innovative Real Time Interface (RTI) approach using the concept of the Hardware-In-the-Loop (HIL) has been presented for a fast dynamic response of a closed loop control of the hybrid system. The corroboration of the hybrid system is validated experimentally, using a real photovoltaic panel connected to a PEM fuel cell emulator and battery. The PVs are controlled by the perturbation and observation Maximum Power point (MPP) technique and the PEM fuel cell is controlled through a boost DC-DC converter using current mode control. The whole system is implemented on the dSPACE 1103 platform for real-time interface and control strategies. The overall behavior of the hybrid system has been critically analyzed and corroboration of the simulated and experimental results have been presented.
    • NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles

      Ahmad, Farhan; Kurugollu, Fatih; Kerrache, Chaker Abdelaziz; Sezer, Sakir; Liu, Lu; Coventry University; University of Derby; Universit Amar Telidji Laghouat, 243326 Laghouat, Laghouat, Algeria; Queen's University Belfast; University of Leicester (IEEE, 2021-01-05)
      Internet-of-Vehicles (IoV) is a novel technology to ensure safe and secure transportation by enabling smart vehicles to communicate and share sensitive information with each other. However, the realization of IoV in real-life depends on several factors, including the assurance of security from attackers and propagation of authentic, accurate and trusted information within the network. Further, the dissemination of compromised information must be detected and vehicle disseminating such malicious messages must be revoked from the network. To this end, trust can be integrated within the network to detect the trustworthiness of the received information. However, most of the trust models in the literature relies on evaluating node or data at the application layer. In this study, we propose a novel hybrid trust management scheme, namely, NOTRINO, which evaluates trustworthiness on the received information in two steps. First step evaluates trust on the node itself at transport layer, while second step computes trustworthiness of the data at application layer. This mechanism enables the vehicles to efficiently model and evaluate the trustworthiness on the received information. The performance and accuracy of NOTRINO is rigorously evaluated under various realistic trust evaluation criteria (including precision, recall, F-measure and trust). Furthermore, the efficiency of NOTRINO is evaluated in presence of malicious nodes and its performance is benchmarked against three hybrid trust models. Extensive simulations indicate that NOTRINO achieve over 75% trust level as compared to benchmarked trust models where trust level falls below 60% for a network with 35% malicious nodes. Similarly, 92% precision and 87% recall are achieved simultaneously with NOTRINO for the same network, comparing to benchmark trust models where precision and recall falls below 87% and 85% respectively.
    • Efficient resampling for fraud detection during anonymised credit card transactions with unbalanced datasets

      Mrozek, Petr; Panneerselvam, John; Bagdasar, Ovidiu; University of Derby; University of Leicester (IEEE, 2020-12-30)
      The rapid growth of e-commerce and online shopping have resulted in an unprecedented increase in the amount of money that is annually lost to credit card fraudsters. In an attempt to address credit card fraud, researchers are leveraging the application of various machine learning techniques for efficiently detecting and preventing fraudulent credit card transactions. One of the prevalent common issues around the analytics of credit card transactions is the highly unbalanced nature of the datasets, which is frequently associated with the binary classification problems. This paper intends to review, analyse and implement a selection of notable machine learning algorithms such as Logistic Regression, Random Forest, K-Nearest Neighbours and Stochastic Gradient Descent, with the motivation of empirically evaluating their efficiencies in handling unbalanced datasets whilst detecting credit card fraud transactions. A publicly available dataset comprising 284807 transactions of European cardholders is analysed and trained with the studied machine learning techniques to detect fraudulent transactions. Furthermore, this paper also evaluates the incorporation of two notable resampling methods, namely Random Under-sampling and Synthetic Majority Oversampling Techniques (SMOTE) in the aforementioned algorithms, in order to analyse their efficiency in handling unbalanced datasets. The proposed resampling methods significantly increased the detection ability, the most successful technique of combination of Random Forest with Random Under-sampling achieved the recall score of 100% in contrast to the recall score 77% of model without resampling technique. The key contribution of this paper is the postulation of efficient machine learning algorithms together with suitable resampling methods, suitable for credit card fraud detection with unbalanced dataset.
    • Explaining probabilistic Artificial Intelligence (AI) models by discretizing Deep Neural Networks

      Saleem, Rabia; Yuan, Bo; Kurugollu, Fatih; Anjum, Ashiq; University of Derby; University of Leicester (IEEE, 2020-12-30)
      Artificial Intelligence (AI) models can learn from data and make decisions without any human intervention. However, the deployment of such models is challenging and risky because we do not know how the internal decisionmaking is happening in these models. Especially, the high-risk decisions such as medical diagnosis or automated navigation demand explainability and verification of the decision making process in AI algorithms. This research paper aims to explain Artificial Intelligence (AI) models by discretizing the black-box process model of deep neural networks using partial differential equations. The PDEs based deterministic models would minimize the time and computational cost of the decision-making process and reduce the chances of uncertainty that make the prediction more trustworthy.
    • Transverse-momentum and event-shape dependence of D-meson flow harmonics in Pb–Pb collisions at √sNN = 5.02 TeV

      Acharya, S.; Adamová, D.; Adler, A.; Adolfsson, J.; Aggarwal, M.M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; et al. (Elsevier BV, 2020-12-29)
      The elliptic and triangular flow coefficients v2 and v3 of prompt D0, D+, and D*+ mesons were measured at midrapidity (|y|<0.8) in Pb–Pb collisions at the centre-of-mass energy per nucleon pair of √sNN = 5.02 TeV with the ALICE detector at the LHC. The D mesons were reconstructed via their hadronic decays in the transverse momentum interval 1 <p_T < 36 GeV/c in central (0–10%) and semi-central (30–50%) collisions. Compared to pions, protons, and J/ψ mesons, the average D-meson v_n harmonics are compatible within uncertainties with a mass hierarchy for p_T ≤ 3 GeV/c, and are similar to those of charged pions for higher p_T. The coupling of the charm quark to the light quarks in the underlying medium is further investigated with the application of the event-shape engineering (ESE) technique to the D-meson v2 and p_T-differential yields. The D-meson v2 is correlated with average bulk elliptic flow in both central and semi-central collisions. Within the current precision, the ratios of per-event D-meson yields in the ESE-selected and unbiased samples are found to be compatible with unity. All the measurements are found to be reasonably well described by theoretical calculations including the effects of charm-quark transport and the recombination of charm quarks with light quarks in a hydrodynamically expanding medium.
    • A survey of interpretability of machine learning in accelerator-based high energy physics

      Turvill, Danielle; Barnby, Lee; Yuan, Bo; Zahir, Ali; University of Derby (IEEE, 2020-12-28)
      Data intensive studies in the domain of accelerator-based High Energy Physics, HEP, have become increasingly more achievable due to the emergence of machine learning with high-performance computing and big data technologies. In recent years, the intricate nature of physics tasks and data has prompted the use of more complex learning methods. To accurately identify physics of interest, and draw conclusions against proposed theories, it is crucial that these machine learning predictions are explainable. For it is not enough to accept an answer based on accuracy alone, but it is important in the process of physics discovery to understand exactly why an output was generated. That is, completeness of a solution is required. In this paper, we survey the application of machine learning methods to a variety of accelerator-based tasks in a bid to understand what role interpretability plays within this area. The main contribution of this paper is to promote the need for explainable artificial intelligence, XAI, for the future of machine learning in HEP.
    • Large-scale Data Integration Using Graph Probabilistic Dependencies (GPDs)

      Zada, Muhammad Sadiq Hassan; Yuan, Bo; Anjum, Ashiq; Azad, Muhammad Ajmal; Khan, Wajahat Ali; Reiff-Marganiec, Stephan; University of Derby; University of Leicester (IEEE, 2020-12-28)
      The diversity and proliferation of Knowledge bases have made data integration one of the key challenges in the data science domain. The imperfect representations of entities, particularly in graphs, add additional challenges in data integration. Graph dependencies (GDs) were investigated in existing studies for the integration and maintenance of data quality on graphs. However, the majority of graphs contain plenty of duplicates with high diversity. Consequently, the existence of dependencies over these graphs becomes highly uncertain. In this paper, we proposed graph probabilistic dependencies (GPDs) to address the issue of uncertainty over these large-scale graphs with a novel class of dependencies for graphs. GPDs can provide a probabilistic explanation for dealing with uncertainty while discovering dependencies over graphs. Furthermore, a case study is provided to verify the correctness of the data integration process based on GPDs. Preliminary results demonstrated the effectiveness of GPDs in terms of reducing redundancies and inconsistencies over the benchmark datasets.