• Language model-based automatic prefix abbreviation expansion method for biomedical big data analysis

      Anjum, Ashiq; University of Derby (Elsevier, 2019-03-28)
      In biomedical domain, abbreviations are appearing more and more frequently in various data sets, which has caused significant obstacles to biomedical big data analysis. The dictionary-based approach has been adopted to process abbreviations, but it cannot handle ad hoc abbreviations, and it is impossible to cover all abbreviations. To overcome these drawbacks, this paper proposes an automatic abbreviation expansion method called LMAAE (Language Model-based Automatic Abbreviation Expansion). In this method, the abbreviation is firstly divided into blocks; then, expansion candidates are generated by restoring each block; and finally, the expansion candidates are filtered and clustered to acquire the final expansion result according to the language model and clustering method. Through restrict the abbreviation to prefix abbreviation, the search space of expansion is reduced sharply. And then, the search space is continuous reduced by restrained the effective and the length of the partition. In order to validate the effective of the method, two types of experiments are designed. For standard abbreviations, the expansion results include most of the expansion in dictionary. Therefore, it has a high precision. For ad hoc abbreviations, the precisions of schema matching, knowledge fusion are increased by using this method to handle the abbreviations. Although the recall for standard abbreviation needs to be improved, but this does not affect the good complement effect for the dictionary method.
    • 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.
    • Learning networks for complex interconnections

      Mocanu, Decebal Constantin; Exarchakos, Georgios; Liotta, Antonio (2016)
    • Learning to program using immersive approaches: A case study learning SAS®, IBM Bluemix and Watson Analytics

      Self, Richard; University of Derby (Verlag der Technischen Universität Graz, 2016-06-28)
      Learning to program is an activity which needs the learner to develop a range of new skills. Traditionally, this has been achieved in Universities by a presenting a series of structured lectures and tutorials covering the syntax and grammar of the language. This approach often leads to disengagement by many of the weaker students. It is becoming clear that this may not be the most effective approach in the twenty first century as a result of the continuous development of software packages which leads to the need to continually revise the teaching materials. In addition, modern millennial students demand engaging modes of learning that al-so prepare them for employment. This paper evaluates an approach which pro-vides a directed, immersive learning approach that mirrors the real world of em-ployment, develops both the requisite technical skills together with the fundamen-tal soft skills necessary for employment and prepares the students for lifelong learning and development and maintenance of new skills and languages. It also provides an intensely engaging environment that allows students to demonstrate the wide range of technical and soft skills that are necessary for a successful ca-reer. This approach also leads to high levels of achievement from the students and reduces stress levels in the academics leading the courses. The approach should be applicable to most STEM subjects which require the use of specialist software packages.
    • A learning-based mac for energy efficient wireless sensor networks

      Galzarano, Stefano; Fortino, Giancarlo; Liotta, Antonio (Springer, Cham, 2014)
    • Linear and non-linear flow mode in Pb–Pb collisions at √sNN = 2.76 TeV

      Andrews, Lee; Barnby, Lee; Evans, David; Graham, Katie; Jones, Peter; Jusko, Anton; Krivda, Marian; Lietava, Roman; Villalobos, Orlando; Willsher, Emily; et al. (2017-10-10)
    • Linear clique-width for hereditary classes of cographs

      Brignall, Robert; Korpelainen, Nicholas; Vatter, Vincent; University of Derby (Wiley, 2016-03-28)
      The class of cographs is known to have unbounded linear clique-width. We prove that a hereditary class of cographs has bounded linear clique-width if and only if it does not contain all quasi-threshold graphs or their complements. The proof borrows ideas from the enumeration of permutation classes.
    • Link quality aware channel allocation for multichannel body sensor networks.

      Gao, Weifeng; Zhao, Zhiwei; Min, Geyong; Cao, Yue; Duan, Hancong; Liu, Lu; Long, Yimiao; Yin, Guangqiang; University of Electronic Science and Technology of China; University of Exeter; et al. (Elsevier, 2017-02-21)
      Body Sensor Network (BSN) is a typical Internet-of-Things (IoT) application for personalized health care. It consists of economically powered, wireless and implanted medical monitoring sensor nodes, which are designed to continually collect the medical information of the target patients. Multichannel is often used in BSNs to reduce the spectrum competition of the tremendous sensor nodes and the problem of channel assignment has attracted much research attention. The health sensing data in BSNs is often required to be delivered to a sink node (or server) before a certain deadline for real time monitoring or health emergency alarm. Therefore, deadline is of significant importance for multichannel allocation and scheduling. The existing works, though designed to meet the deadline, often overlook the impact of the unreliable wireless links. As a result, the health sensing data can still be overdue because of the scheduled lossy links. Besides, potential collisions in the schedules also incur considerable delay in delivering the sensing data. In this paper, we propose a novel deadline- driven Link quality Aware Channel Assignment scheme (LACA), where link quality, deadlines and collisions are jointly considered. LACA prioritizes links with urgent deadlines and heavy collisions. Besides, LACA allows the exploition of the spare slots for retransmissions on lossy links, which can further reduce the retransmission delay. Extensive simulation experiments show that compared to the existing approaches, LACA can better utilize the wireless spectrum and achieve higher packet delivery ratio before the deadline.
    • Live sound loudspeaker array optimization for consistent directional coverage with diffuse radiation characteristics.

      Hill, Adam J.; Hawksford, Malcolm O. J.; University of Derby; University of Essex (Institute of Acoustics, 2018-11)
      A central aim of sound reinforcement systems is to deliver consistent tonality across a wide audience area. Loudspeaker arrays are commonly used to meet this goal, where the upper and lower frequency bounds that can be spatially controlled are dictated by inter-element spacing and array width, respectively. This work focuses on the calculation of frequency-dependent complex coefficients for each array element using a modified Fourier technique to achieve a frequency-independent radiation pattern across an array’s functional region. In order to ensure efficiency, temporally diffuse impulses are utilized within the optimization procedure to avoid clustering of radiated energy at the center of an array and to provide a diffuse radiated field while maintaining the desired directional characteristics. Example applications for subwoofer arrays are presented, although the technique is applicable to any frequency range across the audible spectrum.
    • Live sound subwoofer system performance quantification.

      Hill, Adam J.; University of Derby (Audio Engineering Society, 2018-05-14)
      The general aim of live sound reinforcement is to deliver an appropriate and consistent listening experience across an audience. Achieving this in the subwoofer range (typically between 20 – 100 Hz) has been the focus of previous work, where techniques have been developed to allow for consistent sound energy distribution over a wide area. While this provides system designers with a powerful set of tools, it brings with it many potential metrics to quantify performance. This research identifies key indicators of subwoofer system performance and proposes a single weighted metric to quantify overall performance. Both centrally-distributed and left/right configurations are analyzed using the new metric to highlight functionality.
    • A load-balancing mechanism for distributed SDN control plane using response time.

      Cui, Jie; Lu, Qinghe; Zhong, Hong; Tian, Miaomiao; Liu, Lu; University of Derby; Anhui University (IEEE, 2018-10-16)
      Software-Defined Networking (SDN) has become a popular paradigm for managing large-scale networks including cloud servers and data centers because of its advantages of centralized management and programmability. The issues of scalability and reliability that a single centralized controller suffers makes distributed controller architectures emerge. One key limitation of distributed controllers is the statically configured switch-controller mapping, easily causing uneven load distribution among controllers. Previous works have proposed load-balancing methods with switch migration to address this issue. However, the higher-load controller is always directly considered as the overloaded controller that need to shift its load to other controllers, even if it has no response time delay. The pursuit of absolute load-balancing effect can also result in frequent network delays and service interruptions. Additionally, if there are several overloaded controllers, just one controller with the maximum load can be addressed within a single load-balancing operation, reducing load-balancing efficiency. To address these problems, we propose SMCLBRT, a load-balancing strategy of multiple SDN controllers based on response time, considering the changing features of real-time response times versus controller loads. By selecting the appropriate response time threshold and dealing with multiple overloading controllers simultaneously, it can well solve load-balancing problem in SDN control plane with multiple overloaded controllers. Simulation experiments exhibit the effectiveness of our scheme.
    • Local maximizers of generalized convex vector-valued functions.

      Bagdasar, Ovidiu; Popovici, Nicolae; University of Derby; Babes-Bolyai University (Yokohama Publishers, 2017-12)
      Any local maximizer of an explicitly quasiconvex real-valued function is actually a global minimizer, if it belongs to the intrinsic core of the function's domain. In this paper we show that similar properties hold for componentwise explicitly quasiconvex vector-valued functions, with respect to the concepts of ideal, strong and weak optimality. We illustrate these results in the particular framework of linear fractional multicriteria optimization problems.
    • Local maximum points of explicitly quasiconvex functions

      Bagdasar, Ovidiu; Popovici, Nicolae; University of Derby (Springer, 2014-08-17)
      This work concerns generalized convex real-valued functions defined on a nonempty convex subset of a real topological linear space. Its aim is twofold: first, to show that any local maximum point of an explicitly quasiconvex function is a global minimum point whenever it belongs to the intrinsic core of the function’s domain and second, to characterize strictly convex normed spaces by applying this property for a particular class of convex functions.
    • A location aware fast PMIPv6 for low latency wireless sensor networks

      Kang, Byungseok; University of Derby (IEEE, 2019-06-28)
      Recently, mobile sensor networks (MSN) have been actively studied due to the emergence of mobile sensors such as Robomote and robotic sensor agents (RSAs). The research on existing mobile sensor networks mainly focuses on solving the coverage hole, which is a problem that occurs in the existing stationary sensor network (SSN). These studies have disadvantages in that they cannot make the most use of the mobile ability given to the moving sensors. In order to solve this problem, there is a proposal for sensing a wider area than a fixed sensor network by giving the moving sensor continuous mobility. However, the research is still in the early stage, and communication path to the sink node and data transmission problems. In this paper, we propose a location-aware fast PMIPv6 (LA-FPMIPv6) protocol that enables efficient routing and data transmission in a mobile sensor network environment composed of mobile sensors with continuous mobility. In the proposed protocol, the fixed sensor is arranged with the moving sensor so that the fixed sensor transmits the sensing data to the sink node instead of the moving sensor. For performance evaluation, the LA-FPMIPv6 is compared with existing methods through mathematical analysis and computer simulation. The results of the performance evaluation show that the LA-FPMIPv6 effectively reduces the handover latency, signaling cost, and buffering cost compared with the conventional methods.
    • Location based services analytics: Students as co-producers and partners in research

      Self, Richard; University of Derby (2015-08)
      A perennial question in teaching is that of inspiring students to become innova-tive in their thinking and work. This paper presents one approach to developing excellence in undergraduate analytics research into a leading-edge topic which has had few publications to-date. The resultant analytics has provided valuable in-sights into the accuracy profiles of Location Services in smart devices and identi-fied important consequences of the levels of accuracy that Assisted GPS (A-GPS) exhibits. Consolidation of the research of eight students by this author has resulted in approximately 3000 data points, from which it is possible to develop significant analytical insights which are of value to a wide range of potential us-ers. It has also resulted in a raised academic and research profile for the Universi-ty of Derby in the LBS field.
    • Long-time series aerosol optical depth retrieval from AVHRR data over land in North China and Central Europe

      Xue, Yong; He, Xingwei; de Leeuw, Gerrit; Mei, Linlu; Che, Yahui; Rippin, Wayne; Guang, Jie; Hu, Yincui; University of Chinese Academy of Sciences; University of Derby; et al. (Elsevier, 2017-07-06)
      An algorithm for the retrieval of the aerosol optical depth over land (ADL) using radiances at the top of the atmosphere (TOA) measured by the Advanced Very High Resolution Radiometer (AVHRR) is proposed. AVHRR is the only satellite sensor providing nearly continuous global coverage since June 1979, which could generate the longest aerosol climate data records currently available from operational satellites. In the implementation of the ADL algorithm, an analytical model is used which couples an atmospheric radiative transfer model and a land surface reflectance parameterization. The radiation field can be separated into three parts: direct radiance, single-scattered radiance, and multiple-scattered. Each of these parts is individually parameterized. To obtain the surface reflectance in an automatic retrieval procedure over land for AVHRR, the aerosol scattering effect at 3.75 μm was assumed to be negligible and relationships between the surface reflectances at 0.64 μm and 3.75 μm were evaluated for different surface types and the authors propose to use these to obtain the surface reflectance at the shorter wavelength. The 0.64 μm surface reflectance was then used in a radiative transfer model to compute AOD at that wavelength using six different aerosol types, where optimal estimation (OE) theory is applied to minimize the difference between modeled and measured radiances. The ADL algorithm is applied to re-calibrated Level 1B radiances from the AVHRRs on-board the TIROS-N and the Metop-B satellites to retrieve the AOD over North China and Central Europe. The results show that the AOD retrieved from these two instruments are in agreement with co-located AOD values from ground-based reference networks. Over North China, using AERONET sites, 58% of the ADL AOD values are within an expected error (EE) range of ±(0.05 + 20%) and 53% are within the EE range of ±(0.05 + 15%). For GAW-PFR (World Meteorological Organization, WMO, Global Atmosphere Watch, GAW) sites, part of the European ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure) sites, 79% of the ADL AOD values are within the EE range of ±(0.05 + 20%) and 75% are within the EE range of ±(0.05 + 15%). Not surprisingly, the agreement is better over Europe with generally lower AOD values. An additional cross comparison of the AOD results with MODIS (MODerate-resolution Imaging Spectroradiometer) DeepBlue aerosol products shows that the spatial distributions of the two AOD datasets are similar, but with generally lower values for ADL and lower coverage. The temporal variation of the annual mean AOD over selected AERONET sites shows that ADL values are generally between 0.2 and 0.5 over North-Eastern China and trace the MODIS and AERONET data for the overlapping years quite well.
    • Longitudinal asymmetry and its effect on pseudorapidity distributions in Pb–Pb collisions at √sNN = 2.76 TeV

      ALICE Collaboration; Barnby, Lee; STFC Daresbury Laboratory (Elsevier, 2018-03-22)
      First results on the longitudinal asymmetry and its effect on the pseudorapidity distributions in Pb–Pb collisions at √sNN = 2.76 TeV at the Large Hadron Collider are obtained with the ALICE detector. The longitudinal asymmetry arises because of an unequal number of participating nucleons from the two colliding nuclei, and is estimated for each event by measuring the energy in the forward neutron-Zero-Degree-Calorimeters (ZNs). The effect of the longitudinal asymmetry is measured on the pseudorapidity distributions of charged particles in the regions |η|<0.9, 2.8 < η < 5.1 and -3.7 < η < 1.7 by taking the ratio of the pseudorapidity distributions from events corresponding to different regions of asymmetry. The coefficients of a polynomial fit to the ratio characterise the effect of the asymmetry. A Monte Carlo simulation using a Glauber model for the colliding nuclei is tuned to reproduce the spectrum in the ZNs and provides a relation between the measurable longitudinal asymmetry and the shift in the rapidity (y0) of the participant zone formed by the unequal number of participating nucleons. The dependence of the coefficient of the linear term in the polynomial expansion, c1, on the mean value of y0 is investigated.
    • LQR controller design for quad-rotor helicopters.

      E. Okyere; A. bousbaine; G. T. Poyi; A.K. Joseph; J.M. Andrade; University of Derby (The Institute of Engineering and Technology., 2018-06-22)
      This paper presents an analysis and performance of a LQR control algorithm for quadrotor helicopters. For a successful analysis, first the dynamic model has been developed for the quadcopter and then the controller was designed, tuned and tested. In tuning the LQR, much attention was given to the feedback gain matrix (K). The controller’s performance wasverified in terms of delay time, rise time, overshoot, settling time and tolerance limits. The overall performance of theLQR controller was analysed.
    • M2M-REP: Reputation system for machines in the internet of things.

      Azad, Muhammad Ajmal; Bag, Samiran; Hao, Feng; Salah, Khaled; Newcastle University; Khalifa University (2018-08-14)
      In the age of IoT (Internet of Things), Machine-to-Machine (M2M) communication has gained significant popularity over the last few years. M2M communication systems may have a large number of autonomous connected devices that provide services without human involvement. Interacting with compromised, infected and malicious machines can bring damaging consequences in the form of network outage, machine failure, data integrity, and financial loss. Hence, users first need to evaluate the trustworthiness of machines prior to interacting with them. This can be realized by using a reputation system, which evaluates the trustworthiness of machines by utilizing the feedback collected from the users of the machines. The design of a reliable reputation system for the distributed M2M communication network should preserve user privacy and have low computation and communication overheads. To address these challenges, we propose an M2M-REP System (Machine to Machine REPutation), a privacy-preserving reputation system for evaluating the trustworthiness of autonomous machines in the M2M network. The system computes global reputation scores of machines while maintaining privacy of the individual participant score by using secure multi-party computation techniques. The M2M-REP system ensures correctness, security and privacy properties under the malicious adversarial model, and allows public verifiability without relying on a centralized trusted system. We implement a prototype of our system and evaluate the system performance in terms of the computation and bandwidth overhead.
    • Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor.

      Mukhtar, Naila; Mehrabi, Mohamad; Kong, Yinan; Anjum, Ashiq; University of Derby; Macquarie University (MDPI, 2018-12-25)
      Security of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning models