• Privacy verification of photoDNA based on machine learning

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; University of Derby, College of Engineering and Technology; University of Essex, School of Computer Science and Electronic Engineering (The Institution of Engineering and Technology (IET), 2019-10-09)
      PhotoDNA is a perceptual fuzzy hash technology designed and developed by Microsoft. It is deployed by all major big data service providers to detect Indecent Images of Children (IIOC). Protecting the privacy of individuals is of paramount importance in such images. Microsoft claims that a PhotoDNA hash cannot be reverse engineered into the original image; therefore, it is not possible to identify individuals or objects depicted in the image. In this chapter, we evaluate the privacy protection capability of PhotoDNA by testing it against machine learning. Specifically, our aim is to detect the presence of any structural information that might be utilized to compromise the privacy of the individuals via classification. Due to the widespread usage of PhotoDNA as a deterrent to IIOC by big data companies, ensuring its ability to protect privacy would be crucial. In our experimentation, we achieved a classification accuracy of 57.20%.This result indicates that PhotoDNA is resistant to machine-learning-based classification attacks.
    • GORTS: genetic algorithm based on one-by-one revision of two sides for dynamic travelling salesman problems

      Xu, Xiaolong; Yuan, Hao; Matthew, Peter; Ray, Jeffrey; Bagdasar, Ovidiu; Trovati, Marcello; University of Derby; Nanjing University of Posts and Telecommunications, Nanjing, China; Edge Hill University, Ormskirk, UK (Springer, 2019-09-21)
      The dynamic travelling salesman problem (DTSP) is a natural extension of the standard travelling salesman problem, and it has attracted significant interest in recent years due to is practical applications. In this article, we propose an efficient solution for DTSP, based on a genetic algorithm (GA), and on the one-by-one revision of two sides (GORTS). More specifically, GORTS combines the global search ability of GA with the fast convergence feature of the method of one-by-one revision of two sides, in order to find the optimal solution in a short time. An experimental platform was designed to evaluate the performance of GORTS with TSPLIB. The experimental results show that the efficiency of GORTS compares favourably against other popular heuristic algorithms for DTSP. In particular, a prototype logistics system based on GORTS for a supermarket with an online map was designed and implemented. It was shown that this can provide optimised goods distribution routes for delivery staff, while considering real-time traffic information.
    • Integration and evaluation of QUIC and TCP-BBR in longhaul science data transfers

      Lopes, Raul H. C.; Franqueira, Virginia N. L.; Duncan, Rand; Jisc, Lumen House; University of Derby, College of Engineering and Technology; Brunel University London, College of Engineering, Design and Physical Sciences (EDP Sciences, 2019-09-17)
      Two recent and promising additions to the internet protocols are TCP-BBR and QUIC. BBR defines a congestion policy that promises a better control in TCP bottlenecks on long haul transfers and can also be used in the QUIC protocol. TCP-BBR is implemented in the Linux kernels above 4.9. It has been shown, however, to demand careful fine tuning in the interaction, for example, with the Linux Fair Queue. QUIC, on the other hand, replaces HTTP and TLS with a protocol on the top of UDP and thin layer to serve HTTP. It has been reported to account today for 7% of Google’s traffic. It has not been used in server-to-server transfers even if its creators see that as a real possibility. Our work evaluates the applicability and tuning of TCP-BBR and QUIC for data science transfers. We describe the deployment and performance evaluation of TCP-BBR and comparison with CUBIC and H-TCP in transfers through the TEIN link to Singaren (Singapore). Also described is the deployment and initial evaluation of a QUIC server. We argue that QUIC might be a perfect match in security and connectivity to base services that are today performed by the Xroot redirectors.
    • First observation of an attractive interaction between a proton and a cascade baryon

      Acharya, S.; Adamová, D.; Adhya, S. P.; Adler, A.; Adolfsson, J.; Aggarwal, M. M.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; et al. (American Physical Society (APS), 2019-09-13)
      This Letter presents the first experimental observation of the attractive strong interaction between a proton and a multistrange baryon (hyperon) Ξ−. The result is extracted from two-particle correlations of combined p−Ξ−⊕¯p−¯Ξ+ pairs measured in p−Pb collisions at √sNN=5.02 TeV at the LHC with ALICE. The measured correlation function is compared with the prediction obtained assuming only an attractive Coulomb interaction and a standard deviation in the range [3.6, 5.3] is found. Since the measured p−Ξ−⊕¯p−¯Ξ+ correlation is significantly enhanced with respect to the Coulomb prediction, the presence of an additional, strong, attractive interaction is evident. The data are compatible with recent lattice calculations by the HAL-QCD Collaboration, with a standard deviation in the range [1.8, 3.7]. The lattice potential predicts a shallow repulsive Ξ− interaction within pure neutron matter and this implies stiffer equations of state for neutron-rich matter including hyperons. Implications of the strong interaction for the modeling of neutron stars are discussed.
    • Towards a trusted unmanned aerial system using blockchain (BUAS) for the protection of critical infrastructure

      Barka, Ezedin; Kerrache, Chaker Abdelaziz; Benkraouda, Hadjer; Shuaib, Khaled; Ahmad, Farhan; Kurugollu, Fatih; College of Information Technology, United Arab Emirates University; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Cyber Security Research Group, University of Derby, UK (Wiley, 2019-07-29)
      With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyber attacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of Unmanned Aerial Vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, Blockchain (BC) has been proposed as a revolutionary technology which can be integrated within IoT to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is two-fold. (1) survey of the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them. (2) propose a novel Blockchain-based solution to ensure the security of, and the trust between the UAVs and their relevant ground control stations (GCS). Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted Blockchain-based Unmanned Aerial Systems (UASs).
    • Realization of blockchain in named data networking-based internet-of-vehicles

      Ahmad, Farhan; Kerrache, Chaker Abdelaziz; Kurugollu, Fatih; Hussain, Rasheed; Cyber Security Research Group, University of Derby, UK; Department of Mathematics and Computer Science, University of Ghardaia, Algeria; Institute of Information Systems, Innopolis University, Russia (IEEE, 2019-07-15)
      The revolution of Internet-of-vehicles (IoV) has stimulated a substantial response from academia, research and industry due to its massive potential to improve overall transportation. Current IoV faces huge challenges due to its reliance on the IP-based network architecture. Therefore, Named Data Networking (NDN) is proposed as a promising architecture to solve issues posed by IP-based systems. Recently, Blockchains (BCs) are utilized within IoV to increase network security. However, the integration of BC within NDN-enabled IoV is still an open research problem. In this study, we proposed a novel tier-based architecture known as “Blockchain in NDN-enabled Internet-of-Vehicles (BINDN)” which can support BC within NDN-enabled IoV. BINDN can be used as a reference architecture to design security solutions in NDN-enabled IoV using BC. Further, it provides an extensive set of applications including IoV security, trust management and privacy enhancements. Moreover, we highlighted major challenges and issues when integrating BC within NDN-enabled IoV.
    • High-performance time-series quantitative retrieval from satellite images on a GPU cluster

      Xue, Yong; Liu, Jia; Ren, Kaijun; Song, Junqiang; Windmill, Christopher; Merritt, Patrick; University of Derby (IEEE, 2019-07-12)
      The quality and accuracy of remote sensing instruments continue to increase, allowing geoscientists to perform various quantitative retrieval applications to observe the geophysical variables of land, atmosphere, ocean, etc. The explosive growth of time-series remote sensing (RS) data over large-scales poses great challenges on managing, processing, and interpreting RS ‘‘Big Data.’’ To explore these time-series RS data efficiently, in this paper, we design and implement a high-performance framework to address the time-consuming time-series quantitative retrieval issue on a graphics processing unit cluster, taking the aerosol optical depth (AOD) retrieval from satellite images as a study case. The presented framework exploits the multilevel parallelism for time-series quantitative RS retrieval to promote efficiency. At the coarse-grained level of parallelism, the AOD time-series retrieval is represented as multidirected acyclic graph workflows and scheduled based on a list-based heuristic algorithm, heterogeneous earliest finish time, taking the idle slot and priorities of retrieval jobs into account. At the fine-grained level, the parallel strategies for the major remote sensing image processing algorithms divided into three categories, i.e., the point or pixel-based operations, the local operations, and the global or irregular operations have been summarized. The parallel framework was implemented with message passing interface and compute unified device architecture, and experimental results with the AOD retrieval case verify the effectiveness of the presented framework.
    • A survey of deep learning solutions for multimedia visual content analysis.

      Nadeem, Muhammad Shahroz; Franqueira, Virginia N. L.; Zhai, Xiaojun; Kurugollu, Fatih; University of Derby; University of Essex (IEEE, 2019-06-24)
      The increasing use of social media networks on handheld devices, especially smartphones with powerful built-in cameras, and the widespread availability of fast and high bandwidth broadband connections, added to the popularity of cloud storage, is enabling the generation and distribution of massive volumes of digital media, including images and videos. Such media is full of visual information and holds immense value in today’s world. The volume of data involved calls for automated visual content analysis systems able to meet the demands of practice in terms of efficiency and effectiveness. Deep Learning (DL) has recently emerged as a prominent technique for visual content analysis. It is data-driven in nature and provides automatic end-to-end learning solutions without the need to rely explicitly on predefined handcrafted feature extractors. Another appealing characteristic of DL solutions is the performance they can achieve, once the network is trained, under practical constraints. This paper identifies eight problem domains which require analysis of visual artefacts in multimedia. It surveys the recent, authoritative, and best performing DL solutions and lists the datasets used in the development of these deep methods for the identified types of visual analysis problems. The paper also discusses the challenges that DL solutions face which can compromise their reliability, robustness, and accuracy for visual content analysis.
    • A comparative analysis of trust models for safety applications in IoT-enabled vehicular networks

      Ahmad, Farhan; Adnane, Asma; Hussain, Rasheed; Kurugollu, Fatih; University of Derby; Loughborough University; Innopolis University (IEEE, 2019-06-13)
      To achieve these goals, VANET requires a secure environment for authentic, reliable and trusted information dissemination among the network entities. However, VANET is prone to different attacks resulting in the dissemination of compromised/false information among network nodes. One way to manage a secure and trusted network is to introduce trust among the vehicular nodes. To this end, various Trust Models (TMs) are developed for VANET and can be broadly categorized into three classes, Entity-oriented Trust Models (ETM), Data oriented Trust Models (DTM) and Hybrid Trust Models (HTM). These TMs evaluate trust based on the received information (data), the vehicle (entity) or both through different mechanisms. In this paper, we present a comparative study of the three TMs. Furthermore, we evaluate these TMs against the different trust, security and quality-of-service related benchmarks. Simulation results revealed that all these TMs have deficiencies in terms of end-to-end delays, event detection probabilities and false positive rates. This study can be used as a guideline for researchers to design new efficient and effective TMs for VANET.
    • Multiclass disease predictions based on integrated clinical and genomics datasets

      Anjum, Ashiq; Subhani, Moeez; University of Derby (IARIA, 2019-06-02)
      Clinical predictions using clinical data by computational methods are common in bioinformatics. However, clinical predictions using information from genomics datasets as well is not a frequently observed phenomenon in research. Precision medicine research requires information from all available datasets to provide intelligent clinical solutions. In this paper, we have attempted to create a prediction model which uses information from both clinical and genomics datasets. We have demonstrated multiclass disease predictions based on combined clinical and genomics datasets using machine learning methods. We have created an integrated dataset, using a clinical (ClinVar) and a genomics (gene expression) dataset, and trained it using instancebased learner to predict clinical diseases. We have used an innovative but simple way for multiclass classification, where the number of output classes is as high as 75. We have used Principal Component Analysis for feature selection. The classifier predicted diseases with 73% accuracy on the integrated dataset. The results were consistent and competent when compared with other classification models. The results show that genomics information can be reliably included in datasets for clinical predictions and it can prove to be valuable in clinical diagnostics and precision medicine.
    • Stability and pinning synchronization analysis of fractional order delayed Cohen–Grossberg neural networks with discontinuous activations

      Pratap, A.; Raja, R.; Cao, J.; Lim, C.P.; Bagdasar, O.; University of Derby (Elsevier, 2019-05-11)
      This article, we explore the asymptotic stability and asymptotic synchronization analysis of fractional order delayed Cohen–Grossberg neural networks with discontinuous neuron activation functions (FCGNNDDs). First, under the framework of Filippov theory and differ- ential inclusion theoretical analysis, the global existence of Filippov solution for FCGNNDDs is studied by means of the given growth condition. Second, by virtue of suitable Lyapunov functional, Young inequality and comparison theorem for fractional order delayed linear system, some global asymptotic stability conditions for such system is derived by limiting discontinuous neuron activations. Third, the global asymptotic synchronization condition for FCGNNDDs is obtained based on the pinning control. At last, two numerical simula- tions are given to verify the theoretical findings.
    • Consumer-facing technology fraud: Economics, attack methods and potential solutions

      Mohammed Aamir, Ali; Muhammad AJmal, Azad; Mario Parreno, Centeno; Feng, Hao; Aad Van, Moorsel; Newcastle University; Derby University; University of Warwick (Elsevier, 2019-05-11)
      The emerging use of modern technologies has not only benefited society but also attracted fraudsters and criminals to misuse the technology for financial benefits. Fraud over the Internet has increased dramatically, resulting in an annual loss of billions of dollars to customers and service providers worldwide. Much of such fraud directly impacts individuals, both in the case of browser-based and mobile-based Internet services, as well as when using traditional telephony services, either through landline phones or mobiles. It is important that users of the technology should be both informed of fraud, as well as protected from frauds through fraud detection and prevention systems. In this paper, we present the anatomy of frauds for different consumer-facing technologies from three broad perspectives - we discuss Internet, mobile and traditional telecommunication, from the perspectives of losses through frauds over the technology, fraud attack mechanisms and systems used for detecting and preventing frauds. The paper also provides recommendations for securing emerging technologies from fraud and attacks.
    • Global projective lag synchronization of fractional order memristor based BAM neural networks with mixed time varying delays

      Pratap, A.; Raja, R.; Sowmiya, C.; Bagdasar, Ovidiu; Jinde, Cao; Rajchakit, G.; University of Derby (Wiley InterScience, 2019-05-03)
      This paper addresses Master-Slave synchronization for some memristor- based fractional-order BAM neural networks (MFBNNs) with mixed time varying delays and switching jumps mismatch. Firstly, considering the inherent characteristic of FMNNs, a new type of fractional-order differential inequality is proposed. Secondly, an adaptive switching control scheme is designed to realize the global projective lag synchronization goal of MFBNNs in the sense of Riemann-Liouville derivative. Then, based on a suitable Lyapunov method, under the framework of set-valued map, differential inclusions theory, fractional Barbalat’s lemma and proposed control scheme, some new projective lag synchronization criteria for such MFBNNs are obtained. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed theoretical analysis.
    • Improved Kalman filter based differentially private streaming data release in cognitive computing.

      Wang, Jun; Luo, Jing; Liu, Xiaozhu; Li, Yongkai; Liu, Shubo; Zhu, Rongbo; Anjum, Ashiq; University of Derby; South-Central University for Nationalities; Wuhan University of Technology; et al. (Elsevier, 2019-04-04)
      Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.
    • 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.
    • Mittag-Leffler state estimator design and synchronization analysis for fractional order BAM neural networks with time delays

      Pratap, A.; Dianavinnarasi, J.; Raja, R.; Rajchakit, G.; Cao, J.; Bagdasar, Ovidiu; University of Derby (Wiley InterScience, 2019-03-20)
      This paper deals with the extended design of Mittag-Leffler state estimator and adaptive synchronization for fractional order BAM neural networks (FBNNs) with time delays. By the aid of Lyapunov direct approach and Razumikhin-type method a suitable fractional order Lyapunov functional is constructed and a new set of novel sufficient condition are derived to estimate the neuron states via available output measurements such that the ensuring estimator error system is globally Mittag-Leffler stable. Then, the adaptive feedback control rule is designed, under which the considered FBNNs can achieve Mittag-Leffler adaptive synchronization by means of some fractional order inequality techniques. Moreover, the adaptive feedback control may be utilized even when there is no ideal information from the system parameters. Finally, two numerical simulations are given to reveal the effectiveness of the theoretical consequences.
    • Optimizing wide-area sound reproduction using a single subwoofer with dynamic signal decorrelation

      Hill, Adam J.; Moore, J.B.; University of Derby (Audio Engineering Society, 2019-03-10)
      A central goal in small room sound reproduction is achieving consistent sound energy distribution across a wide listening area. This is especially difficult at low-frequencies where room-modes result in highly position-dependent listening experiences. While numerous techniques for multiple-degree-of-freedom systems exist and have proven to be highly effective, this work focuses on achieving position-independent low-frequency listening experiences with a single subwoofer. The negative effects due to room-modes and comb-filtering are mitigated by applying a time-varying decorrelation method known as dynamic diffuse signal processing. Results indicate that spatial variance in magnitude response can be significantly reduced, although there is a sharp trade-off between the algorithm’s effectiveness and the resulting perceptual coloration of the audio signal.
    • Measuring K0sK± interactions using pp collisions at √s=7 TeV

      Boca, G.; Bock, F.; Bogdanov, A.; Boldizsár, L.; Bolozdynya, A.; Bombara, M.; Bonomi, G.; Bonora, M.; Borel, H.; Borissov, A.; et al. (Elsevier, 2019-03-10)
      We present the first measurements of femtoscopic correlations between the KS0 and K± particles in pp collisions at √s=7 TeV measured by the ALICE experiment. The observed femtoscopic correlations are consistent with final-state interactions proceeding solely via the a0(980) resonance. The extracted kaon source radius and correlation strength parameters for KS0K− are found to be equal within the experimental uncertainties to those for KS0K+. Results of the present study are compared with those from identical-kaon femtoscopic studies also performed with pp collisions at √s=7 TeV by ALICE and with a KS0K± measurement in Pb–Pb collisions at √sNN=2.76 TeV. Combined with the Pb–Pb results, our pp analysis is found to be compatible with the interpretation of the a0(980) having a tetraquark structure instead of that of a diquark.
    • Pervasive blood pressure monitoring using Photoplethysmogram (PPG) Sensor

      Riaz, Farhan; Azad, Muhammad; Arshad, Junaid; Imran, Muhammad; Hassan, Ali; Rehmad, Saad; Derby (Elsevier, 2019-03-08)
      Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95\%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.
    • On some results concerning the polygonal polynomials.

      Andrica, Dorin; Bagdasar, Ovidiu; Babeș-Bolyai University; University of Derby (Technical University of Cluj-Napoca., 2019-02-13)
      In this paper we define the $n$th polygonal polynomial $P_n(z) = (z-1)(z^2-1)\cdots(z^n-1)$ and we investigate recurrence relations and exact integral formulae for the coefficients of $P_n(z)$ and for those of the Mahonian polynomials $Q_n(z)=(z+1)(z^2+z+1)\cdots(z^{n-1}+\cdots+z+1)$. We also explore numerical properties of these coefficients, unraveling new meanings for old sequences and generating novel entries to the Online Encyclopedia of Integer Sequences (OEIS). Some open questions are also formulated.