• BIRDS-bridging the gap between information science, information retrieval and data science

      Frommholz, Ingo; Liu, Haiming; Melucci, Massimo; University of Bedfordshire; University of Padova (Association for Computing Machinery, Inc, 2020-07-30)
      The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor-mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration.
    • IoT for 5G/B5G applications in smart homes, smart cities, wearables and connected cars

      Uddin, Hasna; Gibson, Marcia; Safdar, Ghazanfar Ali; Kalsoom, Tahera; Ramzan, Naeem; Ur-Rehman, Masood; Imran, Muhammad Ali; University of Bedfordshire; University of West of Scotland; University of Glasgow (Institute of Electrical and Electronics Engineers Inc., 2019-10-07)
      Internet of things (IoT) is referred to as smart devices connected to the internet. A smart device is an electronic device, which may connect to other devices or are part of a network such as Wi-Fi. The increase of IoT devices has helped with advancing technology in many areas of society. Application of IoT in 5G/B5G devices has provided many benefits such as providing new ideas that can become projects for tech companies, generating big data (large volume of data which can be used to reveal trends, patterns and associations) and providing various ways of communicating. This has also had an impact on how companies improve their business with the use of advanced technology. However, the rapid growth of IoT has introduced a new platform for cybercriminals to attack. There has been published security measures on IoT to help deal with such risks and vulnerabilities. This survey paper will explore IoT in relation to smart homes, smart cities, wearables and connected cars. The benefits, risks and vulnerabilities will be discussed that comes along with using such devices connected to the internet.
    • Survey on security and privacy issues in cyber physical systems

      Nazarenko, Artem A.; Safdar, Ghazanfar Ali; Nova University of Lisbon; University of Bedfordshire (American Institute of Mathematical Sciences, 2019-04-16)
      The notion of Cyber-Physical Systems (CPS) is proposed by the National Scientific Foundation to describe a type of systems which combine hardware and software components and being the next step in development of embedded systems. CPS includes a wide range of research topics ranging from signal processing to data analysis. This paper contains a brief review of the basic infrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. As CPS reflect the processes of the physical environment onto the cyber space, virtualisation as an important tool for abstraction plays crucial role in CPS. In this context paper presents the challenges associated with mobility and vritualisation; accordingly three main types of virtualisation, namely network, devices and applications virtualisation are presented in the paper. These aspects are tightly coupled with security and safety issues. Therefore, different threats, attack types with corresponding subtypes and possible consequences are discussed as well as analysis of various approaches to cope with existing threats is introduced. In addition threat modelling approaches were also in scope of this work. Furthermore, needs and requirements for safety-critical CPS are reviewed. Thus the main efforts of this paper are directed on introducing various aspects of the CPS with regard to security and safety issues.
    • Security challenges in cyber systems

      Safdar, Ghazanfar Ali; Kalsoom, Tahera; Ramzan, Naeem; University of Bedfordshire; University of the West of Scotland (Institute of Electrical and Electronics Engineers Inc., 2020-09-29)
      CPS (Cyber-Physical Systems) is proposed by the NSF (National Scientific Foundation) to describe a type of necessities which conglomerates hardware and software components and being the next step in development of embedded systems. CPS includes a wide range of research topics from signal processing to data analysis. This paper contains a brief review of the basic infrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. As CPS reflect the processes of the physical environment onto the cyber space, virtualisation as important tool for abstraction plays crucial role in CPS. In this context paper presents the challenges associated with mobility and vritualisation; accordingly, three main types of virtualisation, namely network, devices and applications virtualisation are presented in the paper. The main focus of the paper is made on security. Different threats, attack types and possible consequences are discussed as well as analysis of various approaches to cope with existing threats is introduced. Furthermore, needs and requirements for safety-critical CPS are reviewed.
    • Dynamic mechanics of HK-2 cell reaction to HG stimulation studied by atomic force microscopy

      Yang, Fan; Wang, Jiajia; Qu, Kaige; Yang, Xue; Liu, Chuanzhi; Wang, Ying; Song, Zhengxun; Xu, Hongmei; Chen, Yujuan; Wang, Zuobin; et al. (Royal Society of Chemistry, 2020-10-02)
      Renal tubular cell injury by exposure to high glucose (HG) stimulation mainly accounts for diabetic nephropathy (DN). To understand the mechanism of injury by HG, quantitative characterization has commonly focused on the cells that are already impaired, which ignores the signals for the process of being injured. In this study, the architecture and morphology of HK-2 cells were observed dynamically by multiple imaging methods. AFM (atomic force microscopy)-based single-cell force spectroscopy was employed to investigate the dynamic mechanics quantitatively. The results showed that the Young's modulus increased continuously from 2.44 kPa up to 4.15 kPa for the whole period of injury by HG, while the surface adhesion decreased from 2.43 nN to 1.63 nN between 12 h and 72 h. In addition, the actin filaments of HK-2 cells exposed to HG depolymerized and then nucleated with increasing Young's modulus. The absence of cell pseudopodia coincided with the reduced cell adhesion, strongly suggesting close relationships between the cell architecture, morphology and mechanical properties. Furthermore, the stages of cell reactions were identified and assessed. Overall, the dynamic mechanics of the cells facilitate the identification of injured cells and the assessment of the degree of injury for accurate diagnoses and treatments.
    • Entity-aware capsule network for multi-class classification of big data: a deep learning approach

      Jaiswal, Amit Kumar; Tiwari, Prayag; Garg, Sahil; Hossain, M. Shamim; University of Bedfordshire; University of Padova; École de technologie supérieure, Montréal; King Saud University (Elsevier B.V., 2020-11-20)
      Named entity recognition (NER) is one of the most challenging natural language processing (NLP) tasks, as its performance is related to constantly evolving languages and dependency on expert (human) annotation. The diverse and dynamic content on the web significantly raises the need for a more generalized approach—one that is capable of correctly classifying terms in a corpus and feeding subsequent NLP tasks, such as machine translation, query expansion, and many other applications. Although extensively researched in recent times, the variety of public corpora available nowadays provides room for new and more accurate methods to tackle the NER problem. This paper presents a novel method that uses deep learning techniques based on the capsule network architecture for predicting entities in a corpus. This type of network groups neurons into so-called capsules to detect specific features of an object without reducing the original input unlike convolutional neural networks and their ‘max-pooling’ strategy. Our extensive evaluation on several benchmarked datasets demonstrates how competitive our method is in comparison with state-of-the-art techniques and how the usage of the proposed architecture may represent a significant benefit to further NLP tasks, especially in cases where experts are needed. Also, we explore NER using a theoretical framework that leverages big data for security. For the sake of reproducibility, we make the codebase open-source.
    • A study on the effects of tumor-derived exosomes on hepatoma cells and hepatocytes by atomic force microscopy

      Ju, Tuoyu; Wang, Shuwei; Wang, Jiajia; Yang, Fan; Song, Zhengxun; Xu, Hongmei; Chen, Yujuan; Zhang, Jingran; Wang, Zuobin; Changchun University of Science and Technology; et al. (Royal Society of Chemistry, 2020-12-07)
      Tumor-derived exosomes (exos) are closely related to the occurrence, development and treatment of tumors. However, it is not clear how the exosomes affect the physical properties, which lead to the deterioration of the target cells. In this paper, atomic force microscopy (AFM) was used to study the effects of exosomes in HCC-LM3 cells and other cells (SMMC-7721 and HL-7702). The results showed that the HCC-LM3-exos (the exosomes secreted by HCC-LM3 cells, 50 μg mL-1) significantly promoted the proliferation and migration of HCC-LM3 cells. HCC-LM3-exos also promoted the proliferation and migration of SMMC-7721 and HL-7702 cells at 1000 and 1500 μg mL-1, respectively. With an increase in time and concentration, the proliferation effect was more significant. On comparing the mechanical properties of the three types of cells (HCC-LM3, SMMC-7721 and HL-7702 cells), the degradation degree and migration ability of the cells were from high to low in the above order. In turn, the surface roughness of the cells decreased, and adhesion and elastic modulus increased. With an increase in treatment time, surface roughness increased, while adhesion and elastic modulus decreased. These suggested that the HCC-LM3-exos could change the mechanical properties of cells, leading to their deterioration, and enhance their migration and invasion ability. In this paper, the effects of exosomes were analyzed from the perspective of the physical parameters of cells, which provide a new idea to study cancer metastasis and prognosis.
    • Dynamic causality knowledge graph generation for supporting the chatbot healthcare system

      Yu, Hong Qing; University of Bedfordshire (Springer Science and Business Media Deutschland GmbH, 2020-10-31)
      With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions.
    • Personally identifiable information security in cloud computing

      Feng, Xiaohua; Zhang, Xiangrui (2020-12-18)
      A cyber security application in Personally identifiable information) PII is attracting more and more attention and related to majority people’s everyday activities. The paper is introduced the trends of cyber security in cloud computing and in particular, focus on the responsibility of data privacy, especially in European Union countries. As the impact is on data protection which includes organisation based in the union, or has branches in the union or provides services to the union residents. The paper is also introduced the updated recent development content in our society which caused the impact that we have to deliver ISO standards; for instance, ISO/IEC 27018 and so on. A consequence of the standard is that regular practices of risk assessment need to be carried out in a regular base; such as an annually assessment. Keywords- Data protection, personal privacy, cryptography, cloud computing, cyber security, security policy, Trustworthiness, data service, personally identifiable information (PII), and ISO 27018
    • Mobile computing and IoT: radio spectrum requirement for timely and reliable message delivery over Internet of Vehicles (IoVs)

      Eze, Elias Chinedum; Sant, Paul; Zhang, Sijing; Feng, Xiaohua; Shukla, Mitul; Eze, Joy C.; Liu, Enjie; University of Bedfordshire (Springer, 2020-01-01)
      With the envisioned era of internet of things, all aspects of Intelligent Transportation Systems will be connected to improve transport safety, relieve traffic congestion, reduce air pollution, enhance the comfort of transportation and significantly reduce road accidents. In internet of vehicles, regular exchange of current position, direction, velocity and so on, enables mobile vehicles to predict an upcoming accident and alert the human drivers in time or proactively take precautionary actions to avoid the accident. The actualisation of this concept requires the use of channel access protocols that can guarantee reliable and timely broadcast of safety messages. This study investigates the application of network coding concept to increase content of every transmission and achieve improved broadcast reliability with less number of retransmissions. In particular, the authors proposed Code Aided Retransmission-based Error Recovery (CARER) scheme, introduced a request-to-broadcast/clear-to-broadcast (RTB/CTB) handshake to overcome hidden node problem and reduce packets collision rate. In order to avoid broadcast storm problem associated with the use of RTB/ CTB packet in a broadcast transmission, they developed a rebroadcasting metric used to successfully select a vehicle to rebroadcast the encoded message. The performance of CARER protocol is clearly shown with detailed theoretical analysis and further validated with simulation experiments.
    • Artificial intelligence cyber security strategy

      Feng, Xiaohua; Feng, Yunzhong; Dawam, Edward Swarlat; University of Bedfordshire; Hebei Normal University (IEEE, 2020-11-11)
      Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments’ strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
    • Smart city lane detection for autonomous vehicle

      Dawam, Edward Swarlat; Feng, Xiaohua; University of Bedfordshire (IEEE, 2020-11-11)
      One of AI branch, Computer Vision-based recognition systems is necessary for security in Autonomous Vehicles (AVs). Traffic sign recognition systems are popularly used in AVs because it ensures driver safety and decrease vehicles accidents on roads. However, the inability of AVs to accurately detect road signs and pedestrian behaviour has led to road crashes and even death in recent times. Additionally, as cities become smarter, the traditional traffic signs dataset will change considerably, as theGoogle, 2020se vehicles and city infrastructure introduce modern facilities into their operation. In this paper, we introduce a computer vision based road surface marking recognition system to serve as an added layer of data source from which AVs will make decisions. We trained our detector using YOLOv3 running in the cloud to detect 25 classes of Road surface markings using over 25,000 images. The results of our experiment demonstrate a robust performance in terms of the accuracy and speed of detection. The results of which will consolidate the traffic sign recognition system, thereby ensuring more reliability and safety in AVs decision making. New algorithm using Deep Learning technology in Artificial intelligence (AI) application is implemented and tested successfully.
    • A new algorithm for private cloud

      Feng, Xiaohua (IJETAE, 2014-10-08)
      An operative encryption algorithm is the goal that cyber security researchers have been seeking. In this paper, we briefly report a newly developed encryption algorithm. An implementation has been carried out, the testing result shown this algorithm is effective. We focus on the processing speed and discuss the comparison with others. It has been recognized as an feasible algorithm, which is able to applied to practical circumstances.
    • μECM process investigation considering pulse signal features and EDL capacitance

      Mortazavi, Mina; Ivanov, Atanas (Springer, 2019-05-22)
      Micro-electrochemical machining (μECM) is a controlled anodic dissolution process between electrodes. The anodic dissolution, which follows Faraday’s laws of electrolysis, depends on characteristics of the electrodes materials, electrolyte properties, and pulse signal features. μECM is a challenging multidisciplinary task in which quality of the process and features of the finished products depend on a complex relation between different machining parameters including, electrical features of pulse signal, chemical features of electrolyte, physical features of tools, and thermodynamic features of the process. In this paper, influential machining parameters will be reviewed briefly, and pulse signal features will be investigated and analyzed considering the behavior of the electrode-electrolyte interface. The interface has capacitive feature and plays an important role in micromachining performance. The proposed simulation work presents the requirement for the pulse on-time in order to provide the maximum possible charging-discharging time for the capacitive behavior of the electrode-electrolyte interface.
    • Improving utility of GPU in accelerating industrial applications with user-centered automatic code translation

      Yang, Po; Dong, Feng; Codreanu, Valeriu; Williams, David; Roerdink, Jos B.T.M.; Liu, Baoquan; Anvari-Moghaddam, Amjad; Min, Geyong; University of Bedfordshire; SURFsara; et al. (IEEE Computer Society, 2017-07-24)
      Small to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are hard to use. Little attentions have been paid to the applicability, usability, and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system (GPSME) for inexperienced users to utilize the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.
    • Evaluation of autoparallelization toolkits for commodity GPUs

      Williams, David; Codreanu, Valeriu; Yang, Po; Liu, Baoquan; Dong, Feng; Yasar, Burhan; Mahdian, Babak; Chiarini, Alessandro; Zhao, Xia; Roerdink, Jos B.T.M.; et al. (Springer Verlag, 2014-12-31)
      In this paper we evaluate the performance of the OpenACC and Mint toolkits against C and CUDA implementations of the standard PolyBench test suite. Our analysis reveals that performance is similar in many cases, but that a certain set of code constructs impede the ability of Mint to generate optimal code. We then present some small improvements which we integrate into our own GPSME toolkit (which is derived from Mint) and show that our toolkit now out-performs OpenACC in the majority of tests.
    • The dark web: cyber-security intelligence gathering opportunities, risks and rewards

      Epiphaniou, Gregory; French, Tim; Maple, Carsten; University of Bedfordshire (University of Zagreb, 2014-12-31)
      We offer a partial articulation of the threats and opportunities posed by the so-called Dark Web (DW). We go on to propose a novel DW attack detection and prediction model. Signalling aspects are considered wherein the DW is seen to comprise a low cost signaling environment. This holds inherent dangers as well as rewards for investigators as well as those with criminal intent. Suspected DW perpetrators typically act entirely in their own self-interest (e.g. illicit financial gain, terrorism, propagation of extremist views, extreme forms of racism, pornography, and politics; so-called 'radicalisation'). DWinvestigators therefore need to be suitably risk aware such that the construction of a credible legally admissible, robust evidence trail does not expose investigators to undue operational or legal risk.
    • Towards DR inventor: a tool for promoting scientific creativity

      O’Donoghue, D.P.; Saggion, H.; Dong, Feng; Hurley, D.; Abgaz, Y.; Zheng, X.; Corcho, O,; Zhang, J.J.; Careil, J.M.; Mahdian, B.; et al. (Jozef Stefan Institute, 2014-12-31)
      We propose an analogy-based model to promote creative scientific reasoning among its users. Dr Inventor aims to find novel and potentially useful creative analogies between academic documents, presenting them to users as potential research questions to be explored and investigated. These novel comparisons will thereby drive its users’ creative reasoning. Dr Inventor is aimed at promoting Big-C Creativity and the H-creativity associated with true scientific creativity.
    • Study of a microstrip patch antenna with multiple circular slots for portable devices

      Farooq, Waqas; Ur-Rehman, Masood; Abbasi, Qammer Hussain; Maqbool, Khawaja Qasim; Qaraqe, Khalid; University of Bedfordshire; Texas A & M University, Qatar; Bahria University (Institute of Electrical and Electronics Engineers Inc., 2015-03-16)
      This paper presents the design and study of a high performance microstrip rectangular patch antenna for the 2.5 GHz ISM band. The proposed antenna make use of small multiple circular slots embedded in the radiator to enhance the performance of the traditional patch antenna. Introduction of the new multi-slot arrangement offers a low profile antenna with reduced size, improved impedance matching, broadband operation. A comparatively wider -10 dB impedance bandwidth of 95 MHz is achieved. The maximum achievable gain is 6.8 dBi.Good performance, light weight, mechanically rugged and inexpensive design make this antenna a potential candidate for wireless portable devices.
    • Ontology driven personal health knowledge discovery

      Yu, Hong Qing; Zhao, Xia; Deng, Zhikun; Dong, Feng; University of Bedfordshire (Springer Verlag, 2015-08-04)
      With fast development of smart sensor devices and mobile applications, all different kinds of information related to humans can be founded on the Internet that can be seen as a universal data repository or called Web of Data. Health or healthcare related data are not exceptional in the Web of Data age. The most important and valuable data comes from IoT such as sensors and mobile activity tracking applications to support developing self-health risk detection and management applications. This paper presents a comprehensive ontology driven knowledge discovery framework in personal health domain, which aims to reason and discover health knowledge from various data sources of IoT. The framework contains a sensor oriented Personal Wellness Knowledge Ontology and data integration architecture to complete a whole lifecycle of health knowledge detecting and reasoning path. In addition, a cloud computing based parallel semantic lifting algorithm is described for illustrating the semantic data generation process in detail.