• Analysis of the relationship between Saudi twitter posts and the Saudi stock market

      AL-Rubaiee, Hamed Saad; Qiu, Renxi; Li, Dayou; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-02-04)
      Sentiment analysis has become the heart of social media research and many studies have been applied to obtain users' opinion in fields such as electronic commerce and trade, management and also regarding political figures. Social media has recently become a rich resource in mining user sentiments. Social opinion has been analysed using sentiment analysis and some studies show that sentiment analysis of news, documents, quarterly reports, and blogs can be used as part of trading strategies. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with the Saudi stock market in order to carry out and illustrate the relationship between Saudi tweets (that is standard and Arabian Gulf dialects) and the Saudi market index. To the best of our knowledge, this is the first study performed on Saudi tweets and the Saudi stock market.
    • Antenna and propagation considerations for amateur UAV monitoring

      Abolhasan M; Zhao, Nan; Yang, Xiaodong; Ren, Aifeng; Zhang, Zhiya; Zhao, Wei; Hu, Fangming; Ur-Rehman, Masood; Abbas, Haider; Xidian University; et al. (Institute of Electrical and Electronics Engineers Inc., 2018-05-18)
      The broad application spectrum of unmanned aerial vehicles is making them one of the most promising technologies of Internet of Things era. Proactive prevention for public safety threats is one of the key areas with vast potential of surveillance and monitoring drones. Antennas play a vital role in such applications to establish reliable communication in these scenarios. This paper considers line-of-sight and non-line-of-sight threat scenarios with the perspective of antennas and electromagnetic wave propagation.
    • Anti-degranulation response of herbal formula in RBL-2H3 cells

      Hu, Jing; Chen, Yujuan; Zhu, Jiajing; Gao, Mingyan; Li, Jiani; Song, Zhengxun; Xu, Hongmei; Wang, Zuobin; Changchun University of Science and Technology; University of Warwick; et al. (Elsevier, 2019-12-28)
      Allergic diseases not only bring serious economic burden to the patients, but also consume a lot of substantial resources of social medical systems. Thus, the prevention and treatment of allergic diseases are imperative. In this study, the anti-degranulation activity of herbal formula was evaluated using the rat basophil leukemia cells (RBL-2H3) as in vitro model. The morphological and biophysical properties of RBL-2H3 cells before and after treatment with herbal formula were also determined. Notably, the herbal formula exhibits clearly inhibited degranulation by RBL-2H3 cells in a concentration-dependent manner without cytotoxic effect. Therefore, this herbal formula can be used as an alternative and promising therapeutic agent to ameliorate allergic diseases.
    • Application of atomic force microscope in diagnosis of single cancer cells

      Lu, Zhengcheng; Wang, Zuobin; Li, Dayou; ; University of Bedfordshire; Changchun University of Science and Technology (American Institute of Physics Inc., 2020-09-04)
      Changes in mechanical properties of cells are closely related to a variety of diseases. As an advanced technology on the micro/nano scale, atomic force microscopy is the most suitable tool for information acquisition of living cells in human body fluids. AFMs are able to measure and characterize the mechanical properties of cells which can be used as effective markers to distinguish between different cell types and cells in different states (benign or cancerous). Therefore, they can be employed to obtain additional information to that obtained via the traditional biochemistry methods for better identifying and diagnosing cancer cells for humans, proposing better treatment methods and prognosis, and unravelling the pathogenesis of the disease. In this report, we review the use of AFMs in cancerous tissues, organs, and cancer cells cultured in vitro to obtain cellular mechanical properties, demonstrate and summarize the results of AFMs in cancer biology, and look forward to possible future applications and the direction of development.
    • Application of Error-Correcting Codes (ECCs) for efficient message transmission in Vehicular Ad Hoc Networks (VANETs)

      Muhammad, Shehu Jabaka; Zhang, Sijing; Dyo, Vladimir; University of Bedfordshire (Springer, 2018-09-29)
      In this paper, we presented an adaptive application of forward error code (FEC) for efficient message transmission in vehicular ad hoc networks (VANETs). Our solution is a combination of automatic retransmission request (ARQ) with FEC at the MAC layer. The proposed scheme used the existing channel condition, an estimate of the maximum number of transmissions before the message deadline elapses and message type as an index in code lookup ensemble (CLE) to get an optimum code (optCode) for the current transmission. Furthermore, the system also set the transmission timeout delay RTT , encode the message with the optCode and transmit. However, if the transmission timeout delay elapses before receiving an ACK/NAK, the scheme will return to the initial stage for feasible retransmission of the message. We evaluated the scheme and compared it with the static FEC for reliable and timely safety message transmission; our system outperformed the static FEC in all cases that we have considered.
    • An approach to locating delayed activities in software processes

      Jin, Yun-Zhi; Zhou, Hua; Yang, Hong-Ji; Zhang, Sijing; Ge, Ji-Dong; Yunnan University; Key Laboratory for Software Engineering of Yunnan Province; Research Center of Cloud Computing of Yunnan Province; Bath Spa University; University of Bedfordshire; et al. (Chinese Academy of Sciences, 2017-09-21)
      Activity is now playing a vital role in software processes. To ensure the high-level efficiency of software processes, a key point is to locate those activities that own bigger resource occupation probabilities with respect to average execution time, called delayed activities, and then improve them. To this end, we firstly propose an approach to locating delayed activities in software processes. Furthermore, we present a case study, which exhibits the high-level efficiency of the approach, to concretely illustrate this new solution. Some beneficial analysis and reasonable modification are developed in the end.
    • Aquacold - a crowdsourced query understanding and query construction tool for the linked data web

      Collis, Nick; Frommholz, Ingo; University of Bedfordshire (CEUR-WS, 2017-12-31)
      The Linked Data Web promises a disseminated, dynamic, ever expanding knowledge base where relationships between content and entities can be expressed and queried using formal logic rules. The data formats and exchange protocols that comprise the Linked Data Web have existed for over 15 years and grown to incorporate over 149 billion triples but there is no established 'mainstream' system that allows regular non-technical users to query the linked data web using natural language. In this paper we introduce Aquacold (Aggregated Query Understanding And Construction Over Linked Data), a novel Linked Data query tool designed to fill this gap. Aquacold combines a simple browsing interface for exploring, filtering and sorting linked data with query labelling which allows the user to store a natural language representation of the underlying SPARQL query. A search interface allows users to write guided natural language queries, which are converted into SPARQL queries to retrieve results from the linked data source. A set of voting tools are presented for each query and associated result set to surface the most accurate templates and relegate those that are less accurate. This crowdsourced approach for labelling, saving and voting on linked data query templates enables regular, non-technical users to make complex natural language queries over the linked data web without having to use a structured language such as SPARQL. In this paper we provide an overview of the system and discuss an early prototype.
    • Arabic text classification methods: systematic literature review of primary studies

      Alabbas, Waleed; al-Khateeb, Haider; Mansour, Ali; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2017-01-05)
      Recent research on Big Data proposed and evaluated a number of advanced techniques to gain meaningful information from the complex and large volume of data available on the World Wide Web. To achieve accurate text analysis, a process is usually initiated with a Text Classification (TC) method. Reviewing the very recent literature in this area shows that most studies are focused on English (and other scripts) while attempts on classifying Arabic texts remain relatively very limited. Hence, we intend to contribute the first Systematic Literature Review (SLR) utilizing a search protocol strictly to summarize key characteristics of the different TC techniques and methods used to classify Arabic text, this work also aims to identify and share a scientific evidence of the gap in current literature to help suggesting areas for further research. Our SLR explicitly investigates empirical evidence as a decision factor to include studies, then conclude which classifier produced more accurate results. Further, our findings identify the lack of standardized corpuses for Arabic text; authors compile their own, and most of the work is focused on Modern Arabic with very little done on Colloquial Arabic despite its wide use in Social Media Networks such as Twitter. In total, 1464 papers were surveyed from which 48 primary studies were included and analyzed.
    • 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.
    • Atomic force acoustic microscopy reveals the influence of substrate stiffness and topography on cell behavior

      Liu, Yan; Li, Li; Chen, Xing; Wang, Ying; Liu, Mengnan; Yan, Jin; Cao, Liang; Wang, Lu; Wang, Zuobin; Changchun University of Science and Technology; et al. (Beilstein-Institut, 2019-11-26)
      The stiffness and the topography of the substrate at the cell-substrate interface are two key properties influencing cell behavior. In this paper, atomic force acoustic microscopy (AFAM) is used to investigate the influence of substrate stiffness and substrate topography on the responses of L929 fibroblasts. This combined nondestructive technique is able to characterize materials at high lateral resolution. To produce substrates of tunable stiffness and topography, we imprint nanostripe patterns on undeveloped and developed SU-8 photoresist films using electron-beam lithography (EBL). Elastic deformations of the substrate surfaces and the cells are revealed by AFAM. Our results show that AFAM is capable of imaging surface elastic deformations. By immunofluorescence experiments, we find that the L929 cells significantly elongate on the patterned stiffness substrate, whereas the elasticity of the pattern has only little effect on the spreading of the L929 cells. The influence of the topography pattern on the cell alignment and morphology is even more pronounced leading to an arrangement of the cells along the nanostripe pattern. Our method is useful for the quantitative characterization of cell-substrate interactions and provides guidance for the tissue regeneration therapy in biomedicine.
    • Atomic force microscopy imaging of the G-banding process of chromosomes

      Wang, Bowei; Li, Jiani; Dong, Jianjun; Yang, Fan; Qu, Kaige; Wang, Ying; Zhang, Jingran; Song, Zhengxun; Hu, Hongmei; Wang, Zuobin; et al. (Springer Science and Business Media, 2020-10-24)
      The chromosome is an important genetic material carrier in living individuals and the spatial conformation (mainly referring to the chromosomal structure, quantity, centromere position and other morphological information) may be abnormal or mutated. Thus, it may generate a high possibility to cause diseases. Generally, the karyotype of chromosome G-bands is detected and analyzed using an optical microscope. However, it is difficult to detect the G-band structures for traditional optical microscopes on the nanometer scale. Herein, we have studied the detection method of chromosome G-band samples by atomic force microscopy (AFM) imaging. The structures of chromosome G-banding are studied with different trypsin treatment durations. The experiment result shows that the treatment duration of 20 s is the best time to form G-band structures. The AFM images show the structures of chromosome G-bands which cannot be observed under an optical microscope. This work provides a new way for the detection and diagnosis of chromosome diseases on the nanometer scale.
    • Autonomous arial vehicles in smart cities: potential cyber-physical threats

      Dawam, Edward Swarlat; Feng, Xiaohua; Li, Dayou; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-01-24)
      Autonomous aerial vehicles (AAV) are aircraft systems whose aircrew is replaced by autonomous computer systems and a radio link, thereby managed remotely from a ground station. This mode of transportation has recently been adopted as a means of transportation in a pioneering initiative by Dubai with more smart cities expected to adopt this mode of transportation shortly as it is believed to show potentials to transform urban transportation and future mobility in smart cities. The concern, however, is, the security of these systems and the smart city infrastructure they depend on for their operations. Certainly, the introduction of AAVs into smart cities raises many new cybersecurity questions that are in need of investigation and answers. It is, therefore, the purpose of this paper to explore potential cyber-physical security threats and the challenges that need to be confronted before this mode of transportation is fully integrated into the smart cities. A methodology to investigate on a large scale the cybersecurity attack vectors of such systems is presented based on four categories of systems that are critical to AAV operations, as well as their impacts and how to counter such attacks. What follows is a summary of the countermeasures that should be implemented to guarantee the safety of those systems.
    • Autonomous vehicles' forensics in smart cities

      Feng, Xiaohua; Dawam, Edward Swarlat; Li, Dayou; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2020-04-02)
      Autonomous vehicles (AVs) are capable of sensing their environment and navigating without any human inputs. When accidents occur between AVs, road infrastructures, or human subjects, liability is decided based on accident forensics. This accident forensics is carried out by the acquisition of sensor data generated within the AVs and through its communication between vehicles to a vehicle (V2V) and vehicle to infrastructure (V2I) with a centralised data hub in smart cities that collects and stores this data thereby aiding the relevant authorities in informed decision making. However, practices mostly employed in extracting this information are unprofessional when compared to other areas of digital forensics. In this paper, we designed and implemented a non-invasive mechanism for the collection and storage of forensic data from AVs within smart cities. This mechanism is efficient, secure, and preserves the privacy of data generated by the AV.
    • Battery-assisted electric vehicle charging: data driven performance analysis

      Ali, Junade; Dyo, Vladimir; Zhang, Sijing (2020-11-10)
      As the number of electric vehicles rapidly increases, their peak demand on the grid becomes one of the major challenges. A battery-assisted charging concept has emerged recently, which allows to accumulate energy during off-peak hours and in-between charging sessions to boost-charge the vehicle at a higher rate than available from the grid. While prior research focused on the design and implementation aspects of battery- assisted charging, its impact at large geographical scales remains largely unexplored. In this paper we analyse to which extent the battery-assisted charging can replace high-speed chargers using a dataset of over 3 million EV charging sessions in both domestic and public setting in the UK. We first develop a discrete-event EV charge model that takes into account battery capacity, grid supply capacity and power output among other parameters. We then run simulations to evaluate the battery-assisted charging performance in terms of delivered energy, charging time and parity with conventional high-speed chargers. The results indicate that in domestic settings battery-assisted charging provides 98% performance parity of high-speed chargers from a standard 3 kW grid connection with a single battery pack. For non-domestic settings, the battery-assisted chargers can provide 92% and 99% performance parity of high-speed chargers with 10 battery packs using 3kW and 7kW grid supply respectively.
    • Bayesian averaging over Decision Tree models for trauma severity scoring

      Schetinin, Vitaly; Jakaite, Livija; Krzanowski, Wojtek (Elsevier, 2017-12-21)
      Health care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the “gold” standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions.
    • Bayesian averaging over decision tree models: an application for estimating uncertainty in trauma severity scoring

      Schetinin, Vitaly; Jakaite, Livija; Krzanowski, Wojtek; University of Bedfordshire; University of Exeter (Elsevier, 2018-01-11)
      Introduction For making reliable decisions, practitioners need to estimate uncertainties that exist in data and decision models. In this paper we analyse uncertainties of predicting survival probability for patients in trauma care. The existing prediction methodology employs logistic regression modelling of Trauma and Injury Severity Score(external) (TRISS), which is based on theoretical assumptions. These assumptions limit the capability of TRISS methodology to provide accurate and reliable predictions. Methods We adopt the methodology of Bayesian model averaging and show how this methodology can be applied to decision trees in order to provide practitioners with new insights into the uncertainty. The proposed method has been validated on a large set of 447,176 cases registered in the US National Trauma Data Bank in terms of discrimination ability evaluated with receiver operating characteristic (ROC) and precision–recall (PRC) curves. Results Areas under curves were improved for ROC from 0.951 to 0.956 (p = 3.89 × 10−18) and for PRC from 0.564 to 0.605 (p = 3.89 × 10−18). The new model has significantly better calibration in terms of the Hosmer–Lemeshow Hˆ" role="presentation"> statistic, showing an improvement from 223.14 (the standard method) to 11.59 (p = 2.31 × 10−18). Conclusion The proposed Bayesian method is capable of improving the accuracy and reliability of survival prediction. The new method has been made available for evaluation purposes as a web application.
    • Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

      Schetinin, Vitaly; Jakaite, Livija; Krzanowski, Wojtek; University of Bedfordshire; University of Exeter (IOS Press, 2018-05-17)
      Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems.
    • Benefits, barriers and guideline recommendations for the implementation of serious games in education for stakeholders and policymakers

      Tsekleves, Emmanuel; Cosmas, John; Aggoun, Amar (Blackwell Publishing Ltd, 2014-10-24)
      Serious games and game-based learning have received increased attention in recent years as an adjunct to teaching and learning material. This has been well echoed in the literature with numerous articles on the use of games and game theory in education. Despite this, no policy for the incorporation of serious games in education exists to date. This review paper draws from the literature to provide guideline recommendations that would help educators and policymakers in making the first step towards this.
    • Between virtual and real: exploring hybrid interaction and communication in virtual worlds

      Christopoulos, Athanasios; Conrad, Marc; Shukla, Mitul; University of Bedfordshire (Inderscience Publishers, 2016-03-01)
      In this paper we aim to explore the potential advantages of interactions on student engagement and provide guidance to educators who seek interactive and immersive learning experiences for their students through the use of hybrid virtual learning approaches. We define as hybrid virtual learning the educational model where students are co-present and interacting simultaneously both within a virtual world and the physical classroom receiving stimuli related to the learning material in the virtual world from both directions. In order to achieve our aim, we categorised interactions in various categories and observed the complex network of interactions which can be developed in a virtual world when groups of people are working together in order to achieve different goals. The findings suggest that students spontaneously tend to use the interaction channels only when it is deemed to be necessary.
    • Bibliometric-enhanced information retrieval: 7th international BIR workshop

      Mayr, Philipp; Frommholz, Ingo; Cabanac, Guillaume; Leibniz Institutefor the Social Sciences; University of Bedfordshire; University of Toulouse (ACM, 2018-08-01)
      The Bibliometric-enhanced Information Retrieval (BIR) workshop series has started at ECIR in 2014 and serves as the annual gathering of IR researchers who address various information-related tasks on scientific corpora and bibliometrics. We welcome contributions elaborating on dedicated IR systems, as well as studies revealing original characteristics on how scientific knowledge is created, communicated, and used. This report presents all accepted papers at the 7th BIR workshop at ECIR 2018 in Grenoble, France.