• Interference system for high pressure environment

      Kumpulainen, Tero; Singh, Amandeep; März, Thomas; Dong, Litong; Li, Dayou; Reuna, Jarno; Vihinen, Jorma; Levänen, Erkki; Tampere University; InnoLas Laser GmbH; et al. (Elsevier Ltd, 2021-05-29)
      Laser interference patterning or lithography has been used in variety of the applications using, patterning, masking and processing structures at top of material. It offers fast processing over as large areas can be processed simultaneously. Additionally, fine patterns are possible to achieve both in micro and sub-micro scale. In this manuscript is presented novel concept to combine interference patterning and high-pressure processing environment. With aid of high-pressure system, it is possible to control processing environment and add co-solvents in desired state (liquid, gas, supercritical) and use developed system as controlled reactive environment in the future studies. Two systems were developed and assembled for testing and proofing the concept. The results of the two 4-beam interference systems (lens- and mirror-based) are presented and compared.
    • Analysis of cybercrime in Nigeria

      Hamisu, Muhammad; Idris, Abubakar Muhammad; Mansour, Ali; Olalere, Morufu; University of Bedfordshire; Federal University of Technology, Minna, Nigeria (Institute of Electrical and Electronics Engineers Inc., 2021-05-25)
      Nigeria has both the largest economy and population in Africa, and this contribute to the growth and fast expansion of ICT and the use of Internet in Nigeria. Like other technologies, Internet has been used by both good and bad actors. The use of internet and computer to commit crime is costing global economy the loss of billions of dollars. In Nigeria, the majority of the population use the Internet for good but some few are using it to commit criminal activities such as Fraud. Cybercriminals in Nigeria, widely called Yahoo Boys in the country specialize in Internet fraud that target mostly International victims. The Nigeria government is stepping efforts to bring an end the activities of these criminals as their actions tarnishes the image of the country. While the efforts of the government had yielded some positive results, the threat of Cybercrime in Nigeria is still high, as criminals continue to take advantage of flaws in the law enforcement tactical approach in addressing the crime. This paper discusses an overview of Cybercrime in Nigeria, the common types of Cybercrime that is perpetuated from the country and the reason of doing so. It also discusses the government's success and areas of strength in its fight against Cybercrime and highlight the areas of weaknesses. Recommendations and suggestions are made on how law enforcement and the government at large can improve to tackle Cybercrime better in Nigeria.
    • Detecting advance fee fraud using NLP bag of word model

      Hamisu, Muhammad; Mansour, Ali; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2021-05-25)
      Advance Fee Fraud (AFF) is a form of Internet fraud prevalent within the Cybercrimes domain in literature. Evidence shows that huge financial assets are stolen from the global economy as a result of AFF. Consequently, this paper presents a fraudulent email classifier (FEC) that detects and classifies an email as fraudulent or non-fraudulent using Natural Language Process (NLP) model referred to as Bag-of-Words (BoW). The classifier is designed and trained to detect and classify AFF that originate from known sources using Nigeria as a Case study. Dataset is obtained and used for the training while testing the classifier logs. Experimentally, the classifier was trained using various machine learning algorithms with BoW generated as predictors. By selecting the best algorithms, the classifier was tested and found to perform satisfactorily.
    • Comparative analysis of scheduling algorithms for radio resource allocation in future communication networks

      Ashfaq, Khuram; Safdar, Ghazanfar Ali; Ur-Rehman, Masood; ; University of Bedfordshire; University of Glasgow (PeerJ, 2021-05-18)
      Wireless links are fast becoming the key communication mode. However, as compared to the wired link, their characteristics make the traffic prone to time- and location-dependent signal attenuation, noise, fading, and interference that result in time varying channel capacities and link error rate. Scheduling algorithms play an important role in wireless links to guarantee quality of service (QoS) parameters such as throughput, delay, jitter, fairness and packet loss rate. The scheduler has vital importance in current as well as future cellular communications since it assigns resource block (RB) to different users for transmission. Scheduling algorithm makes a decision based on the information of link state, number of sessions, reserved rates and status of the session queues. The information required by a scheduler implemented in the base station can easily be collected from the downlink transmission. This paper reflects on the importance of schedulers for future wireless communications taking LTE-A networks as a case study. It compares the performance of four well-known scheduling algorithms including round robin (RR), best channel quality indicator (BCQI), proportional fair (PF), and fractional frequency reuse (FFR). The performance of these four algorithms is evaluated in terms of throughput, fairness index, spectral efficiency and overall effectiveness. System level simulations have been performed using a MATLAB based LTE-A Vienna downlink simulator. The results show that the FFR scheduler is the best performer among the four tested algorithms. It also exhibits flexibility and adaptability for radio resource assignment.
    • The influence of different liquid environments on the atomic force microscopy detection of living bEnd.3 cells

      Jin, Yan; Sun, Baishun; Xie, Chenchen; Liu, Yan; Song, Zhengxun; Xu, Hongmei; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire (Royal Society of Chemistry, 2021-05-10)
      Atomic force microscopy (AFM) is one of the most important tools in the field of biomedical science, and it can be used to perform the high-resolution three-dimensional imaging of samples in liquid environments to obtain their physical properties (such as surface potentials and mechanical properties). The influence of the liquid environment on the image quality of the sample and the detection results cannot be ignored. In this work, quantitative imaging (QI) mode AFM imaging and mechanical detection were performed on mouse brain microvascular endothelial (bEnd.3) cells in different liquid environments. The gray-level variance product (SMD2) function was used to evaluate the imaging quality of the cells in liquids with different physical properties, and the variations in cell mechanical properties were quantitatively analyzed. An AFM detection liquid containing less ions and organics compared with the traditional culture medium, which is beneficial for improving the imaging quality, is introduced, and it shows similar mechanical detection results within 3 h. This can greatly reduce the detection costs and could have positive significance in the field of AFM living-cell detection.
    • Response of bEnd.3 cells to growing behavior on the graphene oxide film with 2-D grating structure by two-beam laser interference

      Yan, Jin; Cao, Liang; Wang, Lu; Xie, Chengcheng; Liu, Yan; Song, Zhengxun; Xu, Hongmei; Weng, Zhankun; Wang, Zuobin; Li, Li (Springer Science and Business Media Deutschland GmbH, 2021-02-22)
      Graphene (G) and its derivatives are important nanomaterials with potential medical applications for biosensors and implanting biomaterials. The hydrophobicity and surface microstructures of substrates have great influences on the biological and physical properties of the surface-bound cells. In this work, we used the two-beam laser interference (TBLI) technique to prepare a two-dimensional (2-D) grating structure on the surface of graphene oxide (GO) film. We investigated the effect of GO and the GO film with the 2-D grating structure substrates on the growth behavior of rat brain microvascular endothelial (bEnd.3) cells. The results demonstrated that the cell spreading area and the number of surface-bound cells were closely related to the hydrophobicity of the substrate and the presence of oxygen-containing functional groups (OCGs). Due to the interaction of laser and GO, the GO in the interference area was transformed into reduced graphene oxide (RGO). The grating-structured GO film significantly affected the direction of cell spreading and morphology. It has a good application prospect as a scaffold in tissue engineering, and promising applications in the fields that require highly directional growth of cells, such as nerve injury repair, tendon repair and regeneration.
    • A numerical study of the effects of oxy-fuel combustion under homogeneous charge compression ignition regime

      Mobasheri, Raouf; Aitouche, Abdel; Peng, Zhijun; Li, Xiang; Centre de Recherche en Informatique Signal et Automatique de Lille; Junia; University of Bedfordshire (SAGE Publications Ltd, 2021-02-16)
      The European Union (EU) has recently adopted new directives to reduce the level of pollutant emissions from non-road mobile machinery engines. The main scope of project RIVER for which this study is relating is to develop possible solutions to achieve nitrogen-free combustion and zero-carbon emissions in diesel engines. RIVER aims to apply oxy-fuel combustion with Carbon Capture and Storage (CCS) technology to eliminate NOx emissions and to capture and store carbon emissions. As part of this project, a computational fluid dynamic (CFD) analysis has been performed to investigate the effects of oxy-fuel combustion on combustion characteristics and engine operating conditions in a diesel engine under Homogenous Charge Compression Ignition (HCCI) mode. A reduced chemical n-heptane-n-butanol-PAH mechanism which consists of 76 species and 349 reactions has been applied for oxy-fuel HCCI combustion modeling. Different diluent strategies based on the volume fraction of oxygen and a diluent gas has been considered over a wide range of air-fuel equivalence ratios. Variation in the diluent ratio has been achieved by adding different percentages of carbon dioxide for a range from 77 to 83 vol.% in the intake charge. Results show that indicated thermal efficiency (ITE) has reduced from 32.7% to 20.9% as the CO2 concentration has increased from 77% to 83% at low engine loads while it doesn’t bring any remarkable change at high engine loads. It has also found that this technology has brought CO and PM emissions to a very ultra-low level (near zero) while NOx emissions have been completely eliminated.
    • Study of NSCLC cell migration promoted by NSCLC-derived extracellular vesicle using atomic force microscopy

      Wang, Shuwei; Wang, Jiajia; Ju, Tuoyu; Yang, Fan; Qu, Kaige; Liu, Wei; Wang, Zuobin; Jilin University; Changchun University of Science and Technology; University of Bedfordshire (Royal Society of Chemistry, 2021-02-16)
      Extracellular vesicles (EVs) secreted by cancer cells play a key role in the cancer microenvironment and progression. Previous studies have mainly focused on molecular functions, cellular components and biological processes using chemical and biological methods. However, whether the mechanical properties of cancer cells change due to EVs remains poorly understood. This work studies the effects of mechanical changes in non-small cell lung cancer (NSCLC) cells after treatment with EVs on migration by atomic force microscopy (AFM). Different concentrations of EVs were added into the experimental groups based on co-culture experiments, while the control group was cultured without EVs for 48 h. Cellular migration was evaluated by wound healing experiments. The cellular morphology, cell stiffness and surface adhesion were investigated by AFM. Cytoskeleton changes were detected by fluorescence staining assay. By comparison to the control group, the cell migration was enhanced. After treatment with EVs, the cell length and height show an upward trend, and the adhesion force and Young's modulus show a downward trend, and filopodia were also detected in the cells. Overall, the EVs promoted the migration of NSCLC cells by regulating cells' physical properties and skeletal rearrangement.
    • Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

      Jakaite, Livija; Schetinin, Vitaly; Hladůvka, Jiří; Minaev, Sergey; Ambia, Aziz; Krzanowski, Wojtek; ; University of Bedfordshire; TU Wien; Stavropol State Medical University; et al. (Nature, 2021-01-27)
      Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.
    • Sit-to-stand intention recognition

      Wang, Zuobin; Li, Dayou; Lu, Hang; Qiu, Renxi; Maple, Carsten; University of Bedfordshire; Changchun University of Science and Technology; Warwick University (Springer Science and Business Media Deutschland GmbH, 2021-01-23)
      Sit-to-stand (STS) difficulties are common among elderly because of the decline of their cognitive capabilities and motor functions. The way to help is to encourage them to practice their own functions and to assist only at the point where they need during STS processes. The provision of such support requires the elderly’s intention of standing up to be recognised and the amount of support as well as the moment when the support would be needed to be predicted. The research presented in this paper focuses on intention recognition as it is difficult due to uncertainties existing in STS processes and differences in individual’s biomechanical features. This paper presents fuzzy logic based self-adaptive approach to the recognition of standing up intention from sensor signals that contain the uncertainties.
    • Cross hashing: anonymizing encounters in decentralised contact tracing protocols

      Ali, Junade; Dyo, Vladimir; University of Bedfordshire (2021-01-16)
      During the COVID-19 (SARS-CoV-2) epidemic, Contact Tracing emerged as an essential tool for managing the epidemic. App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google (influenced by an open-source protocol known as DP3T). This protocol contains two well-documented de-anonymisation attacks. Firstly that when someone is marked as having tested positive and their keys are made public, they can be tracked over a large geographic area for 24 hours at a time. Secondly, whilst the app requires a minimum exposure duration to register a contact, there is no cryptographic guarantee for this property. This means an adversary can scan Bluetooth networks and retrospectively find who is infected. We propose a novel ”cross hashing” approach to cryptographically guarantee minimum exposure durations. We further mitigate the 24-hour data exposure of infected individuals and reduce computational time for identifying if a user has been exposed using k-Anonymous buckets of hashes and Private Set Intersection. We empirically demonstrate that this modified protocol can offer like-for-like efficacy to the existing protocol.
    • Editorial: Recent advances in 2020 2nd International Symposium on Big Data and Artificial Intelligence

      Crabbe, M. James C.; Li, Rita Yi Man; Dong, Rebecca Kechen; Manta, Otilia; Comite, Ubaldo; Oxford University; Hong Kong Shue Yan University; University of South Australia; Romanian-American University; University Giustino Fortunato (Association for Computing Machinery., 2021-01-16)
      The 2020 2nd International Symposium on Big Data and Artificial Intelligence was held in Johannesburg, South Africa, from October 15 - 16, 2020. It was organized by IETI, IDSAI, the University of Johannesburg (South Africa) and JRFM, with joint support from the Real Estate and Economics Research Lab of Hong Kong Shue Yan University, the Sustainable Real Estate Research Center of Hong Kong Shue Yan University, Shandong University of Finance and Economics (Mainland China), Guilin University of Technology (Mainland China), IAOE (Austria), the Department of Sport and Physical Education of Hong Kong Baptist University, Rattanakosin International College of Creative Entrepreneurship of Rajamangala University of Technology Rattanakosin (Thailand), Algebra University College (Croatia), and the Center for Financial and Monetary Research of Romanian Academy (Romania), University Giustino Fortunato (Italy). ISBDAI is there to discuss the challenges and possible solutions to these important issues. The conference focused on Artificial Intelligence, Computer Science, Cloud Computing, Big Data, the Internet of Things and the Mobile Web. The participants and speakers were from many countries and universities, including Mainland China, Hong Kong, Thailand, Romania, Italy, Singapore, Austria, Croatia, Australia, UK, Congo King, Portugal and Cyprus. The conference received a record 505 submissions, with 115 papers accepted for presentation. Positive recommendations of at least two reviewers were considered by the conference committees for acceptance of manuscripts. The Editors express a special gratitude to all the Committee Members and ACM-ICPS, who worked so speedily, efficiently, and professionally in support of the conference. Finally, on behalf of the Organizing Committee, we would like to thank all the authors, speakers, and participants for contributing to the success of ISBDAI 2020.
    • 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
    • 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.
    • Selective anticancer effect of Phellinus linteus on epidermoid cell lines studied by atomic force microscopy: anticancer activity on A431 cancer cells and low toxicity on HaCat normal cells

      Gao, Mingyan; Huang, Yuxi; Hu, Cuihua; Hu, Jing; Wang, Ying; Chen, Yujuan; Song, Guicai; Song, Zhengxun; Wang, Zuobin; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing; et al. (Institute of Electrical and Electronics Engineers Inc., 2020-12-02)
      The research on the morphological and mechanical properties of single cells has provided a crucial way of understanding the cellular physiology and metabolism. In this study, the selective anticancer effects of Phellinus linteus on A431 and HaCat cells and their morphological and mechanical properties were systematically investigated by atomic force microscopy (AFM). Notably, the cell morphology on the micronano scale was observed under both the physiological environment and immobilization conditions. The significant morphological changes of A431 cells from the flat to spherical shape, the increase of cell height, and the decrease of the particles on the cell membrane were confirmed to be related to the cell apoptosis under the treatment of the Phellinus linteus water extract (PLWE). Moreover, the small morphology variations of HaCat cells showed that the PLWE presented a high anticancer effect on A431 cells but low toxicity on HaCat cells, which indicated a potential cell selectivity between cancer and normal cells. This work proved that Phellinus linteus could be used as a potential candidate for selective anticancer 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.
    • Unlink the link between COVID-19 and 5G Networks: an NLP and SNA based approach

      Bahja, Mohammed; Safdar, Ghazanfar Ali; University of Birmingham; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2020-11-18)
      Social media facilitates rapid dissemination of information for both factual and fictional information. The spread of non-scientific information through social media platforms such as Twitter has potential to cause damaging consequences. Situations such as the COVID-19 pandemic provides a favourable environment for misinformation to thrive. The upcoming 5G technology is one of the recent victims of misinformation and fake news and has been plagued with misinformation about the effects of its radiation. During the COVID-19 pandemic, conspiracy theories linking the cause of the pandemic to 5G technology have resonated with a section of people leading to outcomes such as destructive attacks on 5G towers. The analysis of the social network data can help to understand the nature of the information being spread and identify the commonly occurring themes in the information. The natural language processing (NLP) and the statistical analysis of the social network data can empower policymakers to understand the misinformation being spread and develop targeted strategies to counter the misinformation. In this paper, NLP based analysis of tweets linking COVID-19 to 5G is presented. NLP models including Latent Dirichlet allocation (LDA), sentiment analysis (SA) and social network analysis (SNA) were applied for the analysis of the tweets and identification of topics. An understanding of the topic frequencies, the inter-relationships between topics and geographical occurrence of the tweets allows identifying agencies and patterns in the spread of misinformation and equips policymakers with knowledge to devise counter-strategies.
    • Durotaxis behavior of bEnd.3 cells on soft substrate with patterned platinum nanoparticle array

      Wu, Xiaomin; Li, Li; Lei, Zecheng; Yang, Fan; Liu, Ri; Wang, Lu; Zhu, Xinyao; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire; et al. (Springer Science and Business Media, 2020-11-17)
      The directional arrangement of cells has crucial effect in tissue engineering fields such as wound healing and scar repair. Studies have shown that continuous nanostructures have directional regulatory effect on cells, but whether discontinuous nanostructures have the same regulatory effect on cells is also worthy of further study. Here, a series of discontinuous platinum nanoparticles (PtNPs) patterned on the surface of PDMS (PtNPs-PDMS&Glass) and glass (PtNPs-Glass) substrates were developed to investigate the effect on bEnd.3 cell durotaxis. The laser interference lithography and nanotransfer printing method were employed to fabricate the substrates. It was found that about 80% cells orderly arranged on the PtNPs-PDMS&Glass substrate, but only 20% cells orderly arrangement on the PtNPs-Glass substrate, and the number of cells on the PtNPs-PDMS&Glass substrate was five times more than that on the PDMS coated glass substrate (PDMS&Glass). The results suggested that patterning PtNPs on the PDMS substrate not only provided the topographical guidance for cells just like continuous nanostructures, but also promoted cell adhesion and growth. In addition, an improved whole cell coupling model was used to investigate and explain the cell durotaxis from the perspective of mechanism. These findings show the possibility of discontinuous nanostructures in regulating cell arrangement, and offer a useful method for the design of biological functional substrate, as well as help to understand the mechanism of cell durotaxis.
    • Counting calories without wearables: device-free human energy expenditure estimation

      Rahaman, Habibur; Dyo, Vladimir; University of Bedfordshire (2020-11-16)
      Maintaining certain physical activity levels is important to prevent or delay the onset of many medical conditions such as diabetes, or mental health disorders. Traditional calorie estimation methods require wearing devices, such as pedometers, smart watches or smart bracelets, which continuously monitor user activity and estimate the energy expenditure. However, wearable devices may not be suitable for some patients due to the need for periodic maintenance, frequent recharging and having to wear it all the time. In this paper we investigate a feasibility of a device- free human energy expenditure estimation based on RF-sensing, which recognises coarse-grained user activity, such as walking, standing, sitting or resting by monitoring the impact of a person’s activity on ambient wireless links. The calorie estimation is then based on Metabolic Equivalent concept that expresses the energy cost of an activity as a multiple of a person’s basal metabolic rate using Harrison-Benedict model. The experimental evaluation using low cost IEEE 802.15.4 transceivers demonstrated that the approach estimated energy expenditure within an indoor environment within 7.4% to 41.2% range when compared to a FitBit Blaze bracelet.
    • 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.