• Behavior-neutral smart charging of plugin electric vehicles: reinforcement learning approach

      Dyo, Vladimir; University of Bedfordshire (IEEE, 2022-06-16)
      High-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV’s past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user’s charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point.
    • Artificial intelligence robot safety: a conceptual framework and research agenda based on new institutional economics and social media

      Li, Rita Yi Man; Crabbe, M. James C. (Springer, 2022-05-15)
      According to "Huang's law", Artificial intelligence (AI)-related hardware increases in power 4 to 10 times per year. AI can benefit various stages of real estate development, from planning and construction to occupation and demolition. However, Hong Kong's legal system is currently behind when it comes to technological abilities, while the field of AI safety in built environments is still in its infancy. Negligent design and production processes, irresponsible data management, questionable deployment, algorithm training, sensor design and/or manufacture, unforeseen consequences from multiple data inputs, and erroneous AI operation based on sensor or remote data can all lead to accidents. Yet, determining how legal rules should apply to liability for losses caused by AI systems takes time. Traditional product liability laws can apply for some systems, meaning that the manufacturer will bear responsibility for a malfunctioning part. That said, more complex cases will undoubtedly have to come before the courts to determine whether something unsafe should be the manufacturer's fault or the individual's fault, as well as who should receive the subsequent financial and/or non-financial compensation, etc. Since AI adoption has an inevitable relationship with safety concerns, this project intends to shed light on responsible AI development and usage, with a specific focus on AI safety laws, policies, and people's perceptions. We will conduct a systematic literature review via the PRISMA approach to study the academic perspectives of AI safety policies and laws and data-mining publicly available content on social media platforms such as Twitter, YouTube, and Reddit to study societal concerns about AI safety in built environments. We will then research court cases and laws related to AI safety in 61 jurisdictions, in addition to policies that have been implemented globally. Two case studies on AI suppliers that sell AI hardware and software to users of built environment will also be included. Another two case studies will be conducted on built environment companies (a contractor and Hong Kong International Airport) that use AI safety tools. The results obtained from social media, court cases, legislation, and policies will be discussed with local and international experts via a workshop, then released to the public to provide the international community and Hong Kong with unique policy and legal orientations.
    • Predicting cumulative effect of lifestyle risk factors for complex disease

      Effiok, Emmanuel; Liu, Enjie; Hitchcock, Jonathan James; University of Bedfordshire (IET/Wiley, 2022-03-17)
      In medical domain, risk factors are often used to model disease predictions. In order to make the most use of the predictive models, linking the model with real patient data generates personalized disease progression and predictions. However, the risk factors are fragmented all over medical literature, certain risks can be accumulated for a disease and the aggregated probability may increase or decrease the occurrence of a disease. In this paper, we propose a risk predictive framework which forms a base for a complete risk prediction model that can be used for various health applications.
    • Crowdsourced linked data question answering with AQUACOLD

      Collis, Nick; Frommholz, Ingo; University of Bedfordshire; University of Wolverhampton (IEEE, 2021-12-29)
      There is a need for Question Answering (QA) to return accurate answers to complex natural language questions over Linked Data, improving the accessibility of Linked Data (LD) search by abstracting the complexity of SPARQL whilst retaining its expressiveness. This work presents AQUACOLD, a LD QA system which harnesses the power of crowdsourcing to meet this need.
    • Real-time user clickstream behavior analysis based on Apache Storm streaming

      Pal, Gautam; Li, Gangmin; Atkinson, Katie (Springer, 2021-12-22)
      This paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior. User-generated clickstream is first stored in a client site browser. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains. We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.
    • Letter to the editor: gratitude and good outcomes: rediscovering positivity and perspective in an uncertain time

      Minaev, Sergey; Schetinin, Vitaly; Kirgizov, Igor; Grigorova, Alina Nikolaevna; Akselrov, Michael; Gerasimenko, Igor (Springer, 2021-11-08)
    • Smart data analysis and data governance

      Feng, Yunzhong; Feng, Xiaohua; Hebei Normal University; University of Bedfordshire (Association for Computing Machinery, 2021-10-31)
      Data governance is still an outstanding issue currently, especially in big data cases. There is a planned big event in 2022 at Chongli. A Smart Chongli has up and running. In order to run the event properly, information security protection should be taken into account. This research is to consider generalize this issue to the future, including the Smart Chongli winter event, but not only limited there. Because of many Smart Cities services are running by online devices, to protect their data, cyber security is in demand, for the Smart Cities' quantity. As a case study, Smart Chongli is discussed, in terms of participants' data. Analysis of these data needs to pay attention to the "Measures for the Administration of Data Security", "GDPR"and so on. Cyber security should be taken care of, from the start to its final ending. Nevertheless, for Smart Chongli, overall development and management need to consider data security impact. A solution is suggested to fill this gap, according to MKSmart trial project Phase 2 experience. Which could also be applied to other smart cities similar project for the time being. c 2021
    • Bi-layered disulfiram-loaded fiber membranes with antibacterial properties for wound dressing

      Xie, Chenchen; Yan, Jin; Cao, Siyuan; Liu, Ri; Sun, Baishun; Xie, Ying; Qu, Kaige; Zhang, Wenxiao; Weng, Zhankun; Wang, Zuobin; et al. (Springer, 2021-10-29)
      In this study, the bi-layered disulfiram-loaded fiber membranes with the antibacterial activity and different surface wettabilities are prepared using electrospinning technology. In the application of wound dressing, the hydrophilic surface of fiber membranes is beneficial for cell adhesion and drug release to heal the wound. Meanwhile, the outside hydrophobic surface is able to block water penetration to reduce the probability of wound infection. The obtained bi-layered drug-loaded fiber membranes are composed of polyvinylidene fluoride (PVDF) bottom surface and disulfiram (DSF)/polylactic acid (PLA) top surface. To modify the top surface wettability, the oxygen plasma modification of bi-layered membranes was carried out. The morphology, wettability, and chemical compositions of bi-layered drug-loaded fiber membranes were analyzed using the scanning electronic microscope (SEM), drop shape analysis instrument, X-ray diffractometer (XRD), and X-ray photoelectron spectrometer (XPS). The bi-layered disulfiram-loaded membranes showed the potent antibacterial activity in vitro against both Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive). It was found that the bi-layered membranes had good biocompatibility with L929 cells. Thus, the obtained bi-layered disulfiram-loaded fiber membranes are suitable for wound dressing application.
    • Economic development and construction safety research: a bibliometrics approach

      Luo, Fansong; Li, Rita Yi Man; Crabbe, M. James C.; Pu, Ruihui; Hong Kong Shue Yan University; Oxford University; University of Bedfordshire; Shanxi University; Srinakharinwirot University (Elsevier, 2021-10-14)
      The construction industry contributes significantly to economic development worldwide, yet it is one of the most hazardous industries where numerous accidents and fatalities happen every year. Little research to date has shed light on the impact of economic development on construction safety research. In this paper, we conduct an analysis of construction safety articles published in the 21st century via a bibliometrics approach. We have analysed: (1) construction safety in developed and developing countries; (2) the major organisations that have conducted construction safety research; (3) authors and territories of the research and (4) topics in construction safety and future research directions. The largest number of published construction safety documents were published by scholars from the US and China; the total number of published articles by these two countries was 1,125, at 56% of the 2000 articles that were published. Both countries showed high levels of research collaboration. While our results suggest that economic development may drive academic construction safety research, there has been an increase in construction safety research conducted by developing countries in recent years, probably due to an improvement in their economic development. While authors’ keywords evidenced the popularity of research on safety management and climate, the network analysis on all keywords, i.e. keywords given by Web of Science and authors, suggest that construction safety research focused on three areas: construction safety management, the relationship between people and construction safety, and the protection and health of workers’ impact on construction safety. We found that there is a new interdisciplinary research trend where construction safety combines with digital technologies, with the largest number involving deep learning. Other trends focus on machine learning, Building Information Modelling, machine learning and visualisation.
    • StaResGRU-CNN with CMedLMs: a stacked residual GRU-CNN with pre-trained biomedical language models for predictive intelligence

      Ni, Pin; Li, Gangmin; Hung, Patrick C.K.; Chang, Victor; University College London; University of Bedfordshire; Ontario Tech University; Teesside University (Elsevier Ltd, 2021-10-13)
      As a task requiring strong professional experience as supports, predictive biomedical intelligence cannot be separated from the support of a large amount of external domain knowledge. By using transfer learning to obtain sufficient prior experience from massive biomedical text data, it is essential to promote the performance of specific downstream predictive and decision-making task models. This is an efficient and convenient method, but it has not been fully developed for Chinese Natural Language Processing (NLP) in the biomedical field. This study proposes a Stacked Residual Gated Recurrent Unit-Convolutional Neural Networks (StaResGRU-CNN) combined with the pre-trained language models (PLMs) for biomedical text-based predictive tasks. Exploring related paradigms in biomedical NLP based on transfer learning of external expert knowledge and comparing some Chinese and English language models. We have identified some key issues that have not been developed or those present difficulties of application in the field of Chinese biomedicine. Therefore, we also propose a series of Chinese bioMedical Language Models (CMedLMs) with detailed evaluations of downstream tasks. By using transfer learning, language models are introduced with prior knowledge to improve the performance of downstream tasks and solve specific predictive NLP tasks related to the Chinese biomedical field to serve the predictive medical system better. Additionally, a free-form text Electronic Medical Record (EMR)-based Disease Diagnosis Prediction task is proposed, which is used in the evaluation of the analyzed language models together with Clinical Named Entity Recognition, Biomedical Text Classification tasks. Our experiments prove that the introduction of biomedical knowledge in the analyzed models significantly improves their performance in the predictive biomedical NLP tasks with different granularity. And our proposed model also achieved competitive performance in these predictive intelligence tasks.
    • Tracking human motion direction with commodity wireless networks

      Rahaman, Habibur; Dyo, Vladimir; University of Bedfordshire (IEEE, 2021-09-07)
      Detecting when a person leaves a room, or a house is essential to create a safe living environment for people suffering from dementia or other mental disorders. The approaches based on wearable devices, e.g. GPS bracelets may detect such events require periodic maintenance to recharge or replace batteries, and therefore may not be suitable for certain types of users. On the other hand, camera-based systems require illumination and raise potential privacy concerns. In this paper, we propose a device-free walking direction detection approach based on RF-sensing, which does not require a person to wear any equipment. The proposed approach monitors the signal strength fluctuations caused by the human body on ambient wireless links and analyses its spatial patterns using a convolutional neural network to identify the walking direction. The approach has been evaluated experimentally to achieve up to 98% classification accuracy depending on the environment.
    • A non-enzymatic glucose sensor via uniform copper nanosphere fabricated by two-step method

      Yu, Miaomiao; Weng, Zhankun; Hu, Jing; Zhu, Xiaona; Song, Hangze; Wang, Shenzhi; Cao, Siyuan; Song, Zhengxun; Xu, Hongmei; Li, Jinhua; et al. (Elsevier Ltd, 2021-08-10)
      Herein, we explored an effective way to obtain uniform copper nanoparticles by irradiating Cu2O microparticles in ethanol with a 1064 nm laser. The morphology, structure and chemical composition of as-prepared copper nanoparticles were characterized by scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction and X-ray photoelectron spectroscopy. It is interesting that the diameter of obtained spherical copper nanoparticles can be finely tuned by changing the irradiation time. Moreover, we also found that the particle size of copper nanoparticles can be reduced to ~63 nm when the irradiation time is 30 min. Inspired by the fast-developing non-enzymatic glucose sensors, the electrochemical activity of the copper nanoparticles toward glucose in alkaline media was further investigated. Notably, the electrochemical results reveal that the prepared copper nanoparticles possess a good prospect in non-enzymatic glucose sensor.
    • Privacy-preserving identity broadcast for contact tracing applications

      Dyo, Vladimir; Ali, Jahangir; University of Bedfordshire (2021-08-10)
      Wireless Contact tracing has emerged as an important tool for managing the COVID19 pandemic and relies on continuous broadcasting of a person’s presence using Bluetooth Low Energy beacons. The limitation of current contact tracing systems in that a reception of a single beacon is sufficient to reveal the user identity, potentially exposing users to malicious trackers installed along the roads, passageways, and other infrastructure. In this paper, we propose a method based on Shamir secret sharing algorithm, which lets mobile nodes reveal their identity only after a certain predefined contact duration, remaining invisible to trackers with short or fleeting encounters. Through data-driven evaluation, using a dataset containing 18 million BLE sightings, we show that the method drastically reduces the privacy exposure of users. Finally, we implemented the approach on Android phones to demonstrate its feasibility and measure performance for various network densities.
    • Effect of trypsin concentration on living SMCC-7721 cells studied by atomic force microscopy

      Yan, Jin; Xie, Chenchen; Zhu, Jiajing; Song, Zhengxun; Wang, Zuobin; Li, Li (Wiley, 2021-08-05)
      Trypsin is playing an important role in the processes of cancer proliferation, invasion, and metastasis which require the precise information of morphology and mechanical properties on the nanoscale for the related research. In this work, living human hepatoma (SMCC-7721) cells were treated with different concentrations of trypsin solution. The morphology and mechanical properties of the cells were measured via atomic force microscope (AFM). Statistical analyses of measurement data indicated that with the increase of trypsin concentration, the average cell height and the surface roughness were both increased, but the cell viability, the cell surface adhesion and the elasticity modulus were decreased significantly. The force required to puncture the cells was also gradually reduced. It indicates that trypsin not only hydrolyzes the proteins between the cell and the substrate but also the membrane proteins. The results offer valuable clues for the cancerous process study, pathological analysis, and trypsin inhibitor drug development. And this work provides an effective way for overcoming the cell membrane in drug injection for cell-targeted therapy. This article is protected by copyright. All rights reserved.
    • Enable a facile size re-distribution of MBE-grown Ga-droplets via in situ pulsed laser shooting

      Geng, Biao; Shi, Zhenwu; Chen, Chen; Zhang, Wei; Yang, Linyun; Deng, Changwei; Yang, Xinning; Miao, Lili; Peng, Changsi; Soochow University; et al. (Springer, 2021-08-04)
      A MBE-prepared Gallium (Ga)-droplet surface on GaAs (001) substrate is in situ irradiated by a single shot of UV pulsed laser. It demonstrates that laser shooting can facilely re-adjust the size of Ga-droplet and a special Ga-droplet of extremely broad size-distribution with width from 16 to 230 nm and height from 1 to 42 nm are successfully obtained. Due to the energetic inhomogeneity across the laser spot, the modification of droplet as a function of irradiation intensity (IRIT) can be straightly investigated on one sample and the correlated mechanisms are clarified. Systematically, the laser resizing can be perceived as: for low irradiation level, laser heating only expands droplets to make mergences among them, so in this stage, the droplet size distribution is solely shifted to the large side; for high irradiation level, laser irradiation not only causes thermal expansion but also thermal evaporation of Ga atom which makes the size-shift move to both sides. All of these size-shifts on Ga-droplets can be strongly controlled by applying different laser IRIT that enables a more designable droplet epitaxy in the future.
    • Deep neural-network prediction for study of informational efficiency

      Sulaiman, Rejwan Bin; Schetinin, Vitaly; University of Bedfordshire (Springer, 2021-08-03)
      In this paper, we attempt to verify a hypothesis of informational efficiency of financial markets, known as “random walk” introduced by Fama. Such hypotheses could be considered in relation to financial crises. In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive modelling based on Machine Learning (ML). We compare conventional ML techniques and the proposed “deep” neural-network structures grown by Group Method of Data Handling (GMDH). In our experiments a GMDH-type neural-network model has outperformed the conventional ML techniques, which is important for achieving the reliable results of predictive modelling and testing the hypothesis. GMDH-type modelling does not require the knowledge of network structure, as a desired network of near-optimal connectivity is learnt from the data. The experimental results compared in terms of prediction error show that the GMDH-type prediction model has a significantly smaller error than the conventional autoregressive and neural-network models.
    • Content-based technical solution for cyberstalking detection

      Asante, Audrey; Feng, Xiaohua; Catholic University College of Ghana; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2021-07-27)
      The continued usage of technology has led to the rise of cyberstalking. Cyberstalking is seen as traditional method of stalking that has been altered by technology. This crime has now been modernized using technological tools and techniques. The continued increase in cyberstalking in the world today has drawn attention to the need to address this problem. Though studies on this crime have been conducted in the fields of criminology, legal, public health, sociology, and psychology, it still remains a challenge to detect, prevent, and investigate this crime. Traditional stalking methods have been used to combat it, despite the fact that this crime is committed online. Unfortunately, these methods have provided few solutions for detecting and preventing it. The prevalence of this crime, combined with technological advancement, has necessitated the development of technical strategies to mitigate it, protect victims, and assist law enforcement agencies. In this study, a content-based detection framework for cyberstalking is proposed. The framework consists of message identification, filtering, detection (content detection and profiling offender) and evidence modules. It is designed as a forensic readiness framework that can automatically detect cyberstalking, gather evidence and profile potential offenders. The framework employs machine learning, data mining techniques, digital forensics, and profiling to analyze text, image, and media contents, collect evidence, and profile offenders. This framework would not only detect cyberstalking automatically, but it would also be useful as an investigative tool for law enforcement.
    • Semantic Hilbert space for interactive image retrieval

      Jaiswal, Amit Kumar; Liu, Haiming; Frommholz, Ingo; University of Bedfordshire (Association for Computing Machinery, Inc, 2021-07-11)
      The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different system states and their dynamic changes thereof based on observations (such as queries, relevance judgements). We will be able to learn the input multimodal representation and relationships between textual-image queries for retrieving images. Our experiments are conducted on the MIT States and Fashion200k datasets that demonstrate the effectiveness of finding particular images autocratically when the user inputs are semantically expressive.
    • Time series chlorophyll-A concentration data analysis: a novel forecasting model for aquaculture industry

      Eze, Elias Chinedum; Kirby, Sam; Attridge, John; Ajmal, Tahmina; University of Bedfordshire; Chelsea Technology Group (2021-06-29)
      Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.
    • Developing a novel water quality prediction model for a South African aquaculture farm

      Eze, Elias Chinedum; Halse, Sarah; Ajmal, Tahmina; University of Bedfordshire; Abagold Limited (MDPI, 2021-06-28)
      Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.