This paper presents a new sensorless control method for four-wheel steering vehicles. Compared to the existing sensor-based control, this approach improved dynamic stability, manoeuvrability and robustness in case of malfunction of the front steering angle sensor. It also provided a software redundancy and backup solution, as well as improved fault tolerance. The strategy of the sensorless control is based on the sliding mode method to estimate the replacement of the front steering input from the errors between the vehicle's measured and desired values of the vehicle's sideslip angle and yaw rate. The simulation results demonstrate that the observer effectively estimated the front wheel steering angle at both low- and high-speed scenarios in the cornering and lane change manoeuvres. Furthermore, the sensorless control approach can achieve equivalent control performances to the sensor-based controller including a small and stable yaw rate response and zero sideslip angle. The results of the study offer a potential solution for improving manoeuvrability, stability and sensor fault tolerance of four-wheel steering vehicles.

This paper presents a machine learning model for load-level estimation for shear-critical reinforced concrete (RC) beams and slabs using multifractal features of their characteristic crack patterns to automate and provide well-informed decisions for RC damage assessment. Multifractal analysis was conducted on a database of 508 images, of which critical features were extracted from the singularity and generalized dimension spectra. These features are used as predictors for the load-level estimation model. The extreme gradient boosting algorithm yielded the best performance among the four machine learning models considered. The mean of the predicted-to-true ratio for the developed model was 1.04 with a coefficient of variation of 0.27. Upon applying Shapley additive ex-planations, the fractal dimension, information dimension, correlation dimension and the area under the left branch of the singularity spectrum were the critical features influencing load-level estimation. The proposed model can be useful to RC building inspectors.

Aims: This study explores the possibility of using routinely taken blood tests in the diagnosis and triage of patients with suspected musculoskeletal malignancy.Methods: A retrospective study was performed on results of patients who had presented for assessment to a regional musculoskeletal tumour unit. Blood results of patients with a histologically confirmed diagnosis between 2010 and 2020 were retrieved. 33 distinct blood tests were available for model forming. Results were standardised by calculating z-scores. Data were split into a training set (70%) and a test set (30%). The training set was balanced by resampling underrepresented classes. The random forest algorithm performed best and was selected for model forming. Receiver operating characteristic curves were used to find the optimum threshold. Models were calibrated and performance metrics evaluated with confusion tables.Results: 2371 patients formed the study population. 1080 had a malignant diagnosis in one of three categories: sarcoma, metastasis, or haematological malignancy. 1291 had a benign condition. Metastasis could be predicted with an accuracy of 79% (AUC 87%, sensitivity 79%, specificity 80% NPV 91%). Haematological malignancy accuracy 79% (AUC 81%, sensitivity 77%, specificity 79%, NPV 97%). Sarcoma accuracy 64% (AUC 73%, sensitivity 76%, specificity 61%, NPV 88%) and all malignancy accuracy 74% (AUC 80%, sensitivity 72%, specificity 75%, NPV 76%).Conclusion: Routinely performed blood tests can be useful in triage of musculoskeletal tumours and can be used to predict presence of musculoskeletal malignancy.

This paper presents a pioneering experimental proof-of-concept study to validate a novel concept of prestress technology that used only pure bio-based composite materials while achieved consistent prestressed stress distribution within the structure member, and provided in-situ flexibility, improved structural performance, and maximised the rate of utilisation of each material. Industrial level of facilities were used during this development. The prestress is achieved by pressurised/forced lamination of multiple components with different materials and geometrical properties. The prestressing process is activated during the pressure release stage during which the components are interacting with each other, creating different stress statuses that would favour the weaker and adverse the stronger components to maximise the strength exploitation of different materials. Using laminated bamboo and timber as an example pair, twenty-two glulam, non-prestressed and prestressed laminated bambootimber composite beams were manufactured, tested, and analysed to provide an in-depth understanding of the structural behaviours of these novel structural members. Failure modes, yielding, ultimate and serviceability limit loads, and corresponding deflections, as well as the histories of strain development at key positions of the specimens were examined. The experimental study confirmed the feasibility, effectiveness and industrial scal-ability of the proposed technology. The novel concept provides a new approach for developing the prestress technology for bio-based materials, and this experimental study laid the foundation for its future analytical development and numerical studies.

This paper presents an investigation into the effect of faults on the scalability resilience of cloud-based software services. The study introduces an experimental framework using the Application-Level Fault Injection (ALFI) to investigate how the faults at the application level affect the scalability resilience and behaviour of cloud-based software services. Previous studies on scalability analysis of cloud-based software services provide a baseline of the scalability behaviour of such services, allowing to conduct in-depth scalability investigation of these services. Experimental analysis on the EC2 cloud using a real-world cloud-based software service is used to demonstrate the framework, considering delay latency of software faults with two varied settings and two demand scenarios. The experimental approach is explained in detail. Here we simulate delay latency injection with two different times, 800 and 1600 ms, and compare the results with the baseline data. The results show that the proposed approach allows a fair assessment of the fault scenario’s impact on the cloud software service’s scalability resilience. We explain the use of the methodology to determine the impact of injected faults on the scalability behaviour and resilience of cloud-based software services.

Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of structural complexity on the performance and statistical reliability of SVMs applied for text mining. We set a theoretical framework for our analysis. We found experimentally that the statistical reliability and performance reduce exponentially with the increase of the structural complexity of the SVMs. This is an important result for the understanding of how the prediction error of SVM predictive data models behaves.

In order to understand the dynamics of emergence and spreading of socio-technical innovations and population moves it is important to determine the place of origin of these populations. Here we focus on the role of geographical factors, such as land fertility and mountains in the context of human population evolution and distribution dynamics. We use a constrained diffusion-based computational model, computer simulations and the analysis of geographical and land-quality data. Our analysis shows that successful human populations, i.e. those which become dominant in their socio - geographical environment, originate from lands of many valleys with relatively low land fertility, which are close to areas of high land fertility. Many of the homelands predicted by our analysis match the assumed homelands of known successful populations (e.g. Bantus, Turkic, Maya). We also predict other likely homelands as well, where further archaeological, linguistic or genetic exploration may confirm the place of origin for populations with no currently identified urheimat. Our work is significant because it advances the understanding of human population dynamics by guiding the identification of the origin locations of successful populations. (C) 2021 Elsevier Ltd. All rights reserved.

A growing sense of unfairness permeates our quasi-digital society. Despite drivers supporting and motivating ethical practice in the digital technology ecosystem, there are compounding barriers to fairness that, at every level, impact technology innovation, delivery and access. Amongst these are barriers and omissions at the earliest stages of technology intentionality and design; systemic inadequacies in sensing systems that deteriorate performance for individuals based on ethnicity, age and physicality; system design, co-requisite and interface decisions that limit access; biases and inequities in datasets and algorithms; and limiting factors in system function and security. Additionally, there are concerns about unethical and illegal practices amongst digital technology providers: for example, in planned obsolescence and anti-competitive behaviors, failings in data practices and security, and in responses to problematic use and behaviors. It is critical that these failings are identified and addressed to better evolve a fairer future digital technology ecosystem. This paper contributes a perspective on technological stewardship and innovation; it identifies the compounding nature of barriers to fairness in the current digital technology ecosystem, and contrasts these with the non-compounding fairness drivers that, in general, establish minimum requirements.

The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies deve...

Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloud-based software services’ performance requirements. In this work, we use a technical measurement of the scalability of cloud-based software services. Our technical scalability metrics are inspired by metrics of elasticity. We used two cloud-based systems to demonstrate the usefulness of our metrics and compare their scalability performance in two cloud platforms: Amazon EC2 and Microsoft Azure. Our experimental analysis considers three sets of comparisons: first we compare the same cloud-based software service hosted on two different public cloud platforms; second we compare two different cloud-based software services hosted on the same cloud platform; finally, we compare between the same cloud-based software service hosted on the same cloud platform with two different auto-scaling policies. We note that our technical scalability metrics can be integrated into a previously proposed utility oriented metric of scalability. We discuss the implications of our work.

Fitness improving innovations occur in populations of organisms as genetic changes (mutations) that allow better fit with the environmental niche of the organisms. Similarly, fitness improving innovations may occur in the context of human communities as well in terms of socio-economic innovations (e.g. new ways of organizing the military, new products or services) that lead to more efficient use of available resources. Here we explore the link between such innovations and the harshness of the environment, where the populations live. Environmental harshness characterizes the availability of population growth supporting resources in the environment. Our analysis shows that if the harshness of the environment varies smoothly with the distance, the expected extent of fitness improving innovations and of the resource utilization efficiency of populations depends in a combined linear and harmonic manner on the harshness of the environment at the location of origin of the populations. We explore the implications of this result for particular cases of both biological and social environments (e.g. gene drives, business innovation).

Measuring and testing the performance of cloud-based software services is critically important in the context of rapid growth of cloud computing. Scalability, elasticity and efficiency are interrelated aspects of performance of cloud-based software services. Here we present a work that is focused on measuring the scalability of cloud-based software services in technical terms. We introduce technical scalability metrics inspired by earlier technical metrics of elasticity.

Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind?s Alpha Go Zero [1]) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace [2]. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?

Background:: Statistical validity and model complexity are both important concepts to enhanced understanding and correctness assessment of computational models. However, information about these are often missing from publications applying machine learning. Aim: The aim of this study is to show the importance of providing details that can indicate statistical validity and complexity of models in publications. This is explored in the context of citation screening automation using machine learning techniques. Method: We built 15 Support Vector Machine (SVM) models, each developed using word2vec (average word) features --- and data for 15 review topics from the Drug Evaluation Review Program (DERP) of the Agency for Healthcare Research and Quality (AHRQ). Results: The word2vec features were found to be sufficiently linearly separable by the SVM and consequently we used the linear kernels. In 11 of the 15 models, the negative (majority) class used over 80% of its training data as support vectors (SVs) and approximately 45% of the positive training data. Conclusions: In this context, exploring the SVs revealed that the models are overly complex against ideal expectations of not more than 2%-5% (and preferably much less) of the training vectors.

The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

The combination of intra and extra-cellular recording of small neuronal circuits such as stomatogastric nervous systems of the crab (Cancer borealis) is well documented and routinely practised. Voltage sensitive dye imaging (VSDi) is a promising technology for the simultaneous monitoring of neuronal activities in such a system. However, integrating data obtained from optical VSDi and electrophysiological recording of the lateral ventricular nerve (lvn) is a complex and exacting task. Our early work demonstrated some of the concepts and principle involved. In this paper, we examine and report on the results obtained from the application of signal processing techniques to three datasets for which we had VSDi and lvn data. Whilst significant challenges remain, we show that such an approach offers the possibility of real-time monitoring using automated analysis of VSDi data streams without the requirement for either extracellular (lvn) or intracellular recording.

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology.

Understanding the mechanisms of restoration of activity in biological neural systems following exposure to damage is key for design of future neuro-prosthetic devices and restorative treatments. The pyloric rhythm network within the crustacean stomatogastric ganglion is a biological neural system that shows spontaneous restoration of activity following the stopping of inputs from higher control ganglia. We model the restoration of the activity in this network using conductance-based models of neurons and the alteration of conductance parameters of the model. Our analysis shows that this approach works only if some of the conductance values remain constrained following the stopping of higher inputs. Our model also shows that in order to model the restoration of the activity in this network it is not necessary to rely on complicated alterations of the mechanisms of Calcium ionic currents in the model, which was proposed previously.

Most governments implement the latest information communication technology (ICT) to improve the online experience of their citizens and businesses. Governments put great effort into providing user-focused services that are usable, secure and accessible by portable and wireless devices (e.g. tablets, smart phones etc.). However, such devices bring with them specific problems of usability and security that affect how users interact with government digital services (GDS). This paper presents a systematic mapping study, investigating the existing problems of usability and security of GDS accessed through smart devices. It aims to uncover what evaluation methods have been used by researchers and investigate how the trade-off between usability and security is assessed in the context of GDS accessed through smart devices. The paper summarises the current knowledge available with regards to this trade-off over the last ten years. The results of the mapping study help identify several research gaps, leading to areas for new research in the domain of usability and security in the context of GDS.

Background: Since the introduction of the systematic review process to Software Engineering in 2004, researchers have investigated a number of ways to mitigate the amount of effort and time taken to filter through large volumes of literature. Aim: This study aims to provide a critical analysis of text mining techniques used to support the citation screening stage of the systematic review process. Method: We critically re-reviewed papers included in a previous systematic review which addressed the use of text mining methods to support the screening of papers for inclusion in a review. The previous review did not provide a detailed analysis of the text mining methods used. We focus on the availability in the papers of information about the text mining methods employed, including the description and explanation of the methods, parameter settings, assessment of the appropriateness of their application given the size and dimensionality of the data used, performance on training, testing and validation data sets, and further information that may support the reproducibility of the included studies. Results: Support Vector Machines (SVM), Naïve Bayes (NB) and Committee of classifiers (Ensemble) are the most used classification algorithms. In all of the studies, features were represented with Bag-of-Words (BOW) using both binary features (28%) and term frequency (66%). Five studies experimented with n-grams with n between 2 and 4, but mostly the unigram was used. χ2, information gain and tf-idf were the most commonly used feature selection techniques. Feature extraction was rarely used although LDA and topic modelling were used. Recall, precision, F and AUC were the most used metrics and cross validation was also well used. More than half of the studies used a corpus size of below 1,000 documents for their experiments while corpus size for around 80% of the studies was 3,000 or fewer documents. The major common ground we found for comparing performance assessment based on independent replication of studies was the use of the same dataset but a sound performance comparison could not be established because the studies had little else in common. In most of the studies, insufficient information was reported to enable independent replication. The studies analysed generally did not include any discussion of the statistical appropriateness of the text mining method that they applied. In the case of applications of SVM, none of the studies report the number of support vectors that they found to indicate the complexity of the prediction engine that they use, making it impossible to judge the extent to which over-fitting might account for the good performance results. Conclusions: There is yet to be concrete evidence about the effectiveness of text mining algorithms regarding their use in the automation of citation screening in systematic reviews. The studies indicate that options are still being explored, but there is a need for better reporting as well as more explicit process details and access to datasets to facilitate study replication for evidence strengthening. In general, the reader often gets the impression that text mining algorithms were applied as magic tools in the reviewed papers, relying on default settings or default optimization of available machine learning toolboxes without an in-depth understanding of the statistical validity and appropriateness of such tools for text mining purposes.