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The Virtual Machine Placement (VMP) problem is a challenging optimization task that involves the assignment of virtual machines to physical machines in a cloud computing environment. The placement of virtual machines can significantly affect the use of resources in a cluster, with a subsequent impact on operational costs and the environment. In this paper, we present an improved algorithm for VMP, based on Parallel Ant Colony Optimization (PACO), which makes effective use of parallelization techniques and modern processor technologies. We achieve solution qualities that are comparable with or superior to those obtained by other nature-inspired methods, with our parallel implementation obtaining a speed-up of up to 2002x over recent serial algorithms in the literature. This allows us to rapidly find high-quality solutions that are close to the theoretical minimum number of Virtual Machines.

This paper proposes a new calibration method, called the mixed-binning (MB) method, to pursue high-linearity time-to-digital converters (TDCs) for light detection and ranging (LiDAR) applications. The proposed TDCs were developed using tapped delay-line (TDL) cells in field-programmable gate arrays (FPGAs). With the MB method, we implemented a resolution-adjustable TDC showing excellent linearity in Xilinx UltraScale FPGAs. We demonstrate a 128-channel TDC to show that the proposed method is cost-effective in logic resources. We also developed a software tool to predict the performances of TDL-based TDCs robustly. Results from both software analysis and hardware implementations are in a good agreement and show that the proposed design has great potential for multichannel applications; the averaged DNL_(pk-pk) and INL_(pk-pk) are close to or even less than 0.05 LSB in multichannel designs.

The problem of uniformly placing N points onto a sphere finds applications in many areas. For example, points on the sphere correspond to unit quaternions as well as to the group of rotations SO(3) and the online version of generating uniform rotations (known as "incremental generation") plays a crucial role in a large number of engineering applications ranging from robotics and aeronautics to computer graphics. An online version of this problem was recently studied with respect to the gap ratio as a measure of uniformity. The first online algorithm of Chen et al. was upper-bounded by 5.99 and later improved to 3.69, which is achieved by considering a circumscribed dodecahedron followed by a recursive decomposition of each face. In this paper we provide a more efficient tessellation technique based on the regular icosahedron, which improves the upper-bound for the online version of this problem, decreasing it to approximately 2.84. Moreover, we show that the lower bound for the gap ratio of placing at least three points is the golden ratio, approx. 1.618, and for at least four points is no less than 1.726., Comment: 18 pages, 13 figures

The interdependence between structural mechanics and microstructure solidification is an inherently three-dimensional phenomenon, where the complex physical processes and mechanical interactions can lead to dendrites growing at orientations influenced by twisting and out of plane bending. These effects can have a significant impact on the formation of defects and the overall macroscopic material properties of the structure. However, all attempts to numerically model this process so far have been limited to two-dimensional representations of the problem, which necessitates ignoring any potential behaviour that may arise from these more complex deformation events. For this reason, the two-dimensional numerical methods presented in previous papers, which couple a Finite Volume Structural Mechanics Solver to a Cellular Automata solidification solver, have been expanded so that problems may now be simulated in three dimensions. Results are presented which do not aim to predict any specific mechanism but rather highlight the new capabilities of this improved three-dimensional modelling framework.

An algorithm substitution attack (ASA) can undermine the security of cryptographic primitives by subverting the original implementation. An ASA succeeds when it extracts secrets without being detected. To launch an ASA on signature schemes, existing studies often needed to collect signatures with successive indices to extract the signing key. However, collection with successive indices requires uninterrupted surveillance of the communication channel and a low transmission loss rate in practice. This hinders the practical implementation of current ASAs, thus causing users to misbelieve that the threat incurred by ASA is only theoretical and far from reality. In this study, we first classify a group of schemes called extractable signatures that achieve traditional security (unforgeability) by reductions ending with key extraction, thus demonstrating that there is a generic and practical approach for ASA with this class of signatures. Further, we present the implementation of ASAs in which only two signatures and no further requirements are needed for the extraction of widely used discrete log-based signatures such as DSA, Schnorr, and modified ElGamal signature schemes. Our attack presents a realistic threat to current signature applications, which can also be implemented in open and unstable environments such as vehicular ad hoc networks. Finally, we prove that the proposed ASA is undetectable against polynomial time detectors and physical timing analysis.

A future Internet of Things (IoT) will feature a service-oriented architecture consisting of lightweight computing platforms offering individual, loosely-coupled microservices. Often, an end-user will request a bespoke service that will require a composition of two or more microservices offered by different service providers. However, the underlying complexities of soft compositions and the increased security risks are inherent in such a massively decentralised and distributed architecture. The use of trust management to secure the IoT is well studied in the literature. However, there are limitations to its use in service compositions in the IoT. Specifically, transparent (agnostic) trust composition and decomposition remain key problems for this area. A novel model for trustworthy service compositions in the IoT, SC-TRUST, is therefore proposed to deal with these challenges. In this study, the trust properties of service compositions and the effect of service workflows on transparent trust composition and decomposition are investigated. Based on the findings, relevant trust evaluation functions are derived to guide the compositions. SC-TRUST was implemented in a suitable application and its performance, in terms of the utility derived and the trust accuracy, convergence and resiliency, was evaluated. The results show that SC-TRUST improves the quality of service compositions and adequately mitigates trust-related attacks, thus increasing both efficiency and security.

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD., This work was supported by British Heart Foundation grants [PG/15/104/31913] and [FS/20/20/34626], the University of Cambridge EPSRC Impact Acceleration Account, the Wellcome EPSRC Centre for Medical Engineering at King’s College London [WT 203148/Z/16/Z], the European COST ACTION-Network for Research in Vascular Ageing CA18216 supported by COST (European Cooperation in Science and Technology), and the European Regional Development Fund (project No. 01.2.2-LMTK- 718-01-0030) under a grant agreement with the Research Council of Lithuania (LMTLT). The authors acknowledge: financial support from the Department of Health through the National Institute for Health Research Cardiovascular MedTech Co-operative at Guy’s and St Thomas’ NHS Foundation Trust.

Funding: This work is partially supported by the EU H2020 project DARE, No. 777413; and by Google Cloud Platform research credits program. This work presents three new adaptive optimization techniques to maximize the performance of dispel4py workflows. dispel4py is a parallel Python-based stream-oriented dataflow framework that acts as a bridge to existing parallel programming frameworks like MPI or Python multiprocessing. When a user runs a dispel4py workflow, the original framework performs a fixed workload distribution among the processes available for the run. This allocation does not take into account the features of the workflows, which can cause scalability issues, especially for data-intensive scientific workflows. Our aim, therefore, is to improve the performance of dispel4py workflows by testing different workload strategies that automatically adapt to workflows at runtime. For achieving this objective, we have implemented three new techniques, called Naive Assignment, Staging and Dynamic Scheduling. We have evaluated our proposal with several workflows from different domains and across different computing resources. The results show that our proposed techniques have significantly (up to 10X) improved the performance of the original dispel4py framework. Postprint

We consider bifurcations from the homogeneous solution of a circular or square hyperelastic sheet that is subjected to equibiaxial stretching under either force- or displacement-controlled edge conditions. We derive the condition for axisymmetric necking and show, for the class of strain-energy functions considered, that the critical stretch for necking is greater than the critical stretch for the Treloar-Kearsley (TK) instability and less than the critical stretch for the limiting-point instability. An amplitude equation for the bifurcated necking solution is derived through a weakly nonlinear analysis and is used to show that necking initiation is generally sub-critical. Abaqus simulations are conducted to verify the bifurcation conditions and the expectation that the TK instability should occur first under force control, but when the edge displacement is controlled the TK instability is suppressed, and it is the necking instability that will be observed. It is also demonstrated that axisymmetric necking follows a growth/propagation process typical of all such localization problems., 27 pages, 10 figures. This paper was accepted by Mathematics and Mechanics of Solids on 11 December 2021

This paper demonstrates the capabilities of convolutional neural networks (CNNs) at classifying types of motion starting from time series, without any prior knowledge of the underlying dynamics. The paper applies different forms of deep learning to problems of increasing complexity with the goal of testing the ability of different deep learning architectures at predicting the character of the dynamics by simply observing a time-ordered set of data. We will demonstrate that a properly trained CNN can correctly classify the types of motion on a given data set. We also demonstrate effective generalisation capabilities by using a CNN trained on one dynamic model to predict the character of the motion governed by another dynamic model. The ability to predict types of motion from observations is then verified on a model problem known as the forced pendulum and on a relevant problem in Celestial Mechanics where observational data can be used to predict the long-term evolution of the system.

Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.

We establish a generic upper bound ExpTime for reasoning with global assumptions (also known as TBoxes) in coalgebraic modal logics. Unlike earlier results of this kind, our bound does not require a tractable set of tableau rules for the instance logics, so that the result applies to wider classes of logics. Examples are Presburger modal logic, which extends graded modal logic with linear inequalities over numbers of successors, and probabilistic modal logic with polynomial inequalities over probabilities. We establish the theoretical upper bound using a type elimination algorithm. We also provide a global caching algorithm that potentially avoids building the entire exponential-sized space of candidate states, and thus offers a basis for practical reasoning. This algorithm still involves frequent fixpoint computations; we show how these can be handled efficiently in a concrete algorithm modelled on Liu and Smolka's linear-time fixpoint algorithm. Finally, we show that the upper complexity bound is preserved under adding nominals to the logic, i.e. in coalgebraic hybrid logic., Extended version of conference paper in FCT 2015

To monitor network operation in real-time, power system operators have developed wide-area monitoring systems (WAMS). However, the centralized communication and information processing architecture of WAMS cannot be extended easily to distribution networks. In this aspect, a three-level distributed network monitoring architecture is proposed in this paper, concerning the dynamic analysis of transmission, primary and secondary distribution networks by exploiting measurements of ambient data and transient responses. In the proposed architecture, operators are responsible for the operation and analysis of their own grid but also can share an overview of the system performance to facilitate their operational coordination. Different online and offline applications are supported within the architecture, including small-signal, transient and frequency stability analysis as well as dynamic equivalencing and real-time inertia estimation. Measurement-based algorithms and models are proposed for each case. Finally, the performance of the developed algorithms has been tested by using a combined transmission and distribution power system model.

The use of multimedia data sharing has drastically increased in the past few decades due to the revolutionary improvements in communication technologies such as the 4th generation (4G) and 5th generation (5G) etc. Researchers have proposed many image encryption algorithms based on the classical random walk and chaos theory for sharing an image in a secure way. Instead of the classical random walk, this paper proposes the quantum walk to achieve high image security. Classical random walk exhibits randomness due to the stochastic transitions between states, on the other hand, the quantum walk is more random and achieve randomness due to the superposition, and the interference of the wave functions. The proposed image encryption scheme is evaluated using extensive security metrics such as correlation coefficient, entropy, histogram, time complexity, number of pixels change rate and unified average intensity etc. All experimental results validate the proposed scheme, and it is concluded that the proposed scheme is highly secured, lightweight and computationally efficient. In the proposed scheme, the values of the correlation coefficient, entropy, mean square error (MSE), number of pixels change rate (NPCR), unified average change intensity (UACI) and contrast are 0.0069, 7.9970, 40.39, 99.60%, 33.47 and 10.4542 respectively.

Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.

Machine learning algorithms have been used for detection (and possibly) prediction of Alzheimer’s disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer’s disease using genetic data is still in its primitive stage. The aim of this paper was to evaluate the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer’s disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer’s disease. A systematic review of quantitative studies was conducted using three search engines (PubMed, Web of Science and Scopus), and included studies between 1st of January 2010 and 31st December 2021. Keywords used were ‘Alzheimer’s disease(s)’, ‘GWAS, ‘Artificial intelligence’ and their synonyms. After applying the inclusion/exclusion criteria, 24 studies were included. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). The main findings showed that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.

Traffic congestion in highly populated urban areas is a huge problem these days. A lot of researchers have proposed many systems to monitor traffic flow and handle congestion through different techniques. But the current systems are not reliable enough to perceive traffic signals in real-time. Therefore, we aim to build a system that can efficiently perform real-time environments to solve the traffic congestion problem through signal automation. Since vehicle detection and counting are crucial in any traffic system, we use state-of-the-art deep learning techniques to detect and count vehicles in real-time. We then automate the signal timings by comparing the count of traffic on all sides of a junction. These automated signal timings sufficiently reduce congestion and improve traffic flow. We prepared a dataset of 4500 images and achieved about 91% accuracy by training it on Faster RCNN.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems.

Here, we first aim to explain practical considerations to design and implement a reconfigurable intelligent surface (RIS) in the sub-6 GHz band and then, to demonstrate its real-world performance. The wave manipulation procedure is explored with a discussion on relevant electromagnetic (EM) concepts and backgrounds. Based on that, the RIS is designed and fabricated to operate at the center frequency of 3.5 GHz. The surface is composed of 2430 unit cells where the engineered reflecting response is obtained by governing the microscopic characteristics of the conductive patches printed on each unit cell. To achieve this goal, the patches are not only geometrically customized to properly reflect the local waves, but also are equipped with specific varactor diodes to be able to reconfigure their response when it is required. An equivalent circuit model is presented to analytically evaluate the unit cell’s performance with a method to measure the unit cell’s characteristics from the macroscopic response of the RIS. The patches are printed on six standard-size substrates which then placed together to make a relatively big aperture with approximate planar dimensions of $120 \times 120$ cm2. The manufactured RIS possesses a control unit with a custom-built system that can control the response of the reflecting surface by regulating the performance of the varactor diode on each printed patch across the structure. Furthermore, with an introduction of our test-bed system, the functionality of the developed RIS in an indoor real-world scenario is assessed. Finally, we showcase the capability of the RIS in hand to reconfigure itself in order to anomalously reflect the incoming EM waves toward the direction of interest in which a receiver could be experiencing poor coverage.

We introduce a new research area in visual analytics (VA) aiming to bridge existing gaps between methods of interactive machine learning (ML) and eXplainable Artificial Intelligence (XAI), on one side, and human minds, on the other side. The gaps are, first, a conceptual mismatch between ML/XAI outputs and human mental models and ways of reasoning, and second, a mismatch between the information quantity and level of detail and human capabilities to perceive and understand. A grand challenge is to adapt ML and XAI to human goals, concepts, values, and ways of thinking. Complementing the current efforts in XAI towards solving this challenge, VA can contribute by exploiting the potential of visualization as an effective way of communicating information to humans and a strong trigger of human abstractive perception and thinking. We propose a cross-disciplinary research framework and formulate research directions for VA.

Intelligent Transportation Systems (ITS), one aspect of the Smart City paradigm, aim to improve the efficiency, convenience, and safety of travelers. The integration of (vehicular) communication technologies allows communication between the on-board communication units (OBUs) of vehicles, roadside units (RSUs), and vulnerable road users (VRUs), and contribute to the efficacy of ITS applications. However, these additional sources of information must be reliable and accurate. Security primitives such as confidentiality, integrity, and authenticity are required, but only achievable when supported with a suitable cryptographic key management scheme. This paper presents the design of a decentralized and efficient key management scheme, abbreviated as the DECENT scheme. This scheme provides secure multihop communication in dense and dynamic network environments while functioning in a self-organized manner. Through threshold secret sharing techniques, network nodes act as a distributed trusted third party (TTP) such that a threshold number of nodes can collaborate to execute key management functions. These functions include decentralized node admission and key updating. Novelties include (i) the unique self-healing characteristic, meaning that DECENT is capable of independently recovering from network compromise, and (ii) guidelines for choosing an appropriate security threshold in any deployment scenario which maximizes the level of security while simultaneously guaranteeing that decentralized key management services can be provided.

BACKGROUND: Diminished synaptic gain-the sensitivity of postsynaptic responses to neural inputs-may be a fundamental synaptic pathology in schizophrenia. Evidence for this is indirect, however. Furthermore, it is unclear whether pyramidal cells or interneurons (or both) are affected, or how these deficits relate to symptoms. \ud \ud METHODS: People with schizophrenia diagnoses (PScz) (n = 108), their relatives (n = 57), and control subjects (n = 107) underwent 3 electroencephalography (EEG) paradigms-resting, mismatch negativity, and 40-Hz auditory steady-state response-and resting functional magnetic resonance imaging. Dynamic causal modeling was used to quantify synaptic connectivity in cortical microcircuits. \ud \ud RESULTS: Classic group differences in EEG features between PScz and control subjects were replicated, including increased theta and other spectral changes (resting EEG), reduced mismatch negativity, and reduced 40-Hz power. Across all 4 paradigms, characteristic PScz data features were all best explained by models with greater self-inhibition (decreased synaptic gain) in pyramidal cells. Furthermore, disinhibition in auditory areas predicted abnormal auditory perception (and positive symptoms) in PScz in 3 paradigms. \ud \ud CONCLUSIONS: First, characteristic EEG changes in PScz in 3 classic paradigms are all attributable to the same underlying parameter change: greater self-inhibition in pyramidal cells. Second, psychotic symptoms in PScz relate to disinhibition in neural circuits. These findings are more commensurate with the hypothesis that in PScz, a primary loss of synaptic gain on pyramidal cells is then compensated by interneuron downregulation (rather than the converse). They further suggest that psychotic symptoms relate to this secondary downregulation.

Clean and safe water is vital for our lives and public health. In recent decades, population growth, agriculture, industries, and climate change have worsened freshwater resource depletion and clean water pollution. Several studies have focused on water pollutions risk simulation and prediction in the presence of pollution hotspots. However, the increase and complexity of big data caused by uncertain water quality parameters led to a new efficient algorithm to trace the most accurate pollution hotspots. Therefore, this study proposes to offer different algorithms and comparative studies using Machine Learning (ML) algorithms. Ten different most widely used algorithms, including unsupervised and supervised ML, will be employed to categorize the pollution hotspots for the Terengganu River. Besides, we also validate algorithms' accuracies by improving and changing each parameter in ML algorithms. Our results list all the accurate and efficient ML algorithms for the classification of river pollutions. These results help to facilitate river prediction using efficient and accurate algorithms in various water quality scenario.

The evolution of Web technologies leads to software premature obsolescence requiring technology-independent representations to increase the reuse rates during the development process. They also require integration into service-oriented architectures to exchange information with different Web systems supporting runtime interoperability. Web Applications (WebApps) run on devices with different capabilities and limitations increasing the complexity of the development process. To address these challenges, different proposals have emerged to facilitate the development of WebApps, which is still an open research field with many challenges to address. This paper presents a model transformation architecture based on software standards to automatically generate full stack multi-layered WebApps covering Persistence, Service, and Presentation layers. This transformation architecture also generates the set of test cases to test WebApp business logic. The proposed transformation architecture only requires a UML platform-independent class model as an input to generate fully functional Web applications in a three-tier architecture including the three layers, while most proposals focus on the generation of the Presentation layer. In addition, this architecture employs software industry standards to enable an easy integration into third-party tools and development environments. The transformation Architecture proposed has been empirically validated on the case study of a fully functional travel management WebApp that is generated using a UML class diagram employing a third-party tool integrated into the same integrated development environment.

In a world where organisations are embracing new IT working models such as Bring Your Own Device (BYOD) and remote working, the traditional mindset of defending the network perimeter is no longer sufficient. Zero Trust Architecture (ZTA) has recently emerged as a new security model in which the breach mindset dominates the threat model. By default, the ZTA considers any endpoint (i.e., device), user, or application to be untrusted until proven otherwise. Nonetheless, once proven by the endpoint, using Advanced Persistent Threats (APT), attackers can still take over an authenticated and authorised session via that endpoint. Therefore, they can perform several user/device centric malicious activities in addition to lateral movement rendering the endpoint the Achilles heel of ZTA. To effectively deter APT attack capabilities on the endpoints, this work proposes a Blockchain-enabled Intrusion Detection and Prevention System (BIDPS) that augments ZTA onto endpoints. The BIDPS aims to achieve two core outcomes: first, detect and prevent attackers’ techniques and tactics as per MITRE’s ATT&CK enterprise matrix earlier than the lateral movement stage, and secondly, strip trust out of the endpoint itself and place it on-chain, thus creating an immutable system of explicit trust. To evaluate the effectiveness of the BIDPS, a testbed was built where techniques of over ten APTs attacks were launched against the endpoint. BIDPS has a high rate of success defending against the launched attacks owing to its Blockchain’s immutability, fortifying the detection/prevention processes.

Funding: The first author is supported by an Engineering and Physical Sciences Research Council grant (EP/P015638/1). The second author is supported by a Royal Society University Research Fellowship (URF\R\180015). Let H ≤ Sn be an intransitive group with orbits Ω1, Ω2, ... , Ωk. Then certainly H is a subdirect product of the direct product of its projections on each orbit, H|Ω1 x H|Ω2 x ... x H|Ωk. Here we provide a polynomial time algorithm for computing the finest partition P of the H-orbits such that H = Πc∈P H|c and we demonstrate its usefulness in some applications. Postprint

A significant number of people are suffering from cognitive impairment all over the world. Early detection of cognitive impairment is of great importance to both patients and caregivers. However, existing approaches have their shortages, such as time consumption and financial expenses involved in clinics and the neuroimaging stage. It has been found that patients with cognitive impairment show abnormal emotion patterns. In this paper, we present a novel deep neural network-based system to detect the cognitive impairment through the analysis of the evolution of facial emotions while participants are watching designed video stimuli. In our proposed system, a novel facial expression recognition algorithm is developed using layers from MobileNet and Support Vector Machine (SVM), which showed satisfactory performance in 3 datasets. To verify the proposed system in detecting cognitive impairment, 61 elderly people including patients with cognitive impairment and healthy people as a control group have been invited to participate in the experiments and a dataset was built accordingly. With this dataset, the proposed system has successfully achieved the detection accuracy of 73.3%.

STUDY QUESTIONHow does in vitro culture alter the human ovarian cortical extracellular matrix (ECM) network structure?SUMMARY ANSWERThe ECM composition and architecture vary in the different layers of the ovarian cortex and are remodelled during in vitro culture.WHAT IS KNOWN ALREADYThe ovarian ECM is the scaffold within which follicles and stromal cells are organized. Its composition and structural properties constantly evolve to accommodate follicle development and expansion. Tissue preparation for culture of primordial follicles within the native ECM involves mechanical loosening; this induces undefined modifications in the ECM network and alters cell–cell contact, leading to spontaneous follicle activation.STUDY DESIGN, SIZE, DURATIONFresh ovarian cortical biopsies were obtained from six women aged 28–38 years (mean ± SD: 32.7 ± 4.1 years) at elective caesarean section. Biopsies were cut into fragments of ∼4 × 1 × 1 mm and cultured for 0, 2, 4, or 6 days (D).PARTICIPANTS/MATERIALS, SETTING, METHODSPrimordial follicle activation, stromal cell density, and ECM-related protein (collagen, elastin, fibronectin, laminin) positive area in the entire cortex were quantified at each time point using histological and immunohistological analysis. Collagen and elastin content, collagen fibre characteristics, and follicle distribution within the tissue were further quantified within each layer of the human ovarian cortex, namely the outer cortex, the mid-cortex, and the cortex–medulla junction regions.MAIN RESULTS AND THE ROLE OF CHANCEPrimordial follicle activation occurred concomitantly with a loosening of the ovarian cortex during culture, characterized by an early decrease in stromal cell density from 3.6 ± 0.2 × 106 at day 0 (D0) to 2.8 ± 0.1 × 106 cells/mm3 at D2 (P = 0.033) and a dynamic remodelling of the ECM. Notably, collagen content gradually fell from 55.5 ± 1.7% positive area at D0 to 42.3 ± 1.1% at D6 (P = 0.001), while elastin increased from 1.1 ± 0.2% at D0 to 1.9 ± 0.1% at D6 (P = 0.001). Fibronectin and laminin content remained stable. Moreover, collagen and elastin distribution were uneven throughout the cortex and during culture. Analysis at the sub-region level showed that collagen deposition was maximal in the outer cortex and the lowest in the mid-cortex (69.4 ± 1.2% versus 53.8 ± 0.8% positive area, respectively, P LARGE SCALE DATAN/A.LIMITATIONS, REASONS FOR CAUTIONOvarian cortical biopsies were obtained from women undergoing caesarean sections. As such, the data obtained may not accurately reflect the ECM distribution and structure of non-pregnant women.WIDER IMPLICATIONS OF THE FINDINGSClarifying the composition and architecture signature of the human ovarian cortical ECM provides a foundation for further exploration of ovarian microenvironments. It is also critical for understanding the ECM–follicle interactions regulating follicle quiescence and awakening, leading to improvements in both in vitro activation and in vitro growth techniques.STUDY FUNDING/COMPETING INTEREST(S)Medical Research Council grant MR/R003246/1 and Wellcome Trust Collaborative Award in Science: 215625/Z/19/Z. The authors have no conflicts to declare.TRIAL REGISTRATION NUMBERN/A.

Selective Exposure Theory (Aruguete & Calvo, 2018; Bigné et al., 2020) suggests that on social media, viewers pay most attention to content which aligns with their values and preferences. Individuals engage in self-assessment by comparing themselves to others (Social comparison theory: Festinger, 1954). We predicted that the characteristics of Instagram arrays and participants' own body satisfaction would combine to influence their visual processing of computer-based images. A 3 (Body Shape: Underweight, Average, Overweight) × 2 (Body Part: Face-only; Body-only) repeated measures design was used. We recruited 60 (young) women to view arrays of images as displayed on Instagram [Mage = 20.75 years, SDage = 2.74 years]. A separate, naïve group of 37 participants rated 165 stimulus images on a scale of under-to-over-weight. These normed images were used to create artificial, ecologically-valid 3 × 4 Instagram image arrays containing two of each type of stimulus image. We recorded participants' eye movements with a high degree of spatial and temporal resolution while participants freely engaged with these arrays. We then collected participants' body satisfaction data (Slade et al., 1990). Results demonstrated inter-relationships between eye movement behaviour and Body Shape, Body Part, and body satisfaction. In short, both bottom-up stimulus characteristics and top-down satisfaction impacted measures of processing. Image content was particularly relevant to ‘when’ measures of processing time, whereas body satisfaction was more-influential upon ‘where’ measurements (fixations counts, number of visits per stimulus image). Our study is the first of its kind to show such effects. Future research is needed to understand such effects in clinical and/or non-female users of Instagram and other platforms.

Funding: JKFB partially supported by the Austrian Science Fund under FWF Meitner project M-3338. A representation theorem relates different mathematical structures by providing an isomorphism between them: that is, a one-to-one correspondence preserving their original properties. Establishing that the two structures substantially behave in the same way, representation theorems typically provide insight and generate powerful techniques to study the involved structures, by cross-fertilising between the methodologies existing for each of the respective branches of mathematics. When the related structures have no obvious a priori connection, however, such results can be, by their own nature, elusive. Here, we show how data-mining across distinct web sources (including the Online Encyclopedia of Integer Sequences, OEIS), was crucial in the discovery of two original representation theorems relating event structures (mathematical structures commonly used to represent concurrent discrete systems) to families of sets (endowed with elementary disjointness and subset relations) and to full graphs, respectively. The latter originally emerged in the apparently unrelated field of bioinformatics. As expected, our representation theorems are powerful, allowing to capitalise on existing theorems about full graphs to immediately conclude new facts about event structures. Our contribution is twofold: on one hand, we illustrate our novel method to mine the web, resulting in thousands of candidate connections between distinct mathematical realms; on the other hand, we explore one of these connections to obtain our new representation theorems. We hope this paper can encourage people with relevant expertise to scrutinize these candidate connections. We anticipate that, building on the ideas presented here, further connections can be unearthed, by refining the mining techniques and by extending the mined repositories. Postprint

This chapter discusses the evolving notion of digital strategy and its underpinning digital technologies, outlines an overarching framework for understanding the fundamental reasons why every organization needs a digital strategy, and explores the implications for incumbents and digital native firms alike competing in the digital age. It draws on a comprehensive literature review and over three decades of experience leading research and consultancy projects at the forefront of digital transformation. The framework is based on two fundamental changes in the business environment: the changing nature of the economy and the continuous rapid development of digital technologies. These changes together redefine the “rules of the game”, forcing all organizations to evaluate and reinvent their strategies and business models by exploiting rapidly expanding digital capabilities. In particular, as the digital and physical worlds are increasingly meshed into one, a new cyber-physical environment is emerging, which has profound implication for how digital strategy is developed, executed and evaluated. The rapid development of synthetic virtual worlds and metaverse (including those based on VR/AR, blockchain, crypto currencies and web 3.0) may add a significant new dimension to the evolving business environment. For business leaders, the main challenge is often not in generating more new ideas, but rather in effectively managing the transition to new technologies, new strategies and business models, and new organizational designs. Three emerging approaches for managing the transition in some world leading organizations are discussed. Finally, the theoretical contributions are summarised and three new areas for future research are highlighted.

Funding: This work was supported by the EU Horizon-2020 project TeamPlay (https://www.teamplay-h2020.eu), grant #779882. Non-functional properties, such as energy, time, and security (ETS) are becoming increasingly important for the programming of Cyber-Physical Systems (CPS). This paper describes TeamPlay, a research project funded under the EU Horizon 2020 programme between January 2018 and June 2021.TeamPlay aimed to provide the system designer with a toolchain for developing embedded applications where ETS properties are first-class citizens, allowing the developer to reflect directly on energy, time and security properties at the source code level. In this paper we give an overview of the TeamPlay methodology, introduce the challenges and solutions of our approach and summarise the results achieved. Overall, applying our TeamPlay methodology led to an improvement of up to 18% performance and 52% energy usage over traditional approaches. Postprint

The work presented in the thesis is focused on developing effective data-led simulation approaches for studying the behaviour and properties of auxetic structures and crystals with a particular focus on the Poisson’s ratio, auxeticity and anisotropy. Python based parametric programs are developed and integrated with Finite element modelling (Abaqus) and ab initio quantum mechanical programs (Materials Studio CASTEP) for structure development and data processing. A range of auxetic structures have been studied including missing rib structures in tension, missing rib and mixed cellular structures in compression and self-similar hierarchical structures in compression. A Python program is also developed for calculating and tracking of the area changes for cellular structures under compression and tension loads. The use of Python programs in developing Voronoi random structures and random structures with auxetic behaviors is also presented. The models are used to establish the effect of key dimensional parameters on the deformation process, Poisson’s ratio and stability of auxeticity. The results show that the area analysis is effective in studying the changes in cell shape and area; the areas of the missing rib and honeycomb cell follows a more uniform cell deformation trend in tension than in compression. The area changes of the missing rib model under compression reflect the main deformation stages including the corner edge-cell wall contact. The work shows that deformation and instability auxeticity of normal missing rib structure and mixed structures are associated with the corner edge wall contact. The mixed model showed different beam–wall contact patterns, which contributes to the much higher critical strain of stable auxeticity and overall shape stability. The work with ab initio quantum mechanical program (Materials Studio CASTEP) is focused on developing a Python-based data system for systematic crystal structure processing and establishing the link between crystal structures and key ground state properties of crystals based on first principle calculations with Materials Studio. A range of ground state properties (e.g., elastic constants (Cij), bulk modulus (K), Young’s modulus (E), shear modulus (G), Poisson’s ratio (v), etc.) have been studied with a particular focus on the Poisson’s ratio and anisotropy. The correlation between the ground state elastic parameters and other properties are analysed. Some typical results on key engineering carbides including simple cubic systems (TiC, VC and NbC) are presented together with the mathematical operation to calculate the K, E, G, v. 3D surface constructions of the ground state parameters including anisotropic features are presented with an integrated program. The Python graphical user interface developed is effective for systematic calculation and visualization of the key structures, properties and anisotropy features. The relationship between maxima and minima of Poisson’s ratio and the anisotropy index of a range of carbides showed a good agreement with the other published data based on a large quantity of data. Some compounds with low or negative Poisson’s ratio were identified and detailed structures and properties data are given. The data highlighted the source of uncertainty in Poisson’s ratio and the link between property data. The Data for structures with Negative Poisson’s ratio is briefly presented and analysed including links between the auxetic crystal structure and some macro lattice structure with auxetic behaviors. The procedure for modelling surface energy, oxygen reduction reaction (ORR), crystals with doping elements and the effect of temperatures are also presented and showed a good agreement with published works. Future use of the framework developed, and main research focuses in both FE modelling and ab initio quantum mechanical simulation are discussed.

The medical supply chain management revolves around regulating medical goods and services from the producer to the ultimate user. The supply chain could be defined as a set of resources required to deliver products and services to the customer. Supply chain management is a complex and fragmented process in the medical industry. The medical supply chain enables the procurement of the required resources and delivery of the necessary equipment to the patient by ensuring that the healthcare facility has the required inventory. To complete the process, information and physical goods regarding medical services and products go through a personalised regress process based on the needs of manufacturers, hospitals, providers, insurance companies and regulatory agencies.\ud \ud However, promoting efficiency in the medical supply chain creates substantial opportunities for reducing costs across the organisation by hospitals and private practitioners. The medical supply chain involves the management of downstream and upstream relationships with customers and suppliers to deliver optimised customer value at less cost throughout the supply chain. The standards of the hospital are focused on making sure that the physicians are giving priority to the customers. Medical supply chain management revolves around distributing and procuring medical equipment from the inventory to the patient. The healthcare organisation needs to address the logistic issues, leading to increased supply costs which prevent them from meeting the quality standards within their budgets. This contributes to the complexity of medical supply chain management. However, medical supplies are not managed, which could lead to inadequate data reporting, poor standardisation of products, lack of automation in the process, and increasing requirements for regulation.

Hydrocephalus is a disorder when an excessive amount of cerebrospinal fluid (CSF) accumulates inside the subarachnoid space, which can lead to an enlargement of the ventricular system of the brain and increase the pressure inside the head. Paediatric population, adults, and most elderly ones can be affected by hydrocephalus. This neurological condition can have an excellent diagnosis if treated. However, it also can be life threatening if not treated correctly. With the increasing roll-out of ‘digital hospitals’, electronic medical records, new data capture and analysis technologies, as well as a digitally enabled health consumer, the healthcare workforce is required to become digitally literate to manage the significant changes in the healthcare landscape. In this study, Machine learning techniques are employed for the long-term follow-up for hydrocephalus patients, for which a data set of 3,262 records of ICP signals of shunted patients from Alder Hey Hospital, was used. Six popular machine-learning based classifiers have been evaluated for the classification of monitoring shunted patients and produce the required risk assessments to follow up shunted patients within a supervised learning setting, which are Ensemble Bagged Tree, Ensemble Boosted Tree, Fine Tree, Quadratic SVM, Gaussian SVM and Cubic SVM. The classifier Ensemble Boosted Tree achieved the highest aggregate performance outcomes of accuracy 98.90, sensitivity 100, specificity 100 and precision of 100. The study concludes that using machine learning techniques represents an alternative procedure that could assist healthcare professionals, as well as the specialist nurse and junior doctor to improve the quality of care and follow-up with hydrocephalus disorder.

State of the art optimisation passes for dependently typed languages can help erase the redundant information typical of invariant-rich data structures and programs. These automated processes do not dramatically change the structure of the data, even though more efficient representations could be available. Using Quantitative Type Theory, we demonstrate how to define an invariant-rich, typechecking time data structure packing an efficient runtime representation together with runtime irrelevant invariants. The compiler can then aggressively erase all such invariants during compilation. Unlike other approaches, the complexity of the resulting representation is entirely predictable, we do not require both representations to have the same structure, and yet we are able to seamlessly program as if we were using the high-level structure., 32 pages, accepted for publication at ESOP'23

Deviations between consumers’ information gathering and purchase channels may lead to showrooming and webrooming, where the former refers to obtaining product information in a brick-and-mortar (BM) store but purchasing online while the latter corresponds to the reverse. In this paper, we endogenize consumers’ information gathering and purchase decisions and characterize the optimal information provision decision for an online retailer in the presence of a rival BM store. For instances where showrooming can arise, we f ind that the optimal information level decreases with the fraction of consumers who consider showrooming. Despite the popular belief that showrooming is always detrimental to the BM store, our results suggest that showrooming may increase the profit of the BM store and decrease the profit of the online store. In instances with webrooming, we again find that the optimal information level decreases with the fraction of consumers who consider webrooming but that the profit of the online retailer always decreases with the fraction of consumers who consider webrooming. In addition, we consider the price matching strategy of the BM store and its interplay with the online retailer’s information decisions. Lastly, we briefly extend our work to study settings with return cost, heterogeneity in online shopping cost, all consumers evaluating the product online f irst, endogenized pricing decisions for retailers, and a retailer owning both online and offline channels.

Security authentication defines the process of verifying the identity of a person. For many years, authentication technology has played a crucial role in terms of data security. Existing typical biometric authentication technologies have some limitations. Progress in technology has produced several specific devices with ability to reproduce human biometrics that are currently used, mainly because they are mostly visible and touchable. Therefore, a new biometric method is required to overcome the limitations of current biometric authentication systems. Human brain signals are one of the biometric characteristics that has recently been utilised in diverse Brain-Computer Interface (BCI) applications. Brain-based authentication is achieving more popularity among researchers where research studies have reported considerable accuracy using different BCI methods for authentication purposes. However, there are some limitations in terms of usability, time efficiency, and most importantly the permanency of the method through time. The proposed research suggests two different brain-based authentication methods using picturising patterns and deep breathing patterns as brain states. This process starts with signal acquisition using the above-mentioned brain patterns as user strategies to capture the raw EEG data from the participants, followed by the pre-processing stage for data cleansing and standardisation. For noise removal, filtering techniques and the Independent Component Analysis (ICA) algorithm are utilised to make data ready for the feature extraction. Fast Fourier Transform (FFT), Power Spectrum Density (PSD), and Discrete Wavelet Transform (DWT) are used for the feature extraction from the time-frequency domain data representation. The extracted features are then forwarded to classification methods that include Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Artificial Neural Network (ANN). The proposed picturising and deep breathing methods indicate 88% and 91% accuracy respectively when evaluated over pure unseen data. The results show that the picturising pattern method improves the security level according to invisibility and changeability of the brain-ID using images; and deep breathing patterns method improves the usability and permanency of the system because this pattern can be used anywhere and at any time.

Linear typed $\lambda$-calculi are more delicate than their simply typed siblings when it comes to metatheoretic results like preservation of typing under renaming and substitution. Tracking the usage of variables in contexts places more constraints on how variables may be renamed or substituted. We present a methodology based on linear algebra over semirings, extending McBride's kits and traversals approach for the metatheory of syntax with binding to linear usage-annotated terms. Our approach is readily formalisable, and we have done so in Agda., Comment: In Proceedings Linearity&TLLA 2020, arXiv:2112.14305

Funding: Financial support was provided by Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, PIP 11220150100066 CO), UNMdP (Project EXA903457/18) and FONCYT (PICT 201-0427). 1. During the last decade, convolutional neural networks (CNNs) have revolutionised the application of deep learning (DL) methods to classification tasks and object recognition. These procedures can capture the key features of image data that are not easily visible to the human eye and use them to classify and predict outcomes with exceptional precision. 2. Here, we show for the first time that CNNs provide highly accurate predictions for small-scale genetic differentiation and diversity in Ctenomys australis, a subterranean rodent from central Argentina. Using microsatellite genotypes and high-resolution satellite imagery, we trained a simple CNN to predict local FST and mean allele richness. To identify landscape features with high impact on predicted values, we applied species distribution models to obtain the distribution of suitable habitat. Subsequent use of a machine learning algorithm (random forest) allowed us to identify the attributes that contribute the most to predictions of population genetic metrics. 3. Predictions obtained from the CNN accounted for more than 98% of the variation observed both in FST and mean allele richness values. Random forest regression on landscape metrics indicated that features involving connectivity and consistent prevalence of suitable habitat promoted genetic diversity and reduced genetic differentiation in C. australis. 4. Validation with synthetic data via simulations of genetic differentiation based on the landscape structure of the study area and of a nearby area showed that DL models are able to capture complex relationships between actual data and synthetic data in the same landscape and between synthetic data generated under different landscapes. 5. Our approach represents an objective and powerful approach to landscape genetics because it can extract information from patterns that are not easily identified by humans. Spatial predictions from the CNN may assist in the identification of areas of interest for biodiversity conservation and management of populations. Postprint

We consider notions of freeness and ambiguity for the acceptance probability of Moore-Crutchfield Measure Once Quantum Finite Automata (MO-QFA). We study the injectivity problem of determining if the acceptance probability function of a MO-QFA is injective over all input words, i.e., giving a distinct probability for each input word. We show that the injectivity problem is undecidable for 8 state MO-QFA, even when all unitary matrices and the projection matrix are rational and the initial state vector is real algebraic. We also show undecidability of this problem when the initial vector is rational, although with a huge increase in the number of states. We utilize properties of quaternions, free rotation groups, representations of tuples of rationals as linear sums of radicals and a reduction of the mixed modification of Post's correspondence problem, as well as a new result on rational polynomial packing functions which may be of independent interest., Comment: Accepted journal version (with change of name from Acceptance Ambiguity for Quantum Automata)

Chatbots are increasingly becoming important gateways to digital services and information—taken up within domains such as customer service, health, education, and work support. However, there is only limited knowledge concerning the impact of chatbots at the individual, group, and societal level. Furthermore, a number of challenges remain to be resolved before the potential of chatbots can be fully realized. In response, chatbots have emerged as a substantial research area in recent years. To help advance knowledge in this emerging research area, we propose a research agenda in the form of future directions and challenges to be addressed by chatbot research. This proposal consolidates years of discussions at the CONVERSATIONS workshop series on chatbot research. Following a deliberative research analysis process among the workshop participants, we explore future directions within six topics of interest: (a) users and implications, (b) user experience and design, (c) frameworks and platforms, (d) chatbots for collaboration, (e) democratizing chatbots, and (f) ethics and privacy. For each of these topics, we provide a brief overview of the state of the art, discuss key research challenges, and suggest promising directions for future research. The six topics are detailed with a 5-year perspective in mind and are to be considered items of an interdisciplinary research agenda produced collaboratively by avid researchers in the field.

A point process model based on a class of Cox processes is developed to analyse precipitation data at a point location. The model is constructed using state-dependent exponential pulses that are governed by an unobserved underlying Markov chain. The mathematical formulation of the model where both the arrival rate of the rain cells and the initial pulse depth are determined by the Markov chain is presented. Second-order properties of the rainfall depth process are derived and utilised in model assessment. A method of moment estimation is employed in model fitting. The proposed model is used to analyse 69 years of sub-hourly rainfall data from Germany and 15 years of English rainfall data. The results of the analysis using variants of the proposed model with fixed pulse lifetime and variable pulse duration are presented. The performance of the proposed model, in reproducing second-moment characteristics of the rainfall, is compared with that of two stochastic models where one has exponential pulses and the other has rectangular pulses. The proposed model is found to capture most of the empirical rainfall properties well and outperform the two alternative models considered in our analysis.

There exists widespread misunderstanding about the nature of computers and how and why they are liable to fail. The present approach to the disclosure or discovery and evaluation of evidence produced by computers in legal proceedings is unsatisfactory. The central problem is the evidential presumption that computers are reliable. This presumption is not warranted. To this end, recommendations are proposed to rectify this problem with the aim of increasing the probability of a fair trial. Index words: electronic evidence; computer systems; disclosure; discovery; recommendations for judges; fairness of legal proceedings

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. Then, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better.

In this paper, we present a genetic algorithm (GA) based band power feature sparse learning (SL) approach for classification of electroencephalogram (EEG) (GABSLEEG) in motor imagery (MI) based brain-computer interfacing (BCI). The band power in the alpha and beta bands was extracted from the EEG segments and used as features to construct the SL dictionary, in which the GA was employed for channel selection. The GABSLEEG system was tested in three functional areas: i) classification of MI data and idle state data; ii) performance with decreased training data size; and iii) computational efficiency. The system was evaluated by dividing the data into training, validation, and testing sets. The proposed GABSLEEG model is found to significantly outperform conventional classifiers, including the support vector machine (SVM) classifier in (i-iii), and the random forest (RF) and the k-nearest neighbour (k-NN) classifiers in (i-ii). The GABSLEEG system consistently had a higher classification accuracy, sensitivity, and specificity. The average accuracy of the proposed system was 99.65%, on BCI Competition IV dataset 1 and 96.08% for BCI Competition III dataset IVa with the idle state included as a class, which was on a par with state-of-the-art SL and even deep learning approaches.

We introduce a new framework for solving an important class of computational problems involving finite permutation groups, which includes calculating set stabilisers, intersections of subgroups, and isomorphisms of combinatorial structures. Our techniques are inspired by and generalise 'partition backtrack', which is the current state-of-the-art algorithm introduced by Jeffrey Leon in 1991. But, instead of ordered partitions, we use labelled directed graphs to organise our backtrack search algorithms, which allows for a richer representation of many problems while often resulting in smaller search spaces. In this article we present the theory underpinning our framework, we describe our algorithms, and we show the results of some experiments. An implementation of our algorithms is available as free software in the GraphBacktracking package for GAP., Shorter and reworked version of arXiv:1911.04783; accepted for publication in Journal of Algebra; 35 pages, 9 figures, 4 tables

This article investigates cybersecurity (and risk) in the context of ‘technological singularity’ from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

Dynamic Binary Translation (DBT) is a powerful approach to support cross-architecture emulation of unmodified binaries. However, DBT systems face correctness and performance challenges, when emulating concurrent binaries from strong to weak memory consistency architectures. As a matter of fact, we report several translation errors in QEMU, when emulating x86 binaries on Arm hosts. To address these challenges, we propose an end-to-end approach that provides correct and efficient emulation for weak memory model architectures. Our contributions are twofold: First, we formalize QEMU’s intermediate representation’s memory model, and use it to propose formally verified mapping schemes to bridge the strong-on-weak memory consistency mismatch. Second, we implement these verified mappings in Risotto, a QEMU-based DBT system that optimizes memory fence placement while ensuring correctness. Risotto further improves performance via cross-architecture dynamic linking of native shared libraries and faster yet correct translation of compare-and-swap operations. We evaluate Risotto using multi-threaded benchmark suites and real-world applications, and show that Risotto improves the emulation performance by 6.7% on average over “erroneous” QEMU, while ensuring correctness. Postprint

WebAssembly is gaining more and more popularity, finding applications beyond the Web browser -- for which it was initially designed for.However, its performance, which developers aimed at being comparable to native, requires further tuning, and has not been extensively studied to pinpoint the cause of various overheads.This paper identifies that WebAssembly's unique safety mechanisms, the major one being bounds-checked memory accesses, may introduce up to a 650% overhead.Therefore, we evaluate four popular WebAssembly runtimes against native compiled code.These runtimes have been enriched with modern bounds checking mechanisms and run on three different ISAs, including x86-64, Armv8 and RISC-V RV64GC.We show that performance-oriented runtimes are able to achieve performance within 20% of native on x86_64 platforms, and 35% for Armv8. On RISC-V the V8 runtime can achieve a 17% overhead over native code for simple numeric kernels.For simple numerical kernels, we have shown that there is no significant difference in the WebAssembly performance compared to native code across different ISAs.We also show that in the case of multithreaded scaling of the tested runtimes, which might for example be used to quickly scale up serverless instances for a single function without the overhead of spawning new processes, the default approach taken by WAVM, Wasmtime, and V8 of using the mprotect syscall to resize memory can cause excessive locking in the Linux kernel and so present an alternative userfaultfd-based solution to mitigate this issue.We share our results, and the tools and scripts under an open source license for other researchers to replicate our results, and monitor the progress that WebAssembly runtimes make as this technology evolves.