Pairwise Approach Research Articles

Lightweight Pairwise Key Distribution Scheme for IoTs

Embedding a pairwise key distribution approach in IoT systems is challenging as IoT devices have limited resources, such as memory, processing power, and battery life. This paper presents a secure and lightweight approach that is applied to IoT devices that are divided into Voronoi clusters. This proposed algorithm comprises XOR and concatenation operations for interactive authentication between the server and the IoT devices. Predominantly, the authentication is carried out by the server. It is observed that the algorithm is resilient against man-in-the-middle attacks, forward secrecy, Denial of Service (DoS) attacks, and offers mutual authentication. It is also observed that the given scheme has low communication and computing overheads compared to some existing methods.

Antithrombotic therapy and cardiovascular outcomes after transcatheter aortic valve implantation in patients without indications for chronic oral anticoagulation: a systematic review and network meta-analysis of randomized controlled trials.

As the antithrombotic regimen that may best prevent ischaemic complications along with the lowest bleeding risk offset following transcatheter aortic valve implantation (TAVI) remains unclear, we aimed to compare the safety and efficacy of antithrombotic regimens in patients without having an indication for chronic oral anticoagulation. We conducted a PROSPERO-registered (CRD42021247924) systematic review and network meta-analysis of randomized controlled trials evaluating post-TAVI antithrombotic regimens up to April 2022. We estimated the relative risk (RR) and 95% confidence intervals (95% CIs) using a random-effects model in a frequentist pairwise and network metanalytic approach. We included seven studies comprising 4006 patients with a mean weighted follow-up of 12.9 months. Risk of all-cause death was significantly reduced with dual antiplatelet therapy (DAPT) compared with low-dose rivaroxaban+3-month single antiplatelet therapy (SAPT) (RR 0.60, 95% CI 0.41-0.88), while no significant reduction was observed with SAPT vs. DAPT (RR 1.02, 95% CI 0.67-1.58) and SAPT and DAPT compared with apixaban or edoxaban (RR 0.60, 95% CI 0.32-1.14 and RR 0.59, 95% CI 0.34-1.02, respectively). SAPT was associated with a significant reduction of life-threatening, disabling, or major bleeding compared with DAPT (RR 0.45, 95% CI 0.29-0.70), apixaban or edoxaban alone (RR 0.45, 95% CI 0.25-0.79), and low-dose rivaroxaban+3-month SAPT (RR 0.30, 95% CI 0.16-0.57). There were no differences between the various regimens with respect to myocardial infarction, stroke, or systemic embolism. Following TAVI in patients without an indication for chronic oral anticoagulant, SAPT more than halved the risk of bleeding compared with DAPT and direct oral anticoagulant-based regimens without significant ischaemic offset.

Checking Just Pairs of Threads for Efficient and Scalable Incremental Verification of Multithreaded Programs

Many techniques of automated verification target multithreaded programs, because subtle interactions between threads may trigger concurrency errors such as deadlocks and data races. However, techniques and tools involving systematic exploration of the whole space of possible thread interleavings do not scale to large software systems, despite various clever algorithmic optimizations. A viable approach is to use incremental verification techniques that, in each run, focus just on the recently modified code and the relatively small number of affected execution traces, and therefore can provide results (bug reports) very quickly. In this paper we present a new algorithm for incremental verification of multithreaded programs based on the pairwise approach, whose key idea is systematic exploration of all possible thread interleavings just for specific relevant pairs of threads. We implemented the algorithm with Java Pathfinder as the backend verification tool, and evaluated it on several multithreaded Java programs. Results show that our incremental algorithm (1) can find errors very fast, (2) greatly reduces time needed for complete safety verification, and (3) it can find the same errors as full verification of the whole state space.

What drives the designation of protected areas? Accounting for spatial dependence using a composite marginal likelihood approach

Previous research indicates that policymakers make biodiversity conservation decisions with the goal of minimizing opportunity costs, as opposed to balancing conservation benefits and opportunity costs. However, such research used coarse biodiversity data and did not consider spatial dependence in observed conservation decisions. The present study estimates conservation choice models for two European countries (Spain and Italy) that, for the first time, include fine-resolution indicators of biodiversity conservation benefits and opportunity costs, and that account for spatial dependence using a pairwise composite marginal likelihood approach. For the preferred model specifications, we find that a 1% increase in species richness levels is associated with increases in the probability of protection of 0.59% and 0.22% in Spain and Italy, respectively. We also find evidence of spatial correlation and that accounting for it substantially affects the elasticity effects implied by the logit regression models. Although our findings confirm that protected area designations are consistently negatively associated with the potential for productive land-uses, local biodiversity levels may have played a larger role in protected area location decisions than suggested previously.

Equitable Allocation of Operations and Makespan Minimization for Autonomous Agents

We study the problem of allocating a set of indivisible operations to a set of agents in a fair and efficient manner while also minimizing the makespan. We first present the Operation Trading Algorithm that generates allocations satisfying the DEQx (Duplicated Equitability up to any operation) fairness criterion while also guaranteeing an upper bound of 2 on the makespan for identical agents. The pairwise approach used in this algorithm has the added advantages of being decentralizable and robust. We then define a relaxed version of the DEQ1 (Duplicated Equitability upto some operation) fairness criterion called partial-DEQ1. A market based algorithm is presented to achieve partial-DEQ1 along with Pareto Optimality. Following this, it is shown that the algorithm also guarantees an upper bound of 1.618 on the makespan for 2 non-identical agents. Parametric Pruning further improves the upper bound to 1.5 which is theoretically the best possible upper bound. To the best of our knowledge, these are the first algorithms designed to achieve the mentioned fairness criteria. The algorithms additionally guarantee upper bounds on the makespan. Finally, we show the efficacy of the algorithms in generating allocations with near optimal makespans by numerically evaluating the algorithms on random instances. Note to Practitioners—This paper provides fast algorithms that can be used for task allocation among any number of agents. The algorithms generate allocations that satisfy fairness criteria along with minimizing the makespan, thus achieving two practically important criteria simultaneously. Additionally, some of the algorithms introduced can be implemented in a reactive manner thus making it easy to deal with sudden changes. The algorithms can also be implemented in a decentralized manner in environments where maintaining a centralized situational awareness is difficult due to communication and machine failures.

A pairwise pseudo-likelihood approach for the additive hazards model with left-truncated and interval-censored data

A pairwise pseudo-likelihood approach for the additive hazards model with left-truncated and interval-censored data

How convergent are rice export prices in the international market?

AbstractThis study revisits the issue of long‐term price convergence of rice export prices for India, Pakistan, Thailand, Uruguay, the United States, and Vietnam using a two‐stage pairwise unit root testing approach. To deduce evidence or lack of proof of convergence in price series, we also examine convergence using sigma and beta convergence specified in both unconditional and conditional frameworks. The methodology used is driven by the need to address three key concerns: (i) the likelihood of finding stationary price differentials, (ii) the magnitude of these differentials, and (iii) their speed of adjustment. To evaluate these concerns, we use monthly data for 18 price series drawn from these six countries from September 2011 to February 2021. The evidence points to a lack of international convergence. This gives rise to the possibility that a shield from a general downward export price trend is already in place for some exporters. Furthermore, we find that the likelihood of convergence is greater between pairs of price series that are characterized as high quality or having the same country of origin or having similar market share. Evidence also suggests that a converged pairing is more likely to have a smaller price differential if both price series are for low‐quality rice.

Deep Fake Detection Using Computer Vision-Based Deep Neural Network with Pairwise Learning

Deep learning-based approaches are applied successfully in many fields such as deepFake identification, big data analysis, voice recognition, and image recognition. Deepfake is the combination of deep learning in fake creation, which states creating a fake image or video with the help of artificial intelligence for political abuse, spreading false information, and pornography. The artificial intelligence technique has a wide demand, increasing the problems related to privacy, security, and ethics. This paper has analyzed the features related to the computer vision of digital content to determine its integrity. This method has checked the computer vision features of the image frames using the fuzzy clustering feature extraction method. By the proposed deep belief network with loss handling, the manipulation of video/image is found by means of a pairwise learning approach. This proposed approach has improved the accuracy of the detection rate by 98% on various datasets.

Test scenario generation for feature-based context-oriented software systems

Feature-based context-oriented programming reconciles ideas from context-oriented programming, feature modelling and dynamic software product lines. It offers a programming language, architecture, tools and methodology to develop software systems consisting of contexts and features that can become active at run-time to offer the most appropriate behaviour depending on the actual context of use. Due to their high run-time adaptivity, dedicated tool support to test such systems is needed. Building upon a pairwise combinatorial interaction testing approach from the domain of software product lines, we implement an algorithm to generate automatically a small set of relevant test scenarios, ordered to minimise the number of context activations between tests. We also explore how the generated scenarios can be enhanced incrementally when the software evolves, and how useful the proposed testing approach is in practice.

Comparative study of regression vs pairwise models for surrogate-based heuristic optimisation

Heuristic optimisation is a popular tool for solving problems in the sciences and engineering fields. These algorithms explore the search space by sampling solutions, evaluating their fitness, and biasing the search in the direction of promising solutions. However, in many cases, this fitness function involves executing expensive computational calculations, drastically reducing the reasonable number of evaluations. In this context, surrogate models have emerged as an excellent alternative to alleviate these computational problems. This paper addresses the formulation of surrogate problems as both regression models that approximate fitness (surface surrogate models) and a novel way to connect classification models (pairwise surrogate models). The pairwise approach can be directly exploited by some algorithms, such as Differential Evolution, in which the fitness value is not actually needed to drive the search, and it is sufficient to know whether a solution is better than another one or not. Based on these modelling approaches, we have conducted a multidimensional analysis of surrogate models under different configurations: different machine learning algorithms (regularised regression, neural networks, decision trees, boosting methods, and random forests), different surrogate strategies (encouraging diversity or relaxing prediction thresholds), and compare them for both surface and pairwise surrogate models. The experimental part of the article includes the benchmark problems already proposed for the SOCO’2011 competition in continuous optimisation and a simulation problem included in the recent GECCO’2021 Industrial Challenge. This paper shows that the performance of the overall search, when using online machine learning-based surrogate models, depends not only on the accuracy of the predictive model but also on both the kind of bias towards positive or negative cases and how the optimisation uses those predictions to decide whether to execute the actual fitness function.

Investigation on factors related to poor CPAP adherence using machine learning: a pilot study

To improve patients’ adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.

Security and privacy concerns in mobile payment services

Mobile technology is becoming more central to human life, especially with the current ongoing global pandemic which points out that there will be an increasing need for mobile payment service as it requires little or no contact in making payments. However, mobile payment is not among the most commonly used mobile utilities because of the security and privacy concerns posed by mobile payment. The prime aim of this study is to determine the security and privacy concerns with the highest existence and then address them by identifying a rational and objectively agreed approach on how to resolve the problems discovered. A sensitive Analytical Hierarchy Process (AHP) Pairwise Comparison approach will be used to solve the security and privacy challenges associated with mobile payment services. This study shows that leakage of personal data is the highest risk concern by most users of the mobile payment system (MPS) while loss of “intended purchase” has the lowest risk concern from what we could derive. Keywords: AHP Model; Mobile Technology; Mobile Payment Services; Pairwise Comparison.

An approach based on virtual samples for gasoline discrimination using physicochemical properties or distillation curves

Forensic cases such as fuel theft may require comparison of gasoline samples to decide whether or not gasolines seized at different locations are from the same origin. For example, a police investigation may need to identify if a gasoline sample seized from a tanker truck is the same as a sample from a filling station tank. This work proposes a novel methodology based on gasoline physicochemical properties or distillation curves enabling comparison of gasoline samples in pairs. A pair-wise comparison approach generally requires repeated measurements. Aiming to overcome the need for numerous repeated measurements, a computational approach to generate virtual samples from real gasoline measurements was developed. Measurement uncertainties of the methods were considered in this approach. The real and virtual samples were then used to compose the data set evaluated by pattern recognition techniques. Principal component analysis (PCA) was applied for exploratory studies and soft independent modeling of class analogy (SIMCA) was used for classification. Fifty-four common gasoline samples containing anhydrous ethanol fuel (27–30%v/v) collected from fuel stations located in the state of Pernambuco (Brazil) were used in this study. The 1431 possible pairs of samples were evaluated by SIMCA, with between 96% and 99% of them completely distinguishable when physicochemical assays and distillation curves were employed, respectively. These results suggest that the proposed methodology could be useful for helping in investigations requiring this type of gasoline comparison as fast as possible. Moreover, the strategy developed could also be extended to other applications that require discrimination of samples in pairs.

Proposing a new LSGDM framework based on BWM with hesitant fuzzy information for prioritizing blockchain adoption barriers in supply chain

Due to the vast advantages of Blockchain technology, especially transparency of information and the ability to track transactions, this technology has been considered in several areas, including supply chain management (SCM). However, due to the emergence of this technology, numerous barriers and limitations have hindered its use. Due to the special conditions of developing countries (such as Iran), there is still a need for independent research to localize these barriers in these countries. On the other hand, most of the research has focused more on identifying barriers and less on prioritizing them. Examining the specific features of the Blockchain technology field shows that there is a need to provide a decision-making framework based on large-scale group decision-making methods (LSGDM), which, in addition to simultaneously paying attention to the advantages of previous approaches (including the participation of different groups of stakeholders; the consensus reaching process (CRP) as well as the uncertainty and doubt of the stakeholders in the evaluations), can increase the applicability of the method by reducing the number of pairwise comparisons. Therefore, while identifying and localizing the list of barriers in Iran, the LSGDM method along with the proposed consensus framework can be used to aggregate the opinions between the experts in a network and create a relative consensus between them. The use of hesitant fuzzy (HF) sets has made it possible to model the uncertainty and hesitancy in the opinions of experts. Also, using the Best-Worst method (BWM) instead of the pairwise comparison approach between all criteria, has reduced the number of pairwise comparisons and provided other advantages such as reduced inconsistency and higher reliability. The results of applying this framework in Iran showed that lack of awareness and customer orientation, scalability and lack of management participation and support are the main barriers to the implementation of the blockchain for SCM in this developing country. The results can help decision makers and policy makers in this area to plan more accurately.

A data‐driven fault detection and fault‐tolerant control scheme for large‐scale systems and its application on multi‐area interconnected power systems

Stimulated by the enhancing requirements for system safety as well as process reliability in complex industrial processes, this paper investigates a data-driven fault detection and fault-tolerant control scheme for large-scale systems. To this end, a pair-wise decomposition approach is first presented based on the inclusion principle, which expands the original large-scale system into a set of disjointed pair-wise subsystems. Then, for the pair-wise subsystems, the observer-based residual generators and observer-based state feedback controllers for fault detection and fault-tolerant control purpose are further developed in a decentralized way. On this basis, the fault-tolerant controller for the original large-scale system is obtained by the coordination and contraction of the decentralized observer-based state feedback controllers. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated through the case study on a multi-area interconnected power system.

Comparison of the safety and efficacy of antithrombotic regimens following TAVR in patients without having an indication for chronic oral anticoagulation

Abstract Aims To compare the safety and efficacy of antithrombotic regimens following transcatheter aortic valve replacement (TAVR) in patients without having an indication for chronic oral anticoagulation Methods and results We conducted a Prospero-registered systematic review and network meta-analysis of randomized controlled trials evaluating post-TAVR antithrombotic regimens up to March 2021. We estimated the relative risk and 95% confidence intervals using a fixed effect model in a frequentist pairwise and network metanalytic approach. We included 6 studies comprising of 3,777 patients with a mean weighted follow-up of 13.3 months. Single antiplatelet therapy (SAPT) was associated with a significant reduction of life-threatening, disabling, or major bleeding compared to dual antiplatelet therapy (DAPT) (Risk Ratio [RR] 0.44, 95% confidence interval [CI]: 0.28–0.69), apixaban (RR: 0.47, 95% CI 0.26–0.84) and low-dose rivaroxaban + 3-month SAPT (RR: 0.30, 95% CI: 0.16–0.57). Risk of all-cause death was significantly reduced with DAPT compared to low-dose rivaroxaban + 3-month SAPT (RR: 0.60, 95% CI: 0.41–0.88) and a consistent reduction was observed with SAPT and DAPT compared to apixaban (RR: 0.60, 95% CI: 0.31–1.16 and RR: 0.58, 95% CI: 0.32–1.04, respectively). There were no differences between the various regimens with respect to myocardial infarction and stroke. Apixaban significantly reduced the risk of pulmonary embolism, valve thrombosis and grade 3 or 4 reduced leaflet motion. Conclusion Following TAVR in patients without an indication for chronic oral anticoagulant, SAPT was associated with the lowest risk of bleeding compared to DAPT and direct oral anticoagulant-based regimens without significant ischemic offset. Funding Acknowledgement Type of funding sources: None.

Unravelling the Relations between and Predictive Powers of Different Testing Variables in High Performance Concrete Experiments: The Data-Driven Analytical Methods

This study proposes and applies a systematic data analysis methodology to analyse experimental data for high-performance concrete (HPC) samples with different admixtures for offshore fan foundation grouting materials uses. In contrast with other relevant research, including experimental studies, the materials physics and chemistry studies, or cementitious material portfolio determination studies, this data-driven analysis provides a deep exploration of the experimental variables associated with the test data. To offer complete and in-depth perspectives, several methods are employed for the data analyses, including correlation analysis, cosine similarity analysis, simple linear regression (SLR) modelling, and heat map and heat-based tabularised visualisations; the outcome is a proposed methodology that is easily implementable. The results from these methods are validated using a pairwise comparison approach (PCA) to avoid unnecessary interference between data variables. There are several potential contributions from this work, including insights for cohered groups of variables, techniques for double check and ‘third check’, an established ‘knowledge base’ consisting of 504 SLR predictive models with their effectiveness (significance) and prediction accuracy (data-model fitness) used in practical applications, an alternative visualisations of the results, three data transforms which can be omitted in a future analysis, and three valuable theory-linking perspectives (e.g., for the relationships between destructive and non-destructive tests with respect to the variable categories). The implication that some variables are interchangeable will make future experiments less labour intensive and time consuming for pre-project HPC material testing.

Linkage of multiple electronic health record datasets using a "spine linkage" approach compared to all "pairwise linkages".

ObjectivesTo compare two approaches for linking multiple datasets: using all "pairwise linkages", linking each dataset to every other dataset; versus linking each dataset to a designated "spine dataset", by: considering the differences between these approaches, and illustrating using real-world data on patients undergoing emergency bowel cancer surgery.
 ApproachWe linked an administrative hospital dataset (Hospital Episode Statistics; HES) capturing patients admitted to hospitals in England, and two clinical datasets comprising patients undergoing emergency bowel surgery (National Emergency Laparotomy Audit; NELA) and patients diagnosed with bowel cancer (National Bowel Cancer Audit; NBOCA), with study period from 31 October 2013 to 30 April 2018. We compared pairwise linkage to spine linkage, designating HES as the spine dataset, by considering the number of eligible patients linked by each approach, characteristics of linked patients, levels of missing data, and whether analysis results were sensitive to the approach used.
 ResultsThe spine linkage approach resulted in an analysis cohort of 15,826 patients, equating to 98.3% of the 16,100 patients identified with the pairwise linkage approach. Of 274 additional patients captured in the pairwise approach, approximately two-thirds were only in the emergency surgery dataset (NELA) and one-third were only in the bowel cancer dataset (NBOCA). There were no systematic differences in patient characteristics between these analysis cohorts. Associations of patient and tumour characteristics with mortality, complications, and length of stay were not sensitive to the linkage approach. When eligibility criteria were applied before linkage, spine linkage included 14,509 patients (90.0% compared to pairwise linkage).
 ConclusionSpine linkage can be an efficient alternative to pairwise linkage, if case ascertainment in the spine dataset and data quality of linkage variables are high. These aspects should be systematically evaluated in the nominated spine dataset before spine linkage. Results are sensitive to order of linkage steps.

IDENTIFYING THE OPTIMAL PLACEMENT OF SPATIAL WIND ENERGY FARMS IN SELANGOR, MALAYSIA

Electrical energy is indispensable for the economic growth and well-being of humanity. However, there is a growing concern with the conventional method of generating electricity which is by burning coal and other fossil fuels. In the coming decades, the world will experience a transition to cleaner energy, away from the use of fossil fuels. One of the clean and renewable energies that is getting more popular and advancing in technology is wind energy. Wind energy requires a proper location to operate in order to be profitable and have the desired impact on society. In search of a suitable location for wind energy, a Geographical Information System based Multi-Criteria Decision Making (GIS-MCDM) approach was utilized in this paper to explore the suitable areas for wind energy development in Selangor. Eight parameters were chosen to address this concern, including terrain elevation, terrain slope, road network, airport locations, forests, settlements, water bodies and average wind speed. All of these criteria were weighted using the pairwise comparison approach in MCDM, and the decisionmaking process was facilitated by the criteria utilizing the ArcGIS-weighted overlay tool. The study has identified seven potential sites, but only four sites that are practically located the wind energy site, which are Banting, Jeram, Ijok and Kerling. GIS-MCDM can assist the decision-maker in designing the optimal placement of wind farms in real scenarios.

Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics.

Transmission is a driver of tuberculosis (TB) epidemics in high-burden regions, with assumed negligible impact in low-burden areas. However, we still lack a full characterization of transmission dynamics in settings with similar and different burdens. Genomic epidemiology can greatly help to quantify transmission, but the lack of whole genome sequencing population-based studies has hampered its application. Here, we generate a population-based dataset from Valencia region and compare it with available datasets from different TB-burden settings to reveal transmission dynamics heterogeneity and its public health implications. We sequenced the whole genome of 785 Mycobacterium tuberculosis strains and linked genomes to patient epidemiological data. We use a pairwise distance clustering approach and phylodynamic methods to characterize transmission events over the last 150 years, in different TB-burden regions. Our results underscore significant differences in transmission between low-burden TB settings, i.e., clustering in Valencia region is higher (47.4%) than in Oxfordshire (27%), and similar to a high-burden area as Malawi (49.8%). By modeling times of the transmission links, we observed that settings with high transmission rate are associated with decades of uninterrupted transmission, irrespective of burden. Together, our results reveal that burden and transmission are not necessarily linked due to the role of past epidemics in the ongoing TB incidence, and highlight the need for in-depth characterization of transmission dynamics and specifically tailored TB control strategies.