Concurrent Model Research Articles

Nurturing Pre-Service and In-Service Education as The Pathway to Fostering The Teacher Leadership Skills

Professional development is claimed by teachers as urgent, considering that the profession itself refers to a concrete manifestation of a combination of educational legislation, pedagogical theory, and practice in schools. This research aims to analyze the effect of pre-service and in-service education on teacher professionalism. This type of research is qualitative. This study uses a mixed methods approach with a concurrent transformative strategy model. The population includes 251 school principals. The sampling technique used was convenience sampling by distributing questionnaires to 202 returning schools. Data was collected through questionnaires, written tests, interviews, and document studies. Data analysis used Structural Equation Modeling (SEM) and interpretive descriptive models. The results of the data analysis are; first, the influence of pre-service education is significant and positive on teacher leadership. Likewise, the effect on professionalism was also substantial and positive. Second, in-service education is known to significantly and positively impact teacher leadership and professionalism. Third, teacher leadership was found to significantly and positively affect professionalism. It was concluded that pre and service education completely and significantly affected professionalism through teacher leadership. The results of this study can be used as a guideline for increasing teacher professionalism in terms of leadership.

Putting Weak Memory in Order via a Promising Intermediate Representation

We investigate the problem of developing an "in-order" shared-memory concurrency model for languages like C and C++, which executes instructions following their program order, and is thus more amenable to reasoning and verification compared to recent complex proposals with out-of-order execution. We demonstrate that it is possible to fully support non-atomic accesses in an in-order model in a way that validates all compiler optimizations that are performed in single-threaded code (including irrelevant load introduction). The key to doing so is to utilize the distinction between a source model (with catch-fire semantics) and an intermediate representation (IR) model (with undefined value for racy reads) and formally establish the soundness of mapping from source to IR. As for relaxed atomic accesses, an in-order model must forbid load-store reordering. We discuss the rather limited performance impact of this fact and present a pragmatic approach to this problem, which, in the long term, requires a new kind of hardware store instructions for implementing relaxed stores. The source and IR semantics proposed in this paper are based on recent versions of the promising semantics, and the correctness proofs of the mappings from the source to the IR and from the IR to Armv8 are mechanized in Coq. This work is the first to formally relate an in-order source model and an out-of-order IR model with the goal of having an in-order source semantics without any performance overhead for non-atomics.

Measuring student well-being in adolescence: proposal of a five-factor integrative model based on PISA 2018 survey data

Much debate exists concerning the factorial dimensionality of student well-being. We contribute to this debate by drawing on PISA 2018 data from a total of 61,722 students, 2528 schools, and nine countries. For our investigation, we test multiple associated measurement models for convergent, discriminant, and concurrent validity, model fit, and measurement invariance. For the PISA 2018 sample, we found very little variation in student well-being both between schools and between countries suggesting that variation in student well-being exists predominantly within respective school contexts. In addition, our findings support the notion that student life-satisfaction should be split into separate general and school-based factors in a Bi-Dimensional Life-Satisfaction Model. Moreover, results suggest that PISA 2018 data can be used to successfully measure a Four-Factor Hedonic Model of Student Well-being, tackling both cognitive (general life-satisfaction and satisfaction with school factors) and affective features (positive and negative affect). Finally, we propose a Five-Factor Integrative Model of Student Well-being that supports a broader conceptualization of student well-being that includes life-satisfaction, both positive and negative affect, and eudaemonia. We tested this model and found that it met the requirements for scalar invariance across male and female gender groups. However, for all 36 inter-country tests of measurement invariance, metric invariance was only reached nine times (25%), and scalar invariance was reached once (2.8%). Implications and recommendations for follow up research are provided.

CF4FL: A Communication Framework for Federated Learning in Transportation Systems

Federated Learning (FL) is a promising technique to enhance the safety and efficiency of intelligent transportation systems. While FL has been extensively studied, the communication and networking challenges related to the operations of FL in dynamic yet dense vehicular networks remain under-explored. Limited storage and communication capacities of individual vehicles throttle the timely training of an FL model in distributed vehicular networks. In this paper, we present a communication framework for FL (CF4FL) in transportation systems. CF4FL aims to accelerate the convergence of FL training process through the innovation of two complementary networking components: (i) a deadline-driven vehicle scheduler (DDVS), and (ii) a concurrent vehicle polling scheme (CVPS). DDVS identifies a subset of vehicles for local model training in each iteration of FL, with the aim of minimizing data loss while respecting the deadline constraints derived from vehicles’ storage, computation, and energy budgets. CVPS takes advantage of multiple antennas on an edge server to enable concurrent local model transmissions in dynamic vehicular networks, thereby reducing the airtime overhead of each FL iteration. We have evaluated CF4FL through a blend of experimentation and simulation. Trace-driven simulation shows that, compared to existing scheduling and transmission schemes, CF4FL reduces the convergence time of FL training by 39%.

In-service training governance, for elementary school teachers in Indonesia

This research seeks to explore the governance of in-service training for elementary school teachers so they can be involved in various types, techniques, and training materials. The research design was categorized as mixed methods using both quantitative and qualitative methods. Applied to the concurrent transformative strategy model. The author analyzes perspectives from various frameworks, to get comprehensive results. The finding is that there are still a group of elementary school teachers who have not received in-service training, some have repeatedly received in-service training, and some have even received in-service training more than 5 times. The governance of in-service training in the form of a roadmap has the values of fairness, equity and proportionality in distributing training services to elementary school teachers which are supported by digital-based platforms. So that teachers can easily access, download, document training materials and evidence, and the government as teacher managers can use their track record to assess professionalism and give them the opportunity to become teacher leaders.

Concurrent topology optimization design for CNT orientation and CNTRC layout

In this study, the topology optimization (TO) design of carbon nanotube (CNT) orientation and material layout for carbon nanotube-reinforced composites (CNTRCs) is performed for the first time. The equivalent performance of CNTRCs is calculated by the extended easy-to-implement rule of mixtures. Then, by considering two independent angle variables, a 3-dimensional (3D) concurrent optimization model for CNT orientation and material layout is constructed. To decrease the possibility of falling into local optimal solutions, a discrete interval method is proposed to divide the search space into specific subintervals, and thus the original optimization problem is changed into material selection and subinterval angle optimization problems. In addition, different from conventional fiber-reinforced composites, CNTRCs employ nanoscale filler as reinforcement, which makes it more beneficial to achieve local material strengthening. Therefore, to decrease the possibility of local optimal solutions and achieve local structural strengthening, a concurrent TO model involving CNTRCs and isotropic materials under the volume constraint is proposed to achieve the optimal balance between structural stiffness and economy. Subsequently, the sensitivities of structural compliance with respect to design, selection, and angle variables are derived. Also, the effectiveness of the proposed method is verified by 2D and 3D examples.

Isla: integrating full-scale ISA semantics and axiomatic concurrency models (extended version)

Architecture specifications such as Armv8-A and RISC-V are the ultimate foundation for software verification and the correctness criteria for hardware verification. They should define the allowed sequential and relaxed-memory concurrency behaviour of programs, but hitherto there has been no integration of full-scale instruction-set architecture (ISA) semantics with axiomatic concurrency models, either in mathematics or in tools. These ISA semantics can be surprisingly large and intricate, e.g. 100k+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$+$$\\end{document} lines for Armv8-A. In this paper we present a tool, Isla, for computing the allowed behaviours of concurrent litmus tests with respect to full-scale ISA definitions, in the Sail language, and arbitrary axiomatic relaxed-memory concurrency models, in the Cat language. It is based on a generic symbolic engine for Sail ISA specifications. We equip the tool with a web interface to make it widely accessible, and illustrate and evaluate it for Armv8-A and RISC-V. The symbolic execution engine is valuable also for other verification tasks: it has been used in automated ISA test generation for the Arm Morello prototype architecture, extending Armv8-A with CHERI capabilities, and for Iris program-logic reasoning about binary code above the Armv8-A and RISC-V ISA specifications. By using full-scale and authoritative ISA semantics, Isla lets one evaluate litmus tests using arbitrary user instructions with high confidence. Moreover, because these ISA specifications give detailed and validated definitions of the sequential aspects of systems functionality, as used by hypervisors and operating systems, e.g. instruction fetch, exceptions, and address translation, our tool provides a basis for developing concurrency semantics for these. We demonstrate this for the Armv8-A instruction-fetch and virtual-memory models and examples of Simner et al.

Modeling Cues May Reduce Sway Following Sit-To-Stand Transfer for People with Parkinson's Disease.

Cues are commonly used to overcome the effects of motor symptoms associated with Parkinson's disease. Little is known about the impact of cues on postural sway during transfers. The objective of this study was to identify if three different types of explicit cues provided during transfers of people with Parkinson's disease results in postural sway more similar to healthy controls. This crossover study had 13 subjects in both the Parkinson's and healthy control groups. All subjects completed three trials of uncued sit to stand transfers. The Parkinson's group additionally completed three trials of sit to stand transfers in three conditions: external attentional focus of reaching to targets, external attentional focus of concurrent modeling, and explicit cue for internal attentional focus. Body worn sensors collected sway data, which was compared between groups with Mann Whitney U tests and between conditions with Friedman's Tests. Sway normalized with modeling but was unchanged in the other conditions. Losses of balance presented with reaching towards targets and cueing for an internal attentional focus. Modeling during sit to stand of people with Parkinson's disease may safely reduce sway more than other common cues.

Retraction

Retraction: Feng X, Shi H, Wang J, Wang S. Green intelligent financial system construction paradigm based on deep learning and concurrency models. J Concurrency Computat Pract Exper. 2021; 33(12).The above article, published online on 28 Apr 2020 Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/cpe.5784) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies were found, including general logical flaws and irrelevant citations, making the motivation and conclusion of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.

WiscSort: External Sorting for Byte-Addressable Storage

We present WiscSort, a new approach to high-performance concurrent sorting for existing and future byte-addressable storage (BAS) devices. WiscSort carefully reduces writes, exploits random reads by splitting keys and values during sorting, and performs interference-aware scheduling with thread pool sizing to avoid I/O bandwidth degradation. We introduce the BRAID model which encompasses the unique characteristics of BAS devices. Many state-of-the-art sorting systems do not comply with the BRAID model and deliver sub-optimal performance, whereas WiscSort demonstrates the effectiveness of complying with BRAID. We show that WiscSort is 2-7 x faster than competing approaches on a standard sort benchmark. We evaluate the effectiveness of key-value separation on different key-value sizes and compare our concurrency optimizations with various other concurrency models. Finally, we emulate generic BAS devices and show how our techniques perform well with various combinations of hardware properties.

A U.S. National Study of Family Resilience During the COVID-19 Pandemic.

The COVID-19 pandemic has significantly disrupted the lives of children and their caregivers. Recent research has examined the impact of the pandemic on child and caregiver functioning but there is a paucity of work examining the impact of the pandemic on the broader family system. The current study examined family resilience during the COVID-19 pandemic across three aims: Aim 1 tested whether meaning, control, and emotion systems form a unitary family adaption factor, Aim 2 evaluated a concurrent model of family resilience, and Aim 3 examined whether parent gender and vaccination status moderated paths in the final model. A nationally representative sample of U.S. parents (N = 796; 51.8% fathers, M age = 38.87 years, 60.3% Non-Hispanic White) completed a cross-sectional survey about themselves and one child (5-16 years old) between February-April 2021, including measures of COVID-19 family risk and protective factors, pre-existing family health vulnerabilities, race, COVID-19 stressors, and family adaptation. Confirmatory Factor Analysis demonstrated that the meaning (i.e., family making meaning of COVID-19), control (i.e., stability in routines), and emotional (i.e., family support) facets of family adaptation are unique but related. A path model revealed that there were concurrent effects from COVID-19 exposure, pre-existing vulnerabilities, and racial diversity status to the family protective, vulnerability, and adaptation variables. Additionally, parent COVID-19 vaccination status altered the association between pre-existing family health vulnerabilities and the family protective factor. Overall, results underscore the importance of examining pre-existing and concurrent risk and protective factors for family resilience during a stressful, global, and far-reaching event.

Novel specification tests for synchronous additive concurrent model formulation based on martingale difference divergence

This paper presents new specification tests for a general synchronous additive concurrent model formulation. As a novelty, our proposal does not require a preliminary model or error structure estimation. No tuning parameters are involved either. We develop a suitable test statistic using the martingale difference divergence coefficient. As a result, this statistic measures the departure from the conditional mean independence in the concurrent model framework, considering the information of all observed time instants. In particular, global as well as partial dependence tests are introduced. Then, we allow one to quantify the effect of a group of covariates or to apply covariates selection one by one. We obtain its asymptotic distribution under the null and propose a bootstrap algorithm to compute the p-values in practice. Through simulations, we illustrate our method, and its performance is compared to existing competitors. In addition, we use this in the analysis of three real datasets related to gait data, flu activity, and casual bike rentals.

Identification of piecewise-linear mechanical oscillators via Bayesian model selection and parameter estimation

The problem of identifying single degree-of-freedom (SDOF) nonlinear mechanical oscillators with piecewise-linear (PWL) restoring forces is considered. PWL nonlinear systems are a class of models that specify or approximate nonlinear systems via a set of locally-linear maps, each defined over different operating regions. They are useful in modelling hybrid phenomena common in practical situations, such as, systems with different modes of operation, or systems whose dynamics change because of physical limits or thresholds. However, identifying PWL models can be a challenging task when the number of operating regions and their partitions are unknown. This paper formulates the identification of oscillators with PWL restoring forces as a task of concurrent model selection and parameter estimation, where the selection of the number of linear regions is treated as a model selection task and identifying the associated system parameters as a task of parameter estimation. In this study, PWL maps in restoring forces with up to four regions are considered, and the task of model selection and parameter estimation task is addressed in a Bayesian framework. A likelihood-free Approximate Bayesian Computation (ABC) scheme is followed, which is easy to implement and provides a simplified way of doing model selection. The proposed approach has been demonstrated using two numerical examples and an experimental study, where ABC has been used to select models and identify parameters from among four SDOF PWL systems with different number of PWL regions. The results demonstrate the flexibility of using the proposed Bayesian approach for identifying the correct model and parameters of PWL systems, in addition to furnishing uncertainty estimates of the identified parameters.

Communicating Actor Automata - Modelling Erlang Processes as Communicating Machines

Brand and Zafiropulo's notion of Communicating Finite-State Machines (CFSMs) provides a succinct and powerful model of message-passing concurrency, based around channels. However, a major variant of message-passing concurrency is not readily captured by CFSMs: the actor model. In this work, we define a variant of CFSMs, called Communicating Actor Automata, to capture the actor model of concurrency as provided by Erlang: with mailboxes, from which messages are received according to repeated application of pattern matching. Furthermore, this variant of CFSMs supports dynamic process topologies, capturing common programming idioms in the context of actor-based message-passing concurrency. This gives a new basis for modelling, specifying, and verifying Erlang programs. We also consider a class of CAAs that give rise to freedom from race conditions.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction.

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

Functional concurrent hidden Markov model

Functional concurrent hidden Markov model

Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP

Practitioners have used hidden Markov models (HMMs) in different problems for about sixty years. Moreover, conditional random fields (CRFs) are an alternative to HMMs and appear in the literature as different and somewhat concurrent models. We propose two contributions: First, we show that the basic linear-chain CRFs (LC-CRFs), considered as different from HMMs, are in fact equivalent to HMMs in the sense that for each LC-CRF there exists an HMM—that we specify—whose posterior distribution is identical to the given LC-CRF. Second, we show that it is possible to reformulate the generative Bayesian classifiers maximum posterior mode (MPM) and maximum a posteriori (MAP), used in HMMs, as discriminative ones. The last point is of importance in many fields, especially in natural language processing (NLP), as it shows that in some situations dropping HMMs in favor of CRFs is not necessary.

Reaching for the Sky: Maximizing Deep Learning Inference Throughput on Edge Devices with AI Multi-Tenancy

The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive growth in data generation at the edge of the Internet over the past decade. Intelligent real-time analysis of such a high volume of data, particularly leveraging highly accurate deep learning (DL) models, often requires the data to be processed as close to the data sources (or at the edge of the Internet) to minimize the network and processing latency. The advent of specialized, low-cost, and power-efficient edge devices has greatly facilitated DL inference tasks at the edge. However, limited research has been done to improve the inference throughput (e.g., number of inferences per second) by exploiting various system techniques. This study investigates system techniques, such as batched inferencing, AI multi-tenancy, and cluster of AI accelerators, which can significantly enhance the overall inference throughput on edge devices with DL models for image classification tasks. In particular, AI multi-tenancy enables collective utilization of edge devices’ system resources (CPU, GPU) and AI accelerators (e.g., Edge Tensor Processing Units; EdgeTPUs). The evaluation results show that batched inferencing results in more than 2.4× throughput improvement on devices equipped with high-performance GPUs like Jetson Xavier NX. Moreover, with multi-tenancy approaches, e.g., concurrent model executions (CME) and dynamic model placements (DMP), the DL inference throughput on edge devices (with GPUs) and EdgeTPU can be further improved by up to 3× and 10×, respectively. Furthermore, we present a detailed analysis of hardware and software factors that change the DL inference throughput on edge devices and EdgeTPUs, thereby shedding light on areas that could be further improved to achieve high-performance DL inference at the edge.

Thermal and mass transfer aspects of MHD flow across an exponentially stretched sheet with chemical reaction

This investigation deals with the two-dimensional steady problem incorporating the magneto hydrodynamic effect with three distinct boundary layer streams of fluid across an exponentially stretched sheet under the influence of thermal radiation and chemical reactions. The concurrent flow model also accounts for the effect of Thermophoresis and Brownian movement, caused by temperature and concentration gradients between two boundaries, respectively. By using suitable similarity transformation variables, the nonlinear systems of governing PDEs are transformed into a system of nonlinear ODEs. The findings, obtained by using the Bvp4c strategy, are analysed and depicted graphically and through tables. Furthermore, the Nusselt number, Sherwood number and drag coefficient, which are crucial for industrial applications, are also shown in the table to demonstrate the impact of dimensionless physical parameters. In response to a steady rise in Lewis number and chemical reaction parameter, the heat transit rate tends to decline while the species transit rate increases.

Concurrent multi-material and multi-scale design optimization of fiber-reinforced composite material and structures for minimum structural compliance

With the development of additive manufacturing technology for fiber-reinforced composite materials, topology optimization of fiber-reinforced composite laminates involving multiple materials and variable stiffness is gaining increasing attention. This study proposes an effective methodology for a Multi-scale and Multi-material Composite Anisotropic Penalization (MMCAP) model to investigate multi-scale and multi-material design optimization of a fiber-reinforced variable stiffness (VS) composite structure to minimize structural compliance. In the concurrent MMCAP model, the macroscopic multi-material structural topology and microscopic discrete fiber laying angle selection are introduced as independent design variables and optimized simultaneously. The modified Solid Isotropic Material with Penalization (SIMP) and the Discrete Material Optimization (DMO) approaches are utilized at the macro- and micro-scales, respectively, to realize a clear macroscopic multi-material structural topology and microscopic specific discrete fiber laying angle selection. Multi-material fiber-reinforced plastic (FRP) materials, such as carbon fiber–reinforced plastic (CFRP) and glass fiber–reinforced plastic (GFRP), are considered two types of solid materials in terms of structural volume cost. Sensitivity analysis of the structural compliance concerning the variables of the two geometrical scales is performed using the analytical sensitivity analysis method. The DMO approach is utilized to couple two geometrical scales: macroscopic topology and microscopic material selection. The capabilities of the proposed MMCAP are demonstrated by concurrent multi-material and multi-scale design optimization of composite panels. The influence of the number of discrete fiber laying angles on the structural compliance and optimized topology configuration is also been discussed. Numerical studies showed that the proposed MMCAP scheme can effectively realize multi-material and multi-scale design optimization of fiber-reinforced composite structure with achieving a clear macroscopic multi-material structural topology and microscopic fiber laying angle. The proposed MMCAP scheme provides a new implementation strategy for lightweight, multi-material, and multi-scale design optimization of composite materials, considering the design and manufacturing collaboration through additive manufacturing technology.