Average Performance Measures Research Articles

Age-related changes in reading performance in normally sighted Arabic-speaking adults

ABSTRACT Clinical relevance Reading performance is important for evaluating near vision and predicting and prescribing near-vision correction. There is a significant gap in the literature on Arabic reading performance and its associated effects. Background Normative data from control participants serve as the baseline for future studies involving groups with specific characteristics. This study aimed to assess baseline measures of Arabic reading performance. Methods In total, 428 normally sighted adults aged 19–83 years read the Balsam Alabdulkader – Leat chart to measure their reading performance. The participants were divided into five age groups. The outcome measures were the maximum reading speed in standard-length words per minute, critical print size, reading acuity, and reading accessibility index. Results The average reading performance measures were as follows: 171 ± 27 standard-length words per minute for maximum reading speed, 0.01 logarithm of the minimum angle of resolution for critical print size, −0.17 logarithm of the minimum angle of resolution for reading acuity, and 1.02 for the accessibility index. There were significant differences among the age groups, indicating a dependency on age and showing a plateau for the young adult group and a decline with age. The accessibility index had an almost perfect correlation with maximum reading speed and a weak but significant relationship with reading acuity and critical print size. Conclusion Arabic exhibits an age-dependent trend in reading speed. However, owing to differences in orthography, visual demands, and reading strategies, the magnitude of the decrease and increase differs, potentially accounting for the variations between languages, especially when compared to English. This study serves as a foundation for future studies on Arabic reading performance. Future studies should examine the reading performance in patients with low vision, study the effects of common diseases on reading ability, evaluate the effectiveness of reading devices, and assess improvements in vision rehabilitation.

Identifying Informatively Easy and Informatively Hard Concepts

In this article, we leverage ideas from the theory of coevolutionary computation to analyze interactions of students with problems. We introduce the idea of informatively easy or hard concepts. Our approach is different from more traditional analyses of problem difficulty such as item analysis in the sense that we consider Pareto dominance relationships within the multidimensional structure of student–problem performance data rather than average performance measures. This method allows us to uncover not just the problems on which students are struggling but also the variety of difficulties different students face. Our approach is to apply methods from the Dimension Extraction Coevolutionary Algorithm to analyze problem-solving logs of students generated when they use an online software tutoring suite for introductory computer programming called problets . The results of our analysis not only have implications for how to scale up and improve adaptive tutoring software but also have the promise of contributing to the identification of common misconceptions held by students and thus, eventually, to the construction of a concept inventory for introductory programming.

Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks

Energy efficiency is a key challenging task while designing routing model in Wireless Sensor Network (WSN). Various energy efficiency routing approaches are designed to transfer the data packets in a secure path through cluster head (CH), but to increase the lifespan of network and to maintain high scalability poses a complex task in WSN environment. Hence, an energy efficient and trust based routing model is designed using proposed Exponentially-Ant Lion Whale Optimization (E-ALWO) algorithm to route the data packets to receiver. However, the E-ALWO algorithm is derived by the integration of Exponentially Weighted Moving Average (EWMA) concept with Ant Lion Optimization (ALO) and Whale Optimization algorithm (WOA), respectively. However, the proposed model performs the routing process through CH such that the selection of CH is made using ALWO algorithm based on the constraints, namely energy and delay. The optimal and the secure route used for the data transferring process is computed using proposed E-ALWO algorithm based on the fitness measure. The fitness function considers the factors, namely energy, trust, delay, and distance. Moreover, the path with maximal fitness value is accepted as the routing path, by which the data is forwarded to sink node through CH. The average performance measure achieved by the proposed method by considering without attack scenario in terms of delay, residual energy, throughput, and trust is 0.1484sec, 0.4998J, 42984kbps, and 0.5885.

Mixed memory control chart based on auxiliary information for simultaneously monitoring of process parameters: An application in glass field

Cumulative sum (CUSUM) and exponentially weighted moving (EWMA) control charts, known as memory control charts, are famous to monitor a small-to-moderate shift in the process parameters (location and/or dispersion). The EWMA control chart based on auxiliary information denoted as EWMAAIB(1) and EWMAAIB(2) are advanced forms of the classical EWMA control chart to monitor the process location and dispersion, respectively. Likewise, the combined mixed EWMA-CUSUM (CMEC) control chart is used to simultaneously monitor the process parameters. This study presents the CMEC control chart based on auxiliary information, symbolized as CMECAIB control chart for simultaneous monitoring of the process parameters. The proposed CMECAIB control chart used EWMAAIB(1) and EWMAAIB(2) control charts plotting statistics as inputs in the classical CUSUM control charts. The Monte Carlo simulation is used as a computational technique for numerical results. The proposed CMECAIB control chart based on average run length performance measure is evaluated against other control charts such as CMEC, combined double mixed EWMA-CUSUM, combined CUSUM, maximum double EWMA, maximum EWMA based on auxiliary information (MaxEWMAAIB) control and maximum Hybrid EWMA based on auxiliary information (MaxHEWMAAIB) control charts. The comparison revealed the superiority of the proposed CMECAIB control chart. Besides, the CMECAIB control chart performs better as the correlation coefficient increases. Likewise, the proposed CMECAIB control chart has a better diagnostic ability for possible directions of changes against other control charts. It is vital to mention that some existing control charts such as classical CUSUM, CMEC, and maximum CUSUM are special cases of proposed CMECAIB control chart with the specific parameter’s values. Finally, to demonstrate the vitality of the proposed study from a practical point of view, a real-life application in the glass bottle manufacturing industry is also provided for users and practitioners.

Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses.

The prospective identification of children likely to develop schizophrenia is a vital tool to support early interventions that can mitigate the risk of progression to clinical psychosis. Electroencephalographic (EEG) patterns from brain activity and deep learning techniques are valuable resources in achieving this identification. We propose automated techniques that can process raw EEG waveforms to identify children who may have an increased risk of schizophrenia compared to typically developing children. We also analyse abnormal features that remain during developmental follow-up over a period of ∼4 years in children with a vulnerability to schizophrenia initially assessed when aged 9 to 12 years. EEG data from participants were captured during the recording of a passive auditory oddball paradigm. We undertake a holistic study to identify brain abnormalities, first by exploring traditional machine learning algorithms using classification methods applied to hand-engineered features (event-related potential components). Then, we compare the performance of these methods with end-to-end deep learning techniques applied to raw data. We demonstrate via average cross-validation performance measures that recurrent deep convolutional neural networks can outperform traditional machine learning methods for sequence modeling. We illustrate the intuitive salient information of the model with the location of the most relevant attributes of a post-stimulus window. This baseline identification system in the area of mental illness supports the evidence of developmental and disease effects in a pre-prodromal phase of psychosis. These results reinforce the benefits of deep learning to support psychiatric classification and neuroscientific research more broadly.

On the Relationships Between Average Channel Capacity, Average Bit Error Rate, Outage Probability, and Outage Capacity Over Additive White Gaussian Noise Channels

In the theory of wireless communications, average performance measures (APMs) are widely utilized to quantify the performance gains/impairments in various fading environments under various scenarios, and to comprehend how the factors arising from design/implementation affect system performance. To the best of our knowledge, it has not been yet discovered in the literature how these APMs relate to each other. In this article, to supplement the APM analyses available in the literature we propose that one APM can be calculated using the other APMs instead of using the end-to-end SNR distribution. Particularly, using the Lamperti’s transformation (LT), we propose a tractable approach, which we call LT-based APM analysis, to identify a relationship between any two given APMs such that it is irrespective of SNR distribution. Thereby, we introduce some novel relationships among average channel capacity (ACC), average bit error rate (ABER) and outage probability/capacity (OP/OC) performances, and accordingly present how to obtain ACC from ABER performance and how to obtain OP/OC from ACC performance in fading environments. We demonstrate that the ACC of any communications system can be evaluated empirically without using end-to-end SNR distribution. We consider some numerical examples and simulations to validate our newly derived relationships.

Supervised Filter Learning for Coronary Artery Vesselness Enhancement Diffusion in Coronary CT Angiography Images

In medical imaging, vesselness diffusion is usually performed to enhance the vessel structures of interest and reduce background noises, before vessel segmentation and analysis. Numerous learning-based techniques have recently become very popular for coronary artery filtering due to their impressive results. In this work, a supervised machine learning method for coronary artery vesselness diffusion with high accuracy and minimal user interaction is designed. The fully discriminative filter learning method jointly learning a classifier the weak learners rely on and the features of the classifier is developed. Experimental results demonstrate that this scheme achieves good isotropic filtering performances on both synthetic and real patient Coronary Computed Tomography Angiography (CCTA) datasets. Furthermore, region growing-based segmentation approach is performed over filtered images obtained by using different schemes. The proposed diffusion scheme is able to achieve higher average performance measures (87.8% ± 1.5% for Dice, 86.5% ± 1.3% for Precision and 88.5% ± 2.6% for Sensitivity). In conclusion, the developed diffusion method is capable of filtering coronary artery structures and suppressing nonvessel tissues, and can be further used in clinical practice as a real-time CCTA images preprocessing tool.

SLDeep: Statement-level software defect prediction using deep-learning model on static code features

Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose a new technique called as Statement-Level software defect prediction using Deep-learning model (SLDeep). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). We conducted experiments using 119,989 C/C++ programs within Code4Bench. The programs comprise 2,356,458 lines of code of which 292,064 lines are faulty. The benchmark comprises a diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data (that is, fault-proneness of new statements) with average performance measures 0.979, 0.570, and 0.702 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer's burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable.

Efficiency assessment of knowledge intensive business services industry in Italy: data envelopment analysis (DEA) and financial ratio analysis

PurposeWithin the service sectors, Knowledge Intensive Business Services (KIBS) play an important role in local and regional economies as sources of competitive advantages and providing knowledge-intensive inputs to the business process of small and medium-sized enterprises. This study aims to analyze the changes in financial performance of KIBS industry in Italy over the period from 2012 to 2017.Design/methodology/approachThis paper examines the efficiency of the KIBS firms by applying data envelopment analysis (DEA) to compute the Malmquist Productivity Index for the period under investigation. The DEA-based Malmquist productivity analysis is applied at firm level using a sample consisting 1.674 companies, representative of the Italian KIBS sector and related to three different NACE activity code (72-computing services; 73-research and development; 74 other professional business activities). The efficiency measures are then used to characterize KIBS firm financial performance through the analysis of average productivity patterns grouped by Italian geographical regions. The Malmquist productivity measures are decomposed into two components: efficiency change and technical change index. The overall analysis is coupled with a financial ratio analysis approach, selecting return on equity (ROE) and leverage ratio as descriptor to validate the results and better characterize differences in efficiency patterns among geographic-based groups of KIBS companies.FindingsOver the period 2015-2017, the results show that the average annual growth of the overall Malmquist productivity index was positive in nine Italian regions that represent only 17 per cent of the total KIBS firms selected. On the other side, a decrease of the average performance measure is observed for the five geographic areas that contribute to 75.7 per cent of the total sample. In general, the technological change component, as a measure of innovation, strongly limits the productivity growth behavior of KIBS industry for all geographic regions. The use of selected financial ratio does not provide additional insight to the performance investigation and further in-depth studies are needed to better evaluate the correlation between average productivity results and regional business dynamics.Practical implicationsThe study investigates the applicability of DEA-based Malmquist indices to the analysis of the productivity behavior of KIBS industry at regional level. It will be of value to provide first evidence to the policymakers to understand industry growth pattern in time frame selected and relate them to additional business factors to detect specific industry constraints.Originality/valueThe analysis in this paper contributes to the existing body of knowledge on industry performance measurement by applying specific analytical techniques to the productivity of Italian KIBS companies. The paper also contributes to the limited body of academic literature investigating KIBS industry at national level proposing a methodological framework that constitutes a first attempt to track average productivity behavior at regional level.

Developing a statistical model for average student performance in 2 general subjects in schools around Akure Metropolis, Nigeria

Academic achievement of students in schools is believed to be on the decline across the country. The blame can easily be placed at the doorstep of students, but the schools are also responsible. The research work develops a statistical model which can be used to assess average students’ performance in two subjects in 10 schools around Akure metropolis, Nigeria. Latent factors were extracted from students’ scores in 2 subjects in the 10 schools using principal component method. The factor loadings were then used to construct average performance measures for the schools.9 latent factors were extracted and found to account for over 95% of variations in student academic performance in the schools. A model of performance measure was constructed using the 9 factors. Findings of the research showed that while most factors contributed positively to higher achievements in English language the case of Mathematics was different. Majority of the factors actually affected achievement in Mathematics negatively. Only 1 factor had negative effect on achievements in English language out of 9 while 5 out of 9 affected Mathematics negatively. This showed that there were more factors affecting achievement in Mathematics than English language.

Speed-Accuracy Tradeoff

The rationale for Fitts’ law is that pointing tasks have the information-theoretic analogy of sending a signal over a noisy channel, thereby matching Shannon’s capacity formula. Yet, the currently received analysis is incomplete and unsatisfactory: There is no explicit communication model for pointing; there is a confusion between central concepts of capacity (a mathematical limit), throughput (an average performance measure), and bandwidth (a physical quantity); and there is also a confusion between source and channel coding so that Shannon’s Theorem 17 can be misinterpreted. We develop an information-theoretic model for pointing tasks where the index of difficulty (ID) is the expression of both a source entropy and a zero-error channel capacity. Then, we extend the model to include misses at rate ε and prove that ID should be adjusted to (1−ε)ID. Finally, we reflect on Shannon’s channel coding theorem and argue that only minimum movement times, not performance averages, should be considered.

Upgrade Evaluation of Traffic Signal Assets: High-resolution Performance Measurement Framework

Agencies that have large-scale traffic signal systems under their purview often have to face asset upgrade decisions. As one of the most advanced traffic control technologies, Adaptive Traffic Control Systems (ATCS) are among the options that must be taken into consideration. Having in mind the complexity of benefits and costs stemming from ATCS investments, there is a need for information-rich performance measures (PM) used in the evaluation and decision-making. However, individual PMs are often not suitable for evaluating the multidimensionality of ATCS operations, due the inherent variability of ATCS control parameters. To expand the range of PMs used in ATCS evaluation, this research develops a new PM, i.e., average arrivals on green ratio, and proposes a refinement of average delay PM to account for queue formation. The paper also presents an application framework for a multi-criteria analysis, assuming a combination of the proposed and existing PMs. In addition to presenting the analytical PM formulation, the evaluation methodology uses microsimulation for a case study comparison between actuated-coordinated and ATCS operations. The results include a comparison between previous and proposed PMs, based on the processed simulation data as well as field data. In conclusion, the proposed PMs have a high transferability potential, low data collection cost, and high data quality, thus being suitable for use in decision processes for signal asset investment. Finally, this research opens up further opportunities for advancing decision-support methods for traffic operations asset management.

A novel POMDP-based server RAM caching algorithm for VoD systems

In contrast to other services on the Internet, streaming media service needs to fetch data from local disks more frequently, since it always lasts long and the bit rate is quite high. In addition, because of the much slower reading/writing speed of disk than random access memory (RAM), adopting advisable RAM caching policy can efficiently reduce disk I/O. In this paper, we study the problem of reducing disk I/O by using a novel approach. We first provide a new popularity estimate algorithm. Then a formal optimization problem about average disk I/O is presented, and a suboptimal caching algorithm for a special case of the problem is given. Furthermore, a partially observable Markov decision process (POMDP) model is constructed for the caching problem. Based on the model, popularity is taken advantage of to predict clients’ randomized behaviors, data replacing decisions are made when the defined observations occur, and the impact of caching actions on disk performance for future infinite steps is assessed. The method of event-based optimization is applied in search of the optimal stochastic policy. Disk I/O, as the long-run average performance measure, is optimized by applying the policy-gradient algorithm. The simulation results illustrate that data required by clients could be better predicted and lower disk I/O could be achieved by using the model proposed in this paper.

Performance analysis of dual-branch selection diversity system using novel mathematical approach

In this paper, novel mathematical approach for evaluation of probability density function (PDF) of instantaneous signal-to-interference ratio (SIR) at the receiver output in interference-limited environment is proposed. Dual-branch selection combining (SC) receiver operating over correlated Weibull fading channels applying SIR algorithm is considered. Analytical expression for joint PDF of desired signal and interference at the receiver output is derived and used for evaluation of PDF of instantaneous SIR. The expression for PDF of SIR is used for system performance analysis via outage probability, average bit error probability (ABEP) and average output SIR as system performance measures. Numerical results are graphically presented showing the effects of fading severity, average SIR at the input and level of correlation on the diversity receiver performance. In addition, results obtained for the PDF of instantaneous SIR in this paper, are compared to the results when the PDF of instantaneous SIR is directly calculated.

An intelligent energy management system for large-scale charging of electric vehicles

The problem of large-scale charging of electric vehicles (EVs) with consumer-imposed charging deadlines is considered. An architecture for the intelligent energy management system (iEMS) is introduced. The iEMS consists of an admission control and pricing module, a scheduling module that determines the charging sequence, and a power dispatch module that draws power from a mix of storage, local renewable energy sources, and purchased power from the grid. A threshold admission and greedy scheduling (TAGS) policy is proposed to maximize operation profit. The performance of TAGS is analyzed and evaluated based on average and worst-case performance measures and the optimality of TAGS is established for some instances. Numerical simulations demonstrate that TAGS achieves noticeable performance gains over benchmark techniques.

A fast and accurate performance analysis of beaconless IEEE 802.15.4 multi-hop networks

We develop an approximate analytical technique for evaluating the performance of multi-hop networks based on beaconless IEEE 802.15.4 ( the “ZigBee” PHY and MAC), a popular standard for wireless sensor networks. The network comprises sensor nodes, which generate measurement packets, relay nodes which only forward packets, and a data sink (base station). We consider a detailed stochastic process at each node, and analyse this process taking into account the interaction with neighbouring nodes via certain time averaged unknown variables (e.g., channel sensing rates, collision probabilities, etc.). By coupling the analyses at various nodes, we obtain fixed point equations that can be solved numerically to obtain the unknown variables, thereby yielding approximations of time average performance measures, such as packet discard probabilities and average queueing delays. The model incorporates packet generation at the sensor nodes and queues at the sensor nodes and relay nodes. We demonstrate the accuracy of our model by an extensive comparison with simulations. As an additional assessment of the accuracy of the model, we utilize it in an algorithm for sensor network design with quality-of-service (QoS) objectives, and show that designs obtained using our model actually satisfy the QoS constraints (as validated by simulating the networks), and the predictions are accurate to well within 10% as compared to the simulation results in a regime where the packet discard probability is low.

Multi-basin and regional calibration based identification of distributed precipitation–runoff models for hourly runoff simulation: calibration and transfer of full and partial parameters

Identification of distributed precipitation–runoff models for hourly runoff simulation based on transfer of full parameters (FP) and partial parameters (PP) are lacking for boreal mid-Norway. We evaluated storage–discharge relationships based model (Kirchmod), the Basic-Grid-Model (BGM) and a simplified Hydrologiska Byråns Vattenbalansavdelning (HBV) model for multi-basins (26 catchments). A regional calibration objective function, which uses all streamflow records in the region, was used to optimize local calibration parameters for each catchment and regional parameters yielding maximum regional weighted average (MRWA) performance measures (PM). Based on regional median Nash–Sutcliffe efficiency (NSE) and NSEln (for log-transformed series) for the calibration and validation periods, the Kirchmod model performed better than the others. Parsimony of the Kirchmod model provided less parameter uncertainty for the FP case but did not guarantee parameter identifiability. Tradeoffs between parsimony and performance were observed despite advantages of parsimony to reduce parameter correlations for the PP, which requires preliminary sensitivity analysis to identify which parameters to transfer. There are potential advantages of using the MRWA method for parameter transfer in space. However, temporal validation indicated marked deterioration of the PM. The tradeoffs between parameter transfers in space and time substantiate both spatial and temporal validation of the regional calibration methodology.

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

AbstractRegionalization for prediction in ungauged basins at hourly resolution is important for water resources management (e.g., floods and hydropeaking). In the research reported in this paper, calibration of 26 catchments (39–3,090 km2) in mid-Norway was performed using hourly records and three spatially distributed (1×1 km2) precipitation–runoff models, as follows: (1) first-order nonlinear system model, (2) Hydrologiska Byrans Vattenballansavdelning (HBV) model, and (3) basic grid model. Four regionalization methods for each model [(1) parameter set yielding maximum regional weighted average performance measures (PMs), (2) regional median of optimal parameters, (3) nearest neighbor (NN), and (4) physical similarity (PS)] were evaluated and compared with three benchmarks. Parameter transfer from best regional donor and from an ideal best arbitrary single-donor, and local calibration (LC) were as benchmarks. The PS attributes include hypsometric curves, land use, drainage density, catchment area, ter...

Hypergraph and Protein Function Prediction with Gene Expression Data

Most network-based protein (or gene) function prediction methods are based on the assumption that the labels of two adjacent proteins in the network are likely to be the same. However, assuming the pairwise relationship between proteins or genes is not complete. The information a group of genes that show very similar patterns of expression and tend to have similar functions (i.e. the functional modules) is missed. The natural way overcoming the information loss of the above assumption is to represent the gene expression data as the hypergraph. Thus, in this paper, the three un-normalized, random walk, and symmetric normalized hypergraph Laplacian based semi- supervised learning methods applied to hypergraph constructed from the gene expression data in order to predict the functions of yeast proteins are introduced. Experiment results show that the average accuracy performance measures of these three hypergraph Laplacian based semi-supervised learning methods are the same. However, their average accuracy performance measures of these three methods are much greater than the average accuracy performance measures of un-normalized graph Laplacian based semi-supervised learning method (i.e. the baseline method of this paper) applied to gene co-expression network created from the gene expression data. 

Race, Genomics, and Philosophy of Science

Race, Genomics, and Philosophy of Science