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In this paper, we propose a novel keyframe extraction extraction method based on the gray wolf optimization (GWO) algorithm, addressing the challenge of information loss in traditional methods due to redundant and similar frames. The proposed method GWOKConvLSTM prioritizes speed, accuracy, and compression efficiency while preserving semantic information. Inspired by wolf behavior, we construct a fitness function that minimizes reconstruction error and achieves optimal compression ratios below 8%. Compared to traditional methods, our GWO method achieves the lowest reconstruction error for a given compression rate, providing a concise and visually coherent summary of keyframes while maintaining consistency across similar motions. Additionally, we propose a template-based method for video classification tasks, achieving the highest accuracy when combined with pre-trained CNNs and ConvLSTM. Our method effectively prevents dynamic background noise from affecting keyframe selection, leading to significantly improve video classification performance using deep neural networks. [ABSTRACT FROM AUTHOR]

Inter‐rater reliability (IRR) is one of the commonly used tools for assessing the quality of ratings from multiple raters. However, applicant selection procedures based on ratings from multiple raters usually result in a binary outcome; the applicant is either selected or not. This final outcome is not considered in IRR, which instead focuses on the ratings of the individual subjects or objects. We outline the connection between the ratings' measurement model (used for IRR) and a binary classification framework. We develop a simple way of approximating the probability of correctly selecting the best applicants which allows us to compute error probabilities of the selection procedure (i.e., false positive and false negative rate) or their lower bounds. We draw connections between the IRR and the binary classification metrics, showing that binary classification metrics depend solely on the IRR coefficient and proportion of selected applicants. We assess the performance of the approximation in a simulation study and apply it in an example comparing the reliability of multiple grant peer review selection procedures. We also discuss other possible uses of the explored connections in other contexts, such as educational testing, psychological assessment, and health‐related measurement, and implement the computations in the R package IRR2FPR. [ABSTRACT FROM AUTHOR]

This research presents a closed-form mathematical framework for assessing the performance of a wireless communication system in the presence of multipath fading channels with an instantaneous signal-to-noise ratio (SNR) subjected to the inverse power Lomax (IPL) distribution. It is demonstrated that depending on the channel parameters, such a model can describe both severe and light fading covering most cases of the well-renowned simplified models (i.e., Rayleigh, Rice, Nakagami-m, Hoyt, α − μ , Lomax, etc.). This study provides the exact results for a basic statistical description of an IPL channel, including the PDF, CDF, MGF, and raw moments. The derived representation was further used to assess the performance of a communication link. For this purpose, the exact expression and their high signal-to-noise ratio (SNR) asymptotics were derived for the amount of fading (AoF), outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC). The closed-form and numerical hyper-Rayleigh analysis of the IPL channel is performed, identifying the boundaries of weak, strong, and full hyper-Rayleigh regimes (HRRs). An in-depth analysis of the system performance was carried out for all possible fading channel parameters' values. The practical applicability of the channel model was supported by comparing it with real-world experimental results. The derived expressions were tested against a numerical analysis and statistical simulation and demonstrated a high correspondence. [ABSTRACT FROM AUTHOR]

The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary (ϵ , δ) constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility. [ABSTRACT FROM AUTHOR]

The primary objective of this manuscript is to find the service quality factors, which influence customer satisfaction, promotional efforts taken by the retailer, and the perception of customers towards the organized retail sector. This investigation uses both offline and online retail sales datasets. Particularly, in the case of the offline retail sale dataset, an energy‐efficient analysis is conducted to collect scalable, accurate, and real‐time data. The collected data is used in enhancing several aspects of e‐commerce operations, including customer experience optimization and inventory management, which results in better decision‐making with increased efficiency. After data collection, the outliers are eliminated by implementing the z‐score technique. The removal of outliers increases the variability of collected data which reduces statistical power. The statistically sufficient data are given to the Randomized Grasshopper Optimization Algorithm (RGOA) for optimal instance selection. Finally, the selected optimal instances are given to the deep neural network (DNN) model for future customer behaviour prediction. The efficacy of the RGOA‐DNN model is analysed by using evaluation measures like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean percentage error (MPE) and mean absolute percentage error (MAPE). The numerical analysis states that the RGOA‐DNN model obtained a minimal error rate with MSE of 0.09 and 0.10, RMSE of 0.13 and 0.14, MAE of 0.12 and 0.16, MPE of 0.10 and 0.11 and MAPE of 0.08 and 0.10 on the offline and online retail sales datasets. The RGOA‐DNN model has a minimal error rate in future customer behaviour prediction related to the conventional regression models. Furthermore, the elimination of inactive instances or selection of optimal instances reduces the model complexity to linear and computational time to 22.10 and 23.12 s on the offline and online retail sales datasets. [ABSTRACT FROM AUTHOR]

Tumor‐informed mutation‐based approaches are frequently used for detection of circulating tumor DNA (ctDNA). Not all mutations make equally effective ctDNA markers. The objective was to explore if prioritizing mutations using mutational features—such as cancer cell fraction (CCF), multiplicity, and error rate—would improve the success rate of tumor‐informed ctDNA analysis. Additionally, we aimed to develop a practical and easily implementable analysis pipeline for identifying and prioritizing candidate mutations from whole‐exome sequencing (WES) data. We analyzed WES and ctDNA data from three tumor‐informed ctDNA studies, one on bladder cancer (Cohort A) and two on colorectal cancer (Cohorts I and N). The studies included 390 patients. For each patient, a unique set of mutations (median mutations/patient: 6, interquartile 13, range: 1–46, total n = 4023) were used as markers of ctDNA. The tool PureCN was used to assess the CCF and multiplicity of each mutation. High‐CCF mutations were detected more frequently than low‐CCF mutations (Cohort A: odds ratio [OR] 20.6, 95% confidence interval [CI] 5.72–173, p = 1.73e−12; Cohort I: OR 2.24, 95% CI 1.44–3.52, p = 1.66e−04; and Cohort N: OR 1.78, 95% CI 1.14–2.79, p = 7.86e−03). The detection‐likelihood was additionally improved by selecting mutations with multiplicity of two or above (Cohort A: OR 1.55, 95% CI 1. 14–2.11, p = 3.85e−03; Cohort I: OR 1.78, 95% CI 1.23–2.56, p = 1.34e−03; and Cohort N: OR 1.94, 95% CI 1.63–2.31, p = 2.83e−14). Furthermore, selecting the mutations for which the ctDNA detection method had the lowest error rates, additionally improved the detection‐likelihood, particularly evident when plasma cell‐free DNA tumor fractions were below 0.1% (p = 2.1e−07). Selecting mutational markers with high CCF, high multiplicity, and low error rate significantly improve ctDNA detection likelihood. We provide free access to the analysis pipeline enabling others to perform qualified prioritization of mutations for tumor‐informed ctDNA analysis. [ABSTRACT FROM AUTHOR]

Abstract The differential privacy (DP) literature often centers on meeting privacy constraints by introducing noise to the query, typically using a pre-specified parametric distribution model with one or two degrees of freedom. However, this emphasis tends to neglect the crucial considerations of response accuracy and utility, especially in the context of categorical or discrete numerical database queries, where the parameters defining the noise distribution are finite and could be chosen optimally. This paper addresses this gap by introducing a novel framework for designing an optimal noise probability mass function (PMF) tailored to discrete and finite query sets. Our approach considers the modulo summation of random noise as the DP mechanism, aiming to present a tractable solution that not only satisfies privacy constraints but also minimizes query distortion. Unlike existing approaches focused solely on meeting privacy constraints, our framework seeks to optimize the noise distribution under an arbitrary $$(\epsilon , \delta )$$ ( ϵ , δ ) constraint, thereby enhancing the accuracy and utility of the response. We demonstrate that the optimal PMF can be obtained through solving a mixed-integer linear program. Additionally, closed-form solutions for the optimal PMF are provided, minimizing the probability of error for two specific cases. Numerical experiments highlight the superior performance of our proposed optimal mechanisms compared to state-of-the-art methods. This paper contributes to the DP literature by presenting a clear and systematic approach to designing noise mechanisms that not only satisfy privacy requirements but also optimize query distortion. The framework introduced here opens avenues for improved privacy-preserving database queries, offering significant enhancements in response accuracy and utility.

With touchscreens being installed in aircraft flight decks, reach-and-turbulence-related challenges arise. Using the ISO 9241-411 multidirectional selection task (a 2D Fitts' task), we quantified the impact of vibration on touchscreen target selection throughput (a performance score combining both speed and accuracy) and error rate in a cockpit layout. 24 participants completed the task under 2 vibration levels (helicopter level flight versus static), 2 hand support methods (using the thumb, while holding onto the screen's edge, versus using the index finger freehand), 4 touchscreen types (two avionic and two consumer touchscreens), 2 touchscreen positions (main instrument panel versus pedestal), and 4 target sizes (0.8, 1, 1.5 and 2 cm). We found average throughput values of 6.5 bits per second (bps) in static conditions, versus 5.7 bps under vibration, and average error rates of 10.3% in static conditions, versus 16.6% under vibration. Similar to prior work, we found an exponential increase in error rate with decreasing target size. Larger target sizes helped mitigate the impact of vibration. We did not find evidence of a benefit to anchoring the hand on the touchscreen's bezel edge, compared to the freehand baseline, under vibration or static conditions. Under vibration, the pedestal outperformed the main instrument panel position, with higher throughput and lower error rate. In static conditions, the two positions performed similarly. This work contributes to vibration mitigation methods when interacting with touchscreens in the aviation context. [ABSTRACT FROM AUTHOR]

Graphics processing units (GPUs) are the reference architecture to accelerate high-performance computing applications and the training/interference of convolutional neural networks. For both these domains, performance and reliability are two of the main constraints. It is believed that the only way to increase reliability is to sacrifice performance, e.g., using redundancies. We show in this paper that this is not always the case. As a very promising result, we found that most GPUs performance improvements also bring the benefit of increasing the number of executions correctly completed before experiencing a silent data corruption (SDC). We consider four different common GPUs' performance optimizations: architectural solutions, software implementations, compiler optimizations, and threads degree of parallelism. We compare different implementations of a variety of parallel codes and, through beam experiments and applications profiling, we show that the performance improvement typically (but not necessarily) increases the GPU SDC rate. Nevertheless, for the vast majority of the configurations the performance gain is much higher than the SDC rate increase, allowing to process a higher amount of correct data. As we show, the programmer choices can increase up to 25 × the number of correctly completed executions without redesigning the algorithm nor including specific hardening solutions. [ABSTRACT FROM AUTHOR]

The usual t-test or F-test can not be used to analyze unreplicated two-level factorial designs, since all the observations are used to estimate the factor effects and no observation is left to estimate the error variance. To overcome this difficulty, various procedures have been proposed in the literature and several simulation studies have been carried out to compare the performance of these methods. The results of these studies have been inconclusive, and no test is widely accepted as a "best" test. In this paper, we present results that show theoretically that no test has high power against all possible alternatives; i.e. no test can detect all patterns of active effects. Therefore, in the absence of any prior information concerning active and inactive effects, no test can be preferred to any other test based on power and the choice of a test should be based on other considerations, such as ease of use, control of individual or experimental error rate, the purpose of the experiment, etc. [ABSTRACT FROM AUTHOR]

Quantum key distribution, which is based on the laws of physics, has revolutionized the protection of communication information. However, before QKD can be extensively utilized, a number of critical challenges must be overcome, namely, the noise of channels. Key qubits are distorted by passing through noisy channels, which leads to errors and significantly impact the security, which is the most crucial aspect of QKD. In this paper, we utilize quantum gates and environment-assisted measurement to enhance the performance of QKD in the presence of noise. We present the details of our methods for both prepare-and-measure (BB84) and entanglement-based (BBM92) QKD protocols to demonstrate how they can be combined with various types of common QKD protocols. We have considered applying quantum gates after the noisy channel to recover the damped key qubits and both before and after the noisy channel to prepare and recover the key qubits. We demonstrate the significant reduction in the error rate of the proposed schemes compared to QKD protocols with no protection. Furthermore, to achieve a protection scheme that is independent of the intensity of the noisy channel, we consider applying environment-assisted measurement to the noisy channel, followed by a recovery process via quantum gates, where explicit formula of the protection variable to achieve an error rate of zero is calculated and the corresponding success probability is analyzed. To assess our proposed schemes for BB84 and BBM92 protocols, we analyze three key performance indicators: error rate, QKD success probability, and eavesdropper’s success probability. These metrics provide insights into the effectiveness, correctness, and security of our methods, ensuring reliable and secure quantum communication. [ABSTRACT FROM AUTHOR]

Antisaccade, which is described as looking at the opposite location of the target, is an eye movements paradigm used for assessing cognitive functions in schizophrenia. Initiation and sustainment of saccades in antisaccade are managed by frontal and parietal cortical areas. Antisaccade abnormalities are well-established findings in schizophrenia. However, studies in the early phases of psychotic disorders and clinical/familial risk for psychosis reported inconsistent findings. The current systematic review aimed to review the results of studies investigating antisaccade error rates in first-episode psychosis (FEP), individuals with ultra-high-risk for psychosis (UHR P), and familial-high-risk for psychosis (FHR P) compared to healthy controls. A meta-analysis of 17 studies was conducted to quantitatively review antisaccade errors in FEP, UHR-P and FHR P. The error rate (Hedges'g) was compared between the total of 860 FEP, UHR P, FHR P, and 817 healthy controls. Hedges' g for effect size, I2 for estimating the percentage of variability, and publication bias were evaluated through the R software. The outcomes of this meta-analysis suggested that FEP is associated with a robust deficit in the antisaccade error rate (g = 1.16, CI = 0.95–1.38). Additionally, both the clinical and familial high-risk groups showed small but significant increases in AS errors (g = 0.26, CI = 0.02–0.52 and g = 0.34, CI = 0.13–0.55, respectively). The large effect size estimated for FEP was compatible with previously reported results in chronic schizophrenia patients. Additionally, relatives had abnormalities with small to medium effect sizes and significant differences. The current findings suggest that antisaccade errors might be a potential endophenotype for psychotic disorders. [ABSTRACT FROM AUTHOR]

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the smoothly clipped absolute deviation (SCAD) penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes. [ABSTRACT FROM AUTHOR]

Privacy and security are the most concerning topics while using cloud-based applications. Malware detection in cloud applications is important in identifying application malware activity. So, a novel Goat-based Recurrent Forensic Mechanism (GbRFM) is used to detect the attack and provide the attack type in cloud-based applications. At first, the dataset is pre-processed in the hidden phase, and the errorless features are extracted. The proposed model also trains the output of the hidden layer to identify and classify the malware. The wild goat algorithm enhances the identification rate by accurately detecting the attack. Using the NSL-KDD data, the preset research was verified, and the outcomes were evaluated. The performance assessment indicates that the developed model gained a 99.26% accuracy rate for the NSL-KDD dataset. Moreover, to validate the efficiency of the proposed model, the outcomes are compared with other techniques. The comparison analysis proved that the proposed model attained better results. [ABSTRACT FROM AUTHOR]

Lomba Nasional Kreativitas Mahasiswa (LO Kreatif) merupakan ajang tahunan yang diadakan oleh APTISI 7 Jatim untuk mahasiswa perguruan tinggi swasta di Indonesia. Meskipun proses pendaftaran lomba telah beralih ke platform digital, penelitian terbaru menunjukkan keterbatasan performa website LO Kreatif saat menghadapi lebih dari 1000 pengguna secara bersamaan. Evaluasi performansi menggunakan metode load testing mengidentifikasi dua faktor utama yang berkontribusi pada penurunan performa, yaitu penggunaan file load pada halaman web dan penggunaan data gambar, proses query yang kompleks, serta penggunaan library. Penelitian ini mengusulkan solusi dengan merancang arsitektur microservice sebagai alternatif terhadap arsitektur monolitik yang digunakan saat ini. Analisis terhadap penelitian sebelumnya menunjukkan bahwa arsitektur monolitik lebih cocok untuk aplikasi dengan jumlah pengguna kecil, sedangkan arsitektur microservice menawarkan skalabilitas dan reliabilitas yang lebih baik. Melalui Design Science Research Methodology (DSRM), penelitian ini melibatkan enam tahapan, mulai dari identifikasi permasalahan hingga komunikasi hasil kepada pemangku kepentingan. Hasil evaluasi menunjukkan bahwa arsitektur microservice berhasil meningkatkan throughput tanpa mengalami penurunan, meskipun rata-rata error rate sebesar 8.52% masih memerlukan perbaikan. Dengan demikian, arsitektur microservice dapat menjadi solusi untuk meningkatkan performansi website LO Kreatif, namun perhatian terhadap aspek perangkat keras juga krusial untuk mendapatkan performansi optimal.

Background: This project is part of a broader effort to develop a new electronic registry for ophthalmology in the KwaZulu-Natal (KZN) province in South Africa. The registry should include a clinical decision support system that reduces the potential for human error and should be applicable for our diversity of hospitals, whether electronic health record (EHR) or paper-based. Methods: Post-operative prescriptions of consecutive cataract surgery discharges were included for 2019 and 2020. Comparisons were facilitated by the four chosen state hospitals in KZN each having a different system for prescribing medications: Electronic, tick sheet, ink stamp and handwritten health records. Error types were compared to hospital systems to identify easily-correctable errors. Potential error remedies were sought by a four-step process. Results: There were 1307 individual errors in 1661 prescriptions, categorised into 20 error types. Increasing levels of technology did not decrease error rates but did decrease the variety of error types. High technology scripts had the most errors but when easily correctable errors were removed, EHRs had the lowest error rates and handwritten the highest. Conclusion: Increasing technology, by itself, does not seem to reduce prescription error. Technology does, however, seem to decrease the variability of potential error types, which make many of the errors simpler to correct. Contribution: Regular audits are an effective tool to greatly reduce prescription errors, and the higher the technology level, the more effective these audit interventions become. This advantage can be transferred to paper-based notes by utilising a hybrid electronic registry to print the formal medical record.

This paper proposed a micro‐LED pixel circuit utilizing low‐temperature polycrystalline oxide (LTPO) technology. Unlike conventional designs, our approach leverages coupling effects to nullify threshold voltages in both segments without signal line augmentation. A more stable emission current is achieved through altered emission methods, yielding an error rate below 4%. [ABSTRACT FROM AUTHOR]

This research presents a closed-form mathematical framework for assessing the performance of a wireless communication system in the presence of multipath fading channels with an instantaneous signal-to-noise ratio (SNR) subjected to the inverse power Lomax (IPL) distribution. It is demonstrated that depending on the channel parameters, such a model can describe both severe and light fading covering most cases of the well-renowned simplified models (i.e., Rayleigh, Rice, Nakagami-m, Hoyt, α−μ, Lomax, etc.). This study provides the exact results for a basic statistical description of an IPL channel, including the PDF, CDF, MGF, and raw moments. The derived representation was further used to assess the performance of a communication link. For this purpose, the exact expression and their high signal-to-noise ratio (SNR) asymptotics were derived for the amount of fading (AoF), outage probability (OP), average bit error rate (ABER), and ergodic capacity (EC). The closed-form and numerical hyper-Rayleigh analysis of the IPL channel is performed, identifying the boundaries of weak, strong, and full hyper-Rayleigh regimes (HRRs). An in-depth analysis of the system performance was carried out for all possible fading channel parameters’ values. The practical applicability of the channel model was supported by comparing it with real-world experimental results. The derived expressions were tested against a numerical analysis and statistical simulation and demonstrated a high correspondence.

Background In today’s world, the abundance of data in many areas of society has increased the need to use efficient technological tools to analyse, accurately and clearly present information. The objective was to develop a virtual platform to increase efficiency in the control of medical records (MR) and reduce errors in the recording of clinical data. To improve the quality of medical care in public health centres. Method The research used a methodology that follows a four-stage process for the development of the medical records management software. It started with planning where the user stories and their priority were defined. In the design phase, the system architecture and code structure were developed. Subsequently, in the coding phase, pair programming with incremental implementation was emphasised. Finally, in the testing phase, automatic and manual integration tests were carried out, documented by means of acceptance tests to ensure the quality of the developed software. Results The web-based platform has led to a significant increase in the number of patients seen, resulting in significant improvements in resource management at the health centres. In addition, the reduction in the incidence of errors highlights the superior accuracy in recording and tracking medical information, promoting a safer and more efficient care environment for patients. Conclusions The main focus of this study is the management of medical records, with the aim of reducing the time spent on registration, ensuring secure access to information, reducing documentation errors and facilitating instant data retrieval through a web-based platform. The results show the effectiveness of this methodology and its positive impact on the management of medical records.

Alertness has been construed as one of three fundamental components of attention. When generated by a warning signal, phasic changes in alertness ubiquitously decrease reaction time. But how does it do so? Based on earlier findings, in 1975, Posner proposed a theory of phasic alertness with two postulates: (i) phasic alertness does not affect the accumulation of information; (ii) phasic alertness accelerates when a response based on the accumulating information will be generated. When targets are continuously presented, this theory predicts that alertness will reduce reaction at the expense of an increase in errors—that is, generate a speed–accuracy trade-off. Los and Schut, Cognitive Psychology, 57, 20–55, (2008), while endorsing Posner's theory, claimed to have failed to replicated the tell-tale trade-off reported by Posner et al. Memory and Cognition, 1, 2-12, (1973, Experiment 1). The primary goal of this commentary was to use all the data from Los and Schut to see if the predicted speed-accuracy trade-off would be verified or not. With the increased power, it was confirmed that the conditions that benefited the most in reaction time from alertness also had higher error rates. It is noteworthy that recent studies have generated replications and extensions of the methods and findings of Posner et al; thus, it appears that the empirical pattern predicted by Posner's theory of phasic alertness is relatively robust. [ABSTRACT FROM AUTHOR]

In this study, the performance analysis of dual-hop power line communication (PLC)/ η − μ and PLC/ λ − μ L-branch systems by using the decode-and-forward (DF) protocol is presented in detail. The introduced work provides novel closed-form expressions of the probability density function (PDF) of the instantaneous signal-to-noise ratio (SNR) of the considered systems. Taking the proposed PDFs into account, we also derive closed-form expressions of the error rate and channel capacity (CC) performance for the dual-hop PLC/ η − μ and PLC/ λ − μ L-branch DF systems. Furthermore, an exhaustive comparative study of different modulation types such as non-coherent M-ary frequency-shift keying, square quadrature amplitude modulation (SQAM), and cross quadrature amplitude modulation (×QAM) is presented by using the proposed closed-form error rate expressions. Also, the exact expressions for the CC with channel inversion with a fixed-rate (CIFR) transmission policy are proposed. Finally, all the theoretical findings are confirmed through numerical results obtained by using the exact expressions. [ABSTRACT FROM AUTHOR]

Based on the cluster analysis algorithm, this paper collects a large amount of sample data from Huizhou buildings and groups these samples using the cluster analysis algorithm so as to identify different categories of architectural culture types. By dividing the group similarity of each category of buildings, the village cultural characteristics related to each category of buildings are extracted, including information on architectural style, structural features, decorative elements and so on. The results show that the automatic cultural classification feature values are 1.2, 2.5, and 2.9, which highlight the unique characteristics of the Huizhou village culture. Especially in Hongcun and Pai Fang Lane buildings, the cultural features are more significant, with matrix classification degrees of 4.7 and 4.022, respectively, fully highlighting the cultural heritage of these buildings. In addition, the clustering analysis algorithm has only a 9.5% error rate, and the classification accuracies all exceed the high level of 0.9, showing its excellent performance in the extraction of various cultural features.

Forensic science is undergoing an evolution in which a long-standing “trust the examiner” focus is being replaced by a “trust the scientific method” focus. This shift, which is in progress and still partial, is critical to ensure that the legal system uses forensic information in an accurate and valid way. In this Perspective, we discuss the ways in which the move to a more empirically grounded scientific culture for the forensic sciences impacts testing, error rate analyses, procedural safeguards, and the reporting of forensic results. However, we caution that the ultimate success of this scientific reinvention likely depends on whether the courts begin to engage with forensic science claims in a more rigorous way. [ABSTRACT FROM AUTHOR]

At present, most of the research on mine wireless signal detection only considers the ideal additive Gaussian white noise channel and Rayleigh fading channel. The signal detection has high bit error rate and complex network structure. In order to solve the above problems, a mine wireless signal detection method based on dual path network (DPN) is proposed. The method uses dual path network receiver (DPNR) to optimize the overall performance of the orthogonal frequency division multiplexing (OFDM) receiver and solve the problem of error accumulation in conventional receivers. Firstly, the residual (Res) block's shortcut is used to perform a convolution of shallow features, and the feature map after one convolution is added to the feature map after multiple convolutions. Secondly, the shallow layer of the Dense block is reused. The convolution calculation of the Dense block is performed to obtain the feature map after the convolution calculation. Finally, the feature maps of the two are fused into a new feature map, which extracts more features at the expense of less complexity, thereby improving detection performance. The experimental results show the following points. ① In OFDM systems, the bit error rate of DPNR is lower than that of conventional receivers. When the signal-to-noise ratio is 13, the bit error rate is zero. When the signal-to-noise ratio is greater than 7, the error rate of DPNR is reduced by more than one order of magnitude compared to conventional receivers in mine environments. When the signal-to-noise ratio is greater than 11, the bit error rate of DPNR is more than one order of magnitude lower than that of conventional receivers under additive Gaussian white noise. ② In the multi-carrier/offset orthogonal amplitude modulation of communication system filter banks, the error rate of DPNR is reduced by more than two orders of magnitude compared to conventional receivers, indicating its good robustness. ③ As the signal-to-noise ratio increases, the bit error rate of DPNR and residual neural network (ResNet) receivers is lower than that of densely connected convolutional networks (DenseNet) receivers. The bit error rate of DPNR is lower in the final stage when the signal-to-noise ratio is greater than 13. ④ At higher signal-to-noise ratios, the bit error rate of DPNR is much lower than that of deep receivers. When the signal-to-noise ratio is greater than 8, the bit error rate of DPNR is reduced by more than one order of magnitude compared to deep receivers.

Applications for the Internet of Vehicles (IoV) are progressing from more fundamental ones like those for traffic efficiency, road safety, and information services to more sophisticated ones like autonomous driving and intelligent mobility that have a variety of connectivity requirements. Therefore, IoV communication systems must overcome various obstacles in order to enable a variety of IoV applications. Attacks on TSP servers are the major target of telematics attack threat events. This article analyses data security monitoring from the vehicle end to the TSP platform, as well as gathering and analysing the status and intelligence of linked vehicles, in order to develop a Telematics TSP server attack monitoring system. Experiments show that the error detection rate of the benchmark algorithms decreases significantly as the error rate of vehicle speed data increases, with the exponential smoothing algorithm showing the largest decrease in the error detection rate. However, the proposed combined model is very close to the error detection rate of the benchmark algorithm at the later stages when the error rate is high, with an error detection rate of more than 80%.It provides great assistance for anomaly detection and data recovery, especially in researching key technologies for remote information processing data security and reliability, greatly improving their performance.

The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model's design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model's architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques. [ABSTRACT FROM AUTHOR]

Objective: Oculometric measures (OM) can be extracted from eye movements during presentation of visual stimuli. Studies have indicated the benefit of OM in assessment of neurological disorders, including Amyotrophic Lateral Sclerosis (ALS). We used a new software-based platform for the extraction of OM during patients' assessment. Our objective was to examine the correlation between OM and clinical assessment as a part of a clinical drug trial. Methods: 32 ALS patients (mean age 60.75 ± 10.36 years, 13 females), were assessed using a validated score (ALSFRS-R), and a novel software-based oculometric platform (NeuraLight, Israel) as a part of a clinical drug trial. Correlations of ALSFRS-R with OM were calculated and compared with matched healthy subjects' data (N = 129). Results: A moderate correlation was found between ALSFRS-R and corrective saccadic latency (R = 0.52, p = 0.002). Fixation time during smooth pursuit and peak velocity during pro-saccades were both worse in ALS patients versus healthy subjects (mean (SD)=0.34(0.06) vs. 0.3(0.07), p = 0.01, and 0.41(0.05) vs. 0.38(0.07), p = 0.04, respectively). Patients with bulbar symptoms (N = 14) had a decreased pro-saccade gain compared with patients without bulbar symptoms (mean (SD)=0.1 (0.04) vs. 0.93 (0.07), p = 0.01), and a larger error rate of anti-saccade movement (mean (SD)=0.42 (0.21) vs. 0.28 (0.16), p = 0.04). Conclusions: Oculometric measures correlated with the clinical assessment and were different from data of healthy subjects. Further studies are warranted to establish the role of oculometrics in the evaluation of patients with ALS and other neurodegenerative disorders, and its possible use in clinical trials. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Voice signals are the most convenient and essential method to establish the communication between human beings. The linguistic characteristics of the human being can be recognized with the help of voice signal analysis such as the ascent of language, health status, emotions, age, gender, etc. Gender is an unprecedented peculiarity of the speakers. This peculiarity can be recognized through the analysis of the voice signals. This paper presents the precision value of gender, i.e., transgender, female and male. The live voice samples for females and males are recorded as well as the live voice samples for the transgender in India are recorded first time because the voice samples of the transgenders are not available at the authenticated sources. The error factor and accuracy is also calculated for the proposed model, which is utilized for the gender identification of the speakers by analyzing the voice signals. In the proposed work, bidirectional long short-term memory (BiLSTM) architecture with recurrent neural network (RNN) model is used as a classification algorithm with mel frequency cepstral coefficients (MFCCs). The MFCC is utilized as a trait of the signals used to identify the gender of the human beings. The outcomes of the proposed model are not dependent on the content of the articulation and the dialect of the speakers. The precision values for female, male and transgender, the accuracy and error rate of the proposed model, depend on applied quantities of voice signals in testing and training datasets. The average precision value is 93.91%, 94.70%, 97.43% and 95.35%for the male, transgender, female and proposed model, respectively. It is found that the female precision value is the highest, and the male has the lowest. Further, the average error rate of the proposed model is 0.045. The gender identification accuracy is 95.52%. [ABSTRACT FROM AUTHOR]

Assistance systems have the potential to support workers in various assembly tasks. Especially in mass customized prefabrication, the correct objects need to be selected and positioned accurately. Using conventional paper plans as assembly guidance, skilled workers need to spend precious time carefully reading plans for the individually designed elements. Augmented reality (AR) systems can help to integrate information for assembly tasks directly into the work environment, which should improve time efficiency and error rates. However, AR systems are often not ergonomic or accurate enough (e.g. smartglasses) or expensive (e.g. laser projectors). In the present study, the effect of a simple projection based spatial augmented reality (SAR) system was studied for building prefabricated wall elements under laboratory conditions, using printed CAD drawings as reference. Each subject built a timber frame wall element with dimensions close to reality. Using the SAR system, the completion times were reduced by nearly 50%, the error rate of subjects with a low experience level decreased and the reported task load measured according to the NASA raw task load index using the SAR system was significantly lower. [ABSTRACT FROM AUTHOR]

A situation giving rise to a violation of the normality assumption in discriminant analysis is that which involves count observations. For a two-variable case involving count observations, this paper presents a new discriminant analysis approach when one variable is observed conditional on the other. Two cases involving Poisson-Binomial and Poisson-Poisson distributions were considered. The derived allocation rules are based on the resulting joint distribution of the two count variables. Applicability of the suggested allocation rules in discriminant analysis involving count data and its performance in comparison with Fisher linear discriminant rule was studied under different conditions. Results obtained show promising applicability of the suggested allocation rules when compared with the Fisher linear discriminant method.

Non-orthogonal multiple access (NOMA) was identified as a technology enabler for the fifth generation and beyond networks, due to the prominent advantages achieved when integrating NOMA with other wireless technologies. In this article, we investigate the interplay between NOMA and generalized space shift keying (GSSK) in a hybrid NOMA-GSSK (N-GSSK) network. Specifically, we provide a comprehensive analytical framework and propose a novel energy-based N-GSSK detector for the reliable realization of N-GSSK systems. The proposed receiver is energy-efficient and enjoys low complexity, as it exploits the energy of the received signals and does not require the knowledge of NOMA signals. To quantify its efficiency, we further investigate the performance of the proposed detector in terms of pairwise error probability, bit error rate union bound, and achievable rate. The accuracy of the developed mathematical framework is corroborated through Monte-Carlo simulations, which show that N-GSSK outperforms conventional NOMA and GSSK, particularly in terms of spectral efficiency.

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