Average Running Time Research Articles

Racial disparities among patients on venovenous extracorporeal membrane oxygenation in the pre–Coronavirus Disease 2019 and Coronavirus Disease 2019 eras: A retrospective registry review

ObjectivesAlthough many studies have addressed such disparities caused by COVID-19, to our knowledge, no study has focused on the association of race on outcomes for patients with COVID-19 requiring venovenous extracorporeal membrane oxygenation support. The goal of this study was to assess association of race on death and duration on venovenous extracorporeal membrane oxygenation in both the pre–COVID-19 and COVID-19 eras. MethodsWe retrospectively reviewed the Extracorporeal Life Support Organization registry and included adults (≥18 years) who required venovenous extracorporeal membrane oxygenation between January 2019 and April 2021. We performed descriptive statistics and multivariable logistic regression. Our primary outcomes were death and extracorporeal membrane oxygenation duration. ResultsA total of 7477 patients were included after excluding 340 patients (4.3%) who were missing race data. In the COVID-19 era, 1474 of 2777 COVID-19–positive patients (53.1%) died. Our regression model suggested somewhat of a protective effect on death for Black and multiple race patients. Additionally, a diagnosis of COVID-19 and patients in the COVID-19 era in general, irrespective of COVID-19 diagnosis, had higher odds of death. Hispanic patients had the longest average venovenous extracorporeal membrane oxygenation run times. ConclusionsOur study using data from the international Extracorporeal Life Support Organization Registry provides updated data on patients supported with venovenous extracorporeal membrane oxygenation in the pre–COVID-19 and COVID-19 eras between 2019 and 2021 with a focus on race. Patients in the COVID-19 era group also had higher mortality compared with those in the pre–COVID-19 era even after being adjusted for COVID-19 diagnosis. Black and multiple races appeared somewhat protective in terms of death. Hispanic race was associated with longer venovenous extracorporeal membrane oxygenation duration.

The Spherical Evolutionary Multi-Objective (SEMO) Algorithm for Identifying Disease Multi-Locus SNP Interactions.

Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.

Intelligent big data visual analytics based on deep learning

Abstract In this paper, we first constructed a deep learning model, optimized the LSTM model to get the BiLSTM model based on the long and short-term memory network, and used the generative adversarial network to calculate the probability distribution of data. Then, the advantages of deep learning in intelligent big data visualization and analysis are explored from the dimensions of data preprocessing, dimension anchor layout, coordinate expansion and data analysis. Finally, the efficiency of the deep learning model is compared with that of other algorithms using indicators such as accuracy and recall, and the feasibility of this paper’s method is verified by empirical analysis using intelligent transportation data as an example. The results show that the model in this paper achieves an accuracy rate of 95.5%, the loss rate is stable at 0.2% to 0.4%, and the average running time is maintained at 20ms, which are all better than other models. The predicted and real values of traffic data for the Deep-STCL model using deep learning basically match, indicating that the deep learning model has obvious advantages in data visualization and analysis.

Adaptive feature extraction method for capsule endoscopy images

The traditional feature-extraction method of oriented FAST and rotated BRIEF (ORB) detects image features based on a fixed threshold; however, ORB descriptors do not distinguish features well in capsule endoscopy images. Therefore, a new feature detector that uses a new method for setting thresholds, called the adaptive threshold FAST and FREAK in capsule endoscopy images (AFFCEI), is proposed. This method, first constructs an image pyramid and then calculates the thresholds of pixels based on the gray value contrast of all pixels in the local neighborhood of the image, to achieve adaptive image feature extraction in each layer of the pyramid. Subsequently, the features are expressed by the FREAK descriptor, which can enhance the discrimination of the features extracted from the stomach image. Finally, a refined matching is obtained by applying the grid-based motion statistics algorithm to the result of Hamming distance, whereby mismatches are rejected using the RANSAC algorithm. Compared with the ASIFT method, which previously had the best performance, the average running time of AFFCEI was 4/5 that of ASIFT, and the average matching score improved by 5% when tracking features in a moving capsule endoscope.

On Restricted Averages of Dedekind Sums

Abstract We investigate the averages of Dedekind sums over rational numbers in the set $\mathscr {F}_{\alpha }(Q) = \{\,{v}/{w}\in \mathbb {Q}: 0<w\leq Q\,\}\cap \lbrack 0, \alpha )$ for fixed $\alpha \leq 1/2$. In previous work, we obtained asymptotics for $\alpha =1/2$, confirming a conjecture of Ito in a quantitative form. In the present article we extend our former results, first to all fixed rational $\alpha $ and then to almost all irrational $\alpha $. As an intermediate step we obtain a result quantifying the bias occurring in the second term of the asymptotic for the average running time of the by-excess Euclidean algorithm, which is of independent interest.

A Blended Instructional Design for Civil Litigation Combining Multi-Objective Optimization Algorithms in a Rule of Law Context

Abstract The current traditional means of teaching adopt the indoctrination teaching mode. Students passively accept information, learning enthusiasm is not high, and the traditional teaching mode needs to be reconstructed urgently. This paper constructs a blended teaching mode for civil litigation based on the WeChat public platform and provides the implementation process for the blended teaching mode. In order to enable students to obtain civil litigation-related resources more in line with the learning characteristics of the WeChat platform, a multi-objective learning path optimization algorithm is proposed, and the discrete binary particle swarm algorithm is used to optimize and solve the multi-objective learning path. The teaching experiment method is utilized to select the research object and method to study the teaching effect of the blended teaching mode and then obtain the students’ satisfaction with the blended teaching mode. The results show that through the BPSO algorithm, after the number of iterations reaches 150 times, its fitness value is around 10-5, the average running time of the 20 test functions is around 500ms, and the mean value of students’ satisfaction with the blended teaching mode is 9.31 points. The blended teaching mode of civil litigation in the context of the rule of law can help students better establish the thinking of the rule of law and promote the improvement of students’ learning ability, and multi-objective optimization can help students better obtain the optimal learning path.

A secure solution based on load-balancing algorithms between regions in the cloud environment

The problem treated in this article is the storage of sensitive data in the cloud environment and how to choose regions and zones to minimize the number of transfer file events. Handling sensitive data in the global internet network many times can increase risks and minimize security levels. Our work consists of scheduling several files on the different regions based on the security and load balancing parameters in the cloud. Each file is characterized by its size. If data is misplaced from the start it will require a transfer from one region to another and sometimes from one area to another. The objective is to find a schedule that assigns these files to the appropriate region ensuring the load balancing executed in each region to guarantee the minimum number of migrations. This problem is NP-hard. A novel model regarding the regional security and load balancing of files in the cloud environment is proposed in this article. This model is based on the component called “Scheduler” which utilizes the proposed algorithms to solve the problem. This model is a secure solution to guarantee an efficient dispersion of the stored files to avoid the most storage in one region. Consequently, damage to this region does not cause a loss of big data. In addition, a novel method called the “Grouping method” is proposed. Several variants of the application of this method are utilized to propose novel algorithms for solving the studied problem. Initially, seven algorithms are proposed in this article. The experimental results show that there is no dominance between these algorithms. Therefore, three combinations of these seven algorithms generate three other algorithms with better results. Based on the dominance rule, only six algorithms are selected to discuss the performance of the proposed algorithms. Four classes of instances are generated to measure and test the performance of algorithms. In total, 1,360 instances are tested. Three metrics are used to assess the algorithms and make a comparison between them. The experimental results show that the best algorithm is the “Best-value of four algorithms” in 86.5% of cases with an average gap of 0.021 and an average running time of 0.0018 s.

Color Analysis of Cloud Brocade Pattern by Image Style Transfer

With the continuous improvement of the level of science and technology, the design method of cloud brocade pattern has gradually changed from the traditional process of color halo, white, and gold stranding to the modern design process, such as the synthesis of cloud brocade line pattern based on the transfer of image style. But back to reality, this method still has problems such as blurred outline and mixed colors, which is not conducive to the transfer of cloud brocade style pictures. Based on this, the paper will use the cloud brocade pattern style transfer color optimization model to analyze the color of the cloud brocade pattern in order to get a better cloud brocade style effect map. The results show that the average similarity of the local migration algorithm is 0.348, while the average similarity of the local migration algorithm based on color optimization is 0.378, which is 8.62% higher than that of the local migration algorithm. After 1600 iterations, the average running time of the local migration algorithm is 13.65s, and the running time of the local migration algorithm for color optimization is 12.46s. It can be seen that the local migration algorithm based on color optimization has obvious advantages in both comprehensive similarity and running time and can provide new ideas and references for the current design of Yunjin pictures. Doi: 10.28991/HIJ-2023-04-04-07 Full Text: PDF

Iterative magnitude pruning-based light-version of AlexNet for skin cancer classification

Convolutional Neural Networks (CNN) with different architectures have shown promising results in skin cancer diagnosis. However, CNN has a high computational cost, which makes the need for a light version of CNN a desirable step. This version can be used on small devices, such as mobile phones or tablets. A light version can be created using pruning techniques. In this study, iterative magnitude pruning (IMP) is utilized. This method depends on pruning the network iteratively. The IMP method is applied on AlexNet with transfer learning (TL) and data augmentation. The proposed IMP AlexNet with TL is applied on three different skin cancer datasets which are PAD-UFES-20, MED-NODE, and PH2 dataset. The datasets used are a combination of smartphone, dermoscopic, and non-dermoscopic images. Different CNN versions are applied on the same datasets for comparison with IMP AlexNet. The CNNs used are VGG-16, ShuffleNet, SqueezNet, DarkNet-19, DarkNet-53, and Inception-v3. The proposed IMP AlexNet achieved accuracies of 97.62%, 96.79%, and 96.75%, with accuracy losses of 1.53%, 2.3%, and 2.2%, respectively, compared to the original AlexNet. In addition, the proposed IMP AlexNet requires less running time and memory usage than the traditional AlexNet. The average running time for IMP AlexNet is 0.45 min, 0.28 min, and 0.3 min, for PAD-UFES-20, MED-NODE, and PH2 datasets, respectively. The average RAM usage with IMP AlexNet is 1.8 GB, 1.6 GB, and 1.7 GB, respectively. IMP AlexNet accelerates the average running time by approximately 15 times that of the traditional AlexNet and reduces the average RAM used by 40%.

An Improved Hashing Approach for Biological Sequence to Solve Exact Pattern Matching Problems

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

Cost-efficient fixed-width confidence intervals for the difference of two Bernoulli proportions

ABSTRACT We study the properties of confidence intervals (CIs) for the difference of two Bernoulli distributions’ success parameters, , in the case where the goal is to obtain a CI of a given half-width while minimising sampling costs when the observation costs may be different between the two distributions. We propose three different methods for constructing fixed-width CIs: (i) a two-stage sampling procedure, (ii) a sequential method that carries out sampling in batches, and (iii) an -stage “look-ahead” procedure. Under diverse scenarios, our proposed algorithms obtain significant cost savings versus their baseline counterparts. Furthermore, for the scenarios under study, our sequential-batches and -stage “look-ahead” procedures approximately obtain the nominal coverage while meeting the desired width requirement. Our sequential-batching method is more efficient than the “look-ahead” method computationally, with average running times an order-of-magnitude faster over the scenarios tested. We illustrate our procedures on a case study comparing generic and brand-name drugs.

A Dynamic Path Optimization Model of IOT Delivery Vehicles for E-commerce Logistics Distribution

Logistics and distribution is a vital link to guarantee the stable supply of the e-commerce market and the healthy development of the industry. With the constant growth of the e-commerce, the efficiency and service quality of logistics and distribution have been paid more and more attention to. Therefore, the study firstly Considering distribution fixed cost, transportation penalty cost and carbon emission cost, the vehicle routing optimization model is transformed into the lowest transportation cost model, then uses an improved traditional artificial fish swarm algorithm to find the optimum way for this model, and finally verifies its performance and applicability through experiments. The performance test results show that the algorithm finds the optimal solution 3589 and 3590 in 63 and 78 iterations in the Oxford Robot Car dataset and Apollo Scape dataset, respectively; the average running time of the algorithm is 11.864s and 11.967s in the 10 operation time tests; in the operation function test, the algorithm. The algorithm was able to overcome the local optimal solution problem. The applicability simulation shows that this algorithm stabilizes after 53 iterations, the minimum cost of the optimal solution of the model is $41,224, and the total distance of distribution is 9035 km. The research algorithm is fast in finding the optimal value, which is close to it, indicating that the algorithm is highly efficient and reliable, and can greatly optimize the path of e-commerce logistics delivery vehicles, and give a theoretical foundation for the optimization of logistics delivery paths in other industries.

MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template

Fiducial marker detection in electron micrographs becomes an important and challenging task with the development of large-field electron microscopy. The fiducial marker detection plays an important role in several steps during the process of electron micrographs, such as the alignment and parameter calibrations. However, limited by the conditions of low signal-to-noise ratio (SNR) in the electron micrographs, the performance of fiducial marker detection is severely affected. In this work, we propose the MarkerDetector, a novel algorithm for detecting fiducial markers in electron micrographs. The proposed MarkerDetector is built upon the following contributions: Firstly, a wavelet-based template generation algorithm is devised in MarkerDetector. By adopting a shape-based criterion, a high-quality template can be obtained. Secondly, a robust marker determination strategy is devised by utilizing statistic-based filtering, which can guarantee the correctness of the detected fiducial markers. The average running time of our algorithm is 1.67 seconds with promising accuracy, indicating its practical feasibility for applications in electron micrographs.

The smart city old industrial buildings renovation: based on improved greedy algorithm

With urban development and industrial restructuring, many old industrial buildings are left unused, making the renewal of such buildings a crucial aspect of urban construction. To meet the growing need for intelligent and efficient urban construction, this study proposes a greedy algorithm that considers the update of action spaces (AP-GA) to optimise the basic work of old building renovation – the layout of rows of tiles. The algorithm is optimised using the idea of action space update and backtracking. Real testing shows that the optimisation method provides the highest optimisation rate (18.20%) for AP-GA and reduces the number of cut bricks. Although the running time is slightly longer than that of the original algorithm, the brick integrity of the layout is significantly improved. When compared with other algorithms, the optimised AP-GA has the shortest average running time of 580.1 μs, demonstrating its effectiveness in the layout of rows of bricks. This new algorithm provides a more efficient and excellent method for the renewal and renovation of old industrial buildings, broadening the research perspective in the field.

W-IQ: Wither-logic based issue queue for RISC-V superscalar out-of-order processor

The rise of RISC-V Instruction Set Architecture (ISA) motivates research on improving the performance of the RISC-V processors. Issue Queue (IQ) is an essential factor affecting the Instructions Per Clock (IPC) and delay. This paper proposes a Withering-logic Based Issue Queue (W-IQ) to improve IPC while reducing the delay. We further evaluate the IPC of W-IQ with the XCVU440 FPGA. The average running time of the SPEC2006 of the W-IQ is 34% shorter than that of the non-compaction IQ with position-based selection logic. Furthermore, for a 65 nm CMOS process, the W-IQ delay is 27% lower than compaction IQ with the serial age-aware selection logic.

Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms

In this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points. The convergence analysis, finite termination properties, and stability of the MHTP are established under the restricted isometry property of the measurement matrix. Simulations are performed in noiseless and noisy environments using synthetic data, in which the successful frequencies, average runtime, and phase transition of the MHTP are considered. Standard test images are also used to test the reconstruction capability of the MHTP in terms of the peak signal-to-noise ratio. Numerical results indicate that the MHTP is competitive with several mainstream thresholding and greedy algorithms, such as hard thresholding pursuit, compressive sampling matching pursuit, subspace pursuit, generalized orthogonal matching pursuit, and Newton-step-based hard thresholding pursuit, in terms of recovery capability and runtime.

Consistent Matrix: A Feature Selection Framework for Large-Scale Datasets

Large-scale data processing based on limited computing resources has always been a difficult problem in data mining, where feature selection is often used as an effective data compressing mechanism. For granular computing of big data, discernibility matrix and dependency degree are the most representative methods for matrix-based and feature importance degree-based feature selection, respectively. However, their temporal and space complexities are high and often lead to poor performance. In this paper, a novel feature selection framework for large scale data processing with linear complexities was proposed for the first time. Firstly, a much more concise fuzzy granule set, called fuzzy arithmetic covering, was introduced to reduce computational costs. Then a new matrix-based feature selection framework, namely consistent matrix, was proposed for general rough set models. As a result, a heuristic attribute reduction algorithm, i.e., HARCM, was designed accordingly. Compared with six state-of-the-art algorithms for feature selection, the average running time of the newly proposed algorithm was reduced up to 2913 times, with a comparable or even better classification performance.

Medical image segmentation using hybrid Contrast Limited Adaptive Histogram Equalization (CLAHE) and expectation maximization algorithm

Early detection of diseases in men and women can improve treatment and reduce the risk involved in human life. Nowadays techniques which are non-invasive in nature are popularly used to detect the various types of diseases. Histopathological analysis plays a major role in finding the nature of the disease through medical images. Manual interpretation of these medical imaging takes time, is tedious, subjective, and can have human errors. It has also been discovered that the interpretation of these images varies amongst diagnostic labs. As computer power and memory capacity have increased, methodologies and medical image processing techniques have been developed to interpret and analyse these images as a substitute for human involvement. The challenge lies in devising an efficient pre-processing technique that helps in analysing, processing and preparing the medical image for further diagnostics. This research provides a hybrid technique that reduces noise in the NITFI medical image by using a 2D adaptive median filter at level 1. The edges of the filtered medical image are preserved using the modified CLAHE algorithm which preserves the local contrast of the image. Expectation Maximization (EM) algorithm extracts the ROI part of the image which helps in easy and accurate identification of the disease. All the three steps are run over the 3D image slices of a NIFTI image. The proposed method proves that it achieves close to ideal RMSE, PSNR and UQI values as well as achieves an average runtime of 37.193 seconds for EM per slice.

SAFTE: A self-injection based anti-fuzzing technique

Fuzzing refers to a collection of software testing methodologies that have been specifically developed to automatically detect implementation bugs, and is utilized by developers as a means of evaluating the security and stability of applications. Unfortunately, hacker can also exploit fuzzing techniques to crash bugs, and analyze zero-day vulnerabilities from it to carry out cyber attacks. As a result, researchers make use of anti-fuzzing technologies to slow down and frustrate these malicious aggressors. However, the main aim of these methods is to design a source-level defense strategy, which means this work requires not only the source codes but also considering the programming language. This restricts users from protecting the released binary executable they do not own from malicious analysis.In this paper, we perform a systematic analysis of software protection techniques and design a novel self-injection based anti-fuzzing techniques, called SAFTE. Different from the former research, SAFTE proposes a new way to hide the feedback or crash-related information by the means of dynamic loading and execution. First, a memory block is allocated, followed by the duplication of the image that is stored in the .data into the allocated local memory. Second, the memory is altered to enable execution permissions, thereby facilitating the execution of the image that is stored in the memory. As a consequence of this approach, let the operational and procedural status of the program is hidden, thereby precluding the fuzzer from obtaining accurate feedback or crash-related information. Therefore, it makes the fuzzer completely useless. Our evaluation on popular fuzzers demonstrates that SAFTE is capable of obviating the majority of extant fuzzers, and exhibits higher efficacy in comparison to the most advanced anti-fuzzing techniques presently available. Meanwhile, the size of the program is better than the other methods(size:18048KB,average run time per run:0.056ms), which was modified by SATFE only increases no more than 85% and the running time only increases 30%.

A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control

For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and the calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more-efficient and -robust compared to supervised learning mechanisms such as deep learning. We present a hierarchical reinforcement learning framework combined with Episodic Memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrated that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.