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An Efficient Autoscaling Cross-Browser Testing Cloud Platform based on Selenium Grid, Kubernetes and KEDA

Journal of information science and Engineering 2023年第5期39卷 1061-1077页

作者： Lin, Chia-Yu Lee, Shin-Jie Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Computer and Network Center National Cheng Kung University Tainan701 Taiwan

Cross-browser testing not only is one of the most common non-functional testing methods in the field of software testing, but also the testing method that requires large amounts of resources, in terms of hardware and time. Basically, based on Selenium Grid, Kubernetes and KEDA auto-scaler, a cross-browser testing platform can be quickly built. However, through our empirical study of this style of platform, we observed three signifi-cant problems in terms of its reliability and efficiency: the Health-Check problem, the Ses-sion-Queue problem, and the Cooldown problem. This paper suggests solutions to these problems. The experimental result shows a 2.27 times improvement in reliability and a decrease in execution time for 61.5%. Moreover, the overall execution time is also 54.2% less comparing with Selenium's Dynamic Grid. © 2023 Institute of information science. All rights reserved.

关键词： Selenium

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Markovian and non-Markovian master equations versus an exactly solvable model of a qubit in a cavity

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Physical Review Applied 2024年第1期22卷 014028-014028页

作者： Zihan Xia Juan Garcia-Nila Daniel A. Lidar Center for Quantum Information Science & Technology Department of Electrical & Computer Engineering Department of Chemistry Department of Physics & Astronomy

quantum master equations are commonly used to model the dynamics of open quantum systems, but their accuracy is rarely compared with the analytical solution of exactly solvable models. In this work, we perform such a comparison for the damped Jaynes-Cummings model of a qubit in a leaky cavity, for which an analytical solution is available in the one-excitation subspace. We consider the non-Markovian time-convolutionless master equation up to the second (Redfield) and fourth orders as well as three types of Markovian master equations: the coarse-grained, cumulant, and standard rotating-wave approximation (RWA) Lindblad equations. We compare the exact solution to these master equations for three different spectral densities: impulse, Ohmic, and triangular. We demonstrate that the coarse-grained master equation outperforms the standard RWA-based Lindblad master equation for weak coupling or high qubit frequency (relative to the spectral density high-frequency cutoff ωc), where the Markovian approximation is valid. In the presence of non-Markovian effects characterized by oscillatory, nondecaying behavior, the time-convolutionless approximation closely matches the exact solution for short evolution times (in units of ωc−1) even outside the regime of validity of the Markovian approximations. For long evolution times, all master equations perform poorly, as quantified in terms of the trace-norm distance from the exact solution. The fourth-order time-convolutionless master equation achieves the top performance in all cases. Our results highlight the need for reliable approximation methods to describe open-system quantum dynamics beyond the short-time limit.

关键词： Cavity quantum electrodynamics Open quantum systems & decoherence quantum information processing Jaynes-Cummings model Lindblad equation Markovian processes Non-Markovian processes quantum master equation

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An Efficient Long Short-Term Memory and Gated Recurrent Unit Based Smart Vessel Trajectory Prediction Using Automatic Identification System Data

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computers, Materials & Continua 2024年第10期81卷 1789-1808页

作者： Umar Zaman Junaid Khan Eunkyu Lee Sajjad Hussain Awatef Salim Balobaid Rua Yahya Aburasain Kyungsup Kim Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea Department of Environmental IT Engineering Chungnam National UniversityDaejeon34134Republic of Korea Autonomous Ship Research Center Samsung Heavy IndustriesDaejeon34051Republic of Korea ICAROS Center(PRISMA Lab) University of Naples Federico IINaples80131Italy Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazan45142Saudi Arabia

Maritime transportation,a cornerstone of global trade,faces increasing safety challenges due to growing sea traffic *** study proposes a novel approach to vessel trajectory prediction utilizing Automatic Identification System(AIS)data and advanced deep learning models,including Long Short-Term Memory(LSTM),Gated Recurrent Unit(GRU),Bidirectional LSTM(DBLSTM),Simple Recurrent Neural Network(SimpleRNN),and Kalman *** research implemented rigorous AIS data preprocessing,encompassing record deduplication,noise elimination,stationary simplification,and removal of insignificant *** were trained using key navigational parameters:latitude,longitude,speed,and *** aware processing through trajectory segmentation and topological data analysis(TDA)was employed to capture dynamic *** using a three-month AIS dataset demonstrated significant improvements in prediction *** GRU model exhibited superior performance,achieving training losses of 0.0020(Mean Squared Error,MSE)and 0.0334(Mean Absolute Error,MAE),with validation losses of 0.0708(MSE)and 0.1720(MAE).The LSTM model showed comparable efficacy,with training losses of 0.0011(MSE)and 0.0258(MAE),and validation losses of 0.2290(MSE)and 0.2652(MAE).Both models demonstrated reductions in training and validation losses,measured by MAE,MSE,Average Displacement Error(ADE),and Final Displacement Error(FDE).This research underscores the potential of advanced deep learning models in enhancing maritime safety through more accurate trajectory predictions,contributing significantly to the development of robust,intelligent navigation systems for the maritime industry.

关键词： Trajectory prediction AIS data smart vessel deep learning LSTM GRU

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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Mining Erasable Itemsets from a Product Database with Quantitative Components 24

Mining Erasable Itemsets from a Product Database with Quanti...

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11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hong, Tzung-Pei Chen, Da Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan Department of Computer Science and Engineering Department of Electrical and Control China Steel Inc. China Library and Information Center National University of Kaohsiung Taiwan

ISBN: (纸本)9798400717550

Erasable itemset mining is one of the most well-known methods in data mining for optimizing limited materials. After mining erasable itemsets, the manager can rearrange the production plan effectively. However, in real applications, traditional mining methods neglect some critical factors, such as costs or quantities of components forming a product. This thesis thus considers mining erasable itemsets from a product database with quantitative components. An erasable profit-based itemset mining (EPIM) algorithm is proposed to mine erasable itemsets with low profits by analyzing the relationships between products and components. In experiments, a synthetic dataset was used to compare the performance in execution speed and memory usage under various threshold settings. It can also be easily generalized to find erasable itemsets satisfying the other criteria with a quantitative product database. © 2024 ACM.

关键词： Mine safety

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Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

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computers, Materials & Continua 2023年第6期75卷 5379-5393页

作者： Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang Department of Computer Science COMSATS University IslamabadPakistan Faculty of Computing Riphah International UniversityFaisalabadPakistan Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science UET TaxilaTaxilaPakistan Department of Software Engineering UET TaxilaTaxilaPakistan Software Engineering Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942KSA Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a *** a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid ***,optimal path selection to route traffic between the origin and destination is *** research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network ***,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal *** the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal *** model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective ***,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

关键词： Internet of vehicles internet of things fuzzy logic optimization path planning

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Dzyaloshinskii-Moriya-type spin-spin interaction from mixed-parity superconductivity

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Physical Review B 2025年第6期111卷 L060504-L060504页

作者： Jabir Ali Ouassou Takehito Yokoyama Jacob Linder Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences NO-5528 Haugesund Norway Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Center for Quantum Spintronics Department of Physics Norwegian University of Science and Technology NO-7491 Trondheim Norway

Interacting impurity spins adsorbed on surfaces have been suggested as basic components for applications in quantum computation and spintronics. Such spins usually prefer a parallel or antiparallel configuration, but weakly noncollinear alignments are possible due to the Dzyaloshinskii-Moriya interaction (DMI) that arises in the presence of relativistic spin-orbit coupling. Here, we show that an effective Dzyaloshinskii-Moriya-type interaction (DMTI) can emerge purely from superconducting correlations without any spin-orbit interaction. We give an analytical proof and provide a numerical study which shows that DMTI arises in mixed-parity superconductors solely from the superconducting pairing. Moreover, we show that the same effect can be realized in Josephson junctions between s-wave and p-wave superconductors, where a phase bias toggles the DMTI entirely on and off. These results enable a way to engineer spin textures using superconducting order.

关键词： Shear waves

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An Effective Lung Cancer Diagnosis Model Using Pre-Trained CNNs

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computer Modeling in Engineering & sciences 2025年第4期143卷 1129-1155页

作者： Majdi Rawashdeh Muath A.Obaidat Meryem Abouali Dhia Eddine Salhi Kutub Thakur Department of Business Information Technology Princess Sumaya University for TechnologyAmman11941Jordan Department of Computer Engineering Al Yamamah UniversityRiyadh11512Saudi Arabia Department of Computer Science John Jay College and the Graduate CenterThe City University of New YorkNew YorkNY 10019USA Department of Computer Science John Jay College at the City University of NewYorkNewYorkNY 10019USA LIMOSE Laboratory Department of Computer ScienceM’hamed Bougara UniversityBoumerdes35000Algeria Department of Professional Security Studies New Jersey City UniversityJersey CityNJ 07305USA

Cancer is a formidable andmultifaceted disease driven by genetic aberrations and metabolic *** 19% of cancer-related deaths worldwide are attributable to lung and colon cancer,which is also the top cause of death *** malignancy has a terrible 5-year survival rate of 19%.Early diagnosis is critical for improving treatment outcomes and survival *** study aims to create a computer-aided diagnosis(CAD)that accurately diagnoses lung disease by classifying histopathological *** uses a publicly accessible dataset that includes 15,000 images of benign,malignant,and squamous cell carcinomas in the *** addition,this research employs multiscale processing to extract relevant image features and conducts a comprehensive comparative analysis using four Convolutional Neural Network(CNN)based on pre-trained models such as AlexNet,VGG(Visual Geometry Group)16,ResNet-50,and VGG19,after hyper-tuning these models by optimizing factors such as batch size,learning rate,and *** proposed(CNN+VGG19)model achieves the highest accuracy of 99.04%.This outstanding performance demonstrates the potential of the CAD system in accurately classifying lung cancer histopathological *** study contributes significantly to the creation of a more precise CNN-based model for lung cancer identification,giving researchers and medical professionals in this vital sector a useful tool using advanced deep learning techniques and publicly available datasets.

关键词： Lung cancer machine learning computer aided diagnosis CNN medical imaging transfer learning

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Understanding the role of human-inspired heuristics for retrieval models

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Frontiers of computer science 2022年第1期16卷 11-21页

作者： Xiangsheng LI Yiqun LIU Jiaxin MAO Department of Computer Science and Technology Institute for Artificial IntelligenceBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing100084China

Relevance estimation is one of the core concerns of information retrieval(IR)*** existing retrieval models gained much success in both deepening our understanding of information seeking behavior and building effective retrieval systems,we have to admit that the models work in a rather different manner from how humans make relevance ***’information seeking behaviors involve complex cognitive processes,however,the majority of these behavior patterns are not considered in existing retrieval *** bridge the gap between practical user behavior and retrieval model,it is essential to systematically investigate user cognitive behavior during relevance judgement and incorporate these heuristics into retrieval *** this paper,we aim to formally define a set of basic user reading heuristics during relevance judgement and investigate their corresponding modeling strategies in retrieval *** experiments are conducted to evaluate the effectiveness of different reading heuristics for improving ranking *** on a large-scale Web search dataset,we find that most reading heuristics can improve the performance of retrieval model and establish guidelines for improving the design of retrieval models with human-inspired *** study sheds light on building retrieval model from the perspective of cognitive behavior.

关键词： reading heuristics retrieval model cognitive behavior

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