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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

Hydrodynamic analyses of typical underwater gliders

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Journal of Hydrodynamics 2015年第4期27卷 556-561页

作者：陈亚君陈红勋马峥 Petro China research Institute of Petroleum Exploration and Development China Ship scientific research center

The underwater glider changes its weight and the weight distribution through the battery use, to move up and down and forward in the sea. It enjoys many advantages such as a long endurance, and a long operational range with its unique device. The performance of the underwater glider can not evaluated only by the drag, the energy consumption is also one of the key factors. In this paper, the power conversion ratio is proposed according to the transfer efficiency from the gravitational potential energy to the available work, and the performances of three typical underwater gliders are evaluated from multi-angles, such as the drag, the power conversion ratio and the barycenter＇s offset. So the glide performance and the energy consumption in various motion states can be analyzed. The results of this paper can provide a theoretical basis for further study of underwater gliders.

关键词： the underwater glider the drag the energy consumption the power conversion ratio

A Posteriori Parameter Choice Strategy for Nonlinear Monotone Operator Equations

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Acta Mathematicae Applicatae Sinica 2002年第2期18卷 289-294页

作者： Hong-qi Yang, Zong-yi HouDepartment of scientific computing & Computer applications, Zhongshan University, Guangzhou 510275,ChinaDepartment of Mathematics, Pudan University, Shanghai 200433, China Department of scientific computing & Computer applications Zhongshan University Guangzhou China Department of Mathematics Fudan University Shanghai China

In this paper we present an a posteriori parameter choice strategy for nonlinear ill-posed operator equations involving monotone operators. Under certain conditions, this a posteriori parameter choice strategy guarantees the optimal convergence rate O (δ1/2) for Tikhonov-Browder regularization, where δ denotes the noise level of the data perturbation.

关键词： Nonlinear operator equation monotone operator convergence rate a posteriori parameter choice

NAS4RRAM: neural network architecture search for inference on RRAM-based accelerators

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Science China(Information Sciences) 2021年第6期64卷 89-99页

作者： Zhihang YUAN Jingze LIU Xingchen LI Longhao YAN Haoxiang CHEN Bingzhe WU Yuchao YANG Guangyu SUN center for Energy-Efficient computing and applications Peking University Department of Micro/nanoelectronics Peking University center for Brain Inspired Chips Institute for Artificial IntelligencePeking University

The RRAM-based accelerators enable fast and energy-efficient inference for neural ***, there are some requirements to deploy neural networks on RRAM-based accelerators, which are not considered in existing neural networks.(1) Because the noise problem and analog-digital converters/digitalanalog converters(ADC/DAC) affect the prediction accuracy, they should be modeled in networks.(2) Because the weights are mapped to the RRAM cells, they should be quantized, and the number of weights is limited by the number of RRAM cells in the accelerator. These requirements motivate us to customize the hardware-friendly network for the RRAM-based accelerator. We take the idea of network architecture search(NAS) to design networks with high prediction accuracy that meet the requirements. We propose a framework called NAS4RRAM to search for the optimal network on the given RRAM-based accelerator. The experiments demonstrate that NAS4RRAM can apply to different RRAM-based accelerators with different scales. The performance of searched networks outperforms the manually designed ResNet.

关键词： network architecture search (NAS neural networks RRAM-based accelerator hardware noise quantization

Further insight into the convergence of the Fletcher-Reeves method

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Science China Mathematics 1999年第9期42卷 905-916页

作者：戴彧虹 State Key Laboratory of scientific and Engineering computing Institute of Computational Mathematics and Scientific/Engineering Computing Chinese Academy of Sciences Beijing China

The convergence properties of the Fletcher-Reeves method for unconstrained optimization are further studied with the technique of generalized line search. Two conditions are given which guarantee the global convergence of the Fletcher-Reeves method using generalized Wolfe line searches or generalized Arjimo line searches, whereas an example is constructed showing that the conditions cannot be relaxed in certain senses.

关键词： unconstrained optimization conjugate gradient Fletcher-Reeves method generalid line search global convergence

vSpec: workload-adaptive operating system specialization for virtual machines in cloud computing

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Science China(Information Sciences) 2016年第9期59卷 47-62页

作者： Xinkui ZHAO Jianwei YIN Zuoning CHEN Sheng HE College of Computer Science Zhejiang University National Parallel computing Engineering research center Jiangnan Institute of computing Technology

In general, operating systems(OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same *** in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine(VM), which leads to homogeneous system calls on each VM. In addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. In this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, v Spec. There are five classes of VM: CPU-intensive, memory-intensive, I/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. v Spec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of v Spec on benchmarks and real-world applications.

关键词： workload classification operating system specialization workload-aware cloud computing v Spec system performance

SNR changes of VLF radio signals detected onboard the DEMETER satellite and their possible relationship to the Wenchuan earthquake

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Science China Earth Sciences 2009年第6期52卷 754-763页

作者： M. Parrot Laboratory of Physical and Chemical Environment National Center for Scientific Research

Here we used the VLF signal data received by the DEMETER satellite, transmitted from various ground VLF transmitters which are located around China, to study the changes in the signal to noise ratio (SNR) before and after the Wenchuan earthquake, which had a magnitude of 8.0. We also found that the SNRs of different frequency signals decreased significantly over the epicenter region before the earthquake, and reverted to their original levels after the earthquake. This phenomenon may be related to the earthquake.

关键词： SNR VLF Wenchuan earthquake DEMETER satellite seismo-ionospheric phenomenon

A new trust region algorithm for image restoration

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Science China Mathematics 2005年第2期48卷 169-184页

作者： WEN Zaiwen & WANG Yanfei State Key Laboratory of scientific Engineering computing, Institute of Computational Mathematics and scientific/Engineering computing, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100080, China National Key Laboratory on Remote Sensing Science, Institute of Remote Sensing applications, Chinese Academy of Sciences, Beijing 100101, China State Key Laboratory of scientific Engineering computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and System Sciences Chinese Academy of Sciences Beijing China National Key Laboratory on Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing China

The image restoration problems play an important role in remote sensing and astronomical image analysis. One common method for the recovery of a true image from corrupted or blurred image is the least squares error (LSE) method. But the LSE method is unstable in practical applications. A popular way to overcome instability is the Tikhonov regularization. However, difficulties will encounter when adjusting the so-called regularization parameter a. Moreover, how to truncate the iteration at appropriate steps is also challenging. In this paper we use the trust region method to deal with the image restoration problem, meanwhile, the trust region subproblem is solved by the truncated Lanczos method and the preconditioned truncated Lanczos method. We also develop a fast algorithm for evaluating the Kronecker matrix-vector product when the matrix is banded. The trust region method is very stable and robust, and it has the nice property of updating the trust region automatically. This releases us from tedious finding the regularization parameters and truncation levels. Some numerical tests on remotely sensed images are given to show that the trust region method is promising.

关键词： trust region algorithm, image restoration, Lanczos method, Kronecker matrix-vector product, preconditioning.

Knowledge-enriched joint-learning model for implicit emotion cause extraction

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 118-128页

作者： Chenghao Wu Shumin Shi Jiaxing Hu Heyan Huang School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Beijing Engineering research center of High Volume Language Information Processing and Cloud computing applications BeijingChina

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

关键词： emotion cause extraction external knowledge fusion implicit emotion recognition joint learning