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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu school of computer Science and Technology Huaibei Normal University school of computer Science and Information Engineering Changzhou Institute of Technology school of computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

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Digital Communications and Networks 2023年第1期9卷 138-145页

作者： Yueshen Xu Zhiying Wang Honghao Gao Zhiping Jiang Yuyu Yin Rui Li school of computer Science and Technology Xidian UniversityXi'an710126China school of computer Engineering and Science Shanghai UniversityShanghai200444China school of computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

关键词： Mashup recommendation Big data Machine learning Mobile networks Internet-of-Things

Unsupervised spectral feature selection algorithms for high dimensional data

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Frontiers of computer Science 2023年第5期17卷 27-40页

作者： Mingzhao WANG Henry HAN Zhao HUANG Juanying XIE school of computer Science Shaanxi Normal UniversityXi’an 710119China Department of computer Science School of Engineering&Computer ScienceBaylor UniversityWacoTX 76798USA

It is a significant and challenging task to detect the informative features to carry out explainable analysis for high dimensional data,especially for those with very small number of samples.Feature selection especially the unsupervised ones are the right way to deal with this challenge and realize the task.Therefore,two unsupervised spectral feature selection algorithms are proposed in this paper.They group features using advanced Self-Tuning spectral clustering algorithm based on local standard deviation,so as to detect the global optimal feature clusters as far as possible.Then two feature ranking techniques,including cosine-similarity-based feature ranking and entropy-based feature ranking,are proposed,so that the representative feature of each cluster can be detected to comprise the feature subset on which the explainable classification system will be built.The effectiveness of the proposed algorithms is tested on high dimensional benchmark omics datasets and compared to peer methods,and the statistical test are conducted to determine whether or not the proposed spectral feature selection algorithms are significantly different from those of the peer methods.The extensive experiments demonstrate the proposed unsupervised spectral feature selection algorithms outperform the peer ones in comparison,especially the one based on cosine similarity feature ranking technique.The statistical test results show that the entropy feature ranking based spectral feature selection algorithm performs best.The detected features demonstrate strong discriminative capabilities in downstream classifiers for omics data,such that the AI system built on them would be reliable and explainable.It is especially significant in building transparent and trustworthy medical diagnostic systems from an interpretable AI perspective.

关键词： feature selection spectral clustering feature ranking techniques entropy cosine similarity

Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National Science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang school of Software Engineering South China University of Technology school of computer Science Fudan University school of computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) school of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (e.***.chatbots,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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Cognition-driven multimodal personality classification

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Science China(Information Sciences) 2022年第10期65卷 164-177页

作者： Xiaoya GAO Jingjing WANG Shoushan LI Min ZHANG Guodong ZHOU school of computer Science and Technology Soochow University

In this paper, we address a novel task, namely, cognition-driven multimodal personality classification(CMPC), aiming to infer personality traits(e.g., romantic, humorous, and gloomy) shown in real time by a human being from the perspective of cognitive psychology. Specifically, this task is motivated by a cognitive difference phenomenon that humans with different personality traits tend to give different personality-oriented textual descriptions when observing an image. In particular, to tackle the inherent noise challenges in this CMPC task, we propose a tailored reinforcement learning approach, namely, multi-agent Select Net, aiming to integrate the opinion-word and image-region selection strategies to select informative opinion-word and image-region features for CMPC. To justify the effectiveness of our approach, we construct six kinds of multimodal personality classification datasets and conduct extensive experiments on the datasets.Experimental results demonstrate that our approach can significantly outperform other strong competitors,including the state-of-the-art unimodal and multimodal approaches.

关键词： cognitive psychology personality classification multimodal analysis multi-agent reinforcement learning agent-sharing mechanism

Feature-Based Augmentation in Sarcasm Detection Using Reverse Generative Adversarial Network

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computers, Materials & Continua 2023年第12期77卷 3637-3657页

作者： Derwin Suhartono Alif Tri Handoyo Franz Adeta Junior computer Science Department School of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia Cyber Security Program Computer Science DepartmentSchool of Computer ScienceBina Nusantara UniversityJakarta11480Indonesia

Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication.This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network(GAN)based augmentation on diverse datasets,including iSarcasm,SemEval-18,and Ghosh.This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network(RGAN).The proposed RGAN method works by inverting labels between original and synthetic data during the training process.This inversion of labels provides feedback to the generator for generating high-quality data closely resembling the original distribution.Notably,the proposed RGAN model exhibits performance on par with standard GAN,showcasing its robust efficacy in augmenting text data.The exploration of various datasets highlights the nuanced impact of augmentation on model performance,with cautionary insights into maintaining a delicate balance between synthetic and original data.The methodological framework encompasses comprehensive data pre-processing and GAN-based augmentation,with a meticulous comparison against Natural Language Processing Augmentation(NLPAug)as an alternative augmentation technique.Overall,the F1-score of our proposed technique outperforms that of the synonym replacement augmentation technique using NLPAug.The increase in F1-score in experiments using RGAN ranged from 0.066%to 1.054%,and the use of standard GAN resulted in a 2.88%increase in F1-score.The proposed RGAN model outperformed the NLPAug method and demonstrated comparable performance to standard GAN,emphasizing its efficacy in text data augmentation.

关键词： Data augmentation Generative Adversarial Network(GAN) Reverse GAN(RGAN) sarcasm detection

BATON: symphony of random testing and concolic testing through machine learning and taint analysis

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Science China(Information Sciences) 2023年第3期66卷 97-117页

作者： Bihuan CHEN Yang LIU Xin PENG Yijian WU Shengchao QIN school of computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University school of computer Science and Engineering Nanyang Technological University school of Computing Teesside University

Random testing is scalable but often fails to hit corner program behaviors,while systematic testing (e.g.,concolic execution) is promising to cover corner program behaviors but is not scalable to explore all program behaviors.Prior attempts to integrate random testing with systematic testing lack targeted guidance.In this paper,we propose a guided hybrid testing approach,named BATON,to synergize random testing with concolic testing.It integrates the knowledge inside test cases and their executions into a conditional execution graph,and uses such knowledge to guide test case generation.Specifically,we learn classification models for some conditionals in the conditional execution graph in a demand-driven way.These models are used to guide random testing to reach and cover partially-covered conditionals.We further employ targeted concolic testing to cover conditionals that cannot be fully covered by guided random testing.We implemented BATONfor Java and evaluated it on three benchmarks.The results show that BATONimproved branch coverage and mutation score over random testing by 16.2%–29.4%and 19.0%–30.0%,over adaptive random testing by 16.8%–33.8%and 19.4%–34.2%,over concolic testing by 2.3%–29.9%and 2.9%–30.1%,and over simple hybrid testing by 1.6%–14.5%and 1.4%–18.7%.

关键词： system testing random testing concolic testing

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan school of computer and Control Engineering Yantai UniversityYantai264005China school of computer and Information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

Adaptive structured sparse multiview canonical correlation analysis for multimodal brain imaging association identification

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Science China(Information Sciences) 2023年第4期66卷 212-227页

作者： Lei DU Huiai WANG Jin ZHANG Shu ZHANG Lei GUO Junwei HAN the Alzheimer's Disease Neuroimaging Initiative school of Automation Northwestern Polytechnical University school of computer Science Northwestern Polytechnical University

Multimodal brain imaging data can be obtained conveniently through rapidly advancing neuroimaging techniques. These multimodal data, which characterize the brain from distinct perspectives, offer a rare opportunity to comprehensively understand the neuropathology of complex brain disorders. Thus,identifying hidden relationships among multimodal brain imaging data is essential and meaningful. The pairwise correlation between two imaging modalities has been extensively studied. However, the multi-way association among more than three modalities remains unclear and is highly challenging. The difficulty and indeterminacy are largely due to the loss imbalances caused by multiple modalities fusion and the lack of reasonable consideration of the relationship implicated in different brain areas. To address both issues, we propose a structured sparse multiview canonical correlation analysis(SMCCA) with adaptive loss balancing and a novel graph-group penalty. The adaptive loss balancing technique encourages SMCCA to fairly optimize each sub-objective. The graph-group constraint penalizes the brain’s regions of interest(ROIs)hierarchically with different regularizations at different levels. We derive an efficient algorithm and present its convergence. Experimental results on synthetic and real neuroimaging data confirm that, compared with state-of-the-art methods, our method is a better alternative as it identifies higher or comparable correlation coefficients and better canonical weights. Importantly, delivered by the canonical weights, the identified ROIs of each modality show a high correlation to each other and brain disorders, which demonstrates the potential of our method for untangling the intricate relationship among multimodal brain imaging data.

关键词： multimodal brain imaging correlation sparse multiview canonical correlation analysis task imbalance adaptive loss balancing graph-group penalty