In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutio...
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In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and procedure of code generation and categorize existing solutions according to taxonomy from perspectives of architecture, model-agnostic enhancing strategy, metrics, and tasks. In addition, we outline the challenges faced by current dominant large models and list several plausible directions for future research. We hope that this survey may provide handy guidance to understanding, utilizing, and developing deep learning-based code-generation techniques for researchers and practitioners.
Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world a...
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Backdoor attacks pose great threats to deep neural network models. All existing backdoor attacks are designed for unstructured data(image, voice, and text), but not structured tabular data, which has wide real-world applications, e.g., recommendation systems, fraud detection, and click-through rate prediction. To bridge this research gap, we make the first attempt to design a backdoor attack framework, named BAD-FM, for tabular data prediction models. Unlike images or voice samples composed of homogeneous pixels or signals with continuous values, tabular data samples contain well-defined heterogeneous fields that are usually sparse and discrete. Tabular data prediction models do not solely rely on deep networks but combine shallow components(e.g., factorization machine, FM) with deep components to capture sophisticated feature interactions among fields. To tailor the backdoor attack framework to tabular data models, we carefully design field selection and trigger formation algorithms to intensify the influence of the trigger on the backdoored model. We evaluate BAD-FM with extensive experiments on four datasets, i.e.,HUAWEI, Criteo, Avazu, and KDD. The results show that BAD-FM can achieve an attack success rate as high as 100%at a poisoning ratio of 0.001%, outperforming baselines adapted from existing backdoor attacks against unstructured data models. As tabular data prediction models are widely adopted in finance and commerce, our work may raise alarms on the potential risks of these models and spur future research on defenses.
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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Missing values pose a formidable obstacle in multivariate time series analysis. Existing imputation methods rely on entangled representations that struggle to simultaneously capture multiple orthogonal time-series pat...
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Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked d...
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Brain tumor classification is crucial for personalized treatment *** deep learning-based Artificial Intelligence(AI)models can automatically analyze tumor images,fine details of small tumor regions may be overlooked during global feature ***,we propose a brain tumor Magnetic Resonance Imaging(MRI)classification model based on a global-local parallel dual-branch *** global branch employs ResNet50 with a Multi-Head Self-Attention(MHSA)to capture global contextual information from whole brain images,while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor *** features from both branches are processed through designed attention-enhanced feature fusion module to filter and integrate important ***,to address sample imbalance in the dataset,we introduce a category attention block to improve the recognition of minority *** results indicate that our method achieved a classification accuracy of 98.04%and a micro-average Area Under the Curve(AUC)of 0.989 in the classification of three types of brain tumors,surpassing several existing pre-trained Convolutional Neural Network(CNN)***,feature interpretability analysis validated the effectiveness of the proposed *** suggests that the method holds significant potential for brain tumor image classification.
Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in...
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Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital *** work mines real-world consumer feedbacks through social media topics,which is significant to product *** specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a *** primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset ***,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse *** this end,this work combines deep learning and survival analysis to predict the prevalent time of *** propose a specialized deep survival model which consists of two *** first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network ***,a specific loss function different from regular survival models is proposed to achieve a more reasonable *** experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.
Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property *** there is an extensive literature on qualitative properties such as safety and liveness,t...
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Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property *** there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these *** uncertain environments,agents must make judicious decisions based on subjective *** verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted *** addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)***,we transform the FCTLK model checking problem into the FCTL model *** enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational ***,we present correctness proofs and complexity analyses of the proposed ***,we further illustrate the practical application of our approach through an example of a train control system.
Facial recognition systems on learning platforms like Moodle enable fast and efficient evaluation, automating the detection of impersonation in online exams. This project implements a Moodle plugin that performs facia...
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Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much atten...
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Matroid theory has been developed to be a mature branch of mathematics and has extensive applications in combinatorial optimization,algorithm design and so *** the other hand,quantum computing has attracted much attention and has been shown to surpass classical computing on solving some computational ***,crossover studies of the two fields seem to be missing in the *** paper initiates the study of quantum algorithms for matroid property *** is shown that quadratic quantum speedup is possible for the calculation problem of finding the girth or the number of circuits(bases,flats,hyperplanes)of a matroid,and for the decision problem of deciding whether a matroid is uniform or Eulerian,by giving a uniform lower boundΩ■on the query complexity of all these *** the other hand,for the uniform matroid decision problem,an asymptotically optimal quantum algorithm is proposed which achieves the lower bound,and for the girth problem,an almost optimal quantum algorithm is given with query complexityO■.In addition,for the paving matroid decision problem,a lower boundΩ■on the query complexity is obtained,and an O■ quantum algorithm is presented.
Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work ***-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security *** aim to investigate backdoo...
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Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work ***-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security *** aim to investigate backdoor attack methods for image categorization tasks,to promote the development of DNN towards higher *** on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples,and the meticulous data screening by developers,hindering practical attack *** overcome these challenges,this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation(GN-TUAP)*** approach restricts the direction of perturbations and normalizes abnormal pixel values,ensuring that perturbations progress as much as possible in a direction perpendicular to the decision hyperplane in linear *** limits anomalies within the perturbations improves their visual stealthiness,and makes them more challenging for defense methods to *** verify the effectiveness,stealthiness,and robustness of GN-TUAP,we proposed a comprehensive threat *** on this model,extensive experiments were conducted using the CIFAR-10,CIFAR-100,GTSRB,and MNIST datasets,comparing our method with existing state-of-the-art attack *** also tested our perturbation triggers using various defense methods and further experimented on the robustness of the triggers against noise filtering *** experimental outcomes demonstrate that backdoor attacks leveraging perturbations generated via our algorithm exhibit cross-model attack effectiveness and superior ***,they possess robust anti-detection capabilities and maintain commendable performance when subjected to noise-filtering methods.
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