检索结果-南通市图书馆

Chinese Journal of Electronics 2024年第1期33卷 128-138页

作者： Jinxue PENG Yong WANG Jingfeng XUE Zhenyan LIU school of Computer science and technology Beijing Institute of Technology

Cross-platform binary code similarity detection aims at detecting whether two or more pieces of binary code are similar or not. Existing approaches that combine control flow graphs(CFGs)-based function representation and graph convolutional network(GCN)-based similarity analysis are the best-performing ones. Due to a large amount of convolutional computation and the loss of structural information, the use of convolution networks will inevitably bring problems such as high overhead and sometimes inaccuracy. To address these issues, we propose a fast cross-platform binary code similarity detection framework that takes advantage of natural language processing(NLP)and inductive graph neural network(GNN) for basic blocks embedding and function representation respectively by simulating extracting structural features and temporal features. GNN’s node-centric and small batch is a suitable training way for large CFGs, it can greatly reduce computational overhead. Various NLP basic block embedding models and GNNs are evaluated. Experimental results show that the scheme with long short term memory(LSTM)for basic blocks embedding and inductive learning-based Graph SAGE(GAE) for function representation outperforms the state-of-the-art works. In our framework, we can take only 45% overhead. Improve efficiency significantly with a small performance trade-off.

关键词： Training Semantics Process control Binary codes Feature extraction Natural language processing Graph neural networks

同方期刊数据库

Effect of strain on structure and electronic properties of monolayer C_(4)N_(4)

在线全文

学校读者我要写书评

暂无评论

Chinese Physics B 2024年第5期33卷 595-600页

作者：陈昊徐瑛赵家石周丹 school of Physics Changchun University of Science and TechnologyChangchun 130022China school of Computer science and technology Changchun University of Science and TechnologyChangchun 130022China

The first-principles calculations are performed to examine structural,mechanical,and electronic properties at large strain for a monolayer C_(4)N_(4),which has been predicted as an anchoring promising material to attenuate shuttle effect in Li–S batteries stemming from its large absorption energy and low diffusion energy *** results show that the ideal strengths of C_(4)N_(4)under tension and pure shear deformation conditions reach 13.9 GPa and 12.5 GPa when the strains are 0.07 and 0.28,*** folded five-membered rings and diverse bonding modes between carbon and nitrogen atoms enhance the ability to resist plastic deformation of C_(4)N_(4).The orderly bond-rearranging behaviors under the weak tensile loading path along the[100]direction cause the impressive semiconductor–metal transition and inverse semiconductor–metal *** present results enrich the knowledge of the structure and electronic properties of C_(4)N_(4)under deformations and shed light on exploring other two-dimensional materials under diverse loading conditions.

关键词： two-dimensional materials strain effect structural evolution electronic properties

维普期刊数据库

Robust video question answering via contrastive cross-modality representation learning

在线全文

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG school of Information science and technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center school of Computer science and Information Engineering Hefei University of Technology Institutes of Physical science and Information technology Anhui University school of Computer science and technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

Neural network study of the nuclear ground-state spin distribution within a random interaction ensemble

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Nuclear science and Techniques 2024年第3期35卷 216-227页

作者： Deng Liu Alam Noor A Zhen-Zhen Qin Yang Lei school of Mathematics and Physics Southwest University of Science and TechnologyMianyang 621010China school of Nuclear science and technology Southwest University of Science and TechnologyMianyang 621010China

The distribution of the nuclear ground-state spin in a two-body random ensemble(TBRE)was studied using a general classification neural network(NN)model with two-body interaction matrix elements as input features and the corresponding ground-state spins as labels or output *** quantum many-body system problem exceeds the capability of our optimized NNs in terms of accurately predicting the ground-state spin of each sample within the ***,our NN model effectively captured the statistical properties of the ground-state spin because it learned the empirical regularity of the ground-state spin distribution in TBRE,as discovered by physicists.

关键词： Neural network Two-body random ensemble Spin distribution of nuclear ground state

维普期刊数据库

Triphenylphosphine-Assisted Exsolution Engineering on Ruddlesden-Popper Perovskites for Promoting Oxygen Evolution

在线全文

学校读者我要写书评

暂无评论

Energy & Environmental Materials 2024年第4期7卷 433-441页

作者： Juan Bai Jing Shang Jun Mei Dongchen Qi Ting Liao Ziqi Sun school of Chemistry and Physics Queensland University of Technology Centre for Materials science Queensland University of Technology school of Materials science and Engineering Shaanxi University of Science and Technology school of Mechanical Medical and Process Engineering Queensland University of Technology

Metal exsolution engineering has been regarded as a promising strategy for activating intrinsically inert perovskite oxide catalysts toward efficient oxygen evolution *** metal exsolution processes on perovskites are often achieved by using the reducing hydrogen gas; however,this is not effective for the relatively stable phase,such as Ruddlesden-Popper perovskite oxides.

关键词： exsolution oxygen evolution perovskite reconstruction Ruddlesden–Popper

Interface interaction between SiO_(2) and magnetite under high temperature:particle migration and inhibition mechanism

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Iron and Steel Research International 2024年第3期31卷 561-572页

作者： Yao-zu Wang Jian-liang Zhang Qiang Cheng Hui-qing Jiang Zheng-jian Liu Yu-bo Tan school of Intelligent science and technology University of Science and Technology BeijingBeijing 100083China school of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing 100083China school of Advanced Engineering University of Science and Technology BeijingBeijing 100083China

Silicon is one of the main gangue components in iron ore,usually in the form of quartz and *** studies have shown that SiO_(2) has a two-sided effect on the consolidation of pellets during high-temperature oxidation roasting of ***,it is very difficult to capture the structural evolution and migration mechanisms during high-tem-perature roasting process by existing experimental ***,the influence of SiO_(2) on the consolidation behavior of magnetite was studied through a series of roasting experiments and molecular dynamic *** results show that the consolidation index and particle growth index decrease with the increase in SiO_(2) content in the *** 1573 K,the liquid phase promotes the recrystallization growth of hematite at high *** dynamic study shows that it is difficult for quartz SiO_(2) to form sintering neck with Fe_(2)O_(3).When the calcination temperature is higher than 1400 K,the Fe_(2)O_(3)-Fe_(2)SiO_(4) system produces a considerable sintering neck structure after *** atomic migration ability of Fe_(2)SiO_(4) is much higher than that of Fe_(2)O_(3).The higher atomic migration ability of Fe_(2)SiO_(4) is the main reason for the formation of the sintering neck.

关键词： Sintering neck Crystal growth Consolidation Molecular dynamics SiO_(2)

Fixed-time stabilization of output-constrained stochastic high-order nonlinear systems

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第4期67卷 5-20页

作者： Ruiming XIE Shengyuan XU Nanjing University of science and technology school of Automation

In this study, the fixed-time stabilization problem of stochastic high-order nonlinear systems with output constraint and high-order and low-order nonlinearities is addressed. A new coordinate transformation is employed to directly convert output-constrained stochastic systems into an equivalent unconstrained *** fully extracting the characteristics of system nonlinearities and using the stochastic fixed-time stability theorem, a new design and analysis method is constructed to guarantee that the trivial solution of the closed-loop system is stochastically fixed-time stable while fulfilling the output constraint.

关键词： stochastic high-order nonlinear system outputconstraint high-order and low-order nonlinearity fixed-timestabilization finite-time stabilization

Output feedback stabilization of stochastic high-order planar nonlinear systems with stochastic inverse dynamics and output-constraint

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第3期67卷 182-196页

作者： Ruiming XIE Shengyuan XU Nanjing University of science and technology school of Automation

In this paper, we solve the output feedback control problem of stochastic high-order planar nonlinear systems with output constraint and stochastic integral input-to-state stability(SiISS) inverse dynamics. By employing a key coordinate transformation, a stochastic nonlinear system with output constraint and SiISS inverse dynamics is converted into an unconstrained system. By skillfully constructing an observer and adopting SiISS small-gain conditions, we develop a new output feedback control design and analysis method, and prove that all the closed-system signals are bounded almost surely, the output constraint is not violated almost surely, and the equilibrium point of the closed-loop system is stochastically asymptotically stable.

关键词： stochastic high-order planar nonlinear system outputconstraint stochastic inverse dynamics output feedbackstabilization stochastic integral input-to-state stability(SiISS)

Minimal Context-Switching Data Race Detection with Dataflow Tracking

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU school of Computer science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology school of Computer science and technology Wuhan University of Science and Technology school of Future technology South China University of Technology Shenzhen Research Institute of Hunan University school of Computer science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)