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Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenergetic Positron Beams

Vacancy-Type Defects in Thin HfO2 Layers Probed by Monoenerg...

Joint e-Manufacturing and Design Collaboration Symposium (eMDC) & International Symposium on Semiconductor Manufacturing (ISSM)

作者： Akira Uedono Kazuya Kawakami Tatsunori Kuribara Ryu Hasunuma Yosuke Harashima Yasuteru Shigeta Zeyuan Ni Ruolin Yan Hideo Nakamura Akira Notake Tsuyoshi Moriya Koji Michishio Shoji Ishibashi Faculty of Pure and Applied Science University of Tsukuba Tsukuba Ibaraki Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan S-Technology Development Center Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Development Department 1 Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Digital Design Center Tokyo Electron Ltd. Sapporo Hokkaido Japan Software and Data Engineering Division Tokyo Electron Technology Solutions Ltd. Nirasaki Yamanashi Japan Research Institute for Measurement and Analytical Instrumentation National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan Research Center for Computational Design of Advanced Functional Materials National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki Japan

ISBN: (数字)9798331510589

ISBN: (纸本)9798331510596

Behaviors of vacancies during the amorphous-to-crystalline transition of thin HfO 2 layers were studied by monoenergetic positron beams. The HfO 2 layers with 4‒30 nm thickness were fabricated on TiN/Si templates by atomic layer deposition technique. The major vacancy-type defects in the layers were identified as (i) Hf vacancies (V Hf ) coupled with oxygen vacancies (V O s) and (ii) vacancy clusters. After annealing at 500°C, the concentration of vacancy clusters increased, which was attributed to the agglomeration of intrinsic open spaces in the amorphous phase upon the phase transition from amorphous to crystalline phases. The phase transition started near the interface between the HfO 2 layer and bottom electrodes (TiN). After the crystallization, the concentration of vacancy clusters decreased as the annealing temperature increased.

关键词： Electrodes Annealing Atomic layer deposition Positrons Crystallization Tin Semiconductor device manufacture Hafnium compounds

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Dual-domain decoupled fusion network for semantic segmentation of remote sensing images

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Information Fusion 2025年 124卷

作者： Xin Li Feng Xu Jue Zhang Anzhu Yu Xin Lyu Hongmin Gao Jun Zhou College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Engineering and Built Environment Griffith University Nathan Australia School of Surveying and Mapping Information Engineering University Zhengzhou China School of Information and Communication Technology Griffith University Nathan Australia

Semantic segmentation of remote sensing images (RSIs) is a challenging task due to the complexity of spatial structures, diverse object scales, and heterogeneous land-cover patterns. Traditional approaches often struggle to effectively balance fine-grained boundary details and global contextual understanding, especially for high-resolution images. In this paper, we propose DDFNet, a novel dual-domain decoupled fusion network, to address these challenges. DDFNet integrates spatial and frequency domain features through a dynamic decoupling and fusion strategy. Specifically, we introduce a Dual-Domain Decoupled Feature Fusion (DDFF) module that selectively combines high- and low-frequency components from both domains, enabling the model to capture local textures and global semantics. To further enhance segmentation accuracy, we design a High-Order Geometric Prior Generation (HGPG) module, which utilizes gradient and curvature information to improve boundary precision and maintain geometric consistency. Extensive experiments on three benchmark datasets — ISPRS Vaihingen, ISPRS Potsdam, and LoveDA — demonstrate that DDFNet achieves state-of-the-art performance. Ablation studies validate the contributions of the DDFF and HGPG modules, and efficiency analysis shows DDFNet’s strong adaptability to different computational constraints.

关键词： Attention mechanism Feature fusion Remote sensing images Semantic segmentation

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OLinear: A Linear Model for Time Series Forecasting in Orthogonally Transformed Domain

arXiv

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arXiv 2025年

作者： Yue, Wenzhen Liu, Yong Li, Haoxuan Wang, Hao Ying, Xianghua Guo, Ruohao Xing, Bowei Shi, Ji State Key Laboratory of General AI Peking University Beijing100871 China Center for Data Science Peking University Beijing100871 China School of Software BNRist Tsinghua University Beijing100084 China Department of Control Science and Engineering Zhejiang University Hangzhou310058 China

This paper presents OLinear, a linear-based multivariate time series forecasting model that operates in an orthogonally transformed domain. Recent forecasting models typically adopt the temporal forecast (TF) paradigm, which directly encode and decode time series in the time domain. However, the entangled stepwise dependencies in series data can hinder the performance of TF. To address this, some forecasters conduct encoding and decoding in the transformed domain using fixed, dataset-independent bases (e.g., sine and cosine signals in the Fourier transform). In contrast, we utilize OrthoTrans, a data-adaptive transformation based on an orthogonal matrix that diagonalizes the series’ temporal Pearson correlation matrix. This approach enables more effective encoding and decoding in the decorrelated feature domain and can serve as a plug-in module to enhance existing forecasters. To enhance the representation learning for multivariate time series, we introduce a customized linear layer, NormLin, which employs a normalized weight matrix to capture multivariate dependencies. Empirically, the NormLin module shows a surprising performance advantage over multi-head self-attention, while requiring nearly half the FLOPs. Extensive experiments on 24 benchmarks and 140 forecasting tasks demonstrate that OLinear consistently achieves state-of-the-art performance with high efficiency. Notably, as a plug-in replacement for self-attention, the NormLin module consistently enhances Transformer-based forecasters. The code and datasets are available at https://***/r/OLinear. © 2025, CC BY.

关键词： Fourier series

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Sentence Pair Similarity Modeling Based on Weighted Interaction of Multi-semantic Embedding Matrix

Sentence Pair Similarity Modeling Based on Weighted Interact...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Junyu Chen Xiaohong Zhu Jun Sang Lu Gong College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728192284

ISBN: (纸本)9781728185361

In this paper, we focus on measuring the similarity of sentence pair. Noting that a single sentence vector may lose fine-grained semantic information which is important for sentence matching, we propose an embedding matrix to calculate a multi-granularity similarity matrix and find the true semantic alignment of two sentences. We also propose a semantic importance calculation and semantic decomposition that are simple but effective. The proposed model does not require any sparse features or external resources such as WordNet. Compared with other state-of-the-art models, we successfully train in a short time and achieve competitive results on similarity measurement and paraphrase identification tasks. Experiments and visual analysis show the good performance and interpretability of the model.

关键词： Weight measurement Training Visualization Analytical models Semantics Matrix decomposition Sparse matrices

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Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Text Cover Steganography Using Font Color of the Invisible Characters and Optimized Primary Color-intensities 19

Text Cover Steganography Using Font Color of the Invisible C...

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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Rabevohitra, Feno Heriniaina Li, Yantao Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9781728105352

Text cover steganography is concerned with using text support for hiding information. Despite its long and old history, the art of cover writing is still attracting the attention of researchers in the domain of information hiding. Unfortunately, only a few related works exist in terms of text-covered writing. We present a visual attack on the previous related work. Then, we use invisible characters to hide secret data within the text support. We further use a combination and variation of the three primary colors intensities to vehiculate the message when painting non-printing characters. The proposed algorithm makes use of the low-value color spaces to decrease the color variations of the embedded secret message. It also further strengthens the hiddenness of the non-printing characters even when replaced by visible characters. © 2019 IEEE.

关键词： Color

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Two-Stage Attention-Based Model for Code Search with Textual and Structural Features

Two-Stage Attention-Based Model for Code Search with Textual...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Ling Xu Huanhuan Yang Chao Liu Jianhang Shuai Meng Yan Yan Lei Zhou Xu Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

Searching and reusing existing code from a large scale codebase can largely improve developers' programming efficiency. To support code reuse, early code search models leverage information retrieval (IR) techniques to index a large-scale code corpus and return relevant code according to developers' search query. However, IR-based models fail to capture the semantics in code and query. To tackle this issue, developers applied deep learning (DL) techniques to code search models. However, these models either are too complex to determine an effective method efficiently or learning for semantic correlation between code and query *** bridge the semantic gap between code and query effectively and efficiently, we propose a code search model TabCS (Two-stage Attention-Based model for Code Search) in this study. TabCS extracts code and query information from the code textual features (i.e., method name, API sequence, and tokens), the code structural feature (i.e., abstract syntax tree), and the query feature (i.e., tokens). TabCS performs a two-stage attention net-work structure. The first stage leverages attention mechanisms to extract semantics from code and query considering their semantic gap. The second stage leverages a co-attention mechanism to capture their semantic correlation and learn better code/query representation. We evaluate the performance of TabCS on two existing large-scale datasets with 485k and 542k code snippets, respectively. Experimental results show that TabCS achieves an MRR of 0.57 on Hu et al.'s dataset, outperforming three state-of-the-art models CARLCS-CNN, DeepCS, and UNIF by 18%, 70%, 12%, respectively. Meanwhile, TabCS gains an MRR of 0.54 on Husain et al.'s, outperforming CARLCS-CNN, DeepCS, and UNIF by 32%, 76%, 29%, respectively.

关键词： Deep learning Correlation Semantics Syntactics Programming Feature extraction Information retrieval

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Interpretation Area-Guided Detection of Adversarial Samples

Interpretation Area-Guided Detection of Adversarial Samples

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： JiaLi Wei Ming Fan Xi Xu Ang Jia Zhou Xu Lei Xue School of Cyber Science and Engineering Xi'an Jiaotong University China School of Big Data and Software Engineering Chongqing University China The Hong Kong Polytechnic University China

ISBN: (数字)9781728189154

ISBN: (纸本)9781728189161

Deep learning systems are known to be vulnerable to adversarial samples, which are implemented to change the prediction results by adding small perturbations to benign samples. It is significant to defend against an adversarial attack in critical fields such as automatic drive. In this paper, we propose an interpretation area-guided detection method of adversarial samples, which can improve the performance of the typical feature squeezing method by combining the generated interpretation results. Specifically, we divide the input image into two main parts, the interpretation part, and the non-interpretation part. Then we only squeeze the non-interpretation part, which can reduce the side-effect for benign samples. We evaluate our approach on two widely used datasets, and the results demonstrate that our approach outperforms the original feature squeezing method.

关键词： Deep learning Perturbation methods Conferences software quality Feature extraction software reliability Security

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Graph Edit Distance Reward: Learning to Edit Scene Graph 16th

Graph Edit Distance Reward: Learning to Edit Scene Graph

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16th European Conference on Computer Vision, ECCV 2020

作者： Chen, Lichang Lin, Guosheng Wang, Shijie Wu, Qingyao Nanyang Technological University Singapore Singapore Zhejiang University Hangzhou China Huazhong University of Science and Technology Wuhan China School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Beijing China

ISBN: (纸本)9783030585280

Scene Graph, as a vital tool to bridge the gap between language domain and image domain, has been widely adopted in the cross-modality task like VQA. In this paper, we propose a new method to edit the scene graph according to the user instructions, which has never been explored. To be specific, in order to learn editing scene graphs as the semantics given by texts, we propose a Graph Edit Distance Reward, which is based on the Policy Gradient and Graph Matching algorithm, to optimize neural symbolic model. In the context of text-editing image retrieval, we validate the effectiveness of our method in CSS and CRIR dataset. Besides, CRIR is a new synthetic dataset generated by us, which we will publish soon for future use. © 2020, Springer Nature Switzerland AG.

关键词： Semantics

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Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection

arXiv

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arXiv 2022年

作者： Masum, Mohammad Shahriar, Hossain Haddad, Hisham Faruk, Md Jobair Hossain Valero, Maria Khan, Md Abdullah Rahman, Mohammad A. Adnan, Muhaiminul I. Cuzzocrea, Alfredo Wu, Fan School of Data Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Software Engineering and Game Development Kennesaw State University United States Department Computer Science Kennesaw State University United States Department of Electrical and Computer Engineering Florida International University United States Institute of Natural Sciences United International University United States IDEA Lab University of Calabria Rende Italy LORIA Nancy France Department of Computer Science Tuskegee University United States

Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tuning of the hyper-parameters. Manually tuning the hyperparameters is tedious, time-consuming, and computationally expensive. Hence, there is a need for an automatic technique to find optimal hyperparameters for the best use of DNN in intrusion detection. This paper proposes a novel Bayesian optimization-based framework for the automatic optimization of hyperparameters, ensuring the best DNN architecture. We evaluated the performance of the proposed framework on NSL-KDD, a benchmark dataset for network intrusion detection. The experimental results show the framework's effectiveness as the resultant DNN architecture demonstrates significantly higher intrusion detection performance than the random search optimization-based approach in terms of accuracy, precision, recall, and f1-score. © 2022, CC BY.

关键词： Deep neural networks

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