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A Linked List Encryption Scheme for Image Steganography without Embedding
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作者 Pengbiao Zhao Qi Zhong +3 位作者 Jingxue Chen Xiaopei Wang Zhen Qin Erqiang Zhou 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期331-352,共22页
Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret in... Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks. 展开更多
关键词 STEGANOGRAPHY ENCRYPTION steganography without embedding coverless steganography
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Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding
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作者 王一铭 黄树锋 +2 位作者 陈煌 杨健 蔡述庭 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第1期287-302,共16页
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete... A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality. 展开更多
关键词 visual security image encryption parallel compressive sensing edge detection embedding
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Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks
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作者 Hanyu Zhou Haochen Li Yaomin Zhao 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第2期90-97,共8页
Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extr... Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels. 展开更多
关键词 PDE discovery Gene Expression Programming Deep Learning Knowledge embedding
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TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment
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作者 Lihong Diao Xinyi Fan +5 位作者 Jiang Yu Kai Huang Edouard C.Nice Chao Liu Dong Li Shuzhen Guo 《Journal of Traditional Chinese Medical Sciences》 CAS 2024年第3期264-274,共11页
Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD patt... Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine. 展开更多
关键词 Qi deficiency pattern Heart failure Biological basis Network embedding Transcriptome
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Insider threat detection approach for tobacco industry based on heterogeneous graph embedding
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作者 季琦 LI Wei +2 位作者 PAN Bailin XUE Hongkai QIU Xiang 《High Technology Letters》 EI CAS 2024年第2期199-210,共12页
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t... In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods. 展开更多
关键词 insider threat detection advanced persistent threats graph construction heterogeneous graph embedding
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A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion
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作者 张哲祎 牟俊 +1 位作者 Santo Banerjee 曹颖鸿 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第2期228-237,共10页
Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is desi... Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works. 展开更多
关键词 chaotic hierarchical encryption multi-medical image encryption differentiated visual effects row-column confusion closed-loop bi-directional diffusion transform domain watermark embedding
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Thin-Shell Wormholes Admitting Conformal Motions in Spacetimes of Embedding Class One
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作者 Peter K. F. Kuhfittig 《International Journal of Astronomy and Astrophysics》 2024年第3期162-171,共10页
This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while ... This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while the surface pressure is negative, resulting in , thereby signaling a violation of the null energy condition, a necessary condition for holding a wormhole open. For a Morris-Thorne wormhole, matter that violates the null energy condition is referred to as “exotic”. For the thin-shell wormholes in this paper, however, the violation has a physical explanation since it is a direct consequence of the embedding theory in conjunction with the assumption of conformal symmetry. These properties avoid the need to hypothesize the existence of the highly problematical exotic matter. 展开更多
关键词 Thin-Shell Wormholes Conformal Symmetry embedding Class One Exotic Matter
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基于t-SNE算法的高校职务科技成果转化绩效评价探索 被引量:1
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作者 陈辉 柯金龙 +2 位作者 夏承鹏 吴合槟 林超辉 《中国高校科技》 2024年第4期65-70,共6页
高校职务科技成果转化绩效评价,是提高职务科技成果转化效率和质量的关键一环。文章构建了高校职务科技成果转化绩效评价指标体系,以入选《赋予科研人员职务科技成果所有权或长期使用权试点单位名单》的试点单位广东工业大学为例,基于t-... 高校职务科技成果转化绩效评价,是提高职务科技成果转化效率和质量的关键一环。文章构建了高校职务科技成果转化绩效评价指标体系,以入选《赋予科研人员职务科技成果所有权或长期使用权试点单位名单》的试点单位广东工业大学为例,基于t-SNE算法进行实例论证。通过评价结果分析,提出一系列相关建议,为高校职务科技成果转化绩效评价提供理论指导和参考。 展开更多
关键词 高校 职务科技成果 科技成果转化 绩效评价 t-sne
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基于t-SNE-VNWOA的船舶柴油机故障诊断
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作者 尚前明 陈家君 邱天 《武汉理工大学学报(交通科学与工程版)》 2024年第1期37-42,共6页
文中提出一种基于t-SNE-VNWOA-LSSVM故障诊断模型,并进行了台架试验.试验设置了正常工况、供气不足、燃烧提前和单缸断油四种工况,将各种工况采集的缸盖振动信号进行快速傅里叶变换(FFT),提取了13个时域和频域特征,利用t分布邻域嵌入算... 文中提出一种基于t-SNE-VNWOA-LSSVM故障诊断模型,并进行了台架试验.试验设置了正常工况、供气不足、燃烧提前和单缸断油四种工况,将各种工况采集的缸盖振动信号进行快速傅里叶变换(FFT),提取了13个时域和频域特征,利用t分布邻域嵌入算法(t-SNE)对数据降维、可视化故障特征.结合鲸鱼优化算法(VNWOA)对分类器(LSSVM)初始参数δ2和γ寻优,搭建其故障识别模型,将遗传算法(GA)和粒子群算法(PSO)的寻优诊断结果与之对比.结果表明:基于t-SNE-VNWOA-LSSVM故障诊断模型精度高达96.57%,且具有良好的稳定性及诊断速度. 展开更多
关键词 柴油机 故障诊断 t-sne VNWOA 振动信号 LSSVM
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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview 被引量:1
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作者 Yue Zhou Xin Luo MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2023年第5期1105-1121,共17页
Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(C... Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field. 展开更多
关键词 Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network
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Future Event Prediction Based on Temporal Knowledge Graph Embedding 被引量:2
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作者 Zhipeng Li Shanshan Feng +6 位作者 Jun Shi Yang Zhou Yong Liao Yangzhao Yang Yangyang Li Nenghai Yu Xun Shao 《Computer Systems Science & Engineering》 SCIE EI 2023年第3期2411-2423,共13页
Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling com... Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen. 展开更多
关键词 Event prediction temporal knowledge graph graph representation learning knowledge embedding
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Extrapolation over temporal knowledge graph via hyperbolic embedding 被引量:1
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作者 Yan Jia Mengqi Lin +5 位作者 Ye Wang Jianming Li Kai Chen Joanna Siebert Geordie Z.Zhang Qing Liao 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第2期418-429,共12页
Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(... Predicting potential facts in the future,Temporal Knowledge Graph(TKG)extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.Intuitively,facts(events)that happened at different timestamps have different influences on future events,which can be attributed to a hierarchy among not only facts but also relevant entities.Therefore,it is crucial to pay more attention to important entities and events when forecasting the future.However,most existing methods focus on reasoning over temporally evolving facts or mining evolutional patterns from known facts,which may be affected by the diversity and variability of the evolution,and they might fail to attach importance to facts that matter.Hyperbolic geometry was proved to be effective in capturing hierarchical patterns among data,which is considered to be a solution for modelling hierarchical relations among facts.To this end,we propose ReTIN,a novel model integrating real-time influence of historical facts for TKG reasoning based on hyperbolic geometry,which provides low-dimensional embeddings to capture latent hierarchical structures and other rich semantic patterns of the existing TKG.Considering both real-time and global features of TKG boosts the adaptation of ReTIN to the ever-changing dynamics and inherent constraints.Extensive experiments on benchmarks demonstrate the superiority of ReTIN over various baselines.The ablation study further supports the value of exploiting temporal information. 展开更多
关键词 EXTRAPOLATION hyperbolic embedding temporal knowledge graph
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Word Embeddings and Semantic Spaces in Natural Language Processing 被引量:1
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作者 Peter J. Worth 《International Journal of Intelligence Science》 2023年第1期1-21,共21页
One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse ... One of the critical hurdles, and breakthroughs, in the field of Natural Language Processing (NLP) in the last two decades has been the development of techniques for text representation that solves the so-called curse of dimensionality, a problem which plagues NLP in general given that the feature set for learning starts as a function of the size of the language in question, upwards of hundreds of thousands of terms typically. As such, much of the research and development in NLP in the last two decades has been in finding and optimizing solutions to this problem, to feature selection in NLP effectively. This paper looks at the development of these various techniques, leveraging a variety of statistical methods which rest on linguistic theories that were advanced in the middle of the last century, namely the distributional hypothesis which suggests that words that are found in similar contexts generally have similar meanings. In this survey paper we look at the development of some of the most popular of these techniques from a mathematical as well as data structure perspective, from Latent Semantic Analysis to Vector Space Models to their more modern variants which are typically referred to as word embeddings. In this review of algoriths such as Word2Vec, GloVe, ELMo and BERT, we explore the idea of semantic spaces more generally beyond applicability to NLP. 展开更多
关键词 Natural Language Processing Vector Space Models Semantic Spaces Word embeddings Representation Learning Text Vectorization Machine Learning Deep Learning
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基于t-SNE的多参数岩体结构面分步聚类方法
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作者 李新正 王述红 +1 位作者 侯钦宽 董福瑞 《岩土力学》 EI CAS CSCD 北大核心 2024年第5期1540-1550,共11页
结构面聚类是进行岩体稳定性评价的重要步骤。常用聚类方法多以产状作为分组依据,忽略了结构面物理特性指标对岩体稳定性的影响。针对分组依据单一化的不足,综合考虑结构面倾向、倾角、迹长、张开度、填充状态和粗糙度的影响,提出一种... 结构面聚类是进行岩体稳定性评价的重要步骤。常用聚类方法多以产状作为分组依据,忽略了结构面物理特性指标对岩体稳定性的影响。针对分组依据单一化的不足,综合考虑结构面倾向、倾角、迹长、张开度、填充状态和粗糙度的影响,提出一种基于学生分布随机邻近嵌入(student-distributed stochastic neighbor embedding,简称t-SNE)的多参数岩体结构面分步聚类方法。首先,利用t-SNE算法对除产状外的结构面特征进行数据降维;进而利用模拟退火算法搜索K-means算法的全局最优初始值,并采用分步聚类思想完成聚类。研究表明:所提方法有效地解决了高维空间样本稀疏的问题,同时保留了数据的局部结构与全局结构。新方法相比于传统方法能对空间分布相似区内结构面的物理特性进行精确划分,分组精度更高,且在避免复杂权重值计算的条件下,能有效区分产状与物理特性参数对岩体稳定性的影响差异。最后,将所提方法应用于中国新疆某露天矿坡结构面实测数据分析中,所得分组结果合理可靠,进一步证明该方法在实际工程中的有效性。研究方法可为多参数岩体结构面的分步聚类提供参考。 展开更多
关键词 岩体结构面 多参数 分步聚类 t-sne K-MEANS算法
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Oxygen functionalization-assisted anionic exchange toward unique construction of flower-like transition metal chalcogenide embedded carbon fabric for ultra-long life flexible energy storage and conversion 被引量:1
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作者 Roshan M.Bhattarai Kisan Chhetri +5 位作者 Nghia Le Debendra Acharya Shirjana Saud Mai Cao Hoang Phuong Lan Nguyen Sang Jae Kim Young Sun Mok 《Carbon Energy》 SCIE EI CAS CSCD 2024年第1期72-93,共22页
The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storag... The metal-organic framework(MOF)derived Ni–Co–C–N composite alloys(NiCCZ)were“embedded”inside the carbon cloth(CC)strands as opposed to the popular idea of growing them upward to realize ultrastable energy storage and conversion application.The NiCCZ was then oxygen functionalized,facilitating the next step of stoichiometric sulfur anion diffusion during hydrothermal sulfurization,generating a flower-like metal hydroxysulfide structure(NiCCZOS)with strong partial implantation inside CC.Thus obtained NiCCZOS shows an excellent capacity when tested as a supercapacitor electrode in a three-electrode configuration.Moreover,when paired with the biomass-derived nitrogen-rich activated carbon,the asymmetric supercapacitor device shows almost 100%capacity retention even after 45,000 charge–discharge cycles with remarkable energy density(59.4 Wh kg^(-1)/263.8μWh cm^(–2))owing to a uniquely designed cathode.Furthermore,the same electrode performed as an excellent bifunctional water-splitting electrocatalyst with an overpotential of 271 mV for oxygen evolution reaction(OER)and 168.4 mV for hydrogen evolution reaction(HER)at 10 mA cm−2 current density along with 30 h of unhinged chronopotentiometric stability performance for both HER and OER.Hence,a unique metal chalcogenide composite electrode/substrate configuration has been proposed as a highly stable electrode material for flexible energy storage and conversion applications. 展开更多
关键词 carbon cloth energy conversion energy storage FLEXIBLE metal embedding ultra-stable
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Enhanced Image Captioning Using Features Concatenation and Efficient Pre-Trained Word Embedding
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作者 Samar Elbedwehy T.Medhat +1 位作者 Taher Hamza Mohammed F.Alrahmawy 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3637-3652,共16页
One of the issues in Computer Vision is the automatic development of descriptions for images,sometimes known as image captioning.Deep Learning techniques have made significant progress in this area.The typical archite... One of the issues in Computer Vision is the automatic development of descriptions for images,sometimes known as image captioning.Deep Learning techniques have made significant progress in this area.The typical architecture of image captioning systems consists mainly of an image feature extractor subsystem followed by a caption generation lingual subsystem.This paper aims to find optimized models for these two subsystems.For the image feature extraction subsystem,the research tested eight different concatenations of pairs of vision models to get among them the most expressive extracted feature vector of the image.For the caption generation lingual subsystem,this paper tested three different pre-trained language embedding models:Glove(Global Vectors for Word Representation),BERT(Bidirectional Encoder Representations from Transformers),and TaCL(Token-aware Contrastive Learning),to select from them the most accurate pre-trained language embedding model.Our experiments showed that building an image captioning system that uses a concatenation of the two Transformer based models SWIN(Shiftedwindow)and PVT(PyramidVision Transformer)as an image feature extractor,combined with the TaCL language embedding model is the best result among the other combinations. 展开更多
关键词 Image captioning word embedding CONCATENATION TRANSFORMER
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Multiplex network infomax:Multiplex network embedding via information fusion
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作者 Qiang Wang Hao Jiang +3 位作者 Ying Jiang Shuwen Yi Qi Nie Geng Zhang 《Digital Communications and Networks》 SCIE CSCD 2023年第5期1157-1168,共12页
For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most ... For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised. 展开更多
关键词 Network embedding Multiplex network Mutual information maximization
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Vertex centrality of complex networks based on joint nonnegative matrix factorization and graph embedding
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作者 卢鹏丽 陈玮 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第1期634-645,共12页
Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlat... Finding crucial vertices is a key problem for improving the reliability and ensuring the effective operation of networks,solved by approaches based on multiple attribute decision that suffer from ignoring the correlation among each attribute or the heterogeneity between attribute and structure. To overcome these problems, a novel vertex centrality approach, called VCJG, is proposed based on joint nonnegative matrix factorization and graph embedding. The potential attributes with linearly independent and the structure information are captured automatically in light of nonnegative matrix factorization for factorizing the weighted adjacent matrix and the structure matrix, which is generated by graph embedding. And the smoothness strategy is applied to eliminate the heterogeneity between attributes and structure by joint nonnegative matrix factorization. Then VCJG integrates the above steps to formulate an overall objective function, and obtain the ultimately potential attributes fused the structure information of network through optimizing the objective function. Finally, the attributes are combined with neighborhood rules to evaluate vertex's importance. Through comparative analyses with experiments on nine real-world networks, we demonstrate that the proposed approach outperforms nine state-of-the-art algorithms for identification of vital vertices with respect to correlation, monotonicity and accuracy of top-10 vertices ranking. 展开更多
关键词 complex networks CENTRALITY joint nonnegative matrix factorization graph embedding smoothness strategy
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Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding
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作者 Jae-Seok Jang Hyuk-Yoon Kwon 《Computer Systems Science & Engineering》 SCIE EI 2023年第9期3471-3489,共19页
Question-answering(QA)models find answers to a given question.The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets.In this paper,w... Question-answering(QA)models find answers to a given question.The necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data sets.In this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given question.Existing studies for the approach performed on the entire dataset or datasets within a category that the question writer manually specifies.In contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the category.Due to the text classification model,we can effectively reduce the search space for finding the answers to a given question.Therefore,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference time.Furthermore,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding models.Using real-world QA data sets,we evaluate the performance of the proposed QA matching model.As a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%point.Moreover,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model. 展开更多
关键词 Question-answering text classification model data augmentation text embedding
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An Unsupervised Writer Identification Based on Generating Clusterable Embeddings
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作者 M.F.Mridha Zabir Mohammad +4 位作者 Muhammad Mohsin Kabir Aklima Akter Lima Sujoy Chandra Das Md Rashedul Islam Yutaka Watanobe 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2059-2073,共15页
The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in... The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems.Due to its importance,numerous studies have been conducted in various languages.Researchers have established several learning methods for writer identification including supervised and unsupervised learning.However,supervised methods require a large amount of annotation data,which is impossible in most scenarios.On the other hand,unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted.This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features.A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images.Furthermore,the trained baseline architecture generates the embedding of the data image,and the K-means algorithm is used to distinguish the embedding of individual writers.The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks.In addition,traditional evaluation metrics are used in the proposed model.Finally,the proposed model is compared with a few unsupervised models,and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data. 展开更多
关键词 Writer identification pairwise architecture clusterable embeddings convolutional neural network
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