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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Zhang, Zhiping Sun, Tao Zheng, Hongyan Liu, Hao Liu, Gengchen Yang, Zhi Wang, Xiaoyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)1891706594

The aim of cross-modal image-text retrieval is to heighten comprehension and to create robust associations between visual and textual content. This process entails a mutual querying and synchronization across various data formats. While conventional techniques have seen enhancements via attention-based approaches, they frequently miss the dynamics between corresponding and non-corresponding entity areas within image-text pairs. These techniques, often fixated on aligning image entities with textual terms, can overlook the congruence of entity characteristics, leading to less accurate localization. This research introduces PNRITR, an innovative strategy for cross-modal retrieval that leverages label learning to discern attributes absent from images, broadening the scope beyond just positive entity areas. PNRITR has delivered notable advancements on the Flickr30K and MS-COCO datasets. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Knowledge based systems

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Towards Demystifying Android Adware: dataset and Payload Location 39

Towards Demystifying Android Adware: Dataset and Payload Loc...

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39th ACM/IEEE International Conference on Automated Software engineering Workshops, ASEW 2024

作者： Wang, Chao Liu, Tianming Zhao, Yanjie Zhang, Lin Du, Xiaoning Li, Li Wang, Haoyu Monash University Clayton Australia Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China The National Computer Emergency Response Beijing China Beihang University Beijing China

ISBN: (纸本)9798400712494

Adware represents a pervasive threat in the mobile ecosystem, yet its inherent characteristics have been largely overlooked by previous research. This work takes a crucial step towards demystifying Android adware. We present a comprehensive, well-annotated Android adware dataset AdwareZoo, which comprises 15,996 adware samples across 118 distinct adware families collected from both security reports and app repositories, providing a robust foundation for in-depth analysis and future research. We identify adware family payloads by isolating packages from adware samples for VirusTotal rescanning. Our analysis unveils critical insights into the adware ecosystem, highlighting distinctive patterns in family naming conventions and exposing the unexpected classification of widely-used ad networks as adware. We identify diverse payload location strategies, with a notable finding that over 30% of adware families employ payloads beyond conventional Java/Kotlin code. Furthermore, we reveal several evasion techniques utilized by ad-ware, including package name obfuscation and dynamic payload loading. This research not only offers a robust foundation for understanding and combating adware but also highlights the pressing need for increased scrutiny of mobile advertising practices. © 2024 ACM.

关键词： Android malware

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Demystifying Ethereum account diversity:observations,models and analysis

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Frontiers of computer science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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A Model Inference Attack Based on Random Sampling in DLaaS 24th

A Model Inference Attack Based on Random Sampling in DLaaS

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Wu, Feng Sun, Shouyue Yang, Jiaxun Wu, Liwen Cui, Lei Qu, Youyang Yao, Shaowen Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Engineering Research Center of Cyberspace Yunnan University Kunming China School of Information Science and Engineering Yunnan University Kunming China Data61 Commonwealth Scientific and Industrial Research Organization BurwoodVIC3166 Australia

ISBN: (纸本)9789819615506

Deep learning as a service (DLaaS) has become an effective business solution in numerous domains. Nevertheless, its service form has been proven vulnerable to model inference by previous research. Existing work on model inference attack relies on strong assumptions, such as the attacker knows the sample variance and model structure, which may not thoroughly reflect the attack’s potential threats. In this paper, we propose MC-infer, a zero-knowledge, real-data-free, and black-box model inference method inspired by Monte-Carlo sampling. In particular, MC-infer feeds random noises obtained from different distributions to the target model, and sniffs the corresponding target distributions according to its feedback. Then many samples are taken in these distributions to obtain enough robust noises. Finally, the distribution represented by the target model is fitted through these noises to perform model inference. Our extensive evaluations demonstrate that MC-infer can effectively infer the target model with less information, and general noise perturbation on the model’s outputs cannot defend against MC-infer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent systems

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Blockchain-Based Cognitive Computing Model for data Security on a Cloud Platform

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computers, Materials & Continua 2023年第12期77卷 3305-3323页

作者： Xiangmin Guo Guangjun Liang Jiayin Liu Xianyi Chen Department of Computer Information and Cyber Security Jiangsu Police InstituteNanjing210031China Jiangsu Electronic Data Forensics and Analysis Engineering Research Center Jiangsu Police InstituteNanjing210031China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and ***,information shoplifting poses significant threats,potentially leading to poor performance and privacy ***-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business *** ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized data Driven Cognitive Computing(HD2C)***,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the ***2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of ***“data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to *** with Blockchain allows quick consensus through smart member selection and *** HD2C paradigm significantly improves the computational processing efficiency of intelligent *** analysis results derived from IIoT datasets confirm HD2C *** compared to other consensus algorithms,the Blockchain PoA’s foundational cost is *** accuracy and memory utilization evaluation results predict the total benefits of the *** comparison to the values 0.004 and 0.04,the value of 0.4 achieves good *** to the experiment results,the number of transactions per second has minimal impact on memory *** findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.

关键词： Blockchain Internet of Things(IoT) blockchain based cognitive computing Hybridized data Driven Cognitive Computing(HD2C) Federated Learning(FL) Proof of Authority(PoA)

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Spatial–temporal variations of NDVI and its response to climate in China from 2001 to 2020

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International Journal of Digital Earth 2022年第1期15卷 1463-1484页

作者： Yuanyuan Wei Shougang Sun Dong Liang Zhaohong Jia School of Internet Anhui UniversityHefeiPeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Anhui UniversityHefeiPeople’s Republic of China School of Computer Science and Technology Anhui UniversityHefeiPeople’s Republic of China

Vegetation plays an important role in global or regional environmental *** this study,the spatial–temporal variations of NDVI and its response to climate in China and its seven sub-regions were investigated based on MODIS NDVI data,ERA5-land precipitation(PRE)and temperature(TEM)data from 2001 to *** inter-annual growth rate of NDVI in China was 0.0021/yr in the past 20 *** inter-annual growth rates of NDVI in seven sub-regions had significant differences at regional or seasonal *** ratio of improved vegetation area to the total studied area reached about 70%.In summer,vegetation degradation was concentrated in East China and Southwest *** vegetation in Central China and South China improved more obviously in autumn than in the other *** vegetation of Northeast China had a remarkable degradation in autumn and winter,especially in *** influence degree of PRE(q=0.54,P<0.01)was greater than that of TEM(q=0.27,P<0.01)in the control of the spatial distribution of *** interaction influence degree q of PRE∩TEM was about 0.71 in the last 20 ***,the PRE and TEM played different roles in vegetation growth in seven sub-regions.

关键词： NDVI spatial–temporal variations climate change MK test Geodetector

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An Automatic Method for Fast Synaptic Signal Detection and Count 22

An Automatic Method for Fast Synaptic Signal Detection and C...

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5th International Conference on big data Technologies, ICBDT 2022

作者： Xu, Xiaofeng Lan, Yihua Li, He Qi, Qinglei Liu, Quan Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang473061 China

ISBN: (纸本)9781450396875

Synapses are the special connection structure between brain neurons, and they are the core element of brain plasticity and information transmission. In order to better understand the process of brain information transmission, it is necessary to quantitatively calculate the synapse, but the automatic identification of a large range of synaptic images is still a difficult problem. According to the characteristics of synaptic signal in microscopy optical mouse brain images, this paper proposes the automatic fast synapse recognition method based on density peak clustering, which solves the identification problem of dense small synaptic signal, and realizes the rapid synapse positioning and counting in a large brain area. The results show that the proposed methods in accuracy and efficiency bring great convenience to the researchers. CCS CONCEPTSĝ€¢Applied computing © 2022 ACM.

关键词： Signal detection

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Synthesizing CTA Image data for Type-B Aortic Dissection using Stable Diffusion Models 46

Synthesizing CTA Image Data for Type-B Aortic Dissection usi...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abaid, Ayman Ali Farooq, Muhammad Hynes, Niamh Corcoran, Peter Ullah, Ihsan University Hospital Galway University of Galway Newcastle Road Ireland University of Galway School of Computer Science Ireland University of Galway School of Engineering College of Science and Engineering Ireland University of Galway Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. Therefore, in this study, the possibility of generating synthetic cardiac CTA images was explored by fine-tuning stable diffusion models based on user defined text prompts, using only limited number of CTA images as input. A comprehensive evaluation of the synthetic data was conducted by incorporating both quantitative analysis and qualitative assessment, where a clinician assessed the quality of the generated data. It has been shown that Cardiac CTA images can be successfully generated using using Text to Image (T2I) stable diffusion model. The results demonstrate that the optimized T2I CTA diffusion model successfully render images with features that are typically unique to acute type B aortic dissection (TBAD) medical conditions. © 2024 IEEE.

关键词： computerized tomography

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