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2nd International Conference on Blockchain and Trustworthy Systems, Blocksys 2020

作者： Li, Biaoqi Fu, Xiaodong Yue, Kun Liu, Li Liu, Lijun Feng, Yong Yunnan Provincial Key Laboratory of Computer Technology Application Kunming650500 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Information Science and Engineering Yunnan University Kunming650091 China

ISBN: (纸本)9789811592126

Peer-to-peer and other distributed systems are known particularly vulnerable to Sybil attack, so is P2P reputation systems. Reputation helps users to make better decisions in P2P systems, but Sybil attacker can obtain multiple identities and pretends to be multiple. It can control more than "one vote" to let others’ reputation change just as the attacker wants. This paper presents a Sybil attacker resistant model for P2P reputation systems by introducing multiple rounds of PoW (Proof-of-Work) verification and dynamic difficulty adjustment. A savvy attacker usually doesn’t carry out attacks without positive returns, so we minimize the expected profit of attackers through the using of puzzles. The costs and benefits are quantified as effectiveness in our model. We explore the effectiveness in two types of comparison, one is in dynamic or static difficulty adjustment, the other is executing once or multiple rounds of verification, and show the effectiveness of our model both analytically and experimentally. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Peer to peer networks

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PlaneSAM: Multimodal Plane Instance Segmentation Using the Segment Anything Model

arXiv

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arXiv 2024年

作者： Deng, Zhongchen Yang, Zhechen Chen, Chi Zeng, Cheng Meng, Yan Yang, Bisheng School of Computer Science and Information Engineering Hubei University Hubei Wuhan430062 China School of Artificial Intelligence Hubei University Hubei Wuhan430062 China Ministry of Education Hubei Wuhan430062 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Hubei Wuhan430072 China Engineering Research Center of Space-Time Data Capturing and Smart Application The Ministry of Education of P.R.C. Hubei Wuhan430072 China Institute of Geospatial Intelligence Wuhan University Hubei Wuhan430072 China

Plane instance segmentation from RGB-D data is a crucial research topic for many downstream tasks, such as indoor 3D reconstruction. However, most existing deep-learning-based methods utilize only information within the RGB bands, neglecting the important role of the depth band in plane instance segmentation. Based on EfficientSAM, a fast version of the Segment Anything Model (SAM), we propose a plane instance segmentation network called PlaneSAM, which can fully integrate the information of the RGB bands (spectral bands) and the D band (geometric band), thereby improving the effectiveness of plane instance segmentation in a multimodal manner. Specifically, we use a dual-complexity backbone, with primarily the simpler branch learning D-band features and primarily the more complex branch learning RGB-band features. Consequently, the backbone can effectively learn D-band feature representations even when D-band training data is limited in scale, retain the powerful RGB-band feature representations of EfficientSAM, and allow the original backbone branch to be fine-tuned for the current task. To enhance the adaptability of our PlaneSAM to the RGB-D domain, we pretrain our dual-complexity backbone using the segment anything task on large-scale RGB-D data through a self-supervised pretraining strategy based on imperfect pseudo-labels. To support the segmentation of large planes, we optimize the loss function combination ratio of EfficientSAM. In addition, Faster R-CNN is used as a plane detector, and its predicted bounding boxes are fed into our dual-complexity network as prompts, thereby enabling fully automatic plane instance segmentation. Experimental results show that the proposed PlaneSAM sets a new state-of-the-art (SOTA) performance on the ScanNet dataset, and outperforms previous SOTA approaches in zero-shot transfer on the 2D-3D-S, Matterport3D, and ICL-NUIM RGB-D datasets, while only incurring a 10% increase in computational overhead compared to EfficientSAM. Ou

关键词： 3D reconstruction

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Hybridizing deep neural network for genes expression classification using histone modification 3

Hybridizing deep neural network for genes expression classif...

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3rd Smart Cities Symposium, SCS 2020

作者： Nair, Rajit Bhagat, Amit Shukla, Piyush Kumar Dutta, Pushan Kumar SoET Jagran Lakecity University Bhopal India Department of Computer Application MANIT Bhopal India Department of Computer Science and Engineering University Institute of Technology RGPV M.P. Bhopal India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Kolkata Kolkata India

ISBN: (纸本)9781839535635

Genes expression datasets are considered to be high dimensional dataset. It is very much needed that before predicting the genes expression dimensionality reduction must be performed. Machine learning algorithms fail to analyze genes expression due to its high dimensionality behaviour. These algorithms have to perform feature selection or feature extraction before classification. To overcome this dimensionality reduction approach deep learning algorithms are implemented which has shown improved performance. This paper is also based on the approach of deep learning algorithm which is based on a classification approach using hybridization of a deep neural network named Hybrid Deep Neural Network (HDNN) in which Convolutional Neural Networks are combined with Recurrent Neural Networks to perform prediction. Parameters like accuracy, AUC, precision, and F1score have shown improved performance than other state of art algorithms with 85.5%, 0.84, 0.81, and 0.79 respectively. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Deep neural networks

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Multi-stage image denoising with the wavelet transform

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Bob Zhang, Yanning Zhang, David School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Guangdong Shenzhen518055 China Department of Computer and Information Science University of Macau 999078 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Guangdong Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which may cause training difficulty. In this paper, we propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and a residual block (RB). DCB uses a dynamic convolution to dynamically adjust parameters of several convolutions for making a tradeoff between denoising performance and computational costs. WEB uses a combination of signal processing technique (i.e., wavelet transformation) and discriminative learning to suppress noise for recovering more detailed information in image denoising. To further remove redundant features, RB is used to refine obtained features for improving denoising effects and reconstruct clean images via improved residual dense architectures. Experimental results show that the proposed MWDCNN outperforms some popular denoising methods in terms of quantitative and qualitative analysis. Codes are available at https://***/hellloxiaotian/MWDCNN. © 2022, CC BY-NC-ND.

关键词： Wavelet transforms

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers

An Effective Classification of Ddos Cloud Based Attack Throu...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Vikas Goel Pragati Goel Raju Ranjan Amit Kumar Sharma Dept. of IT KIET Group of Institutions Delhi-NCR Ghaziabad India Dept. of Computer Application NCRD'S Sterling Institute of Management Studies Nerul Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

The term “cloud computing” refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process.

关键词： computers Cloud computing Technological innovation Web and internet services Intrusion detection Signal processing Denial-of-service attack

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Sentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Miningsentiment Analysis of Amazon Customer Reviews Using Bi-Lstm, Bi-Gru, and Distilbert for the Purpose of Opinion Mining

SSRN

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SSRN 2023年

作者： Tebyani, Sahand Jazayeri, Kian Ahadi, Seyedeh Aridis Çelebi, Erbuğ Jahandar, Farzad Department of Management Information Systems School of Applied Sciences Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Department of Engineering Artificial Intelligence Application and Research Center Cyprus International University Haspolat Turkish Republic of Northern Cyprus Cyprus Khoury College of Computer Science Northeastern University United States Cyprus International University Cyprus

Recurrent Neural Networks (RNNs) have long had a dominant position in the fields of sentiment analysis and Natural Language Processing (NLP). This research gives a thorough comparative analysis of two unique models for opinion mining: Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). Notably, these models have a GloVe-based tokenizer installed to improve their verbal comprehension. We use the Amazon reviews dataset, concentrating on mobile devices and accessories, a rich source of varied user opinions, to assess their effectiveness. We compare their performance to that of the powerful large language model DistilBERT in addition to bespoke models. Our study goes into the complex technique of sentiment analysis to unearth subtle insights. Performance assessments cover a wide range of measures and factors and provide a comprehensive understanding of the models' capabilities. We also contrast how well these models extract highly relevant terms, demonstrating how well they can extract insightful information from user-generated content. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zhiying Xiong Qilin Fan Kai Wang Xiuhua Li Xu Zhang Qingyu Xiong School of Big Data and Software Engineering Chongqing University Chongqing China Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing University Chongqing China School of Computer Science and Technology Harbin Institute of Technology Weihai China Research Institute of Cyberspace Security Harbin Institute of Technology Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China Department of Computer Science University of Exeter Exeter UK

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods.

关键词：

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

引用

TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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地理大数据揭示中国主要城市建成环境物质存量的空间格局

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engineering 2024年第3期34卷 143-153页

作者： Zhou Huang Yi Bao Ruichang Mao Han Wang Ganmin Yin Lin Wan Houji Qi Qiaoxuan Li Hongzhao Tang Qiance Liu Linna Li Bailang Yu Qinghua Guo Yu Liu Huadong Guo Gang Liu Institute of Remote Sensing and Geographical Information Systems School of Earth and Space SciencesPeking UniversityBeijing 100871China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China SDU Life Cycle Engineering Department of Green TechnologyUniversity of Southern DenmarkOdense 5230Denmark Department of Construction Management School of Civil EngineeringTsinghua UniversityBeijing 100084China School of Computer Science China University of GeosciencesWuhan 430074China School of Geographical Sciences East China Normal UniversityShanghai 200062China Land Satellite Remote Sensing Application Center Ministry of Natural ResourcesBeijing 100048China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Geography California State UniversityLong BeachCA 90840USA Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China College of Urban and Environmental Sciences Peking UniversityBeijing 100871China

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate ***,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution *** study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained *** per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form *** is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past ***’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in *** results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

关键词： Urban system engineering Built environment stock Spatial pattern Urban sustainability Big geodata

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The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

The ISCSLP 2022 Intelligent Cockpit Speech Recognition Chall...

引用

International Symposium on Chinese Spoken Language Processing

作者： Ao Zhang Fan Yu Kaixun Huang Lei Xie Longbiao Wang Eng Siong Chng Hui Bu Binbin Zhang Wei Chen Xin Xu Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Beijing Shell Shell Technology Co. Ltd Beijing China WeNet Open Source Community Li Auto Inc. Beijing China

ISBN: (纸本)9798350397970

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce the associated dataset collected from a new-energy vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic contents. We then describe the track arrangement and the baseline system. Specifically, we set up two tracks in terms of allowed model/system size to investigate resource-constrained and -unconstrained setups, targeting to vehicle embedded as well as cloud ASR systems respectively. Finally we summarize the challenge results and provide the major observations from the submitted systems.

关键词： Measurement Industries Visualization Target tracking Speech recognition Linguistics Acoustics

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