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7th International Conference on Image Information Processing, ICIIP 2023

作者： Mehra, Ankush Singh, Gurpreet Badotra, Sumit Kaur, Balwinder Verma, Amit Lovely Professional University Department of Computer Science And Engineering Punjab Phagwara India Bennett University School of Computer Science Engineering And Technology Greater Noida India University Center For Research And Development Chandigarh University Department of Computer Science And Engineering Mohali Gharuan India

ISBN: (纸本)9798350371406

An Internet server, service, or network may be the subject of a distributed denial-of-service (DDoS) assault, in which the attacker attempts to interrupt regular traffic by flooding the target with an excessive amount of data requests. Network security has been severely compromised as a result. We choose the vast majority of network parameters to characterize the traffic in order to get a complete picture of it. As a further step toward lowering the detection time complexity, we employ the PCA approach to compress the features' dimensions. Using principal component analysis (PCA), we can save much of the original data while drastically cutting down on forecast time. After data dimensionality reduction, the RNN is used to train and get a detection model. In comparison to other approaches for detecting DDoS assaults, PCA-RNN performs much better on the real-world dataset, as shown by the evaluation results. © 2023 IEEE.

关键词： Principal component analysis

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Enhance Performance of Ad-hoc Search via Prompt Learning 1

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28th China Conference on Information Retrieval, CCIR 2022

作者： Yang, Shenghao Liu, Yiqun Xie, Xiaohui Zhang, Min Ma, Shaoping Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9783031247552

ISBN: (纸本)9783031247545

Recently, pre-trained language models (PTM) have achieved great success on ad hoc search. However, the performance decline in low-resource scenarios demonstrates the capability of PTM has not been inspired fully. As a novel paradigm to apply PTM to downstream tasks, prompt learning is a feasible scheme to boost PTM’s performance by aligning the pre-training task and downstream task. This paper investigates the effectiveness of the standard prompt learning paradigm on the ad hoc search task. Based on various PTMs, two types of prompts are tailored for the ad hoc search task. Overall experimental results on the MS Marco dataset show the credible better performance of our prompt learning method than fine-tuning based methods and another previous prompt learning based model. Experiments conducted in various resource scenarios show the stability of prompt learning. RoBERTa and T5 deliver better results compared to BM25 using 100 training queries utilizing prompt learning, while fine-tuning based methods need more data. Further analysis shows the significance of the uniformity of tasks’ format and adding continuous tokens into training in our prompt learning method. © 2023, Springer Nature Switzerland AG.

关键词： Learning 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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Flexible Granular Deep Learning Voting Architecture

Flexible Granular Deep Learning Voting Architecture

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2024 International Conference on computer and Applications, ICCA 2024

作者： Kallab, Chadi Haddad, Samir Charafeddine, Jinan Merhej, Joseph Turkey, Nisrine Sayah, Jinane Lebanese American University Department of Computer Science and Mathematics Jbeil Lebanon University of Balamand Computer Science Department Koura Lebanon Research Center Léonard de Vinci Pôle Universitaire Paris La Défense92916 France Lebanese University LaRRIS Laboratory Computer Science Department Fanar Lebanon Lebanese American University Jbeil Lebanon

ISBN: (纸本)9798350367560

The recognition of sleep stages is a very complex and novel topic for many physicians. In an attempt to capitalise on the advances in this field of computing, a number of machine learning algorithms have attempted to replicate the actions that physicians conduct. Deep learning researchers are attempting to create and assess a variety of models and building blocks to assist physicians with their analysis because the polysomnograph's structure is a challenging combination of reasonably comparable components. In this work, we present a broad yet adaptable architecture that leverages the main driving force behind voting systems to enable scientists and doctors to incorporate as many criteria or blocks as required. © 2024 IEEE.

关键词： Sleep research

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JMLKelinci+: Detecting Semantic Bugs and Covering Branches with Valid Inputs Using Coverage-guided Fuzzing and Runtime Assertion Checking

JMLKelinci+: Detecting Semantic Bugs and Covering Branches w...

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作者： Nilizadeh, Amirfarhad Leavens, Gary T. Pǎsǎreanu, Corina S. Noller, Yannic Department of Computer Science College of Engineering and Computer Science University of Central Florida OrlandoFL United States CarnegieMellon University/NASA Ames Research Center CA Moffett Field United States National University of Singapore Singapore

Testing to detect semantic bugs is essential, especially for critical systems. Coverage-guided fuzzing (CGF) and runtime assertion checking (RAC) are two well-known approaches for detecting semantic bugs. CGF aims to generate test inputs with high code coverage. However, while CGF tools can be equipped with sanitizers to detect a fixed set of semantic bugs, they can otherwise only detect bugs that lead to a crash. Thus, the first problem we address is how to help fuzzers detect previously unknown semantic bugs that do not lead to a crash. Moreover, a CGF tool may not necessarily cover all branches with valid inputs, although invalid inputs are useless for detecting semantic bugs. So, the second problem is how to guide a fuzzer to maximize coverage using only valid inputs. However, RAC monitors the expected behavior of a program dynamically and can only detect a semantic bug when a valid test input shows that the program does not satisfy its specification. Thus, the third problem is how to provide high-quality test inputs for a RAC that can trigger potential bugs. The combination of a CGF tool and RAC solves these problems and can cover branches with valid inputs and detect semantic bugs effectively. Our study uses RAC to guarantee that only valid inputs reach the program under test using the program's specified preconditions, and it also uses RAC to detect semantic bugs using specified postconditions. A prototype tool was developed for this study, named JMLKelinci+. Our results show that combining a CGF tool with RAC will lead to executing the program under test only with valid inputs and that this technique can effectively detect semantic bugs. Also, this idea improves the feedback given to a CGF tool, enabling it to cover all branches faster in programs with non-trivial preconditions.1 © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Shuffled Autoregression for Motion Interpolation 48

Shuffled Autoregression for Motion Interpolation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Huang, Shuo Jia, Jia Yang, Zongxin Wang, Wei Wu, Haozhe Yang, Yi Xing, Junliang Tsinghua University Department of Computer Science and Technology Beijing100084 China Beijing National Research Center for Information Science and Technology China Zhejiang University Ccai College of Computer Science and Technology China

ISBN: (纸本)9781728163277

This work aims to provide a deep-learning solution for the motion interpolation task. Previous studies solve it with geometric weight functions. Some other works propose neural networks for different problem settings with consecutive pose sequences as input. However, motion interpolation is a more complex problem that takes isolated poses (e.g., only one start pose and one end pose) as input. When applied to motion interpolation, these deep learning methods have limited performance since they do not leverage the flexible dependencies between interpolation frames as the original geometric formulas do. To realize this interpolation characteristic, we propose a novel framework, referred to as Shuffled AutoRegression, which expands the autoregression to generate in arbitrary (shuffled) order and models any inter-frame dependencies as a directed acyclic graph. We further propose an approach to constructing a particular kind of dependency graph, with three stages assembled into an end-to-end spatial-temporal motion Transformer. Experimental results on one of the current largest datasets show that our model generates vivid and coherent motions from only one start frame to one end frame and outperforms competing methods by a large margin. The proposed model is also extensible to multiple keyframes' motion interpolation tasks and other areas' interpolation. © 2023 IEEE.

关键词： animation human motion motion interpolation neural networks transformer

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Prediction of Passenger Flow During Peak Hours Based on Deep Learning 7th

Prediction of Passenger Flow During Peak Hours Based on Deep...

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7th International Conference on Information science, Communication and Computing, ISCC 2023

作者： Li, Yajing Cheng, Jieren Kou, Yuqing Xia, Dongwan Sheng, Victor S. School of Computer Science and Technology Hainan University Haikou570228 China Hainan Blockchain Technology Engineering Research Center Haikou570228 China Department of Computer Science Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9789819971602

In many deep learning tasks, feature extraction and fusion in multivariate time series is an indispensable part of passenger flow prediction. Because there is also a certain correlation between passenger flow time series, it is very meaningful to successfully capture the time characteristics of the series and the dependencies between each series. Therefore, this study combined Residual Network (ResNet) and Attention Gated Recurrent Unit (Attention GRU) to build a ResGRU model for predicting subway passenger flow data during peak hours. This study has improved ResNet and attention GRU, then designed the ResGRU model architecture. Among them, the subway network topology is constructed by graphs, ResNet is used to capture the hidden spatial features between data, attention GRU captures the deep temporal features between data. This study not only considered passenger flow data, but also added subway network topology data, even weather and air pollution index data. Finally, three time solts of 10, 15 and 30 min were used to forecast the peak passenger flow on the public dataset. The ResGRU model was compared with the single model, the combined model and an ablation experiment. The experimental results demonstrate the high robustness and superiority of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network topology

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Generalized hybrid LiFi-WiFi UniPHY learning framework towards intelligent UAV-based indoor networks

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International Journal of Intelligent Networks 2024年第1期5卷 255-266页

作者： Ahmad, Rizwana Anwar, Dil Nashin Bany Salameh, Haythem Elgala, Hany Ayyash, Moussa Almajali, Sufyan El-Khazali, Reyad Electronic and Electrical Engineering Department University of Cambridge United Kingdom Electrical and Computer Engineering Department University at Albany-State University of New York United States Artificial Intelligence Research Center Al Ain University Al Ain United Arab Emirates Telecommunications Engineering Department Yarmouk University Irbid Jordan Department of Computing Information and Technology Chicago State University ChicagoIL United States Department of Computer Science Princess Sumaya University for Technology Amman Jordan Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates

Advancements in unmanned aerial vehicle (UAV) technology, along with indoor hybrid LiFi-WiFi networks (HLWN), promise the development of cost-effective, energy-efficient, adaptable, reliable, rapid, and on-demand HLWN-capable indoor flying networks (IFNs). To achieve this, a unified physical layer (UniPHY) capable of simultaneous control communication, data transfer, and sensing is crucial. However, traditional block-based decoders, designed independently for LiFi and WiFi, perform poorly in complex and hybrid LiFi-WiFi-enabled UniPHY systems. In this study, we propose an end-to-end learning framework based on convolutional neural networks (CNNs) for UniPHY, which can be trained to serve hybrid LiFi-WiFi transmissions to improve error performance and simplify UAV hardware. In this work, the performance of the proposed framework is assessed and compared with that of the conventional independent block-based communication system. Furthermore, a comprehensive summary of optimal hyper-parameters for efficient training of our learning framework has been provided. It is shown that, with optimal hyper-parameters, the proposed CNN-based framework outperforms the conventional block-based approach by providing a signal-to-noise ratio gain of approximately 7 dB for the LiFi channel and 23 dB for the WiFi channel. In addition, we evaluate the complexity and training convergence for loss and accuracy. © 2024 The Authors

关键词： Data transfer

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On a Structural Approach to the Complex Systems Conceptual Modeling 4

On a Structural Approach to the Complex Systems Conceptual M...

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4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Brodsky, Yury I. Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Simulation Systems and Operation Research Department Moscow Russia

ISBN: (纸本)9798350300918

Conceptual modeling is gaining an essential role in designing large-scale systems, especially as the complexity of the latter increases. The design tool for such systems is the development of ontologies for their subject areas. A problem arises: in our activity, we consider more complex subject areas, and the complexity of ontologies is growing. Is there a limit to this complexity? The design of large-scale systems is already suffocating with complexity - it is difficult to describe the components of the domain, their synthesis is complicated, and even more difficult is the software implementation of the model within the framework of an imperative, object-oriented approach. Debugging the created software system is extremely difficult. The structural theory offers a unified way to describe the subject areas of a broad class of large-scale systems, realizing in this description the principle of 'One in everything and all in One.' Based on this, an end-to-end technology of reporting, synthesis, and software implementation of models of complex, large-scale systems, which is close to CAD methods, is being built. In implementing a software system, it is possible to remain within the framework of declarative programming, avoiding imperative programming, which significantly simplifies its debugging. © 2023 IEEE.

关键词： Large scale systems

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Learned Reverse ISP with Soft Supervision 17th

Learned Reverse ISP with Soft Supervision

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17th European Conference on computer vision, ECCV 2022

作者： Zou, Beiji Zhang, Yue School of Computer Science and Engineering Central South University Changsha410083 China Hunan Engineering Research Center of Machine Vision and Intelligent Medicine Changsha410083 China

ISBN: (纸本)9783031250620

RAW image serves as the foundation for camera imaging, which resides at the very beginning of the pipeline that generates sRGB images. Unfortunately, owing to special considerations, the information-rich RAW images are forfeited by default in most existing applications. To regain the RAW image, some works attempt to restore RAW images from RGB images. They focus on designing handcrafted model-based methods or complicated networks, however, ignoring the special property of RAW image, i.e., high dynamic range. To make up for this deficiency, we introduce a novel soft supervision, derived from the high dynamic range. Specifically, we propose to soften the original ground-truth as a multivariate Gaussian distribution so that networks could learn much more information. Then, we introduce a soft supervision driven network (SSDNet), based on convolution and transformer, for effectively restoring RAW images from RGB images. Quantitative and qualitative results show the promising restoration performance of RGB-to-RAW. In particular, our method achieved fifth place in the S7 track of AIM Reversed ISP Challenge. The source code will be available at https://***/yuezhang98/Learned-Reverse-ISP-with-Soft-Supervision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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