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FAS-reid: Fair Architecture Search for Person Re-IDentification 3

FAS-reid: Fair Architecture Search for Person Re-IDentificat...

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3rd IEEE international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2023

作者： Chen, Shengbo Chen, Shijie Lei, Zhou Shanghai University School of Computer Engineering and Science Shanghai200444 China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9781665490788

The development of deep learning has driven the development of ReID, and more and more excellent methods have been proposed, but most of these are artificially designed network backbones. Automation is a trend in the development of deep learning. This paper propose FAS-ReID(Fair Architecture Search for Person Re-IDentification), which can automatically design and generate a neural network backbone for ReID. This paper compare operation selection to competition, and inject noise to make the competition fairer, while making the architecture of automated search More adaptable to ReID We use TriHard loss to improve the feature extraction ability. Our experiments show that the backbone searched by FAS-ReID is better and reduces the search time. Meanwhile, as for the problem of performance collapse caused by skip-connection enrichment in the search process, FAS-Reid does not use the early stop strategy to avoid performance loss like other solutions, which is also the reason why our method is more reliable and robust. © 2023 IEEE.

关键词： Automation

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Brain-Inspired Hyperdimensional computing for Ultra-Efficient Edge AI

Brain-Inspired Hyperdimensional Computing for Ultra-Efficien...

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ACM/IEEE international conference on Hardware/software Codesign and System Synthesis part of the Embedded Systems Week (ESWEEK)

作者： Amrouch, Hussam Imani, Mohsen Jiao, Xun Aloimonos, Yiannis Fermuller, Cornelia Yuan, Dehao Ma, Dongning Barkam, Hamza E. Genssler, Paul R. Sutor, Peter Univ Stuttgart Chair Semicond Test & Reliabil STAR Stuttgart Germany UC Irvine Bio Inspired Architecture & Syst Irvine CA USA Villanova Univ Dependable Efficient & Intelligent Comp Lab DETAIL Villanova PA USA Univ Maryland Percept & Robot Grp College Pk MD USA

ISBN: (数字)9781665472944

ISBN: (纸本)9781665472944

Hyperdimensional computing (HDC) is rapidly emerging as an attractive alternative to traditional deep learning algorithms. Despite the profound success of Deep Neural Networks (DNNs) in many domains, the amount of computational power and storage that they demand during training makes deploying them in edge devices very challenging if not infeasible. This, in turn, inevitably necessitates streaming the data from the edge to the cloud which raises serious concerns when it comes to availability, scalability, security, and privacy. Further, the nature of data that edge devices often receive from sensors is inherently noisy. However, DNN algorithms are very sensitive to noise, which makes accomplishing the required learning tasks with high accuracy immensely difficult. In this paper, we aim at providing a comprehensive overview of the latest advances in HDC. HDC aims at realizing real-time performance and robustness through using strategies that more closely model the human brain. HDC is, in fact, motivated by the observation that the human brain operates on high-dimensional data representations. In HDC, objects are thereby encoded with high-dimensional vectors which have thousands of elements. In this paper, we will discuss the promising robustness of HDC algorithms against noise along with the ability to learn from little data. Further, we will present the outstanding synergy between HDC and beyond von Neumann architectures and how HDC opens doors for efficient learning at the edge due to the ultra-lightweight implementation that it needs, contrary to traditional DNNs.

关键词： Hyperdimensional computing Embedded Systems Energy-efficient computing Adversarial Attack Voltage Scaling In-memory computing Security Graphs Robotics Computer Vision

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Nirjas: An open source framework for extracting metadata from the source code 12

Nirjas: An open source framework for extracting metadata fro...

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12th international conference on Cloud computing, Data Science and engineering, Confluence 2022

作者： Bhardwaj, Ayush Sahil Pratap, Kaushlendra Mishra, Gaurav Amity University Aset Department Of Computer Science Engineering Noida India Sathyabama University India

ISBN: (纸本)9781665437011

Metadata/Comments are the critical element of any software development process. In this paper, we went on to explain how the metadata/comments in the source code can play an essential role in comprehending the software. We introduced a python based open-source framework 'Nirjas' that helps us in extracting the metadata in a structured manner. There are various syntax, types and widely accepted conventions for adding a comment in the source file of different programming languages. Various edge cases can create noise in our extraction, for which we used Regex to accurately retrieve these metadata. The non-Regex method can give us the result but misses out on accuracy and noise separation. Nirjas can also separate different types of comments, source code and provide us with the details about those comments in terms of the line number, file name, language used, total SLOC, etc. Nirjas is a standalone python framework/library and can be easily and quickly installed using the source installation or using pip (package installer for Python). Nirjas was first created and started out as one of the projects during the Google Summer of Code program and is currently developed and maintained under the FOSSology organization © 2022 IEEE.

关键词： Extraction

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Research of Duplicate Requirement Detection Method 7th

Research of Duplicate Requirement Detection Method

引用

7th international conference on Smart computing and Communication, SmartCom 2022

作者： Rao, Dansheng Bian, Lingfeng Zhao, Hui COMAC Shanghai Aircraft and Research Institute Shanghai201210 China Fudan University Shanghai200433 China Henan University Kaifeng475000 China

ISBN: (纸本)9783031281235

With the rapid development of the software industry, the number of requirements included in the software is also increasing. In particular, in a complex software system such as an avionics software system, dozens of subsystems are usually included, and each subsystem includes hundreds of requirements, so the entire software system will include thousands of requirements. Furthermore, during software development, requirements will undergo frequent changes. When the number of requirements is huge and there are many people from different backgrounds participating in requirements development, it is easy to duplicate requirements. Therefore, in order to prevent redundant development, in the requirements analysis stage, requirements analysts usually need to detect all duplicate requirements in the requirements document and eliminate them, thereby improving the quality of software requirements and saving software development costs. However, when the number of requests is large, manually detecting duplicate requests can be a time-consuming and error-prone task. Aiming at this problem, this paper studies the method of duplicate requirements detection in English requirements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Requirements engineering

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An Enhanced Reversible Data Hiding Algorithm to Maintain Privacy of multimedia in Cloud

An Enhanced Reversible Data Hiding Algorithm to Maintain Pri...

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2022 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Vidhya, A. Kumar, P. Mohan Jeppiaar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This paper proposes high-capacity image-based reversible data-hiding techniques that may be tested in both plaintext and encrypted domains. We proposed methods for maintaining the authenticity and privacy of multimedia material without danger of intrusion. All of these techniques employ encrypted copies of natural photos as a host image on which extra data is encoded, and they all allow for complete reversibility in noise-free retrieval of these natural images. The content owner might use these methods to outsource an embedding algorithm to an untrusted third party in a cloud environment without providing the cover to the embedder since it includes very sensitive information. The owner encrypts the cover picture to keep the data private, and the embedder homomorphically embeds the secret data on the encrypted image coefficients without knowing the host image. The owner might enable anybody with a valid data hiding key to extract the encrypted domain's contained secret data, but only the intended single receiver with the associated decryption key could decode the marked data and disclose the cover. Algorithms are improved to accomplish data embedding in the encrypted transform domain to improve resilience against typical signal processing procedures. This is done using the integer discrete cosine transform in the encrypted domain (e-DCT). When compared to current state-of-the-art reversible data concealing methods, the suggested approaches provide improved embedding capacity and picture quality. In the encrypted domain, the methods perform well in the face of several common noise assaults. © 2022 IEEE.

关键词： Embeddings

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Detection and Mitigation of DDoS attack in software Defined Networking: A Survey

Detection and Mitigation of DDoS attack in Software Defined ...

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2022 international conference on Sustainable computing and Data Communication Systems, ICSCDS 2022

作者： Ashodia, Namita Makadiya, Kishan Marwadi University Department of Cyber Security Gujarat Rajkot India Marwadi University Department of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9781665478847

software Defined Networking (SDN) is an emerging technology, which provides the flexibility in communicating among network. software Defined Network features separation of the data forwarding plane from the control plane which includes controller, resulting centralized network. Due to centralized control, the network becomes more dynamic, and resources are managed efficiently and cost-effectively. Network Virtualization is transformation of network from hardware-based to software-based. Network Function Virtualization will permit implementation, adaptable provisioning, and even management of functions virtually. The use of virtualization of SDN networks permits network to strengthen the features of SDN and virtualization of NFV and has for that reason has attracted notable research awareness over the last few years. SDN platform introduces network security challenges. The network becomes vulnerable when a large number of requests is encapsulated inside packet_in messages and passed to controller from switch for instruction, if it is not recognized by existing flow entry rules. which will limit the resources and become a bottleneck for the entire network leading to DDoS attack. It is necessary to have quick provisional methods to prevent the switches from breaking down. To resolve this problem, the researcher develops a mechanism that detects and mitigates flood attacks. This paper provides a comprehensive survey which includes research relating frameworks which are utilized for detecting attack and later mitigation of flood DDoS attack in software Defined Network (SDN) with the help of NFV. © 2022 IEEE.

关键词： software defined networking

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Monitoring and Warning of Flooding Conditions Using IoT Based System 9

Monitoring and Warning of Flooding Conditions Using IoT Base...

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9th international conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Ashokkumar, N. Arun, V. Prabhu, S. Kalaimagal, V. Srinivasan, Dhamodharan Shanthi, B. Mohan Babu University Department of Electronics and Communication Engineering Tirupati India Mohan Babu University Department of Electrical and Electronics Engineering Tirupati India RLE Motors Pvt.Ltd Tamilnadu India Annamalai University Centralized Instrumentation and Service Lab Tamilnadu India

ISBN: (纸本)9798350397376

To be sure, flooding is one of the most devastating natural calamities that needs to be addressed. It has widespread repercussions for the economy and has caused many deaths. Lack of warning causes a great deal of annual mortality. This study employed the NodeMCU, ultrasonic sensors, and other sensors to detect and measure the depths of flood waters. Here, we measure changes in the river's level with the help of an ultrasonic HC-SR04 sensor and a NodeMCU ESP8266 Microcontroller. The information after processing will be sent to the ThingSpeak IoT Cloud Platform. With this method, river levels may be tracked graphically from any location. The circuit's software was programmed using the Arduino IDE and uploaded to the Arduino microcontroller's memory. The study concluded that the proposed sensor could precisely determine the location of an incoming object and present that information as a distance on the ThingSpeak Cloud Platform. Concurrently, the sensor shows off LED indicators for your eyes to feast over. Based on data from an Arduino Uno, this system uses the Internet of Things to issue warnings to the proper authorities ahead of time of any impending floods. © 2023 IEEE.

关键词： Microcontrollers

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Microsoft Malware Detection using Machine Learning 6

Microsoft Malware Detection using Machine Learning

引用

6th international conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Garg, Aayushi Tahilramani, Nikunj Chauhan, Parth Patel, Ritu Silver Oak University Electronics & Communication Dept Ahmedabad India Aiie Information Communication Technology Dept. Ahmedabad India Sage University Computer Engineering Dept Madhya Pradesh India

ISBN: (纸本)9781665410359

A malware is any software intentionally designed to cause damage to a computer, server, client or network. Malware is very challenging issue and major concern for privacy of data. This detection can give information about Malicious and Benign data. Machine learning contain Evaluation measures like multi-class log loss, Confusion matrix, precision and recall these four measures are used. Exploratory data analysis, ANOVA, Feature engineering and evaluation are methods for classification machine learning models. In this paper Decision tree model is used for the classification. Max depth for 16 value gives more accuracy score 0.63 as compare to Max depth for 32 value which gives accuracy score 0.5. © 2022 IEEE.

关键词： Malware

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ORFPPrediction: Machine Learning Based Online Recruitment Fraud Probability Prediction

ORFPPrediction: Machine Learning Based Online Recruitment Fr...

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2023 international conference on Cognitive computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Wang, Meifeng Wang, Yuanyuan Li, Yazhou Gu, Dingyun Zhu, Yemeng Huaiyin Institute of Technology Institute of Computer and Software Engineering Jiangsu Province Internet of Things Mobile Interconnection Technology Engineering Laboratory Huai'an City Jiangsu223001 China

ISBN: (纸本)9798350306293

In response to the issue of inadequate public datasets concerning imbalances in Internet recruitment scams, this paper constructs an imbalanced experimental dataset, labels it, and proposes an ensemble learning method based on model stacking. This method enhances the prediction accuracy of the original model built with Random Forest. Initially, the method employs label encoding and TF-IDF (Term Frequency- Inverse Document Frequency) to preprocess structured and unstructured data respectively. Then, it utilizes Truncated SVD (Singular Value Decomposition) to reduce the dimensionality of the sparse matrix generated by feature extraction from TF-IDF. Subsequently, decision trees, random forests, and Light Gradient Boosting Machine are utilized as base models, with the random forest employed as the meta-model to integrate the predictions of the base models. This paper presents a comparison of the experimental results from seven machine learning models based on the self-constructed experimental dataset, with model stacking implemented on the random forest that yielded the best experimental result. Through model stacking enhancement of the machine learning models, compared to the random forest model that performed best on the self-constructed experimental dataset, an accuracy of 94.85% was achieved, marking a 7.33% improvement. The experimental results hold significant value and practical implications in understanding and resolving the probability risk issue of recruitment fraud. © 2023 IEEE.

关键词： Learning systems

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Research on Visualization Technology of Data Mining Based on RD-SOM (Radar-Self-Organizing Map) Clustering Algorithm

Research on Visualization Technology of Data Mining Based on...

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Cloud computing, Big Data Applications and software engineering (CBASE), international conference on

作者： Bing Ma Dongsheng Li Xiaotan Liu Yiyuan Mai Shasha Qin PLAA Infantry College PLAA Infantry College Shijiaozhuang Hebei China Changsha Hunan China Hainan Hospital of Chinese PLA General Hospital Sanya Hainan China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

With the continuous development of data storage, analysis, processing and other technologies, people are eager to visualize the mining process of complex data, and data mining visualization technology is gradually applied to all fields of society, which greatly improves the efficiency of data mining [1]. The author starts with data visualization technology, studies data preprocessing and algorithms, and proposes data mining visualization technology of RD-SOM clustering algorithm by analyzing the existing problems in the current SOM clustering algorithm, so as to improve the current algorithm and obtain higher data utilization rate.

关键词： Cloud computing software algorithms Data preprocessing Data visualization Clustering algorithms Memory Big Data applications Data mining software engineering

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