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Analysis of Irregular Menstrual Cycle and Its Consequences

Analysis of Irregular Menstrual Cycle and Its Consequences

引用

2023 international conference on Computer Communication and Informatics, ICCCI 2023

作者： Sneha, M. Sai Sravani, K. Nithya, S. Kaladevi, R. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348217

Women in the 21st century is diagnosed with many kinds of health issues which are later found to be serious. One of the most worrying diseases today is Polycystic Ovarian Syndrome(PCOS) which is highly dangerous to an extent of affecting women's reproductive life to a huge extent. This causes various disorders like infertility, endometrial cancer, etc. The most prevalent symptoms of this disease are oily skin, acne, irregular menses, anxiety disorders, and hypertension. These women are also at increased risk for mood disorders, such as depression and anxiety, and non-alcoholic fatty liver. An estimation states that one in five women suffers from PCOS [1]. Many women wake up and notice the symptoms only at the time of conceiving. This is happening because of the lack of knowledge about this disorder. Our aim is to develop a medium, which helps the end user (Specially made for women) to know their Health level according to their period cycle. data prediction provides the end user with Suggestions based on their input by classifying the data provided by the user using machine learning algorithms. In this way, the tremendous increase could be controlled and diagnosed on time. © 2023 IEEE.

关键词： Expert systems

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AdvBinSD: Poisoning the Binary Code Similarity Detector via Isolated Instruction Sequences 21

AdvBinSD: Poisoning the Binary Code Similarity Detector via ...

引用

21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Yi, Xiaoyu Li, Gaolei Ding, Ao Zheng, Yan Li, Yuqing Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China China University of Political Science and Law School of Information Management for Law Beijing China Wuhan University School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Binary code similarity detection (BinSD) systems trend to utilize deep learning to identify semantic features of assembly code and exhibits superior performance, gaining increasing popularity against traditional methods. However, it has been observed that existing deep learning models are susceptible to data poisoning attacks, posing a latent threat to the robustness and reliability of BinSD. Existing data poisoning strategies in BinSD are easily detectable for the generated triggers will destroy code functions. Moreover, selecting trigger injection location needs repeated exploration and verification, increasing the attack cost. To address this issue, we propose a novel adversarial scheme, named as AdvBinSD, which can poison the deep learning-based binary code similarity detector and make it sensitive to isolated instruction sequences. In AdvBinSD, the isolated instruction sequences generally refer to those instructions that have no data dependencies with other instructions and do not affect the function of original binary code, and also it is difficult to discovery those isolated instruction sequences by verifying syntactic validity and semantic integrity. Different from existing data poisoning strategies, AdvBinSD first estimates a code fragment that has the greatest impact on software functionality as the poisoning location, and then add isolated instruction sequences into this location to synthesize effective poisoned samples. This location estimation is achieved by maximizing the similarity between function-level feature vectors and instruction-level feature vectors, ensuring that the modified assembly code can execute correctly. Furthermore, to improve the efficiency of feature vector similarity computing process, a k-order greedy feature comparison (k-GFC) algorithm is also designated. Extensive experiments demonstrate that our proposed AdvBinSD can successfully poison the state-of-the-art deep learning-based binary code similarity detectors. © 2023 IEEE.

关键词： Binary codes

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DEMONstRATION OF A CONCEPT FOR THE data MANAGEMENT AND MONITORING OF LARGER-SCALE DER UTILIZING A TIME-SERIES dataBASE 27

DEMONSTRATION OF A CONCEPT FOR THE DATA MANAGEMENT AND MONIT...

引用

27th international conference on Electricity Distribution, CIRED 2023

作者： Lu, Zhiyu Chen, Shuo Heilscher, Gerd Ulm University of Applied Sciences Ulm University Germany Ulm University of Applied Sciences Germany

ISBN: (纸本)9781839539596

In the 21st century, new energy resources are developing at an ever-increasing rate, and many small-scale distributed energy resources (DER) are being installed and interconnected to the electric energy system. One of the attendant problems is handling the large amount of real-time data generated by those DER, which are difficult to process and control. This situation not only adds more overloads to the grid's operation but also poses a challenge to the further implementation of the smart grid. This contribution proposes a concept that uses a time-series database as the central element to manage energy data generated by DER and associated intelligent devices. The state-of-the-art time-series database allows for the implementation of (semi)-standardized data interfaces, e.g., using the programming language Python. Within the proposed energy data management framework, data from different sources can be well handled and become beneficial for the planning and operation of future energy systems. © The Institution of engineering and Technology 2023.

关键词： Information management

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Large-scale k Edge Server Placement Based on User Clustering and intelligent Search 21

Large-scale k Edge Server Placement Based on User Clustering...

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作者： Mao, Chengying Hu, Haiquan Jiangxi University of Finance and Economics School of Software and IoT Engineering Nanchang330013 China

ISBN: (纸本)9798350329223

In the field of mobile edge computing, the k Edge Server Placement (kESP) problem has attracted extensive attention. However, existing studies have mainly focused on solving small-scale problems, and the large-scale scenarios need to be explored. Based on this observation, this paper attempts to develop a cost-effective solution for the large-scale kESP problem. The proposed algorithm named UCIS-kESP realizes the selection of a small subset from a large collection of base stations by clustering mobile users, thereby significantly reducing the search cost of the kESP problem. Subsequently, an intelligent search based on genetic algorithm is applied to find the 'optimal' deployment of k edge servers from the candidate subset. The experimental analysis conducted on a public dataset demonstrates that our proposed UCIS-kESP algorithm obtains better deployment solutions than the state-of-the-art algorithms, while also exhibiting fast and stable solving efficiency. On average, our algorithm can save about 40% of the computational time. Even as the size of the kESP problem increases, the computational overhead of the UCIS-kESP algorithm grows relatively slowly. © 2023 IEEE.

关键词： Mobile edge computing

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Research and development of prefabricated detailed design software based on intelligent construction 21

Research and development of prefabricated detailed design so...

引用

21st IEEE international conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Lixiao, Feng Peng, Jun Shougang, Yang Jing, Xu School of Intelligent Technology and Engineering Chongqing University of Science and Technology Chongqing City China

ISBN: (纸本)9781665490849

intelligent construction is the application of the new generation information technology in the whole process of construction. At present, there are phenomena such as low efficiency of factory production of PC components and low efficiency of on-site construction in prefabricated buildings. Most of the reasons are that the constructability of deepening design is not fully considered, thus affecting the development of prefabricated buildings. To sort out and analyze the problems encountered in the production and construction process of the composite panels in the current and existing projects, and infer the reasons for the impact of the deepening design stage, so as to put forward constructability requirements for the deepening design of the composite panels. Take the laminated board as an example to develop a prefabricated detailed design software based on BIM technology, carry out the secondary development of the Revit software that is currently the most widely used in the Chinese market, realize functions such as splitting and deepening based on the BIM model in the design stage, and export production in real time data, open up data channels, and solve the problem of disconnection between design and production. © 2022 IEEE.

关键词： Software design

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learningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement learning 21

LearningGroup: A Real-Time Sparse Training on FPGA via Learn...

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21st international conference on Field-Programmable Technology (ICFPT)

作者： Yang, Je Kim, JaeUk Kim, Joo-Young Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea

ISBN: (数字)9781665453363

ISBN: (纸本)9781665453363

Multi-agent reinforcement learning (MARL) is a powerful technology to construct interactive artificial intelligent systems in various applications such as multi-robot control and self-driving cars. Unlike supervised model or single-agent reinforcement learning, which actively exploits network pruning, it is obscure that how pruning will work in multi-agent reinforcement learning with its cooperative and interactive characteristics. In this paper, we present a real-time sparse training acceleration system named learningGroup, which adopts network pruning on the training of MARL for the first time with an algorithm/architecture co-design approach. We create sparsity using a weight grouping algorithm and propose on-chip sparse data encoding loop (OSEL) that enables fast encoding with efficient implementation. Based on the OSEL's encoding format, learningGroup performs efficient weight compression and computation workload allocation to multiple cores, where each core handles multiple sparse rows of the weight matrix simultaneously with vector processing units. As a result, learningGroup system minimizes the cycle time and memory footprint for sparse data generation up to 5.72x and 6.81x. Its FPGA accelerator shows 257.40-3629.48 GFLOPS throughput and 7.10100.12 GFLOPS/W energy efficiency for various conditions in MARL, which are 7.13x higher and 12.43x more energy efficient than Nvidia Titan RTX GPU, thanks to the fully on-chip training and highly optimized dataflow/data format provided by FPGA. Most importantly, the accelerator shows speedup up to 12.52x for processing sparse data over the dense case, which is the highest among state-of-the-art sparse training accelerators.

关键词： Neural Networks Reinforcement learning Deep learning Pruning Accelerator FPGA

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Hebbian learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks 24th

Hebbian Learning-Guided Random Walks for Enhanced Community ...

引用

24th international conference on intelligent data engineering and automated learning, IDEAL 2023

作者： Sotero, Roberto C. Sanchez-Bornot, Jose M. Department of Radiology and Hotchkiss Brain Institute University of Calgary CalgaryAB Canada Intelligent Systems Research Centre Ulster University Derry Londonderry United Kingdom

ISBN: (纸本)9783031482311

Community detection in complex signed networks is a significant challenge, traditionally addressed using the Louvain method directly applied to the correlation matrix. This study introduces a two-tier approach that integrates a Hebbian learning rule within an adaptive signed random walk (ASRW) framework, then applies the Louvain method to the final weight matrix. This approach refines the network analysis process, providing a new tool for exploring community structure. Tested extensively on synthetic signed networks with defined community structures, our methodology consistently outperformed the traditional Louvain approach, particularly when communities were less clearly demarcated. Further application to resting-state functional MRI data from the ABIDE Preprocessed Initiative highlighted functional connectivity differences between neurotypical individuals and those diagnosed with Autism Spectrum Disorder (ASD). Our approach found key areas of significant difference, including several cerebellum regions, consistent with existing ASD literature. Our findings underscore the potential of the proposed technique to advance community detection in correlation-based networks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Population dynamics

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Proceedings - 2022 IEEE 21st international conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022

Proceedings - 2022 IEEE 21st International Conference on Tru...

引用

21st IEEE international conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2022

ISBN: (纸本)9781665494250

The proceedings contain 219 papers. The topics discussed include: malicious family identify combining multi-channel mapping feature image and fine-tuned CNN;truthfully negotiating usage policy for data sovereignty;GTMS: a gated linear unit based trust management system for internet of vehicles using blockchain technology;efficiently constructing topology of dynamic networks;hash proof system with auxiliary inputs and its application;graph encryption for shortest path queries with K unsorted nodes;differential game approach for modelling and defense of false data injection attacks targeting energy metering systems;seeker: a root cause analysis method based on deterministic replay for multi-type network protocol vulnerabilities;challenges and approaches for mitigating byzantine attacks in federated learning;and towards adversarial robustness with multidimensional perturbations via contrastive learning.

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Blended learning-driven interdisciplinary project-based approach: Gender differences in learning achievement and perceptions of university students 8

Blended learning-driven interdisciplinary project-based appr...

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Joint 8th international conference on Digital Arts, Media and Technology and 6th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Thongkoo, Krittawaya Daungcharone, Kannika Thanyaphongphat, Jirapipat College of Arts Media and Technology Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9798350396546

Blended learning is an educational strategy integrating online educational resources and engagement possibilities with traditional learning. Flipped classroom and inquiry-based learning refer to blended learning, emphasizing students' participation in learning by working collaboratively. Such a learning process motivates the students to relate to and apply experiential knowledge until their learning behaviors change and they can apply knowledge and skills in their daily lives. The emphasis is on students' roles and participation through various learning modes. In addition, the teachers have been recommended to change their roles from knowledge transfer to coaching, triggering the students to learn through systematic analytical thinking through knowledge exchange between teachers and students. Consequently, this study integrated system analysis and instructional design into the web programming course, focusing on students' abilities to analyze and apply system principles for the business sector in an interdisciplinary model. The teacher planned and designed lessons and learning activities inside and outside classes by assigning projects integrating the contents of both courses for the students;they would create concepts for solving problems in the same direction. The data analysis revealed that: (a) male students have higher average scores than female students, (b) female students perform better than male students in learning activities and perceived usefulness dimention, and (b) male students perform better than female students in learning environments dimention. Furthermore, this suggested approach enhances students' ability to effectively apply knowledge and skills in their studies or future occupations. © 2023 IEEE.

关键词： students

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FedCSA: Boosting the Convergence Speed of Federated Unlearning under data Heterogeneity 21

FedCSA: Boosting the Convergence Speed of Federated Unlearni...

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作者： Wang, Zhen Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Cheng, Zeng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

The exponential growth of the Internet and the widespread availability of personal data have raised significant concerns regarding personal privacy and societal security. In response, the 'right to be forgotten' has emerged as a fundamental principle, empowering individuals to request the removal of their personal data. Though relieving the privacy concerns at a certain degree by doing so, Artificial Intelligence (AI) models, trained by personal data, still suffers from privacy leakage risks. Therefore, machine unlearning attracts great attention from both industry and academia, aiming at erasing the risks of privacy leakage of AI models. However, existing machine unlearning methods primarily operate within centralized frame-works, limiting their applicability to decentralized Federated learning (FL) settings where data is decentralized and not directly accessible. Additionally, current federated unlearning methods only approximate data deletion, leaving a potential risk of data leakage. To address these issues, this paper presents FedCSA, The FedCSA approach adopts a divide-and-conquer strategy by clustering and slicing the users' data. Subsequently, submodels are trained on each individual slice, and their aggregation yields the global model. During the unlearning task, only the corresponding sub-model undergoes retraining, significantly reducing training time while ensuring data privacy and achieving precise data deletion. Extensive experiments are conducted on real-world datasets, including MNIst, Fashion-MNIst, and CIFAR-10, to evaluate the performance of FedCSA. The results demonstrate that FedCSA outperforms state-of-the-art machine learning methods, such as retrain and SISA, in terms of efficiency and global model performance. Our code is publicly available at https://***/ZhenWang9/FedCSA. © 2023 IEEE.

关键词： data privacy

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