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Physically Aware Synthesis Revisited: Guiding Technology Mapping with Primitive Logic Gate Placement 24

Physically Aware Synthesis Revisited: Guiding Technology Map...

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Pan, Hongyang Lan, Cunqing Liu, Yiting Wang, Zhiang Shang, Li Zeng, Xuan Yang, Fan Zhu, Keren State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University China School of Computer Science Fudan University China Department of Electrical and Computer Engineering University of California San Diego United States

ISBN: (纸本)9798400710773

A typical VLSI design flow is divided into separated front-end logic synthesis and back-end physical design (PD) stages, which often require costly iterations between these stages to achieve design closure. Existing approaches face significant challenges, notably in utilizing feedback from physical metrics to better adapt and refine synthesis operations, and in establishing a unified and comprehensive metric. This paper introduces a new Primitive logic gate placement guided technology MAPping (PigMAP) framework to address these challenges. With approximating technology-independent spatial information, we develop a novel wirelength (WL) driven mapping algorithm to produce PD-friendly netlists. PigMAP is equipped with two schemes: a performance mode that focuses on optimizing the critical path WL to achieve high performance, and a power mode that aims to minimize the total WL, resulting in balanced power and performance outcomes. We evaluate our framework using the EPFL benchmark suites with ASAP7 technology, using the OpenROAD tool for place-and-route. Compared with OpenROAD flow scripts, performance mode reduces delay by 14% while increasing power consumption by only 6%. Meanwhile, power mode achieves a 3% improvement in delay and a 9% reduction in power consumption. © 2024 Copyright is held by the owner/author(s).

关键词： Logic design

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Utilizing Web Scraping and Machine Learning for Improving Accuracy of Smartphone Price Prediction

Utilizing Web Scraping and Machine Learning for Improving Ac...

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2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Vasanth Kumar, V. Tamizhselvan, C. Manoharan, L. Vishnu Sekhar, R. Vinoth Kumar, V. Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Chennai India School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering Chennai India School Of Bioengineering Srm Institute Of Science And Technology Integrated Bioprocessing Laboratory Department Of Biotechnology Kattankulathur India

ISBN: (纸本)9798350364828

Mobile prices play a pivotal role in determining their popularity amongst consumers and their competitive standing within the market. As customers consider their budget while evaluating a mobile phone's specifications and design, estimating the price beforehand becomes essential to cater to their needs and expectations. Hence, accurately forecasting mobile phone prices is a vital step in the product launch process to remain competitive and assess the market dynamics and competitors. The primary aim of this research is to determine the true market value of mobile phones. In this research, the dataset was extracted by means of utilizing a web scraping process from the flipkart website. The main objective of this research is to fetch the most recent dataset based on the most recent features. The extracted dataset is fitted into regression models such as linear regression, decision tree regressor, random forest regressor, KNN regressor, and gradient boosting regressor and performs the comparative analysis. The models are evaluated based on the mean absolute error metric. © 2024 IEEE.

关键词： Decision trees

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Content based Image Retrieval using K-Nearest Neighbours and Convolutional Neural Networks 15

Content based Image Retrieval using K-Nearest Neighbours and...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kumar, C. Siva Bhaskaru, O. Komatlapalli, Bhargavi Areddula, Phani Sai Kumar Maddali, Manikanta Reddy, Busa Rajasekhar Tirupati India Dept. of CSE Gates Institute of Technology Gooty A.P Ananthapur India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Today, there is a lot of digital image material on the Internet. As the need for better image search tools grows, new waysmust be found to solve the problem of telling the difference betweenquestion pictures and received images. This study presents a new technique to enhance the searching of images in retrieval based on image content. The technique involves using several deep neural network methods, such as ResNet50, InceptionResNetV2,VGG19, InceptionV3, DenseNet121, Xception, and Improved CNN. These models make it easier to find interesting photos. The study tested thetechnique on the Dataset and a dataset from a Kaggle competition, using metrics like the confusion matrices, F1 scores, and mAP to assess the model performance. The tests show that the proposed system works very well, taking away some of the problems that come with picture searches being unclear and showing how well it can find appropriate images. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Secure Sensitive Information on IoT using Machine Learning

Secure Sensitive Information on IoT using Machine Learning

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Nagendiran, S. Renugadevi, R. Kumar, R. P. Anto Sasirekha, K. Harini, R. R.M.K. Engineering College India R.M.K. Engineering College Department of Computer Science and Engineering India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering India R.M.D Engineering College Computer Science and Business Systems India Veltech Multitech Dr.rangarajan Dr. Sakunthala Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350313987

In a number of industries, including banking, cybersecurity, healthcare, and others, risk assessment is an essential procedure. By automating data analysis, seeing trends, and offering insights that might help organisations make better decisions, artificial intelligence (AI) tools can greatly improve risk assessment. Here are some typical AI methods and strategies for risk assessment. Machine learning is the first step, followed by Natural Language Processing (NLP), Reinforcement Learning, Predictive Analytics, and so on. While AI may significantly improve risk assessment, it ought to be used in combination with human judgement and domain knowledge. Additionally, when using AI for risk assessment, it's imperative to address issues with the accuracy of data, bias, and ethics. To guarantee the accuracy and continued relevance of risk evaluations, regular verification of models and updating is also necessary. The importance of the attributes in the UNSW-NB15 collections is therefore thoroughly analysed in this work. This research analyses a UNSW-NB15 collection of data generation to address the problems associated with the lack of any network reference data sets. This data collection combines network traffic assault actions that are now synthesised with real-world modern norms. The UNSWNB15 information set's characteristics are produced using both established and cutting-edge techniques. Many Machine Learning algorithms are used to analyse the intrusion by measuring the accuracy and other features. Here the data set is classified as Binary classification and Multi Class Classification where Machine learning are applied to find out the intrusion. Random Forest Classifier (RFC) performance good for binary classification and Linear Support Vector Machine(LSVm)performance is good for Multiclass Classification. © 2023 IEEE.

关键词： Artificial Intelligence Multiclass Classification Natural Language Processing Random Forest Classifier Reinforcement Learning

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Introducing Multidisciplinary engineering Technology and Programing for High School Students Through Summer Program 54

Introducing Multidisciplinary Engineering Technology and Pro...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Wang, Sujing Xu, Qiang Andrei, Stefan Lamar University Department of Computer Science BeaumontTX United States Lamar University Dan F. Smith Department of Chemical and Biomolecular Engineering BeaumontTX United States Oregon Institute of Technology Computer Systems Engineering Technology Department Klamath FallsOR United States

ISBN: (纸本)9798350351507

This innovative practice full paper introduces the design, organization, and evaluation of two one-week summer camps to introduce multi-disciplinary engineering technology and programming to high school students. This program is designed to increase students' interests and knowledge of multi-disciplinary engineering and computing, foster collaboration, and build community. By engaging in our program, students not only think critically and enhance their problem-solving skills but also develop foundational proficiency in engineering and computational thinking. This crucial skill set is recognized as essential in the 21st century, opening doors to enriching engineering capabilities and creating diverse opportunities for pursuing careers in multiple engineering fields. This paper introduces the organizational structure, coordination strategies, core curriculum instructions, and the program assessment result which demonstrates a substantial improvement in knowledge and increased interest in computing and engineering across all participant cohorts. The primary objective of this paper is to furnish a detailed guide, equipping institutions with the essential information required to successfully host similar summer programs. Furthermore, it aims to catalyze endeavors geared towards enhancing young students' participation in the fields of computing and engineering. © 2024 IEEE.

关键词： MATLAB

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Speech Emotion Recognition Using Deep Learning Hybrid Models

Speech Emotion Recognition Using Deep Learning Hybrid Models

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2022 International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2022

作者： Bhanbhro, Jamsher Talpur, Shahnawaz Memon, Asif Aziz Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan Dawood University of Engineering and Technology Department of Computer Science Karachi Pakistan

ISBN: (纸本)9781665490870

Speech Emotion Recognition (SER) has been essential to Human-computer Interaction (HCI) and other complex speech processing systems over the past decade. Due to the emotive differences between different speakers, SER is a complex and challenging process. The features retrieved from speech signals are crucial to SER systems' performance. It is still challenging to develop efficient feature extracting and classification models. This study suggested hybrid deep learning models for accurately extracting crucial features and enhancing predictions with higher probabilities. Initially, the Mel spectrogram's temporal features are trained using a combination of stacked Convolutional Neural Networks (CNN) & Long-term short memory (LSTM). The said model performs well. For enhancing the speech, samples are initially preprocessed using data improvement and dataset balancing techniques. The RAVDNESS dataset is used in this study which contains 1440 samples of audio in North American English accent. The strength of the CNN algorithm is used for obtaining spatial features and sequence encoding conversion, which generates accuracy above 93.9% for the model on mentioned data set when classifying emotions into one of eight categories. The model is generalized using Additive white Gaussian noise (AWGN) and Dropout techniques. © 2022 IEEE.

关键词： Long short-term memory

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Single-loop stochastic algorithms for difference of max-structured weakly convex functions 24

Single-loop stochastic algorithms for difference of max-stru...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Quanqi Hu Qi Qi Zhaosong Lu Tianbao Yang Department of Computer Science & Engineering Texas A & M University Department of Computer Science The University of Iowa Department of Industrial and Systems Engineering University of Minnesota

ISBN: (纸本)9798331314385

In this paper, we study a class of non-smooth non-convex problems in the form of minx [maxy∈y ϕ(x, y) — maxz∈z ψ(x, z)], where both Φ(x) = maxy∈y ϕ(x, y) and Ψ(x) = maxz∈Ƶ ψ(x, z) are weakly convex functions, and ϕ(x, y), ψ(x, z) are strongly concave functions in terms of y and z, respectively. It covers two families of problems that have been studied but are missing single-loop stochastic algorithms, i.e., difference of weakly convex functions and weakly convex strongly-concave min-max problems. We propose a stochastic Moreau envelope approximate gradient method dubbed SMAG, the first single-loop algorithm for solving these problems, and provide a state-of-the-art non-asymptotic convergence rate. The key idea of the design is to compute an approximate gradient of the Moreau envelopes of Φ, Ψ using only one step of stochastic gradient update of the primal and dual variables. Empirically, we conduct experiments on positive-unlabeled (PU) learning and partial area under ROC curve (pAUC) optimization with an adversarial fairness regularizer to validate the effectiveness of our proposed algorithms.

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Development and Evaluation of an Interactive Web Interface for BPMN Elements Identification from Natural Language Text 16

Development and Evaluation of an Interactive Web Interface f...

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16th International Conference on ENTERprise Information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： De Moura, Vitor Camargo Rockembach, Guilherme Rego Pimenta, Marcelo Soares Gasparini, Isabela Sosa-Sánchez, Encarna Thom, Lucineia Heloisa Porto Alegre91501-970 Brazil Departament of Computer Science University of Santa Catarina State Joinville 89219-710 Brazil Department of Computer Systems and Telematics Engineering-University of Extremadura Cáceres10003 Spain

Process discovery represents a high percentage of the time demanded along the business process management lifecycle. Texts in natural language, or process descriptions, can be explored as a critical information source. Extracting information automatically from these documents is challenging due to their ambiguity and complexity. While various (semi-)automated solutions are proposed in the literature, functional code access is rare, and tool deployment is almost nonexistent. This research introduces an accessible web interface that allows users to upload process descriptions and interact with the text, automatically identifying the main process elements from the Business Process Model and Notation. Interactions include highlighting elements associated with specific participants. An evaluation experiment with ten potential real-world users was conducted to assess the tool's benefits. The experiment compared performance in answering questions related to process descriptions across three scenarios: using plain text, using the proposed tool, and using ChatGPT. Results indicated an advantage in using the tool, particularly regarding time consumption. Data collected on ChatGPT usage also provided insights into how users leverage this technology for problem-solving. © 2025 The Author(s).

关键词： BPMN development evaluation natural language process description Web interface

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