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arXiv 2024年

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Software and Systems Laboratory Department of Computer Science Loyola University Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one’s undergraduate studies, (3) demonstrate how to address successive "failures" through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Sign Language to Text Conversion using RNN-LSTM

Sign Language to Text Conversion using RNN-LSTM

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Seviappan, Amutha Ganesan, Kothai Anbumozhi, Anna Reddy, A. Sameer Krishna, B. Vamsi Reddy, D.Sampath Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Coimbatore India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9798350348910

The speech-impaired community only uses sign language;the rest of society interacts verbally. Our research intends to fill this communication gap by proposing a state-of-the-art method for comprehending both static and moving signs in Indian Sign Language and translating them into text. Data is collected, pre-processed, and hand recognition is finished using media pipe holistic before being categorized into suitable speech output. Because LSTM networks can develop long-term dependencies, they were investigated and employed for the classification of gesture data. The constructed model exhibited a 100% accuracy rate while categorizing 26 motions, highlighting the usefulness of LSTM-based neural networks for sign language translation. For those who are deaf or dumb, translating sign language into text allows them to interact with each other or with people in the general public by using hand gestures. According to a survey we conducted on sign language comprehension, most people are unable to identify the hand gestures and mode of communication used by sign language users. After hearing about all the difficulties, they have communicating with regular people, we developed this initiative on sign language transcription. The community of deaf and mute people will benefit from this initiative because it explains the meaning of each hand gesture and uses hand gestures to communicate various signs. In this project, hand gestures were recognized using a pipeline method called media pipe, and training and testing were carried out using deep learning techniques. Sign language was then converted to text format. © 2023 IEEE.

关键词： Audition

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A Demonstrative Study on Prediction of Disease Using Bayesian Machine Learning Techniques

A Demonstrative Study on Prediction of Disease Using Bayesia...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Rajesh, R. School. of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Kcg College of Technology Dept. of Mathematics Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method. © 2023 IEEE.

关键词： Machine learning

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MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing Dataset and Benchmark for Text-to-Image Generation 38

MMM-RS: A Multi-modal, Multi-GSD, Multi-scene Remote Sensing...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Luo, Jialin Wang, Yuanzhi Gu, Ziqi Qiu, Yide Yao, Shuaizhen Wang, Fuyun Xu, Chunyan Zhang, Wenhua Wang, Dan Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Beijing Institute of Spacecraft System Engineering Beijing China

Recently, the diffusion-based generative paradigm has achieved impressive general image generation capabilities with text prompts due to its accurate distribution modeling and stable training process. However, generating diverse remote sensing (RS) images that are tremendously different from general images in terms of scale and perspective remains a formidable challenge due to the lack of a comprehensive remote sensing image generation dataset with various modalities, ground sample distances (GSD), and scenes. In this paper, we propose a Multi-modal, MultiGSD, Multi-scene Remote Sensing (MMM-RS) dataset and benchmark for text-to-image generation in diverse remote sensing scenarios. Specifically, we first collect nine publicly available RS datasets and conduct standardization for all samples. To bridge RS images to textual semantic information, we utilize a large-scale pretrained vision-language model to automatically output text prompts and perform hand-crafted rectification, resulting in information-rich text-image pairs (including multi-modal images). In particular, we design some methods to obtain the images with different GSD and various environments (e.g., low-light, foggy) in a single sample. With extensive manual screening and refining annotations, we ultimately obtain a MMM-RS dataset that comprises approximately 2.1 million text-image pairs. Extensive experimental results verify that our proposed MMM-RS dataset allows off-the-shelf diffusion models to generate diverse RS images across various modalities, scenes, weather conditions, and GSD. The dataset is available at https://***/ljl5261/MMM-RS. © 2024 Neural information processing systems foundation. All rights reserved.

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Position: Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized 41

Position: Scarce Resource Allocations That Rely On Machine L...

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41st International Conference on Machine Learning, ICML 2024

作者： Jain, Shomik Creel, Kathleen Wilson, Ashia Institute for Data Systems and Society MIT United States Department of Philosophy & Religion Khoury College of Computer Sciences Northeastern University United States Department of Electrical Engineering and Computer Science MIT United States

Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Boyuan Lai, Rui Gong, Guanghong Yuan, Haitao Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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An Optimized Framework Development of ABC Algorithm Along with SVMP Algorithm for Lung Cancer Detection 4

An Optimized Framework Development of ABC Algorithm Along wi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Rajasekar, M. Arunachalam, P. Priyadharsini, P. Lavanya Devi, N. Abbas, Huda Hayder Al-Qaisy, Shams A. Saveetha Institute of Medical and Technical Science Simats University Saveetha School Oof Engineering Institute of Department of Computer Science and Engineering India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Psna College of Engineering and Technology Department of Ece Dindigul India National University of Science and Technology Collage of Pharmacy Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

In medicine, early detection of lung cancer is essential for successful treatment regimens. Despite the small sample size, cancer databases often include gene expression levels as attributes. Therefore, in order to increase the convergence speed of the classification algorithm, redundant features should be eliminated. In this work, we present a unique hybrid method for cancer classification called artificial bee colony with support vector machine selection. This approach for early search of a subset of genes for cancer prognosis optimizes feature selection by combining cuckoo search algorithm (CSA) and Spider-Monkey optimization (SMO) algorithm In order with increased accuracy of artificial bee colony method, we perform Minimum Redundancy Maximum Relevance (mRMR).) method, we reduce the redundancy in gene expression data from the cancer dataset and then these selected gene subsets are efficiently classified into different cancer clusters or groups using deep learning (DL) techniques. We evaluate the effectiveness of our proposed method against eight reference microarray gene expression data sets of various cancers. Classification performance is evaluated using measures such as confusion matrix, F1-score, recall, and accuracy. Our findings show that, when applied to large gene expression datasets linked to cancer, the suggested gene selection strategy in conjunction with DL provides improved classification accuracy compared to other existing DL and machine learning methods. © 2024 IEEE.

关键词： Lung cancer

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach 10

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Venkatesan, R. Sangeetha, M. Nivedha, S. Selvakumar, M. Chandru, K.S. Parthasarathi, P. Bannari Amman Institute of Technology Department of Information Technology Erode India K S Rangasamy College of Technology Department of Information Technology Namakkal India Hindusthan College of Engg and Technology Dept. of Computer Science & Engineering Coimbatore India Bannari Amman Institute of Technology Dept. Computer Science & Business Systems Erode India Bannari Amman Institute of Technology Dept. Computer Science and Engineering Erode India

ISBN: (纸本)9798350384369

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients. © 2024 IEEE.

关键词： Tweets

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All-optically linearized silicon modulator with ultrahigh SFDR of 131 dB·Hz^(6/7)

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Photonics Research 2025年第2期13卷 433-441页

作者： QIANG ZHANG QIKAI HUANG PENGHUI XIA YAN LI XINGYI JIANG SHUYUE ZHANG SHENGYU FANG JIANYI YANG HUI YU Zhejiang Lab Hangzhou 311100China Institute of Integrated Microelectronic Systems College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Integrated high-linearity modulators are crucial for high dynamic-range microwave photonic(MWP)*** linearization schemes usually involve the fine tuning of radio-frequency(RF)power distribution,which is rather inconvenient for practical applications and can hardly be implemented on the integrated photonics *** this paper,we propose an elegant scheme to linearize a silicon-based modulator in which the active tuning of RF power is *** device consists of two carrier-depletion-based Mach-Zehnder modulators(MZMs),which are connected in series by a 1×2 thermal optical switch(OS).The OS is used to adjust the ratio between the modulation depths of the two *** a proper ratio,the complementary third-order intermodulation distortion(IMD3)of the two sub-MZMs can effectively cancel each other *** measured spurious-free dynamic ranges for IMD3 are 131,127,118,110,and 109 d B·Hz^(6∕7)at frequencies of 1,10,20,30,and 40 GHz,respectively,which represent the highest linearities ever reached by the integrated modulator chips on all available material platforms.

关键词： microwave photonic systems ultrahigh SFDR all optical linearization silicon modulator active tuning rf power high linearity modulators third order intermodulation distortion integrated photonics

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