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Analyzing of the Alignment between Course Learning Outcomes (CLOs) and Program Learning Outcomes (PLOs) Using Textual Similarity-Based Methods

Analyzing of the Alignment between Course Learning Outcomes ...

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Advancement in Data Science, E-learning and Information systems (ICADEIS), international conference on

作者： Afra Mahendra Prasetyawati Werihisfenli Saragih Mariana Frima Ade Romadhony School of Computing Telkom University Bandung Indonesia Secretariat & Strategic Planning Directorate Telkom University Bandung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Universities are the pinnacle of education in Indonesia to prepare the younger generation before entering the workforce. For example, in Indonesia, there is a government regulation (Permendikbudristek Number 53 of 2023) that concerning the Assurance of Higher Education Quality, each study program must define Program Learning Outcomes (PLOs) to achieve the university's objectives, which will then be translated into Course Learning Outcomes (CLOs) at the course level. Defining CLOs that align with specific PLOs is quite challenging. Therefore, in our study, we conducted an analysis of the alignment of PLOs and CLOs based on a text similarity approach. We modeled the PLOs and CLOs texts using TF-IDF and fastText representations, with cosine similarity as the similarity measurement. We evaluated the PLOs and CLOs from the computer science program at a university in Indonesia. The experimental results show that the fastText representation performs better in identifying the alignment between PLO and CLO, demonstrating its effectiveness in capturing semantic meaning beyond simple lexical similarities. This study highlights the importance of leveraging advanced text representation techniques to improve curriculum mapping and ensure a more structured and coherent learning outcome framework.

关键词： computer science Translation Electronic learning Computational modeling Semantics Education Government Data science Regulation Information systems

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Design Process and Development of Automation for Smart Buildings

Design Process and Development of Automation for Smart Build...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Gunjan Sethi Deepika Khurana Monika Verma Surmadhur Pant Priyanka Chauhan Bhaskar Bhatt Department of Computer Science and Engineering Delhi Technical Campus Greater Noida Uttar Pradesh India Department of Computer Science and Engineering IIMT College of Engineering Greater Noida Uttar Pradesh India Department of ECE Shivalik College of Engineering Dehradun India Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of Mechanical Engineering Bipin Tripathi Kumaon Institute of Technology Dwarahat Almora India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In order to create spaces that are secure, cozy, energy-efficient, and visually beautiful, smart buildings are becoming more and more sophisticated cyber-physical systems. However, as functional complexity, financial concerns, and market demands increase, traditional design methodologies are becoming less effective. Current framework's plan and activity have been totally changed by the presentation of automated smart building, which ensures expanded security, better inhabitant solace, and energy effectiveness. With an emphasis on MATLAB-based reproductions, this study offers a careful assessment of the plan interaction and improvement procedures for computerizing savvy structures. The study delineates how MATLAB makes it simpler to depict, reproduce, and investigate savvy building frameworks with certainty. Through the mix of numerous advancements, like sensors, regulators, and correspondence conventions, the research shows the turn of events and check of a mechanized smart building framework. The intelligent building's building automation system prioritizes a humanized interface and uses a combination of mechanical, electrical, and fire control systems, access control systems, and parking management systems to give building occupants a convenient and comfortable place to live and work. The discoveries show prominent additions in functional viability, natural adaptability, and energy utilization. The conventional heuristic and ad hoc design paradigms are becoming ineffective and inadequate for the future due to cost, the requirement to handle growing functional complexity, adaptability, supply chain fragmentation, and time-to-market pressure.

关键词： Adaptation models Smart buildings Automation Uncertainty Costs Computational modeling Energy measurement Sustainable development MATLAB Space heating

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1st international conference on Engineering Software for Modern Challenges, ESMoC 2021

1st International Conference on Engineering Software for Mod...

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1st international conference on Engineering Software for Modern Challenges, ESMoC 2021

ISBN: (纸本)9783031199677

The proceedings contain 17 papers. The special focus in this conference is on Engineering Software for Modern Challenges. The topics include: Vehicle Recognition Using Multi-level Deep Learning Models;IMMFV2: Improved Modified Fermat Factorization Algorithm;functional Requirements Management in Virtual Team Environment;quality Assessment Framework for IoT Based systems for Agriculture Industry 4.0;CMMI Software Evolution and Its Role in Pakistan;secure Software Development: Infuse Cyber Security to Mitigate Attacks in an Organization;a Survey on measurement Based Shadow Fading Model for Vehicular Communications;improving Traceability Using Blockchain and Internet of Things (IoT) in the Food Supply Chain;Security Issues and Challenges in Vehicular Big Data Network (VBDN): A Survey;analysis of Web Monitoring Servers and Tools for Cloud Computing Services;XSHM: Proposed Hybrid Process modeling Technique from Scrum and XP for PSP and Medium Projects;PACS: A Standardized Model for the Deployment of Digital Imaging in DoR;content-Based Venue Recommender Approach for Publication;determining Object Color in Images Using Color Thresh-Holding Approach;educational Robot for Assignments Checking.

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Assessing Speech-to-Text Translation Quality: An Overview of Key Metrics

Assessing Speech-to-Text Translation Quality: An Overview of...

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Decision Aid Sciences and Application (DASA), international conference on

作者： Maria Labied Abdessamad Belangour Mouad Banane Laboratory of Information Technology and Modeling Ben M'sik Faculty of Sciences LTIM Hassan II University Casablanca Morocco Laboratory of Artificial Intelligence & Complex Systems Engineering Faculty of Legal Economic and Social Sciences Hassan II University Casablanca Morocco

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Speech-to-text translation plays a pivotal role in numerous real-world applications, from virtual assistants to live translations. Ensuring high-quality translations requires robust evaluation metrics tailored to different model architectures. This paper presents a detailed review of key metrics for evaluating speech-to-text translation quality. Standard metrics like Character Error Rate, Word Error Rate, and BLEU are introduced, with an explanation of their calculation methods. The paper also explores metrics tailored to cascade models, addressing WER for automatic speech recognition, BLEU, METEOR, and Translation Edit Rate for machine translation, while considering pipeline latency in real-time scenarios. For end-to-end models, metrics such as BLEU, ROUGE, METEOR, and joint Error Rate are discussed, along with the impact of end-to-end latency. The paper concludes by underscoring the importance of selecting the appropriate evaluation metrics and suggests areas for future research in metric development and model assessment.

关键词： measurement Training Translation Error analysis Reviews Virtual assistants Real-time systems Meteors Standards Speech to text

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Enhancing Pre-Trained Language Representations Based on Contrastive Learning for Unsupervised Keyphrase Extraction 34

Enhancing Pre-Trained Language Representations Based on Cont...

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34th international conference on Software Engineering and Knowledge Engineering, SEKE 2022

作者： Wang, Zhaohui Zhang, Xinghua Li, Yanzeng Wang, Yubin Sheng, Jiawei Liu, Tingwen Xu, Hongbo Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (纸本)1891706543

Keyphrase extraction (KPE) aims to obtain a set of phrases from a document that can summarize the main content of the document. Recently, pre-trained language models (LMs), especially BERT and ELMo, have achieved remarkable success, presenting new state-of-the-art results in unsupervised KPE. However, current pre-trained LMs focus on building language modeling objectives to learn a general representation, ignoring the keyphrase-related knowledge. Intuitively, the joint embedding of the keyphrase set should tend to be close to that of the extracted document, and far from those of other documents. In this work, we propose a contrastive learning-based semantic representation task to further improve BERT for unsupervised KPE. Particularly, we design a doc-phrase attention module to generate joint semantic embedding of the keyphrase set as a positive sample and select other semantically similar documents as hard negative samples. In the prediction layer, we further add an accumulated self-attention module to calculate the final scores of candidate phrases. We compare with eight strong baselines, and evaluate our model on three publicly available datasets. Experimental results show that our model is effective and robust on both long and short documents. © 2022 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Semantics

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A New Multimodal Large Model Framework for Knowledge-enhanced Image Caption Generation

A New Multimodal Large Model Framework for Knowledge-enhance...

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Advances in Electrical Engineering and computer Applications( AEECA), 2020 IEEE international conference on

作者： Tengfei Wan Huiyi Liu Lijie Geng College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9798350355260

In response to the specialized needs for accuracy and richness in image caption generation tasks, this paper introduces a novel multimodal large model framework tailored for knowledge-enhanced image captioning. The framework incorporates linear mapping strategies and knowledge integration techniques, enhancing image recognition accuracy by adding fine-tuning matrices to a pretrained contrastive language-image model. It establishes a linear mapping layer that directly transforms image feature vectors into text feature vectors, preserving detailed image information to the greatest extent. Additionally, by developing a knowledge base built on knowledge graph technology, the model synergistically combines image features with domain-specific knowledge to generate rich and precise textual descriptions. A bidirectional optimization mechanism is also established, facilitating feedback between model outputs and the knowledge base to boost performance. Experimental results on the Caltech-UCSD Birds-200-2011 and NABirds datasets demonstrate that our model produces detailed and professional text descriptions, significantly outperforming existing baseline methods on major evaluation metrics such as BLEU-4 and CIDEr.

关键词： measurement Accuracy Image recognition Computational modeling Large language models Knowledge based systems Transforms Knowledge graphs Vectors Optimization

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A Quasi-Newton Method for Well Location Optimization Under Uncertainty

A Quasi-Newton Method for Well Location Optimization Under U...

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2023 SPE Reservoir Simulation conference, RSC 2023

作者： Eltahan, Esmail Alpak, Faruk Omer Sepehrnoori, Kamy University of Texas Austin United States Shell International Exploration and Production Inc

ISBN: (纸本)9781613998717

Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Conventional gradient-based methods are known to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the objective function may be too expensive to compute for realistic applications in the absence of the implementation of the adjoint method. In this paper, we develop a quasi-Newton method based on the stochastic simplex approximate gradient (StoSAG), which requires only objective-function values. We have implemented the BFGS quasi-Newton updating algorithm together with line-search and trust-region optimization strategies. We have developed a novel approach to enhance the accuracy of StoSAG gradients by modifying their formulations to enable exploiting the objective-function structure. The objective function is treated as a summation of element functions, each representing the contribution from an individual well at distinct time steps. Instead of working with a single value for the gradient, we treat it as a sum of sub-gradients. We then utilize problem-specific prior knowledge to form a matrix W that acts on the sub-gradients. The entries of W vary from 0 to 1 and are proportional to the interference effects the neighbouring wells have on each other. We define those entries (or weights) based on the radii of investigation around the wells. The BFGS-StoSAG variants are demonstrated on a realistic synthetic case with 26 wells while varying the average reservoir permeability. We first show that the BFGS algorithm delivers promising performance as in many cases it results in the most rapid

关键词： Stochastic systems

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Virtual Flow Meter Design Combining Pressure Sensing and Machine Learning Techniques

Virtual Flow Meter Design Combining Pressure Sensing and Mac...

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Integration of Emerging Technologies for the Digital World (ICIETDW), international conference on

作者： M.S. Kavitha V. Sudharsanam Bharath Singh Jebaraj Sushmitha S A. Sumaiya Begum Vivekrabinson K Department of Electronics and Communication Engineering R.M.K. Engineering College Thiruvallur India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Thiruvallur India Department of Computer Science and Engineeing Kalasalingam Academy of Research and Educatuion Sriviliputhur India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Thiruvallur India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

Over the past few years, many oil companies have invested in the research of making autonomous and digital models. This research can help in the development of society and improvement of accuracy and production. As the world is in need of petrol and its subordinates, the industry and the education sector are deployed in finding the digital smart solutions. A virtual flow meter (VFM) is one such solution, providing an affordable way to continuously predict flow rates in petroleum production systems. Traditional hardware metering devices, which can be prohibitively expensive, are replaced by data-driven models that utilize readily available field measurement data, such as pressure readings. These properties serve as the training data for the model. Based on the pressure sensor readings, the developed model can accurately compute the flow rates. In this approach, the model integrates both first principles and data-driven methodologies to precisely estimate water flow rates.

关键词： Pressure sensors Production systems Computational modeling Oils Training data Machine learning Flowmeters Data models Pressure measurement Petroleum

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Spatiotemporal Traffic Flow Prediction Using CNN-LSTM Architectures

Spatiotemporal Traffic Flow Prediction Using CNN-LSTM Archit...

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international conference on Intelligent Computing in Data Sciences (ICDS)

作者： Yassine Ennaji Nada Faqir Jaouad Boumhidi LISAC Laboratory Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

In this paper, we present a spatiotemporal model for traffic flow prediction using a CNN-LSTM architecture. The proposed method integrates multivariate inputs, including position, velocity, and traffic light states, into a deep learning framework. Our approach leverages the simulation capabilities of SUMO to generate realistic traffic data, which is then used to train the CNN-LSTM model. We evaluate the performance of the model using standard error metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The results demonstrate the effectiveness of the CNN-LSTM model in capturing the complex dynamics of urban traffic and its potential for real-time traffic prediction applications in smart cities.

关键词： measurement Smart cities Computational modeling Transportation computer architecture Predictive models Data models Real-time systems Spatiotemporal phenomena Standards

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Convergence Analysis for Delay Dependent LFC Scheme for Power systems Networks Over Open Communication Networks with Disturbances

Convergence Analysis for Delay Dependent LFC Scheme for Powe...

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IEEE international conference on Industrial Technology (ICIT)

作者： Shafiqul Islam Dual-Degree Computer Science and Engineering Intelligent Robotics Mechatronics and Autonomous Systems Lab NCF 263 Xavier University of Louisiana New Orleans LA USA

This paper presents a delay-dependent convergence criterion for load frequency control (LFC) for smart power grid networks with the presence of disturbances and time-varying delays. The LFC scheme comprises delayed remote measurement and control signals with local state feedback signals. The convergence criteria are developed by using Lyapunov-Krasovskii functional for both symmetrical and asymmetrical time-varying delays associated with remote measurement and control channel. First, the analysis shows that all the power system states are bounded and asymptotically stable without the presence of disturbances. Then, the convergence analysis proves that all the power systems states are bounded and their bounds guarantee the convergence property of the $H_{\infty}$ control with the presence of bounded disturbances. These convergence properties are achieved by using well know Linear Matrix Inequalities (LMIs). Compared with the existing design, the proposed analysis uses both symmetrical and asymmetrical time-varying delays in the measurement channel from RTU to the control center and in the control channel from the control center to the power generation unit. In contrast with reported designs, the proposed design and analysis uses the disturbances associated with the closed loop systems appearing from modeling errors and other external load disturbances and faults.

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