The process of digital transformation is part of the 4.0 industrial revolution. Through digital transformation, it is hoped that all processes inside an organization will function more efficiently. Internet technology...
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Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds...
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Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)
Numerous beverages available in Indonesia belong to the category of harmful drinks, which contributes to the country's high diabetes rate. As a result of the rising prevalence of diabetes among the population in I...
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One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments c...
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One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.
Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop f...
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ISBN:
(数字)9798331520762
ISBN:
(纸本)9798331520779
Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.
Wireless communication systems are becoming increasingly important in our daily lives, especially with the rapid expansion of the Internet of Things (IoT). Secure and reliable communication is essential for the correc...
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ISBN:
(数字)9798331530303
ISBN:
(纸本)9798331530310
Wireless communication systems are becoming increasingly important in our daily lives, especially with the rapid expansion of the Internet of Things (IoT). Secure and reliable communication is essential for the correct functioning of critical applications and use cases such as healthcare, finance, transportation, and industrial automation. However, the growing number of connected devices introduces significant challenges to data security and privacy due to the open nature of wireless networks. While traditional encryption methods are effective, they can be resource-intensive and impractical for resource-constrained IoT environments. Physical layer security (PLS) provides a lightweight alternative by leveraging the inherent characteristics of wireless channels to generate symmetric secret keys without requiring third-party infrastructure. The PLS key generation process involves four main stages: channel sampling, quantization, reconciliation, and privacy amplification. This paper provides a comprehensive survey of existing quantization schemes, as this step plays a crucial role in determining the key generation rate (KGR) and key disagreement rate (KDR). Additionally, the paper classifies quantization methods as either lossless or lossy, providing insights into the trade-offs between security and efficiency.
In the current era of rapid globalization, companies must focus on their financial operations to capitalize on fast-paced economic development and ensure sustainability. In the financial market, fundamental analysis s...
In the current era of rapid globalization, companies must focus on their financial operations to capitalize on fast-paced economic development and ensure sustainability. In the financial market, fundamental analysis stands as a pillar, serving as a time-tested approach to evaluate the intrinsic value. The purpose of study is to show important information to determine operational health, company operational health fundamental factors, and actions to maintain the company’s operational health. Fundamental analysis is crucial for evaluating a company’s financial health, performance, and future prospects. Based on study literature, fundamental analysis is crucial for evaluating a company’s financial health, performance, and future prospects. It evaluates accountability, transparency, and external influences on operations, profitability, and sustainability. Factors like software innovation, employee wellbeing, resource management, macroeconomics, and brand reputation impact operational health, while KPIs and training programs directly have crucial impact for employee productivity to maintaining company’s operational health.
Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of lo...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automat...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.
Currently, Indonesia and the whole world are being hit by the Covid-19 pandemic which has an impact on various fields of life. It affects all sectors, including the education sector. The government through the Ministr...
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Currently, Indonesia and the whole world are being hit by the Covid-19 pandemic which has an impact on various fields of life. It affects all sectors, including the education sector. The government through the Ministry of Education and Culture makes a policy in education in terms of the learning process. Teaching and learning activities that were initially carried out face to face become distance learning which was carried out at home. In this study, a systematic literature review is conducted on automatic assessment of essay answers. Various previous studies discuss the essay answer scoring system that has been developed using various methods. We synthesize the results to enrich our understanding of the automated essay exam scoring system. The expected result of this research is that it can contribute to further research related to the automated essay exam scoring system, especially in terms of considering methods and dataset forms.
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