检索结果-内蒙古大学图书馆

8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Huang, Yuyang Zharfan, Faishal Hendrawan, Hendrawan Gunawi, Haryadi S. Jiang, Junchen University of Chicago Department of Computer Science Chicago United States Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350368499

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application's bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application's *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application's accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application's sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics. © 2024 IEEE.

关键词： Bandwidth

来源：评论

学校读者我要写书评

暂无评论

TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

引用

Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Sentiment Polarity Analysis of Bangla Food Reviews Using Machine and Deep Learning Algorithms 3

Sentiment Polarity Analysis of Bangla Food Reviews Using Mac...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Amin, Al Sarkar, Anik Islam, Md Mahamodul Miazee, Asif Ahammad Islam, Md Robiul Hoque, Md Mahmudul Uttara University Department of Computer Science and Engineering Dhaka Bangladesh National Institute of Technology Silchar India University of Oslo Department of Informatics Oslo Norway Maharishi International University Department of Computer Science Iowa United States CCN University of Science & Technology Department of Computer Science and Engineering Comilla Bangladesh

ISBN: (纸本)9798350388282

The Internet has become an essential tool for people in the modern world. Humans, like all living organisms, have essential requirements for survival. These include access to atmospheric oxygen, potable water, protective shelter, and sustenance. The constant flux of the world is making our existence less complicated. A significant portion of the population utilizes online food ordering services to have meals delivered to their residences. Although there are numerous methods for ordering food, customers sometimes experience disappointment with the food they receive. Our endeavor was to establish a model that could determine if food is of good or poor quality. We compiled an extensive dataset of over 1484 online reviews from prominent food ordering platforms, including Food Panda and HungryNaki. Leveraging the collected data, a rigorous assessment of various deep learning and machine learning techniques was performed to determine the most accurate approach for predicting food quality. Out of all the algorithms evaluated, logistic regression emerged as the most accurate, achieving an impressive 90.91% accuracy. The review offers valuable insights that will guide the user in deciding whether or not to order the food. © 2024 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Fractional Linear Matroid Matching Is in Quasi-NC 32

Fractional Linear Matroid Matching Is in Quasi-NC

引用

32nd Annual European Symposium on Algorithms, ESA 2024

作者： Gurjar, Rohit Oki, Taihei Raj, Roshan Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9783959773386

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma (2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients. © Rohit Gurjar, Taihei Oki, and Roshan Raj;licensed under Creative Commons License CC-BY 4.0.

关键词： Parallel algorithms

来源：评论

学校读者我要写书评

暂无评论

Decentralized control in active distribution grids via supervised and reinforcement learning

引用

Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

来源：评论

学校读者我要写书评

暂无评论

Disentangling Prosody Representations With Unsupervised Speech Reconstruction

引用

IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Hybridization Model of Frame Semantics and deep learning for text semantic similarity calculation 6

Hybridization Model of Frame Semantics and deep learning for...

引用

6th International Conference on computer Information Science and Application technology, CISAT 2023

作者： Haijing, Liu Department of Computer Engineering Taiyuan Institute of Technology Taiyuan China College of computing informatics and media University Technology Mara Malaysia

ISBN: (纸本)9781510668546

Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstream research paradigm, but they suffer from the problem of insufficient understanding of text semantics and thus unclear interpretation of computational results. In this paper, we propose a text semantic encoding model combining Frame semantic theory and deep learning, which enriches the semantic representation within sentences by making full use of the Frame, Frame Elements, lexical units, and inter-Frame relations in the Frame semantic knowledge base and combining them with the Bert model, and interacts with the semantics between sentences through Siamese Transformer encoders. The experiments are validated on MSRParaphraseCorpus and quora-question-pairs, and the results show that the model proposed in this paper outperforms similar models in terms of F1 values. © 2023 SPIE.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLSTM, and CRF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlurrahman, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu Rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides information on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error and inaccurate results. To help gamers get the desired game information and increase customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its performance based on the f1 score evaluation results. The proposed model that we created is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated Recurrent Unit (BiGRU), and fine-tuning Bidirectional Encoder Representations from Transformer (BERT). We then propose another model, BiLSTM-CRF, by examining the effect of using the last four hidden states of BERT and comparing it with the last one hidden state of BERT to be used in the BiLSTM-CRF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CRF on the last four hidden states yielded the best performance, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CRF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

引用

2023 International Conference on Electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad Randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question format and susceptibility to manipulation on this research, we propose an approach that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to create a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro performance of 98%. © 2023 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Classification of Tuna Meat Grade Quality Based on Color Space Using Wavelet and k-Nearest Neighbor Algorithm

Classification of Tuna Meat Grade Quality Based on Color Spa...

引用

2023 International Conference on Smart-Green technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Gede Sujana Eka Putra, I. Ketut Gede Darma Putra, I. Sudarma, Made Sudana, Anak Agung Kompiang Oka Institute of Business and Technology Indonesia Department of Computer Informatics Denpasar Indonesia Udayana University Department of Information Technology Denpasar Indonesia Udayana University Department of Electrical and Computer Engineering Denpasar Indonesia

ISBN: (纸本)9798350382822

Tuna products are one of Indonesia's leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to Robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from RGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better accuracy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：