检索结果-内蒙古大学图书馆

2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhong, Ling Wang, Wen Zhu, Shiqiang Ji, Shanshan Zha, Changhai Wan, Minhong Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

With the wide application of service robots in people's daily life, people are not only satisfied with robots accomplishing tasks independently but also hope that robots can maintain sustainable interaction with human beings. Especially in the field of music, robots need to be equipped with intelligent cognitive skills in decision-making, so that the audience can enjoy immersive appreciation and emotional resonance in the robots performance. Visual perception and natural language understanding are essential for robots to establish human-robot friendly relationships. To enrich the interactive ability of music robots, we design a contactless interaction system based on facial expression recognition, which consists of four modules: voice wake-up, face detection, facial expression recognition, and music mapping. When people wake up the music robot with specific words, the robot will customize appropriate music to perform according to the recognition results of the FER module. As a final step, the interaction system has been successfully applied to the piano humanoid robot independently developed by our team. © 2022 IEEE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning

Fine-grained Category Discovery under Coarse-grained supervi...

引用

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： An, Wenbin Tian, Feng Chen, Ping Tang, Siliang Zheng, Qinghua Wang, QianYing School of Automation Science and Engineering Xi'an Jiaotong University China School of Computer Science and Technology MOEKLNNS Lab Xi'an Jiaotong University China Department of Engineering University of Massachusetts Boston United States College of Computer Science and Technology Zhejiang University China Lenovo Research

Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same granularity. In this paper, we investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. It is also a challenging task since supervised training on coarse-grained categories tends to focus on inter-class distance (distance between coarse-grained classes) but ignore intra-class distance (distance between fine-grained sub-classes) which is essential for separating fine-grained categories. Considering most current methods cannot transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner. Extensive experiments on public datasets show both effectiveness and efficiency of our model over compared methods. Code and data are available at https://***/Lackel/Hierarchical_Weighted_SCL. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Comparative Evaluation of Attribute-Enabled Supervised Classification in Predicting the Air Quality

引用

Spatial Information research 2023年第4期31卷 399-407页

作者： Subbulakshmi, P. Vimal, S. Robinson, Y. Harold Verma, Amit Nayak, Janmenjoy School of computer science and Engineering VIT Chennai campus Tamilnadu India Data Analytics Lab Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam India Department of Computer Science and Engineering Francis Xavier Engineering College Tirunelveli India Department of Computer Science & Engineering University Centre for Research & Development Chandigarh University Gharuan Mohali India Department of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha India

Air pollution demonstrates the appearance of toxins into the air which is blocking human prosperity and the earth. It will portray as potentially the riskiest threats that humanity anytime faced. It makes hurt animals, harvests to thwart these issues in transportation territories need to expect air quality from pollutions utilizing AI systems and IoT. Along these lines, air quality evaluation and assumption has become a huge target for human health factors and also affect internal organs related to respiratory. The accuracy of Air Pollution prediction has been involved with the machine learning techniques and the best accuracy model is identified. The air quality prediction dataset is used for identifying the meteorology air pollution data while the predicted model is involved the decision tree computation for predicting the toxin contents in the region, the Air quality indicator is used to assess the pollution level and monitoring the air quality. The performance analysis shows that the decision tree technique has produced the better results in the performance metrics of Accuracy, precision, recall, and F1-score with the minimized error values while the comparative evaluation of Attribute-enabled classification has identified the best technique for predicting the air quality.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Wen Wang Zheyuan Lin Shanshan Ji Te Li Jason Gu Minhong Wan Chunlong Zhang Zhejiang Lab and Zhejiang Engineering Research Center for Intelligent Robotics Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Canada

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Detecting Free-Riders in Federated Learning Using an Ensemble of Similarity Distance Metrics

Detecting Free-Riders in Federated Learning Using an Ensembl...

引用

Intelligent Cybersecurity Conference (ICSC)

作者： Sarhad Arisdakessian Omar Abdel Wahab Osama Wehbi Azzam Mourad Hadi Otrok Computer and Software Engineering Polytechnique Montréal Montréal Canada Department of CS KU 6G Research Center Khalifa University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

Federated learning (FL) has witnessed an increase in adoption mainly because of its practical implications and ability to leverage large amounts of data while safeguarding their privacy. However, at its core, FL highly relies on the resources of its participants (i.e., clients), that directly influence the accuracy of the global model in any given training task. A significant challenge arises from the presence of clients that do not fully engage in the training process, yet still benefit from the updated models provided by the server. This lack of active participation has the potential to undermine the overall performance of the global model. Furthermore, free-riders create an unfair scenario where honest participants contribute more while receiving the same overall benefit. In this work, we propose a strategy that relies on an ensemble of several distance measures to mitigate the impact of free-riders in FL environments. By integrating multiple distance metrics into a unified ensemble approach, our objective is to detect and identify free-riders effectively. Extensive simulations and experimental results highlight the robustness of our approach.

关键词： Training Data privacy Federated learning Atmospheric measurements Particle measurements Robustness Servers Object recognition Ensemble learning computer security

来源：评论

学校读者我要写书评

暂无评论

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

arXiv

引用

arXiv 2025年

作者： Shahin, Nada Ismail, Leila Lab Department of Computer Science and Software Engineering College of Information Technology UAE University Al-Ain United Arab Emirates Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community. © 2025, CC BY.

关键词： Neural machine translation

来源：评论

学校读者我要写书评

暂无评论

Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

引用

Biomedical engineering Frontiers 2022年第1期3卷 1-13页

作者： Angela Zhang Amil Khan Saisidharth Majeti Judy Pham Christopher Nguyen Peter Tran Vikram Iyer Ashutosh Shelat Jefferson Chen B.S.Manjunath Vision Research Laboratory Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraSanta BarbaraCAUSA Chen Lab Department of NeurosurgeryUniversity of CaliforniaIrvine Medical CenterOrangeCAUSA Santa Barbara Cottage Hospital Santa BarbaraCAUSA

Objective and Impact *** propose an automated method of predicting Normal Pressure Hydrocephalus(NPH)from CT scans.A deep convolutional network segments regions of interest from the *** regions are then combined with MRI information to predict *** our knowledge,this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for *** to their low cost and high versatility,CT scans are often used in NPH *** well-defined and effective protocol currently exists for analysis of CT scans for ***’index,an approximation of the ventricle to brain volume using one 2D image slice,has been proposed but is not *** proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting *** propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these *** segmentation and network features are used to train a model for NPH *** method outperforms the current state-of-the-art by 9 precision points and 29 recall *** segmentation model outperforms the current state-of-the-art in segmenting the ventricle,gray-white matter,and subarachnoid space in CT *** experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process,and network properties can increase NPH prediction accuracy.

关键词： diagnosis network Connectivity

来源：评论

学校读者我要写书评

暂无评论

C++ and Interoperability Between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability Between Libraries: The GraphBLAS C+...

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benjamin Brock Scott McMillan Aydın Buluç Timothy G. Mattson José E. Moreira Parallel Computing Lab Intel Software Engineering Institute Carnegie Mellon University Computational Research Department Lawrence Berkeley National Laboratory EECS Department University of California Berkeley IBM Thomas J. Watson Research Center

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Characterization of Single Event Functional Interrupts in COTS and Rad-Hard ARM Microcontrollers

Characterization of Single Event Functional Interrupts in CO...

引用

IEEE Space Computing Conference (SCC)

作者： Stefania Esquer Jesse K. Mee Heather M. Quinn Brian D. Sierawski Ronald D. Schrimpf Engineering Department Electrical and Computer Vanderbilt University Nashville TN USA SPACER Lab The Air Force Research Laboratory Kirtlan AFB NM USA

ISBN: (数字)9798350384536

ISBN: (纸本)9798350384543

This work evaluated the single event functional interrupt (SEFI) response of the commercial-off-the-shelf (COTS) and radiation hardened Cortex-M4 (M4) micro controllers with the Armv7-M ISA. The micro controllers were exposed to 200 MeV protons. The COTS M4 was further evaluated under carbon ions and alpha particles to assess if the control bits that depend on the software could have a significant influence on the SEFI cross section for this ISA. The SEU results show that both micro controllers can experience multiple-cell upsets (MCUs), which could facilitate the accumulation of multiple-bit upsets (MBUs). MBUs could be a concern even for radiation hardened systems with ECC, because ECC crashes the system (SEFI) to correct MBUs.

关键词： Protons Radiation effects Radiation hardening (electronics) Microcontrollers Alpha particles Ions Software Error correction codes Carbon Testing

来源：评论

学校读者我要写书评

暂无评论

ICT for Improved Maternal Healthcare in Uganda: A Systematic Literature Review

ICT for Improved Maternal Healthcare in Uganda: A Systematic...

引用

IST-Africa

作者： Musa CHEMISTO Kassim KALINAKI Ivan Tim OLOYA Tar JL GUTU Percival EGAU Fred KIRYA Darlius Bosco MWEBESA Rashid KISITU Department of Electronics and Computer Engineering HCI Research Lab Soroti University Soroti Uganda Department of Computer Science Islamic University in Uganda Mbale Uganda Faculty of Information and Communications Technology ISBAT University Kampala Uganda

ISBN: (数字)9781905824731

ISBN: (纸本)9798350356595

This paper adapts a Systematic Literature Review (SLR) to investigate the influence of ICTs on Uganda's maternal health sector development based on the premise that ICTs have supported improvements in maternal health services such as antenatal, pregnancy, and postnatal care. The SLR analysed twenty articles and reports, which were classified using the Search, Appraisal, Synthesis, and Analysis (SALSA) framework and Mendeley referencing tool. Six ICT initiatives in maternal health were identified and evaluated, centred on their developmental contributions. Using a theoretical framework for analysis of ICT-based development initiatives, we categorized explanatory development perceptions about the ICT initiatives. Conceptual categories adopted from the framework include better lives for the poor and improved government services. The categories rationalized inferred contextual literature relating to the developmental contributions of the ICT initiatives. Our findings provide an interpretive understanding of the ICT initiative's significance towards service delivery access and improvement. However, the study was limited by insufficient literature about ICTs in Uganda's maternal health sector. Future studies aim to develop an Artificial intelligence system to support improved maternal healthcare in Uganda and a theoretical framework.

关键词： Training Systematics Costs Bibliographies Decision making Medical services Stakeholders

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：