检索结果-内蒙古大学图书馆

作者： Zheng Huo College of Computing Data Science and Society University of California Berkeley

Recommendation systems are a crucial element in engaging users and maintaining their engagement with e-commerce platforms. By recommending products or services that are likely to be relevant to each user's interests and preferences, the system can help maintain user interest and encourage them to spend more time on the platform. This work analyzes the principles of several off-the-shelf recommendation models, including the collaborative filtering model, the singular value decomposition model, and the rating-based collaborative filtering model. These models play a crucial role in the field of recommendation systems for ecommerce. To gain further insights from the comments of various merchandise items,sentiment analysis techniques and word cloud analysis are applied. Evaluation of these results demonstrates the critical role of recommender systems in shaping the future landscape of ecommerce. Sentiment analysis allows us to identify patterns in user feedback and understand how different factors influence user satisfaction with products or services. Word cloud analysis provides a visual representation of the most frequently mentioned features or keywords in the comments, allowing us to identify trends and patterns in user behavior. By combining these techniques with traditional recommendation models, more accurate and personalized recommendations could be made that better meet user needs and enhance their shopping experience on e-commerce platforms.

关键词： Recommendation System data Analysis E-Commerce Platform

来源：评论

学校读者我要写书评

暂无评论

Integrating data and Rules: A Hybrid Approach for Robust Lane Change Intention Prediction Under Distribution Shift

Integrating Data and Rules: A Hybrid Approach for Robust Lan...

引用

International Conference on Intelligent Transportation

作者： Yao Long Teng Htet Naing Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Lane change intention prediction is critical in improving road safety. In general, this task is achieved via two distinct approaches: The data-based approach and the rule-based approach. Generally, the former outperforms the latter under in-distribution scenarios. However, under out-distribution scenarios, data-based methods tend to perform extremely poorly. Rule-based methods are robust to such problems since they are formulated based on theory. Instead of relying exclusively on each approach, it is more advantageous to combine them to exploit both of their strengths. Thus, in this paper a physics guided hybrid model is proposed for lane change intention prediction to improve model robustness under distribution shift. The effectiveness of the proposed model is demonstrated in the experimental results where the proposed model outperforms rule-based models under in-distribution scenarios by 4%, and outperforms both rule-based models and data-based models in out-distribution scenarios by 3% and 60% respectively in the highD dataset.

关键词： Training Transforms Predictive models Prediction algorithms Robustness Road safety data models Calibration Physics Intelligent transportation systems

来源：评论

学校读者我要写书评

暂无评论

An Investigation on the Potential of KAN in Speech Enhancement

arXiv

引用

arXiv 2024年

作者： Li, Haoyang Hu, Yuchen Chen, Chen Chng, Eng Siong Nanyang Technological University College of Computing and Data Science Singapore

High-fidelity speech enhancement often requires sophisticated modeling to capture intricate, multiscale patterns. Standard activation functions, while introducing nonlinearity, lack the flexibility to fully address this complexity. Kolmogorov-Arnold Networks (KAN), an emerging methodology that employs learnable activation functions on graph edges, present a promising alternative. This work investigates two novel KAN variants based on rational and radial basis functions for speech enhancement. We integrate the rational variant into the 1D CNN blocks of Demucs and the GRU-Transformer blocks of MP-SENet, while the radial variant is adapted to the 2D CNN-based decoders of MP-SENet. Experiments on the VoiceBank-DEMAND dataset show that replacing standard activations with KAN-based activations improves speech quality across both the time-domain and time-frequency domain methods with minimal impact on model size and FLOP, underscoring KAN's potential to improve speech enhancement models. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

来源：评论

学校读者我要写书评

暂无评论

data Processing Efficiency Aware User Association and Resource Allocation in Blockchain Enabled Metaverse over Wireless Communications

arXiv

引用

arXiv 2024年

作者： Qian, Liangxin Zhao, Jun College of Computing and Data Science Nanyang Technological University Singapore

In the rapidly evolving landscape of the Metaverse, enhanced by blockchain technology, the efficient processing of data has emerged as a critical challenge, especially in wireless communication systems. Addressing this need, our paper introduces the innovative concept of data processing efficiency (DPE), aiming to maximize processed bits per unit of resource consumption in blockchain-empowered Metaverse environments. To achieve this, we propose the DPE-Aware User Association and Resource Allocation (DAUR) algorithm, a tailored solution for these complex systems. The DAUR algorithm transforms the challenging task of optimizing the sum of DPE ratios into a solvable convex optimization problem. It uniquely alternates the optimization of key variables like user association, work offloading ratios, task-specific computing resource distribution, bandwidth allocation, user power usage ratios, and server computing resource allocation ratios. Our extensive numerical results demonstrate the DAUR algorithm’s effectiveness in DPE. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

An Objective Metric Towards Music Mood Visualization

An Objective Metric Towards Music Mood Visualization

引用

International Conference on Cyberworlds

作者： Hanqin Wang Alexei Sourin College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

Music visualization is presented in different formats. The mainstream music visualization remains focused on providing animated images for visual pleasure or for enhancing the perception of sheet music with colors. Static visualization of music, on the other hand, has the potential to provide quick insights into the music’s attributes, particularly its mood. However, despite our previous work exploring several approaches to music mood visualization, a major problem still exists in how to objectively evaluate the mood of music pieces and the generated images. Thus, this paper proposes IMEMNextan improvement to the existing framework IMEMNet [1], which serves as a novel and objective metric for music mood visualization. Not only does it allow for a user study-free evaluation of music mood visualization, but the new network also enables for creation of datasets of music-image pairs with the same mood. The new IMEMNext network achieved a significant performance improvement compared to its original implementation.

关键词： Measurement Hands Visualization Computer vision Mood Image color analysis Computational modeling Transformers Music information retrieval Image classification

来源：评论

学校读者我要写书评

暂无评论

Two Tropical data Center Testbeds and data Yields from 2018 to 2024 24

Two Tropical Data Center Testbeds and Data Yields from 2018 ...

引用

Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems

作者： Rongrong Wang Duc Van Le Rui Tan College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400704802

The ever-increasing energy consumption of data centers (DCs) has emerged as a global concern. Currently, many DCs follow conventional practices, characterized by low temperature setpoints, resulting in considerable energy consumption for cooling purposes. In pursuit of energy-efficient DC operation practices, we have built two DC testbeds with full control of the cooling system to facilitate experiments under various operating conditions. The test server rooms are instrumented with sensors in different modalities at both room and server levels to monitor real-time operating conditions. Our experiment results indicate that the air-cooled DCs operated in enclosed buildings can increase temperature setpoint to improve energy efficiency with insignificant impact on server computing performance and reliability. The collected environmental and energy traces from our testbeds have been released as two public datasets. Our analytical results and datasets will contribute to the future design and operation of energy-efficient DCs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pharmacophore-guided de novo drug design with diffusion bridge

arXiv

引用

arXiv 2024年

作者： Wang, Conghao Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore

De novo design of bioactive drug molecules with potential to treat desired biological targets is a profound task in the drug discovery process. Existing approaches tend to leverage the pocket structure of the target protein to condition the molecule generation. However, even the pocket area of the target protein may contain redundant information since not all atoms in the pocket is responsible for the interaction with the ligand. In this work, we propose PharmacoBridge, a phamacophoreguided de novo design approach to generate drug candidates inducing desired bioactivity via diffusion bridge. Our method adapts the diffusion bridge to effectively convert pharmacophore arrangements in the spatial space into molecular structures under the manner of SE(3)-equivariant transformation, providing sophisticated control over optimal biochemical feature arrangements on the generated molecules. PharmacoBridge is demonstrated to generate hit candidates that exhibit high binding affinity with potential protein targets. © 2024, CC BY.

关键词： Bridge approaches

来源：评论

学校读者我要写书评

暂无评论

A New Carry Look-Ahead Adder Architecture Enabling Improved Speed and Energy Efficiency

A New Carry Look-Ahead Adder Architecture Enabling Improved ...

引用

IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Padmanabhan Balasubramanian Douglas Maskell College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

We present a new carry look-ahead adder (NCLA) architecture that makes use of non-uniform-size CLA modules in contrast to the existing CLA architecture which typically makes use of uniform-size CLA modules. This helps to achieve improved speed and energy efficiency for the NCLA architecture compared to the existing CLA architecture without incurring notable area and power penalties. To demonstrate the advantage of NCLA, several adders were implemented ranging from the slow ripple carry adder to the generally considered fastest parallel-prefix adder viz. the Kogge-Stone adder (KSA), and their performance metrics were compared. The 32-bit addition was considered an example, and the different adders were implemented in a semi-custom design method using a 28-nm CMOS standard digital cell library. Based on the synthesis results, the NCLA was found to facilitate significant optimizations in design metrics compared to its high-speed counterparts. To give some examples, the NCLA achieved: (i) a 14.7% reduction in delay and a 13.4% reduction in energy compared to a high-speed CLA while occupying slightly more area, (ii) a 42.1% reduction in delay and a 58.3% reduction in energy compared to a conditional sum adder while occupying 8% more area, (iii) a 14.7% reduction in delay and a 37.7% reduction in energy compared to a speed-optimized carry select adder while requiring 37% less area, and (iv) a 20.2% reduction in energy and a 55.4% reduction in area compared to the KSA.

关键词： Microprocessors Computer architecture Signal processing Energy efficiency Libraries Distance measurement Delays Adders Standards Optimization

来源：评论

学校读者我要写书评

暂无评论

Fast Bipartitioned Hybrid Adder Utilizing Carry Select and Carry Lookahead Logic

Fast Bipartitioned Hybrid Adder Utilizing Carry Select and C...

引用

International Conference on Mathematics and Computers in sciences and in Industry (MCSI)

作者： Padmanabhan Balasubramanian Douglas L. Maskell College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

We present a novel fast bipartitioned hybrid adder (FBHA) that utilizes carry-select and carry-lookahead logic. The proposed FBHA is an accurate adder with a significant part and a less significant part joined together by a carry signal. In an N-bit FBHA, the K-bit less significant part is realized using carry-lookahead adder logic, and the (N – K)-bit significant part is realized using carry-select adder logic. The 32-bit addition was considered as an example operation for this work. Many 32-bit adders ranging from the slow ripple carry adder to the fast parallel-prefix Kogge-Stone adder and the proposed adder were synthesized using a 28-nm CMOS standard cell library and their design metrics were compared. A well-optimized FBHA achieved significant optimizations in design metrics compared to its high-speed adder counterparts and some examples are mentioned as follows: (a) 19.8% reduction in delay compared to a carry-lookahead adder; (b) 19.8% reduction in delay, 24.4% reduction in area, and 19.4% reduction in power compared to a carry-select adder; (c) 45.6% reduction in delay, and 13.5% reduction in power compared to a conditional sum adder; and (d) 46.5% reduction in area, and 29.3% reduction in power compared to the Kogge-Stone adder

关键词： Accuracy Microprocessors Computer architecture Libraries Hybrid power systems Delays Logic Adders Standards Arithmetic

来源：评论

学校读者我要写书评

暂无评论

AI in Education for Corporate Training

AI in Education for Corporate Training

引用

Intelligent Education and Intelligent Research (IEIR), International Conference on

作者： Kuchhal Shourya Zhiqi Shen College of Computing & Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331519827

ISBN: (纸本)9798331519834

The gap between employer demands and the skills highlighted on resumes reveals a major flaw in traditional university education. The current one-size-fits-all approach leaves graduates unprepared for the fast-evolving job market. To address this, we are developing an AI-powered personalized learning platform tailored to corporate training. This platform utilizes a machine learning model that analyzes individual resumes to generate customized course maps based on the user’s unique background and career goals. By aligning educational pathways with specific job requirements, it caters to the growing need for job-specific programs, especially among Gen Z. A Logistic Regression Model is trained on resume datasets to predict suitable professions for users, while tools like Gensim and TensorFlow help construct personalized knowledge graphs of relevant courses. Various graph-based algorithms like PageRank, SimRank, and HITS are used to generate an optimized study map, considering course difficulty and prerequisites. This innovative approach offers a significant leap in helping job seekers refine their skills and meet employer expectations. The platform's findings could ultimately be integrated into corporate training programs, enhancing workforce development and addressing the shortcomings of traditional education systems.

关键词： Training Industries Logistic regression Accuracy Resumes Knowledge graphs Companies Predictive models Prediction algorithms Optimization

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：