this paper presents the design and development of a low-cost and user-friendly Bengali braille embosser for the visually impaired population in Bangladesh. To produce braille text, the embosser employs a shifting mech...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
this paper presents the design and development of a low-cost and user-friendly Bengali braille embosser for the visually impaired population in Bangladesh. To produce braille text, the embosser employs a shifting mechanism and an Embosser head, both controlled by an Arduino microcontroller. A software has also been developed to allow users with basic printer knowledge to operate the embosser. Access to educational resources and reading materials is limited for people with disabilities in Bangladesh, and the availability of Bengali braille writing machines is scarce. the proposed solution aims to address these challenges and improve the learning and communication capabilities of visually impaired individuals.
Multi-Object Tracking (MOT) task is a key issue in computer vision. It has capacious application prospects in survelliance, self-driving, and augmented reality. Benefit from the continuous progress of Deep Neural Netw...
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A new method of constructing a two-dimensional demand matrix is proposed by design of products from perspectives of the products characteristics and requirements at the same time. Taking characteristics of the product...
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Nudge is considered as an intervention to change user behavior and influence decision-making. Mobile apps have become a part of our everyday life. In this pandemic era, governments use mobile apps9; technology to c...
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In the rapidly evolving landscape of computing power networks, applications are becoming more complex and diverse. Consequently, this evolution has led to a growing need for sophisticated intelligent scheduling mechan...
In the rapidly evolving landscape of computing power networks, applications are becoming more complex and diverse. Consequently, this evolution has led to a growing need for sophisticated intelligent scheduling mechanisms to adeptly manage these networks. Utilizing intelligent algorithms has become pivotal in ensuring effective and reliable computational services for these intricate networks. this paper addresses the pressing issue of intelligent scheduling in serverless computing power networks. We explore a computing power network system model based on serveless computing, formulate the intelligent task scheduling problem as a multi-objective optimization problem, present a solution that designs task scheduling policies using the Deep Deterministic Policy Gradient (DDPG) algorithm from reinforcement learning. Additionally, we perform experiments to validate the practicality and effectiveness of our proposed approach.
In-depth analysis and discussion of the integration methods in the implementation of the unified registration of real estate can better provide effective data protection for the real estate management department. Taki...
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the proceedings contain 39 papers presendted at a virtual meeting. the special focus in this conference is on HCI in Business, Government and Organizations. the topics include: COVID-19 AI Inspector;Online Shopping Du...
ISBN:
(纸本)9783031055430
the proceedings contain 39 papers presendted at a virtual meeting. the special focus in this conference is on HCI in Business, Government and Organizations. the topics include: COVID-19 AI Inspector;Online Shopping During COVID-19: A Comparison of USA and Canada;design and Implementation of a Collaborative Idea Evaluation System;electronic Performance Monitoring: Review of theories, Conceptual Framework, and Study Proposal;designing a Workplace Violence Reporting Tool for Healthcare Workers in Hospital Settings;evaluation of the Change in the Quality of Reports withthe Application of Gamification in a Corporative Institution;developing Personas for designing Health Interventions;Attracting Future Students’ Attention by an UX-Optimized Website;holistic Approach to the Social Acceptance of Building Information Modelling Applications;factors that Influence Cookie Acceptance: Characteristics of Cookie Notices that Users Perceive to Affect their Decisions;a Survey-Based Study to Identify User Annoyances of German Voice Assistant Users;Easy Hand Gesture Control of a ROS-Car Using Google MediaPipe for Surveillance Use;user-Centered Assembly Knowledge Documentation: A Graph-Based Visualization Approach;China’s CO2 Emissions Interval Forecasting Based on an Improved Nonlinear Fractional-Order Grey Multivariable Model;building a "Corpus of 7 Types Emotion Co-occurrences Words" of Chinese Emotional Words with Big Data Corpus;predicting the Usefulness of Questions in Q&A Communities: A Comparison of Classical Machine Learning and Deep Learning Approaches;the Corpus of Emotional Valences for 33,669 Chinese Words Based on Big Data;the Economic theoretical Implications of Blockchain and Its Application in Marine Debris Removal;explore the Influence of Smart Contract on Online Lending;better Decision-Making through Collaborative Development of Proposals.
In this paper, we propose a novel method for simplifying the design of adaptive educational games by integrating Knowledge Space theory (KST) with real- and/or build-time knowledge space alterations based on a story m...
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Drug response prediction plays a crucial role in precision medicine, such as cancer analysis and treatment. Due to the uncertainty of drug efficacy and the heterogeneity of cancer, predicting drug response in vitro is...
Drug response prediction plays a crucial role in precision medicine, such as cancer analysis and treatment. Due to the uncertainty of drug efficacy and the heterogeneity of cancer, predicting drug response in vitro is expected to assist in guiding anticancer drug design and understanding cancer biology. Based on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), we propose a deep learning method for predicting drug sensitivity. Specifically, we first construct graphs of drugs and cell lines in the benchmark dataset. And we design a drug feature extraction module based on Graph Transformer and a cell line feature extraction module based on Graph Attention Networks (GAT) to capture the embeddings of drugs and cell lines. Finally, we feed the drug embeddings and cell line embeddings into the module for prediction. Experiment results show that our method outperforms several advanced models in drug response prediction, demonstrating the good performance and enormous application potential in precision medicine. In addition, the results of ablation experiments demonstrate the effectiveness of the modules in our model.
this work addresses the challenge of decomposing time-varying graph signals into their constituent amplitude- and frequency-modulated components while inferring their dynamic functional connectivity structures, known ...
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ISBN:
(数字)9798350353235
ISBN:
(纸本)9798350353242
this work addresses the challenge of decomposing time-varying graph signals into their constituent amplitude- and frequency-modulated components while inferring their dynamic functional connectivity structures, known as graph mode decomposition (GMD). the existing method within the graph signal processing (GSP) framework yields static connectivity structures, limiting its real-life applicability and relying heavily on the user-defined parameter of the total number of existing modes in the signal K. We present a novel method to overcome these limitations, offering dynamic multi-scale connectivity structures and amplitude-frequency components. the approach formulates a variational optimization problem, integrating a prior for time-varying edge weights, and employs a successive scheme for optimization, eliminating the need for a priori specification of the number of components K. the performance of the method is validated on both synthetic and real datasets.
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