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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Satya Prakash Yadav Amit Gupta Caio Dos Santos Nascimento Victor Hugo C. de Albuquerque Mahaveer Singh Naruka Sansar Singh Chauhan Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) GreaterNoida U.P. India Graduate Program in Telecommunications Engineering. (PPGET) Federal Institute of Education Science and Technology of Ceará (IFCE) Fortaleza-CE Brazil Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza Fortaleza/CE Brazil Computer Science & Engineering GL Bajaj Institute of Technology & Management Greater Noida India

Virtual assistants are improving and providing consumers with greater advantages. The comprehension and fulfilment of requests by virtual assistants will increase as voice recognition and natural language processing continue to grow. Virtual assistants are projected to be employed in more commercial activities as speech recognition technology advances. The main goal of developing personal assistant software (virtual assistant) is to use web-based semantic data sources, user-generated content, and knowledge from knowledge libraries. Basically, main objective of making this Voice-Based Virtual Assistant is to make life easier and having a personal assistant to everyone which can perform many tasks. As the end user interacts with a virtual assistant, the AI programming learns from the data provided and improves its ability to forecast the end user's needs. Virtual assistants are often used to do things like add tasks to a calendar, provide information that would normally be found in a website, and operate and monitor Smart Home devices like lighting and cameras and thermostats. Massive volumes of data are required to fuel virtual assistant technologies, which feed Artificial Intelligence (AI) platforms such as machine learning, natural language processing, and speech recognition. Speech recognition has a lengthy history and has seen several key advancements. On smartphones and wearable devices, speech recognition for dictation, search, and voice commands has become a standard feature. Design of a small, large vocabulary speech recognition system that can run quickly, accurately, and with minimum latency on mobile devices.

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The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset

The Efficient Traffic Sign Detection and Recognition for Tai...

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International Conference on Applied System Innovation (ICASI)

作者： Taweelap Suwattanapunkul Lung-Jen Wang International Master Program in Information Technology and Applications National Pingtung University Taiwan Department of Computer Science and Information Engineering National Pingtung University Taiwan

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.

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Study on the Flow, Foaming Characteristics and Structural Strength of Polypropylene Structural Foam Injection Molding by Innovative Nitrogen and Molten Plastic Mixing Mechanism

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Polymers 2023年第9期15卷 2116-2116页

作者： Huang, Po-Wei Peng, Hsin-Shu Hwang, Sheng-Jye Huang, Chao-Tsai Program of Mechanical and Aeronautical Engineering Feng Chia University of Engineering and Science Taichung407102 Taiwan Department of Mechanical and Computer Aided Engineering Feng Chia University of Engineering and Science Taichung407102 Taiwan Department of Mechanical Engineering National Cheng Kung University of Engineering Tainan70101 Taiwan Department of Chemical and Materials Engineering Tamkang University of Engineering New Taipei City251301 Taiwan

Plastic foam molding methods include thermoforming, extrusion and injection molding. Injection foam molding is a one-time molding method with high production efficiency and good product quality. It is suitable for foamed plastic products with complex shapes and strict size requirements. It is also the main method for producing structural bubbles. In this investigation, we developed a structural foam injection molding technology using the gas supply equipment connected to the unique plasticizing mechanism of the injection machine and studied its influence on the specimens’ melt rheology quality and foam structures. In the experiment, the forming material was polypropylene (PP), and the gas for mixing/forming foaming characteristics was nitrogen (N2). Additionally, in order to observe the rheological properties of N2/melt mixing, a melt flow specimen mold cavity was designed and the change in the melt viscosity index was observed using a melt pressure sensing element installed at the nozzle position. With the nitrogen supply equipment connected to a unique plasticizing mechanism, the mixing of gas and molten plastic can be achieved at the screw plasticizing stage, where the foaming effect is realized during the melt-filling process due to the thermodynamic instability of the gas. It was also found that an increase in N2 fill content increased melt fluidity, and the trend of melt pressure and melt viscosity index showed that the higher the gas content, the lower the trend. The foaming characteristic depends on the gas thermodynamic instability and the pressure release, so it can be seen from the melt fill path that, the greater the pressure near the gate, the lower the foaming amount and the internal structure (SEM) after molding;the farther from the gate, the greater the relative increase in the foaming growth/amount. This phenomenon will be more obvious when the N2 fill content is increased. © 2023 by the authors.

关键词： Gases

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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Evolution of emotion semantics

arXiv

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arXiv 2021年

作者： Xua, Aotao Stellar, Jennifer E. Xua, Yang Department of Computer Science University of Toronto Department of Psychology University of Toronto Cognitive Science Program University of Toronto

Humans possess the unique ability to communicate emotions through language. Although concepts like anger or awe are abstract, there is a shared consensus about what these English emotion words mean. This consensus may give the impression that their meaning is static, but we propose this is not the case. We cannot travel back to earlier periods to study emotion concepts directly, but we can examine text corpora, which have partially preserved the meaning of emotion words. Using natural language processing of historical text, we found evidence for semantic change in emotion words over the past century and that varying rates of change were predicted in part by an emotion concept's prototypicality-how representative it is of the broader category of "emotion". Prototypicality negatively correlated with historical rates of emotion semantic change obtained from text-based word embeddings, beyond more established variables including usage frequency in English and a second comparison language, French. This effect for prototypicality did not consistently extend to the semantic category of birds, suggesting its relevance for predicting semantic change may be category-dependent. Our results suggest emotion semantics are evolving over time, with prototypical emotion words remaining semantically stable, while other emotion words evolve more freely. © 2021, CC BY.

关键词： Semantics

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NNSplit-SØREN: Supporting the model implementation of large neural networks in a programmable data plane

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computer Networks 2023年 222卷

作者： Li, David Chunhu Maulana, Muhamad Rizka Chou, Li-Der Information Technology and Management Program Ming Chuan University Taoyuan City333321 Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan City320317 Taiwan

Embedding neural network (NN) models in the data plane is one of the very promising and attractive ways to leverage the computational power of computer network switches. This method became possible with the advent of the P4 language controlling the programmable data plane. However, most data planes today have some constraints, such as a limited set of operations and limited memory size. The computational cost of training large-scale NNs is high. In addition, while complex large-scale NN architectures are often used to improve prediction accuracy, they affect the functional performance of the data plane because of factors such as numerous input parameters and complex model design. Therefore, determining how to reduce the performance cost incurred by implementing large NN models in the data plane is a critical issue that needs to be addressed. This research proposes a technique called Neural Network Split (NNSplit) to solve the performance problems of embedding a large NN in a data plane by splitting the NN layers across multiple data planes. To support layer splitting, a new protocol called SuppORting ComplEx Computation in the Network (SØREN) is also proposed. The SØREN protocol header carries the activation value and bridges the NN layers in all switches. A multi-class classification use case of network traffic is used as the context for the experimental analysis. Experimental results show that compared to non-splitting NN architectures, NNSplit can reduce memory usage by nearly 50% and increase network traffic throughput with the cost of a 14% increase in round-trip time. In addition, when the SØREN protocol is encapsulated into data packets, the average processing time of the switch is 773 µs, which has very little impact on the processing time of the packets. Experimental results also show that the proposed NNSplit–SØREN can support large NN models on the data plane with a small performance cost. © 2022 Elsevier B.V.

关键词： Cost reduction

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The Contribution of Pancasila Education and Civic Education on Value Education for The Students During COVID 19 Pandemic in Digital Era

The Contribution of Pancasila Education and Civic Education ...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Faculty of Economics and Communication Bina Nusantara University Character Building Development Center Mass Communication Program Communication Department Jakarta Indonesia Faculty of Economics and Communication Bina Nusantara University Tourism Department Jakarta Indonesia Bina Nusantara University School of Computer Science Character Building Development Center Computer Science Department Jakarta Indonesia Bina Nusantara University School of Industrial Engineering Industrial Engineering Department Jakarta Indonesia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

When all nations including Indonesia are hit by COVID-19 pandemic in era digital, we are compelled to conduct research with the main purpose, namely how much the contribution or influence of Pancasila Education and Civic Education on Value Education for students during the Covid-19 pandemic. The method used in this research is a quantitative method. The data was obtained by asking the students who were respondents to fill out a questionnaire. 150 students who have attended Pancasila and Citizenship courses were chosen by random sampling. Then the data obtained from the responses of the respondents were processed by the SPSS 22 program, and analyzed by path analysis. From the data processing and path analysis, the results of the research are obtained which prove that Pancasila Education has a significant contribution or influence on Value Education. Civic Education has no significant contribution or influence on Value Education. Based on these findings from this research, it is necessary to increase more intensive and serious efforts in delivering Pancasila Education, especially Civic Education in order to increase the contribution of both education to value education for the students, so that the students as the younger generation do not only have an understanding about Pancasila Education and Civic, but also be able to live the values, especially SPIRIT (Striving for the excellence, Perseverance, Integrity, Respect, Innovation, and Team Work) which are the core values at Bina Nusantara University. © 2022 IEEE.

关键词： Students

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Marvin Borsdorf Zexu Pan Pascal Himmelmann Haizhou Li Tanja Schultz Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time.

关键词： Training Measurement Databases Signal processing Data models Blind source separation Robustness Acoustics Speech processing

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The KIND Dataset: A Social Collaboration Approach for Nuanced Dialect Data Collection 18

The KIND Dataset: A Social Collaboration Approach for Nuance...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Yamani, Asma Z. Alziyady, Raghad AlYami, Reem Albelali, Salma A. Abouhagar, Leina Almulhim, Jawharah Alsulami, Amjad Alfarraj, Motaz Al-Zaidy, Rabeah Department of Information and Computer Science King Fahd University of Petroleum & Minerals Saudi Arabia Center for Integrative Petroleum Research CIPR King Fahd University of Petroleum & Minerals Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum & Minerals Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum & Minerals Saudi Arabia Preparatory Year Program King Fahd University of Petroleum & Minerals Saudi Arabia Department of Computer Science Imam Abdulrahman Bin Faisal University Saudi Arabia Saudi Data & AI Authority Saudi Arabia

ISBN: (纸本)9798891760905

Nuanced dialects are a linguistic variant that pose several challenges for NLP models and techniques. One of the main challenges is the limited amount of datasets to enable extensive research and experimentation. We propose an approach for efficiently collecting nuanced dialectal datasets that are not only of high quality, but are versatile enough to be multipurpose as well. To test our approach we collect the KIND corpus, which is a collection of fine-grained Arabic dialect data. The data is short texts, and unlike many nuanced dialectal datasets, it is curated manually through social collaboration efforts as opposed to being crawled from social media. The collaborative approach is incentivized through educational gamification and competitions for which the community itself benefits from the open source dataset. Our approach aims to achieve: (1) coverage of dialects from under-represented groups and fine-grained dialectal varieties, (2) provide aligned parallel corpora for translation between Modern Standard Arabic (MSA) and multiple dialects to enable translation and comparison studies, (3) promote innovative approaches for nuanced dialect data collection. We explain the steps for the competition as well as the resulting datasets and the competing data collection systems. The KIND dataset is shared with the research community. © 2024 Association for Computational Linguistics.

关键词： Data acquisition

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Physics Representation Learning for Dexterous Manipulation Planning

Physics Representation Learning for Dexterous Manipulation P...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yi Wu Mengsha Hu Runxiang Jin Rui Liu Informatique et Sciences du Numérique (Computer Research Laboratory) CentraléSupelec Paris-Saclay University Gif-Sur-Yvette France Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Department of Computer Science Kent State University Kent OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Dexterous manipulation in robotics, particularly with high degrees of freedom (DoF) devices like the 24-joint Shadow Hand, confronts complexities in search space and execution precision. Humans, however, manipulate objects effortlessly, thanks to their innate grasp of physics. Inspired by this, we introduce the Physics Representation Learning (PRL) framework for robotic hand manipulation. PRL uses physics principles for action conceptualization, such as aligning axes at specific angles (Figure 1). It is a natural language that describes the physics laws, thus bridging the gap between high-level semantics planning and joint-level execution through inverse kinematics (IK). Built on top of the physics-informed action space, PRL deploys a Reinforcement Learning (RL) network with expert demonstrations. Its effectiveness was validated in Nvidia Isaac Sim on four tasks: hammering, unscrewing, sweeping, and pinching. Results show that PRL significantly outperforms conventional joint-control algorithms and RL without demonstrations, underscoring the benefits of physics-based action representations for complex tasks.

关键词： Representation learning Semantics Natural languages Reinforcement learning Kinematics Planning Complexity theory Physics Robots

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