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Addressing Cross-Cultural Design Challenges in Social Media Platforms: A Human-computer Interaction Perspective 16th

Addressing Cross-Cultural Design Challenges in Social Media ...

引用

16th International Conference on Cross-Cultural Design (CCD) Held as Part of the 26th International Conference on Human-computer Interaction (HCII)

作者： Li, Lanjie China Jiliang Univ Sch Arts & Commun Hangzhou Peoples R China

ISBN: (纸本)9783031609008;9783031609015

With the global popularity of social media, people are increasingly relying on social media platforms for their daily information sharing and social interactions. However, due to the differences in people's values, habits and behaviours across cultures, the experience of using social media varies across cultural groups, which poses a challenge for cross-cultural design. To address this issue, this study aims to explore a culturally sensitive human-computer interaction (HCI) approach to promote the effective use of social media platforms globally. Through in-depth understanding of the usage habits and behavioural patterns of users from different cultures, the findings of the study include specific methodological and quantitative analyses to provide an effective design methodology that promotes social media platforms that can be successfully applied in cross-cultural environments and improves the user experience and usability of social media platforms in different cultural contexts. Thus, the usability of social media platforms can be increased and the user experience of users around the world can be improved.

关键词： Cross-cultural design Cultural sensitivity Human-computer interaction (HCI) social media platforms user experience

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CrossPAR: Enhancing Pedestrian Attribute Recognition with Vision-Language Fusion and Human-Centric Pre-training 17th

CrossPAR: Enhancing Pedestrian Attribute Recognition with V...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Ngo, Bach-Hoang Ngo, Si-Tri Le, Phu-Duc Phan, Quang-Minh Tran, Minh-Triet Le, Trung-Nghia University of Science VNU-HCM Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9789819609598

Pedestrian attribute recognition (PAR) is crucial in various applications like surveillance and urban planning. Accurately identifying attributes in diverse and intricate urban environments is challenging despite its significance. This paper introduces a novel network for PAR that integrates a human-centric encoder, trained on extensive human datasets, with a vision-language encoder, trained on substantial text-image pair datasets. We also develop a cross-attention mechanism utilizing a Mixture-of-Experts approach that combines the human-centric encoder’s proficiency in local attribute detection with the vision-language encoder’s ability to comprehend global content. CrossPAR showcases a comparable accuracy result compared to existing techniques across multiple benchmarks, using less training data. These results confirm our approach’s effectiveness and suggest promising avenues for further research and practical applications in the domain of PAR and related fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Urban planning

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Automatic Modulation Recognition Using Parallel Feature Extraction Architecture 18th

Automatic Modulation Recognition Using Parallel Feature Extr...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Tang, Haolin Zhao, Yanxiao Kuzlu, Murat Luo, Changqing Catak, Ferhat Ozgur Wang, Wei Department of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA United States Department of Engineering Technology Old Dominion University NorfolkVA United States Department of Computer Science Virginia Commonwealth University RichmondVA United States Department of Electrical Engineering and Computer Science University of Stavanger Rogaland Norway Department of Computer Science San Diego State University San DiegoCA United States

ISBN: (纸本)9783031714665

Automatic Modulation Recognition (AMR) plays a critical role in wireless communication and can be applied in various applications such as spectrum monitoring and signal surveillance. Recently, different AMR approaches have been proposed based on Deep Learning (DL), which are inspired by the powerful abilities of feature extraction in neural networks, i.e., Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Among these solutions, hybrid methods extract spatial features and temporal features of signals by combining CNN and RNN into a single architecture to provide better performance. However, it is inefficient to extract two types of features using a single architecture since the temporal feature could be degraded after CNN layers, and the spatial feature may be compromised after RNN layers. To overcome this challenge, we propose a parallel neural network architecture for AMR, which extracts spatial features using CNN layers and temporal features using LSTM (Long Short-Term Memory) layers, respectively, in two parallel routes. Afterward, the extracted features will be combined to predict the modulation scheme of a signal. Extensive simulations are conducted on signals with 11 different modulation methods at a wide range of Signal-to-Noise Ratio (SNR) levels to evaluate our proposed parallel feature extraction architecture. In addition, we compare our solution against three other DL-based methods, and the results show that our method outperforms in terms of recognition accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Clinical Cognitive sciences 21st

Clinical Cognitive Sciences

引用

21st International Workshop on Software Engineering and Formal Methods (SEFM)

作者： Pluck, Graham Ariyabuddhiphongs, Kris Chulalongkorn Univ Fac Psychol Bangkok Thailand

ISBN: (纸本)9783031660207;9783031660214

Clinical sciences involved with the mind and brain, including neurology, psychiatry, endocrinology and clinical psychology all frequently deal with cognitive symptoms, side effects, and risk factors. Consequently, there has long been some interaction between those clinical fields and traditional cognitive sciences, focused on computationalist and embodied approaches to understanding natural and machine cognition. Examples include the advances made in understanding the normal cognitive architecture made by studying its breakdown in disease, as well as the enhanced methods of defining and measuring cognitive disorders stemming from understanding the healthy state. Nevertheless, the fields currently fail to fully exploit the potential for mutual advancement. Here we explore the interactions between traditional clinical and cognitive sciences and highlighted strengths of the relationship, and areas that could benefit from greater multidisciplinary emphasis. We argue that original fields of cognitive science (philosophy, linguistics, computer science, anthropology, psychology and neuroscience) remain the core of the multidisciplinary cognitive sciences, but that they can all be applied fruitfully to clinical issues. We explore this in one sample disorder-voice hearing in schizophrenia, showing the potential for clinically applied cognitive sciences. It is our contention that greater achievement is possible, in both academic and applied fields dealing with cognition, if we can foster a mutually symbiotic relationship between the clinical and cognitive sciences.

关键词： Neurology Psychiatry Cognitive disorders Applied sciences Hallucinations Cognitive science

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Hierarchical Attention Decoder for Solving Math Word Problems 4th

Hierarchical Attention Decoder for Solving Math Word Proble...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Li, Jiajia Peng, Letian Wang, Ping Zhao, Hai School of Information Management Wuhan University Wuhan China Center for the Studies of Information Resources Wuhan University Wuhan China Department of Computer Science and Engineering University of California San Diego United States Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9789819787012

To answer math word problems (MWPs), models must formalize equations from the source text of math problems. Recently, the tree-structured decoder has significantly improved model performance on this task by generating the target equation in a tree format. However, current decoders usually ignore the hierarchical relationships between tree nodes and their parents, which hinders further improvement. Thus, we propose a structure called a hierarchical attention tree to aid the generation procedure of the decoder. As our decoder follows a graph-based encoder, our full model is therefore named Graph to Hierarchical Attention Tree (G2HAT). We show that a tree-structured decoder with hierarchical accumulative multi-head attention leads to a significant performance improvement and reaches a significant improvement on various strong baselines on both English MAWPS and Chinese Math23k MWP benchmarks. For further study, we also apply a pre-trained language model to G2HAT, which even results in a new higher performance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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FAT-LSTM: A Multimodal Data Fusion Model with Gating and Attention-Based LSTM for Time-Series Classification 27th

FAT-LSTM: A Multimodal Data Fusion Model with Gating and A...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hosseinzadeh, Pouya Bahri, Omar Boubrahimi, Soukaina Filali Hamdi, Shah Muhammad Department of Computer Science Utah State University LoganUT United States

ISBN: (纸本)9783031781650

The remarkable success of machine learning models has sparked considerable interest in multimodal data fusion techniques. Addressing the challenge of integrating diverse data modalities while enhancing classification performance remains a key focus. In this paper, we introduce FAT-LSTM, a novel framework designed to fuse multimodal data for classification tasks. Leveraging advanced Gating and Attention-based Long Short-Term Memory (LSTM) mechanisms, FAT-LSTM represents a significant improvement in multimodal learning field. Through extensive comparative analysis against established baseline models, our study shows the superior performance achieved by FAT-LSTM. Additionally, thorough ablation analysis provides insights into the inner workings of the model, shedding light on its effectiveness. Empirical validation across multiple space weather datasets further confirms FAT-LSTM’s efficacy across various scenarios. This research highlights the crucial role of multimodal data fusion in effectively addressing real-world challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data fusion

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Robust Remote Sensing Scene Classification with Multi-view Voting and Entropy Ranking 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Wang, Jinyang Wang, Tao Gan, Min Hadjichristofi, George Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science and Engineering European University Cyprus Nicosia1516 Cyprus

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Deep convolutional neural networks have been widely used in scene classification of remotely sensed images. In this work, we propose a robust learning method for the task that is secure against partially incorrect categorization of images. Specifically, we remove and correct errors in the labels progressively by iterative multi-view voting and entropy ranking. At each time step, we first divide the training data into disjoint parts for separate training and voting. The unanimity in the voting reveals the correctness of the labels, so that we can train a strong model with only the images with unanimous votes. In addition, we adopt entropy as an effective measure for prediction uncertainty, in order to partially recover labeling errors by ranking and selection. We empirically demonstrate the superiority of the proposed method on the WHU-RS19 dataset and the AID dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Entropy

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PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolution 17th

PlainUSR: Chasing Faster ConvNet for Efficient Super-Resolu...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Wang, Yan Li, Yusen Wang, Gang Liu, Xiaoguang School of Computer Science Nankai University Tianjin China

ISBN: (纸本)9789819609109

Reducing latency is a roaring trend in recent super-resolution (SR) research. While recent progress exploits various convolutional blocks, attention modules, and backbones to unlock the full potentials of the convolutional neural network (ConvNet), achieving real-time performance remains a challenge. To this end, we present PlainUSR, a novel framework incorporating three pertinent modifications to expedite ConvNet for efficient SR. For the convolutional block, we squeeze the lighter but slower MobileNetv3 block into a heavier but faster vanilla convolution by reparameterization tricks to balance memory access and calculations. For the attention module, by modulating input with a regional importance map and gate, we introduce local importance-based attention to realize high-order information interaction within a 1-order attention latency. As to the backbone, we propose a plain U-Net that executes channel-wise discriminate splitting and concatenation. In the experimental phase, PlainUSR exhibits impressively low latency, great scalability, and competitive performance compared to both state-of-the-art latency-oriented and quality-oriented methods. In particular, compared to recent NGswin, the PlainUSR-L is 16.4× faster with competitive performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolution

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Multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning 24th

Multiple-Round Aggregation of Abstract Semantics for Secur...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhang, Jiao Li, Xiong Liang, Wei School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615445

Federated Learning (FL) provides a valuable framework that allows for the collaborative training of models across distributed networks while maintaining the privacy of the data involved. The concept of secure aggregation is crucial in preserving both the privacy and integrity of data within FL systems. However, the deployment of secure aggregation encounters significant barriers. Conventional secure aggregation methods are primarily designed for single-round operations, which can become prohibitively expensive in scenarios that require multiple rounds. Additionally, these methods often struggle in large-scale environments characterized by heterogeneous data and models. This paper introduces the Multiple-Round Aggregation of Abstract Semantics (MRAAS), a novel scheme designed to address the aforementioned limitations in secure aggregation within heterogeneous FL environments. The MRAAS scheme enhances security and reduces the need for extensive data exchanges by aggregating abstracted model semantics instead of raw model gradients, facilitating secure, multiple rounds of aggregation. Key innovations of MRAAS include an advanced abstraction technique, robustness against participant drop-out, and the use of recyclable secrets. These innovations collectively enhance system resilience and reduce the necessity for frequent cryptographic renewals. Our theoretical evaluations focus on the security features of MRAAS. Empirical validations conducted through comparative analyses utilizing two public datasets confirm the superiority of MRAAS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Unknown Truths and Unknowable Truths 1

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9th International Conference on Logic, Rationality, and Interaction, LORI-IX 2023

作者： Fan, Jie School of Humanities University of Chinese Academy of Sciences Beijing China Institute of Philosophy Chinese Academy of Sciences Beijing China

ISBN: (数字)9783031455582

ISBN: (纸本)9783031455575

Notions of unknown truths and unknowable truths are important in formal epistemology, which are related to each other in e.g. Fitch’s paradox of knowability. Although there have been some logical research on the notion of unknown truths and some philosophical discussion on the two notions, there seems to be no logical research on unknowable truths. In this paper, we propose a logic of unknowable truths, investigate the logical properties of unknown truths and unknowable truths, which includes the similarities of the two notions and the relationship between the two notions, and axiomatize this logic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer circuits

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