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Investigating the Impact of Internal Inputs on Mmultisensory Integration: A Study of External Multisensory Inputs and Internal Arousal 32

Investigating the Impact of Internal Inputs on Mmultisensory...

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32nd Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2024

作者： Azizi, Zahra Chan, Jason Ward, Tomas Setti, Annalisa Insight SFI Research Centre for Data Analytics School of Computing Dublin City University Ireland School of Applied Psychology University College Cork Cork Ireland

Multisensory integration, the brain's ability to combine information from different sensory modalities, is influenced by both external stimuli and internal states, such as arousal, motivation, and emotion. This study explores the balance between internal and external inputs and their effect on multisensory integration, with a focus on arousal. We hypothesize that multisensory integration abilities vary depending on both internal states and external inputs. In our experiment, we tested 23 participants using the Sound-Induced Flash Illusion (external input) and arousal-cued images (internal arousal). Sensory sensitivity was also assessed using the Highly Sensitive Person (HSP) scale. Preliminary findings revealed no significant differences in response accuracy or confidence when participants were presented with cue-induced arousal versus neutral images across different age groups. However, greater sensory sensitivity was linked to enhanced internal judgment. These results suggest that while arousal-cued images may not be the most effective method for inducing internal arousal in this context, individual differences play a role in how internal states affect multisensory integration. To further explore this, we plan to implement the cold-water pressor technique, along with physiological monitoring (GSR and heart rate), to more effectively induce and control internal arousal. By exploring how internal states influence multisensory integration, our findings could inform strategies to improve sensory processing and behavioral outcomes in individuals with deficits, such as those with Parkinson's disease or age-related challenges. These interventions may enhance motor coordination, cognitive function, and quality of life while also providing insights for broader applications in mental health and technology. © 2024 Copyright for this paper by its authors.

关键词： Neurodegenerative diseases

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Generative Deduplication For Socia Media data Selection

Generative Deduplication For Socia Media Data Selection

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Xianming Li, Jing Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre on Data Science & Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761681

Social media data exhibits severe redundancy caused by its noisy nature. It leads to increased training time and model bias in its processing. To address this issue, we propose a novel Generative Deduplication framework for social media data selection by removing semantically duplicate data. While related work involves data selection in task-specific training, our model acts as an efficient pre-processing method to universally enhance social media NLP pipelines. Specifically, we train a generative model via self-supervised learning to predict a keyword to capture the semantics of noisy social media text for deduplication. Meanwhile, time-dimensional Gaussian noise is added to improve training complexity and avoid learning trivial features. Extensive experiments suggest that our model can better reduce training samples while improving performance than baselines. The results show our model's potential to broadly advance social media language understanding in effectiveness and efficiency. © 2024 Association for Computational Linguistics.

关键词： Self-supervised learning

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BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings

BeLLM: Backward Dependency Enhanced Large Language Model for...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Li, Xianming Li, Jing Department of Computing Hong Kong Polytechnic University Hong Kong Research Centre on Data Science and Artificial Intelligence Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9798891761148

Sentence embeddings are crucial in measuring semantic similarity. Most recent studies employed large language models (LLMs) to learn sentence embeddings. Existing LLMs mainly adopted autoregressive architecture without explicit backward dependency modeling. Therefore, we examined the effects of backward dependencies in LLMs for semantic similarity measurements. Concretely, we propose a novel model: backward dependency enhanced large language model (BeLLM). It learns sentence embeddings via transforming specific attention layers from uni- to bi-directional. We extensively experiment across various semantic textual similarity (STS) tasks and downstream applications. BeLLM achieves state-of-the-art performance in varying scenarios. It shows that auto-regressive LLMs benefit from backward dependencies for sentence embeddings. © 2024 Association for Computational Linguistics.

关键词： Embeddings

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TikTok Video Cluster Analysis Based on Trending Topic 9th

TikTok Video Cluster Analysis Based on Trending Topic

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Bakar, Juhaida Abu Sharimi, Nur Azmielia Muhammad Shahril, Mohd Azrul Edzwan Azmi, Nur Syafiqah Harun, Nor Hazlyna Awang, Hapini Bakar, Nur Syafiqah Abu Data Management and Software Solution Research Lab School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Data Science Research Lab School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Faculty of Medicine and Health Sciences Universiti Sains Islam Malaysia Negeri Sembilan Nilai71800 Malaysia

ISBN: (纸本)9789819995882

TikTok is a popular social networking application that offers trend research and is a valuable source for users. However, this is often misconstrued for content, which may not be suitable for children due to misappropriate content. This study aims to improve user perception of TikTok by using topic modelling and clustering techniques to identify trending topics in TikTok videos. The research uses Latent Dirichlet Allocation (LDA) and K-means clustering techniques to enhance the recognition of local and global topics across text documents. The methodology includes data collection, data pre-processing, clustering, topic modelling, and results. Ten subjects associated with trending TikTok videos are displayed using the LDA algorithm, and the generated topics are used to produce an Inter-topic Distance Map. The method’s effectiveness is evaluated using log-likelihood score and perplexity measurements. It has a log-likelihood score of 5579 and a perplexity score of 287. A good model is one with a higher log-likelihood and lower perplexity. The study ex-tracts popular TikTok topics using both the LDA topic modelling technique and the K-means clustering algorithm. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cluster analysis

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MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators 31

MQM-APE: Toward High-Quality Error Annotation Predictors wit...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Lu, Qingyu Ding, Liang Zhang, Kanjian Zhang, Jinxia Tao, Dacheng Southeast University China The University of Sydney Australia Southeast University Shenzhen Research Institute China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although approaches such as GEMBA-MQM (Kocmi and Federmann, 2023a) have shown state-of-the-art performance on reference-free evaluation, the predicted errors do not align well with those annotated by human, limiting their interpretability as feedback signals. To enhance the quality of error annotations predicted by LLM evaluators, we introduce a universal and training-free framework, MQM-APE, based on the idea of filtering out non-impactful errors by Automatically Post-Editing (APE) the original translation based on each error, leaving only those errors that contribute to quality improvement. Specifically, we prompt the LLM to act as (1) evaluator to provide error annotations, (2) post-editor to determine whether errors impact quality improvement and (3) pairwise quality verifier as the error filter. Experiments show that our approach consistently improves both the reliability and quality of error spans against GEMBA-MQM, across eight LLMs in both high- and low-resource languages. Orthogonal to trained approaches, MQM-APE complements translation-specific evaluators such as Tower, highlighting its broad applicability. Further analysis confirms the effectiveness of each module and offers valuable insights into evaluator design and LLMs selection. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Lightweight Dual Grouped Large-Kernel Convolutions for Salient Object Detection Network 31st

Lightweight Dual Grouped Large-Kernel Convolutions for Sali...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Liu, Jiajie Zhang, Zhibin Engineering Research Center of Ecological Big Data Ministry of Education Beijing China Key Laboratory of Wireless Networks and Mobile Computing Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9789819620609

Most existing Salient Object Detection (SOD) methods focus on achieving better performance, often resulting in models with a large number of parameters. However, there is limited research on lightweight models in this field. To address this gap, our goal is to maintain performance while reducing the number of network parameters. Thanks to the development of large-kernel convolutions in recent years, we have improved the U2Net as the base network by adding our lightweight dual large-kernel fusion module. Our module better utilizes the depth information of U2Net and, due to the large receptive field of large-kernel convolutions, better captures the relationships between image elements. This allows our network to remain lightweight while maintaining performance. We designed a large-kernel (DLK) fusion module and a lightweight dual grouped large-kernel Unet network (DGLKUNET). Our network uses SRUS (an improved Residual U-blocks module, RUS) to construct DGLKUNET that predicts image contours and labels. Compared to the base RUS network (U2Net), our network reduces the number of parameters by up to 70% and the computational cost by 40% while maintaining performance. Evaluation results on five datasets demonstrate the performance of our lightweight network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object recognition

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SpaHet: A Software/Hardware Co-design for Accelerating Heterogeneous-Sparsity based Sparse Matrix Multiplication 24

SpaHet: A Software/Hardware Co-design for Accelerating Heter...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Huang, Haoqin Yao, Pengcheng An, Zhaozeng Sun, Yufei Hu, Ao Xu, Peng Zheng, Long Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9798400706011

Sparse general matrix-matrix multiplication is widely used in data mining applications. Its irregular memory access patterns limit the performance of general-purpose processors, thus motivating many FPGA-based hardware innovations in recent years. Nevertheless, existing accelerators fail to efficiently support heterogeneous input matrix sparsity, which is universal in various real-world applications. With in-depth experimental analysis, we observe that their performance is bottlenecked by their fixed tiling mechanisms, which only alleviate the irregularity of one input matrix. Based on the observation, we propose SpaHet, a software/hardware co-design to accelerate heterogeneous-sparsity based sparse matrix multiplication. SpaHet adopts a dual-adaptive sliding window mechanism to cover the reuse characteristics of both input matrices simultaneously. With a specialized exploration algorithm, the window-based mechanism can automatically find the optimal tiling strategy instead of applying a fixed one based on empirical experience. A sparsity-aware merge tree is also proposed to maximize the output matrix reuse via accumulating intermediate results thoroughly. Our results on a Xilinx Alveo U280 accelerator card show that SpaHet outperforms state-of-the-art CPU-, GPU- and FPGA-based solutions by 7.71×, 1.1×, and 2.74× in performance, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Program processors

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Multitask learning for Arabic Dialects Identification and Machine Translation

Multitask learning for Arabic Dialects Identification and Ma...

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International Conference on Artificial Intelligence and Its Practical Applications in the Age of Digital Transformation, I2COMSAPP 2024

作者： Dhleima, Mohamed Tourad, Mohamedou Cheikh Telmoud, Cheikh Abdelkader Ahmed Abdelmounaim, Abdali Nanne, Mohamedade Farouk Scientific Computing Computer Science and Data Science Research Unit University of Nouakchott Nouakchott Mauritania Research Team Control and Informatics for Intelligent Systems and Green Energy FST cadi Ayyad Marrakech Morocco

ISBN: (纸本)9783031714283

Multitask learning (MTL) is a popular approach in natural language processing (NLP) to improve the performance of related functions. This paper presents a comprehensive approach to address dialect identification and translation challenges in Arabic NLP. The proposed model utilizes state-of-the-art transformer architectures for both tasks, ensuring robust performance in capturing contextual dependencies and linguistic nuances inherent in Arabic dialects. The primary objectives are to accurately identify Arabic dialects and facilitate their translation to Modern Standard Arabic (MSA) using a shared representation learning approach. The model architecture consists of a shared embedding layer followed by a transformer encoder for each task, employing multi-head self-attention mechanisms for hierarchical contextual information extraction. The model’s performance is evaluated using the F1-score for dialect classification, and the BLEU-score for translation performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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FedRKG: A Privacy-Preserving Federated Recommendation Framework via Knowledge Graph Enhancement 18th

FedRKG: A Privacy-Preserving Federated Recommendation Framew...

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18th International Conference on Green, Pervasive, and Cloud computing, GPC 2023

作者： Yao, Dezhong Liu, Tongtong Cao, Qi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9789819998951

Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally. Recently, Graph Neural Networks (GNN) have gained popularity in recommendation tasks due to their ability to capture high-order interactions between users and items. However, privacy concerns prevent the global sharing of the entire user-item graph. To address this limitation, some methods create pseudo-interacted items or users in the graph to compensate for missing information for each client. Unfortunately, these methods introduce random noise and raise privacy concerns. In this paper, we propose FedRKG, a novel federated recommendation system, where a global knowledge graph (KG) is constructed and maintained on the server using publicly available item information, enabling higher-order user-item interactions. On the client side, a relation-aware GNN model leverages diverse KG relationships. To protect local interaction items and obscure gradients, we employ pseudo-labeling and Local Differential Privacy (LDP). Extensive experiments conducted on three real-world datasets demonstrate the competitive performance of our approach compared to centralized algorithms while ensuring privacy preservation. Moreover, FedRKG achieves an average accuracy improvement of 4% compared to existing federated learning baselines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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data Archiving Model on Cloud for Video Surveillance Systems with Integrity Check 9th

Data Archiving Model on Cloud for Video Surveillance Systems...

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9th International Conference on computing and Informatics, ICOCI 2023

作者： Katuk, Norliza Omar, Mohd Hasbullah Pozi, Muhammad Syafiq Mohd Chzhan, Ekaterina School of Computing Universiti Utara Malaysia UUM Kedah Sintok06010 Malaysia Data Management and Software Solution Research Lab School of Computing Universiti Utara Malaysia Sintok Malaysia Siberian Federal University Krasnoyarsk660041 Russia

ISBN: (纸本)9789819995882

Video data has grown significantly as a result of the expanding usage of high-resolution cameras and longer retention periods, necessitating effective archiving solutions, such as video surveillance systems, that make data easy to retrieve and maintain data integrity. These systems are relied upon by businesses and private users to protect their assets and guarantee that video recordings are properly archived for later use. In order to solve the issues of data storage, integrity, and retrieval in surveillance systems, this study proposes a data archiving model with an integrity check for video surveillance systems saved on the cloud. The suggested model has four parts: system architecture, data archiving method, data integrity check, and data schema. These components enable metadata production, effective cloud storage, AI-based human identification, and data security. The model’s critical elements are developed, tested, and evaluated in this study to guarantee its dependability and efficiency when managing video surveillance data. Coding the archiving module, testing the software with a pertinent dataset, and creating the integrity check module are all included in the scope. The integrity check module’s performance was tested by comparing compromised and uncompromised data to see how well the model could distinguish between real data and data that had been tampered with. The findings imply that the suggested methodology successfully addresses the problems associated with archiving video surveillance data, improving security, resource management, and data protection. Additionally, it promotes the creation of reliable, tamper-resistant surveillance systems, creating a more dependable digital video surveillance landscape. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Video recording

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