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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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Wearable-Friendly Apnea Severity Classification

Wearable-Friendly Apnea Severity Classification

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Huyen Nhung Hoang Zilu Liang Ubiquitous and Personal Computing Lab Kyoto University of Advanced Science (KUAS) Kyoto Japan

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The prevelence of obstructive sleep apnea (OSA) has significantly increased around the world. The word “apnea” is equivalent to “no breath,” which is the main symptom of this condition. The challenge of detecting sleep disorders from consumer wearable sensors is attracting more and more researchers in the field. Some of them adopted deep learning approaches, while others combined feature engineering with conventional machine learning techniques. A study used at-home oximetry data to estimate the Apnea/Hypopnea Index (AHI) and achieved high intra-class correlation coefficients within 0.889-0.924 [1]. They then classified apnea severity using three typical AHI thresholds: 5 events/hour, 15 events/hour, and 30 events/hour, respectively. Another study using a CNN-based model to classify OSA severity and achieved a macro-accuracy of 80.51 %, a mean sensitivity of 79.95%, a mean specificity of 93.05% [2]. However, compared to clinical equipment, data obtained from wearable devices are low in frequency and resolution, making it challenging to capture all vital information for diagnosing OSA. The development and evaluation of a single model with the ability to reach high diagnostic performance using consumer trackers are still pending. In this study, we aimed to classify the apnea severity from consumer-grade low-granularity oximetry data.

关键词： Performance evaluation Deep learning Correlation coefficient Biomedical equipment Sensitivity Sleep apnea Indexes Wearable devices Wearable sensors

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Deformable Graph Transformer

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5385-5396页

作者： Park, Jinyoung Yun, Seongjun Park, Hyeonjin Kang, Jaewoo Jeong, Jisu Kim, Kyung-Min Ha, Jung-Woo Kim, Hyunwoo J. Korea University Seoul02841 Korea Republic of Amazon SeattleWA98109 United States NAVER NAVER Corporation Bundang13561 Korea Republic of NAVER AI Lab NAVER CLOUD NAVER Corporation Bundang13561 Korea Republic of Korea Advanced Institute of Science and Technology School of Computing Daejeon34141 Korea Republic of

Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the success is limited to small-scale graphs due to the drawbacks of full dot-product attention on graphs such as the quadratic complexity with respect to the number of nodes and message aggregation from enormous irrelevant nodes. To address these issues, we propose Deformable Graph Transformer (DGT) that performs sparse attention via dynamically selected relevant nodes for efficiently handling large-scale graphs with a linear complexity in the number of nodes. Specifically, our framework first constructs multiple node sequences with various criteria to consider both structural and semantic proximity. Then, combining with our learnable Katz Positional Encodings, the sparse attention is applied to the node sequences for learning node representations with a significantly reduced computational cost. Extensive experiments demonstrate that our DGT achieves superior performance on 7 graph benchmark datasets with 2.5 ∼ 449 times less computational cost compared to transformer-based graph models with full attention. © 1979-2012 IEEE.

关键词： Undirected graphs

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Computational Complexity Analysis of Ant Colony Clustering Algorithms: Application to Student's Grouping Problem

Computational Complexity Analysis of Ant Colony Clustering A...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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SpikingMiniLM: energy-efficient spiking transformer for natural language understanding

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Science China(Information Sciences) 2024年第10期67卷 115-128页

作者： Jiayu ZHANG Jiangrong SHEN Zeke WANG Qinghai GUO Rui YAN Gang PAN Huajin TANG College of Computer Science and Technology Zhejiang University The State Key Lab of Brain-Machine Intelligence Zhejiang University Collaborative Innovation Center of Artificial Intelligence Zhejiang University Advanced Computing and Storage Laboratory Huawei Technologies Co. Ltd. College of Computer Science and Technology Zhejiang University of Technology MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University

In the era of large-scale pretrained models, artificial neural networks（ANNs） have excelled in natural language understanding（NLU） tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural networks（SNNs） in NLU——a promising avenue with demonstrated advantages, including reduced power consumption and improved efficiency due to their event-driven characteristics. We propose the SpikingMiniLM,a novel spiking Transformer model tailored for natural language understanding. We first introduce a multi-step encoding method to convert text embeddings into spike trains. Subsequently, we redesign the attention mechanism and residual connections to make our model operate on the pure spike-based paradigm without any normalization technique. To facilitate stable and fast convergence, we propose a general parameter initialization method grounded in the stable firing rate principle. Furthermore, we apply an ANN-to-SNN knowledge distillation to overcome the challenges of pretraining SNNs. Our approach achieves a macro-average score of 75.5 on the dev sets of the GLUE benchmark, retaining 98% of the performance exhibited by the teacher model MiniLMv2. Our smaller model also achieves similar performance to BERTMINIwith fewer parameters and much lower energy consumption, underscoring its competitiveness and resource efficiency in NLU tasks.

关键词： spiking neural networks natural language understanding spiking Transformer spike-based attention multi-step encoding ANN-to-SNN distillation

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Optimized QAOA ansatz circuit design for two-body Hamiltonian problems

Optimized QAOA ansatz circuit design for two-body Hamiltonia...

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International Conference on VLSI Design

作者： Ritajit Majumdar Dhiraj Madan Debasmita Bhoumik Dhinakaran Vinayagamurthy Shesha Raghunathan Susmita Sur-Kolay IBM Quantum IBM India Research Lab Advanced Computing & Microelectronics Unit Indian Statistical Institute

Quantum Approximate Optimization Algorithm (QAOA) is a prospective candidate for providing quantum advantage in finding approximate solutions to optimization problems using near-term quantum devices. In this paper, the goal is to reduce the number of CNOT gates and the depth of quantum ansatz circuits for QAOA. First, we present a generalized QAOA formulation for any Hamiltonian that involves upto two-body interactions, as a graph. The circuit realization of the depth-p QAOA requires $2mp$ CNOT gates where the graph for the Hamiltonian has n vertices and m edges. Presently, a CNOT gate is one of the primary sources of error. We propose a graph-theoretic algorithm which can eliminate $n-1$ CNOTs from the QAOA ansatz while retaining functional equivalence with the original circuit. This improves upon previously proposed Depth First Search (DFS) method by restricting the increase in depth of the circuit, which was a drawback of the method, by $\simeq 84.8\%$ for Erdős-Renyi graphs. Finally, if the underlying connectivity (hardware coupling map) and the initial qubit placement for a hardware are known, then we present how to extend the greedy heuristic method in order to obtain a functionally equivalent circuit with $\simeq 5\%$; fewer SWAP gates for Erdős-Renyi graphs.

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POPA: Expressing High and Portable Performance across Spatial and Vector Architectures for Tensor Computations 24

POPA: Expressing High and Portable Performance across Spatia...

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32nd ACM International Symposium on Field-Programmable Gate Arrays, FPGA 2024

作者： Hao, Xiaochen Rong, Hongbo Zhang, Mingzhe Sun, Ce Jiang, Hong Liang, Yun Peking University China Parallel Computing Lab Intel United States Tsinghua University China University of Science and Technology of China China Intel United States Peking University & Beijing Advanced Innovation Center for Integrated Circuits China

ISBN: (纸本)9798400704185

This paper aims at high and portable performance for tensor computations across spatial (e.g., FPGAs) and vector architectures (e.g., GPUs). The state-of-the-art usually address performance portability across vector architectures (CPUs and GPUs). However, they either miss FPGAs or do not achieve high performance. Without a common architectural abstraction, they program and optimize spatial and vector devices separately, causing low portability. We propose a unified programming framework, POPA, which achieves portability via architectural abstraction and performance via specialization. A parallel dataflow machine is proposed as a unified, abstract hardware target that hides differences of concrete architectures. The machine consists of software-defined systolic arrays and a tensor-specific cache hierarchy, which captures pipeline parallelism and customizable memories on FPGAs, as well as multithreading parallelism on GPUs. The machine is specified in a unified programming model as two dataflow graphs for scheduling compute and data movement, respectively. A compiler then specializes the abstract machine to exploit the properties of FPGAs and GPUs, bridging the gap between the abstract machine and a concrete architecture. We evaluate POPA on several Intel FPGAs and GPUs with high-profile tensor kernels, and this is the first system that achieves >=80% performance of expert-written code or machine peak across architectures, to the best of our knowledge. © 2024 ACM.

关键词： Graphics processing unit

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LSTM-based Glucose Prediction for Prediabetes

LSTM-based Glucose Prediction for Prediabetes

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Cleber Franca Carvalho Thilini Savindya Karunarathna Zilu Liang Faculty of Engineering Kyoto University of Advanced Science Kyoto Japan Ubiquitous and Personal Computing Lab Kyoto University of Advanced Science Kyoto Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Existing glucose prediction models were developed for diabetes populations. Their generalizability to other populations such as prediabetic population is unknown. To fill in the knowledge gap, we developed a glucose prediction model for prediabetic population. We evaluated the internal and external validity of the model. The results showed that the constructed model had good results for predicting glucose levels for prediabetic individuals, with mean absolute error (MAE), root mean square error (RMSE) and normalized RMSE (NRMSE) of 2.66, 4.00 and 0.21, respectively. When applied on other populations, the model achieved comparable performance on type 2 diabetes and better performance on type 1 diabetes in terms of NRMSE.

关键词： Predictive models Glucose Diabetes Root mean square Biomedical monitoring Monitoring Consumer electronics

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Pre-trained Language Model for Chinese Frame Semantic Parsing based on Electric Power Dataset 4

Pre-trained Language Model for Chinese Frame Semantic Parsin...

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4th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Wang, Xiaohui Liang, Yundan Gui, Chenwei Wang, Boya Wang, Gaozhou State Grid Laboratory of Grid Advanced Computing and Applications Beijing China State Grid Corporation of China Beijing China University of International Relations Lab of Intelligent Social Computing Beijing China State Grid Shandong Electric Power Company Information and Telecommunications Company Shandong China

ISBN: (纸本)9798350373233

In the operational landscape of the electric power industry, a wealth of events, operations, and fault reports generates substantial textual data. The application of frame semantic parsing to the power grid system presents challenges, especially given the distinctive characteristics of Chinese text, including entity ambiguity, word segmentation issues, and a misalignment with knowledge in the power systems field. In response to these challenges, we introduce an innovative pipeline specifically designed for Chinese semantic analysis based on an electric power dataset, showcasing significant enhancements over the baseline. This research strives to drive technological advancements, fostering a more sophisticated understanding of textual data within the intricate domain of the Chinese electric power industry, with a particular focus on natural language processing. © 2023 IEEE.

关键词： Pipelines

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A Tightly-Coupled GNSS RTK/INS Positioning Algorithm Based on Adaptive Lag Smoother

A Tightly-Coupled GNSS RTK/INS Positioning Algorithm Based o...

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IEEE Symposium on Intelligent Vehicle

作者： Cheng Ye Wei Li Yu Hu Zhejiang Lab The Research Center for Advanced Computing Systems Hangzhou China The Research Center for Intelligent Computing Systems Institute of Computing Technology Beijing China

How to take into account both the calculation cost and positioning accuracy when driving over a long distance in the scene of changing satellite visibility, such as urban areas and mountain roads, is a research topic worth of attention for intelligent vehicles. In this paper, a tightly-coupled RTK/INS positioning algorithm base on adaptive lag smoother is proposed. By combining the uncertainty of the state to be estimated at the current time and the quantitative score of satellite visibility, the lag-length in smoother can be adjusted adaptively, so as to ensure positioning accuracy while reducing the calculation cost of marginal benefit consumption as much as possible. The proposed algorithm is demonstrated in both the simulator and real-world urban roads. From the experimental results, it is found that the estimation accuracy achieved by the adaptive lag smoother is similar to that of smoothers with long lag length, but the time consumption is reduced by about 30%. Under the same condition that the positioning can be completed in real time, the accuracy of the algorithm in this paper is 27% higher than that of the tightly-coupled RTK/INS system based on extended Kalman filter.

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