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A GTFS data acquisition and processing framework and its application to train delay prediction

International Journal of Transportation science and Technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory data fusion

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Evaluating Question generation models using QA systems and Semantic Textual Similarity

Evaluating Question generation models using QA systems and S...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Safwan Shaheer Ishmam Hossain Sudipta Nandi Sarna Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Question generation based on conversational context is a difficult problem to solve. A widely used technique for generating quality questions using fine-tuned models relies on a suitable answer and the context, usually the passage. But when it comes to conversational settings, the questions generated are not of the highest quality as they lack the contextual element in the question, especially due to the lack of co-reference resolution of the entity. Furthermore, in most of the evaluation techniques for generating questions, there seems to be a lack of utilizing powerful question-answering systems to judge the answerability of the questions generated. The most prevalent metric used for judging machine-generated text against the human gold standard, BLUE, unfortunately doesn't factor in whether a question answering system would be able to answer the question, but instead focuses mostly on the number of substrings that match against each other. Various question generation models following a generalized encoder-decoder architecture were evaluated using semantic textual similarity for both the generated questions and the generated answers. Although higher parameters in a model usually lend to better performance, our experiment displayed that such is not always the case, at least when there is a massive amount of context missing.

关键词： Measurement Computational modeling Conferences Semantics computer architecture Question answering (information retrieval) Context modeling

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EDPP: Intelligent and Robust data Sharing Framework using Enhanced data Preserving Paradigm

EDPP: Intelligent and Robust Data Sharing Framework using En...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Elantamilan, D. Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Vallal P. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai112 India R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Engineering Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9781665495295

Growing amounts of information sources are required as the quantity of available information from various sources, both self governing and heterogeneous data maintenance requirement increases. A major component of the increased interest in big data is the heterogeneity of the data itself. In this work, data communications is researched, in which it is the method of constructing a specific case of direct instruction from an illustration of reference conceptual framework, so that when the eventually results entity conforms with target schema configuration. The logic of schema mappings are high-level expressions used to specify relationships across databases and are often used in this process. The experimental results of this paper provide proper proof to the efficiency of research and the results are acquired based on the real-Time working emulations. © 2022 IEEE.

关键词： Electronic data interchange

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Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions 23

Neural approximation of Wasserstein distance via a universal...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Samantha Chen Yusu Wang Department of Computer Science Engineering University of California - San Diego Halıcıoglu Data Science Institute University of California - San Diego

Learning distance functions between complex objects, such as the Wasserstein distance to compare point sets, is a common goal in machine learning applications. However, functions on such complex objects (e.g., point sets and graphs) are often required to be invariant to a wide variety of group actions e.g. permutation or rigid transformation. Therefore, continuous and symmetric product functions (such as distance functions) on such complex objects must also be invariant to the product of such group actions. We call these functions symmetric and factor-wise group invariant functions (or SFGI functions in short). In this paper, we first present a general neural network architecture for approximating SFGI functions. The main contribution of this paper combines this general neural network with a sketching idea to develop a specific and efficient neural network which can approximate the p-th Wasserstein distance between point sets. Very importantly, the required model complexity is independent of the sizes of input point sets. On the theoretical front, to the best of our knowledge, this is the first result showing that there exists a neural network with the capacity to approximate Wasserstein distance with bounded model complexity. Our work provides an interesting integration of sketching ideas for geometric problems with universal approximation of symmetric functions. On the empirical front, we present a range of results showing that our newly proposed neural network architecture performs comparatively or better than other models (including a SOTA Siamese Autoencoder based approach). In particular, our neural network generalizes significantly better and trains much faster than the SOTA Siamese AE. Finally, this line of investigation could be useful in exploring effective neural network design for solving a broad range of geometric optimization problems (e.g., k-means in a metric space).

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Large Language Model (LLM)-enabled Graphs in Dynamic Networking

arXiv

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arXiv 2024年

作者： Sun, Geng Wang, Yixian Niyato, Dusit Wang, Jiacheng Wang, Xinying Poor, H. Vincent Letaief, Khaled B. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs as different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Novel Text Clustering Approach Based on Bacterial Colony Optimization

A Novel Text Clustering Approach Based on Bacterial Colony O...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： S Dhanalakshmi S Sathiyabama D. Ayyamuthukumar Department of Computer Science Thiruvalluvar Government Arts College Rasipuram Tamilnadu India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

A huge number of text documents are divided into a limited number of groups using the unsupervised learning technique known as text clustering. While the clusters contain different text documents, each cluster contains similar documents. Swarm intelligence (SI) optimization methods have been successfully used to resolve a variety of optimization issues, including difficulties with text document grouping. However, the traditional SI algorithms have many drawbacks including the local optima, low convergence rate, and low accuracy. Hence, the present research work proposes a novel text clustering approach based on bacterial colony optimization (BCO) for separating text documents based on similarity. Three separate text document datasets are used for the experiments, and performance is assessed using three different performance measures. The novel BCO approach produces high accuracy and a quick convergence rate, according to the analysis of the results. The suggested BCO text clustering strategy compares several text clustering methods to analyze strength and robustness.

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Evaluation and Design of Robotic Hand Picking Operations using Intelligent Motor Unit

Evaluation and Design of Robotic Hand Picking Operations usi...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Gnanavel, R. Vinod, D. Nalini, M.K. Dhinakaran, K. Elantamilan, D. R.M.K. College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept of CSE Chennai India BMS College of Engineering Department of ISE Bangalore India Dhanalakshmi College of Eneineering Department of Artificial Intelligence and Data Science Chennai India VallalP. T. Lee Chengalvaraya Naicker Arts and Science College Department of Computer Science Chennai India

ISBN: (纸本)9781665495295

Picking actions performed by industrial robots may be evaluated using a new architecture in this article. It has always been a goal of humankind to imbue their products with lifelike characteristics in an effort to create a substitute for themselves to carry out their commands and to work in difficult environments like the present. The most common image of a robot is that of a machine that resembles a human person in both appearance and operation. Automation is being replaced by robotics in an effort to boost output and ensure consistency in product quality. Despite the fact that every effort is being made to provide industrial robots increasingly anthropomorphic and humanlike traits and superhuman skills, today's industrial robots may not resemble humans in the slightest. A robotic manipulator, more generally known as a robotic arm, is a typical form of industrial robot. Moving joints connect stiff chains in either an open or closed kinematic structure. It is possible to think of a link's structure as a human torso with the waist, upper arm and forearm as joints [1]. The wrist joint at the end of the arm connects end effectors such as a tool and its mounting fixture, or a gripper or any other device for functioning. This workstation features a 6-axis Force/Torque pick and place robot for loading and packaging products. A six-axis Force/Torque design was used to properly evaluate and account for all of the possible loading process concerns when building the pick and place robot. © 2022 IEEE.

关键词： Robotic arms

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Accelerated Policy Gradient: On the Convergence Rates of the Nesterov Momentum for Reinforcement Learning

arXiv

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arXiv 2023年

作者： Chen, Yen-Ju Huang, Nai-Chieh Lee, Ching-Pei Hsieh, Ping-Chun Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Advanced Data Science Institute of Statistical Mathematics Tokyo Japan

Various acceleration approaches for Policy Gradient (PG) have been analyzed within the realm of Reinforcement Learning (RL). However, the theoretical understanding of the widely used momentum-based acceleration method on PG remains largely open. In response to this gap, we adapt the celebrated Nesterov’s accelerated gradient (NAG) method to policy optimization in RL, termed Accelerated Policy Gradient (APG). To demonstrate the potential of APG in achieving fast convergence, we formally prove that with the true gradient and under the softmax policy parametrization, APG converges to an optimal policy at rates: (i) Õ(1/t2) with nearly constant step sizes;(ii) O(e−ct) with time-varying step sizes. To the best of our knowledge, this is the first characterization of the convergence rates of NAG in the context of RL. Notably, our analysis relies on one interesting finding: Regardless of the parameter initialization, APG ends up entering a locally nearly-concave regime, where APG can significantly benefit from the momentum, within finite iterations. Through numerical validation and experiments on the Atari 2600 benchmarks, we confirm that APG exhibits a Õ(1/t2) rate with nearly constant step sizes and a linear convergence rate with time-varying step sizes, significantly improving convergence over the standard PG. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Parallel Longest Common SubSequence Analysis In Chapel

Parallel Longest Common SubSequence Analysis In Chapel

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Soroush Vahidi Baruch Schieber Zhihui Du David A. Bader Department of Computer Science New Jersey Institute of Technology Newark NJ USA Department of Data Science New Jersey Institute of Technology Newark NJ USA

One of the most critical problems in the field of string algorithms is the longest common subsequence problem (LCS). The problem is NP-hard for an arbitrary number of strings but can be solved in polynomial time for a fixed number of strings. In this paper, we select a typical parallel LCS algorithm and integrate it into our large-scale string analysis algorithm library to support different types of large string analysis. Specifically, we take advantage of the high-level parallel language, Chapel, to integrate Lu and Liu's parallel LCS algorithm into Arkouda, an open-source framework. Through Arkouda, data scientists can easily handle large string analytics on the back-end high-performance computing resources from the front-end Python interface. The Chapel-enabled parallel LCS algorithm can identify the longest common subsequences of two strings, and experimental results are given to show how the number of parallel resources and the length of input strings can affect the algorithm's performance.

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Siamese-Transformer Network for Offline Handwritten Signature Verification using Few-shot

Siamese-Transformer Network for Offline Handwritten Signatur...

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International Conference on computer and Information Technology (ICCIT)

作者： Prattoy Majumder AFM Mohimenul Joaa Ehsanur Rahman Rhythm Md Humaion Kabir Mehedi Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Handwritten signature verification is a crucial task with applications spanning authentication, financial transactions, and legal documents. In scenarios where only a single reference signature is available, the challenge of accurate verification becomes pronounced due to variations in writing styles, distortions, and limited labeled data. In this paper, we propose a novel Siamese-Transformer network tailored for handwritten signature verification using few-shot learning. By synergizing Siamese neural networks and Transformer architectures, our model excels in capturing contextual relationships and discerning genuine from forged signatures. A triplet loss function facilitates discriminative feature learning. Convolution layers extract local features from an image, while the transformer component utilizes these local features to capture global dependencies within signatures. Experimental results on benchmark datasets showcase the model’s superior performance in few-shot verification scenarios, marking it as a promising advancement in signature verification and few-shot learning techniques.

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