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12th International Conference on Computational Creativity, ICCC 2021

作者： Wright, George A. Purver, Matthew Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9789895416035

This paper concerns the evaluation of a workspace architecture for generating natural language descriptions, including methods for evaluating both its output and its own self-evaluation. Herein are details of preliminary results from evaluation of an early iteration of the architecture operating in the domain of weather. The domain is not typically seen as creative, but provides a simple testbed for the architecture and evaluation methodology. The program does not yet match humans in terms of fluency of language, factual correctness, and how completely the input is described, but human judges did find the program’s output easier to read than human generated texts. Planned improvements to the program also described in the paper will incorporate self-monitoring and better self-evaluation with the aim of producing descriptions that are more fluently written and more accurate. © ICCC *** rights reserved.

关键词： Architecture

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BloodComm: A Peer-to-Peer Blockchain-based Community for Blood Donation Network

BloodComm: A Peer-to-Peer Blockchain-based Community for Blo...

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International Conference on computer and Information Technology (ICCIT)

作者： Chowdhury Mohammad Abdullah Minhaz Kamal Fairuz Shaiara Abu Raihan Mostofa Kamal Md Azam Hossain Department of Computer Science and Engineering Network and Data Analysis Group (NDAG) Islamic University of Technology Gazipur Bangladesh

Blood transfusion is an integral part of the healthcare system that plays an important role in ensuring the quality of care for patients undergoing a variety of medical procedures and treatments. A large portion of this blood comes from voluntary donors. The existing blood donor management systems are unable to offer a reliable audit trail and traceability. Hence, there is a significant risk that patients may get transfusion of blood from unreliable sources. In this paper, we propose a system built on Ethereum with the goal of creating a decentralized, transparent, traceable, and secure network of blood donors. The platform uses smart contracts to facilitate peer-to-peer interactions. To encourage donors to donate blood more regularly, the system also offers rewards in the form of tokens. Our source code is available in a public Github repository 1 .

关键词： Source coding computer network reliability Smart contracts Medical services Peer-to-peer computing Reliability Information technology

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SPEAR: Design and Implementation of an Advanced Virtual Assistant

SPEAR: Design and Implementation of an Advanced Virtual Assi...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Garima Jain Amita Shukla Nitesh Kumar Bairwa Anamika Chaudhary Ashish Patel Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence and Machine Learning Dronacharya Group of Institutions Greater Noida India Department of Computer Science and Enginnering Netaji Subhash University of Technology Delhi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents the development and evaluation of SPEAR, an advanced voice-activated personal desktop assistant designed to address challenges in existing virtual assistant technology, such as limited language support, low recognition accuracy, and inadequate task automation. Despite recent advancements in Natural Language Processing (NLP) and machine learning, implementation in current systems remains subpar. SPEAR achieves an 88% overall command execution success rate, with a speech recognition accuracy of 92% and an average response time of 1.8 seconds, demonstrating efficiency in real-time interactions. Developed using Python and machine learning libraries, SPEAR supports 15 voice commands across multiple platforms, excelling in note creation (95% success), web searching (94%), and information retrieval (92%). Its natural language understanding score of 0.86 and context retention rate of 75% highlight its conversational capabilities. While the assistant performs well in messaging functionality (85% success) and currently supports three languages, expanding its multilingual capabilities is essential for broader user engagement. Metrics indicate a satisfaction score of 8.2/10 and an average of 12 daily interactions per user, showcasing its practical utility. Additionally, the system's text-to-speech quality (8.5/10) and error recovery rate (82 %) affirm its robustness. These findings contribute valuable insights for future research, focusing on enhancing multilingual support, broadening task categories, and improving context retention, ultimately positioning SPEAR to rival established industry leaders by offering a comprehensive suite of services.

关键词： Accuracy Personal voice assistants Machine learning Information retrieval Natural language processing Real-time systems Robustness Text to speech Time factors Web search

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TREC: transient redundancy elimination-based convolution 22

TREC: transient redundancy elimination-based convolution

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiawei Guan Feng Zhang Jiesong Liu Hsin-Hsuan Sung Ruofan Wu Xiaoyong Du Xipeng Shen Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Computer Science Department North Carolina State University

ISBN: (纸本)9781713871088

The intensive computations in convolutional neural networks (CNNs) pose challenges for resource-constrained devices; eliminating redundant computations from convolution is essential. This paper gives a principled method to detect and avoid transient redundancy, a type of redundancy existing in input data or activation maps and hence changing across inferences. By introducing a new form of convolution (TREC), this new method makes transient redundancy detection and avoidance an inherent part of the CNN architecture, and the determination of the best configurations for redundancy elimination part of CNN backward propagation. We provide a rigorous proof of the robustness and convergence of TREC-equipped CNNs. TREC removes over 96% computations and achieves 3.51× average speedups on microcontrollers with minimal (about 0.7%) accuracy loss.

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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research

arXiv

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arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Design and Implementation of a Vision-Based Vehicle Control System for Operational Commands via Human Keypoints Recognition

Design and Implementation of a Vision-Based Vehicle Control ...

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Intelligent Systems, International Workshop on

作者： Ge Cao Yue Teng Yu Shen Qing Tang Tran Tien Dat Ashraf Uddin Russo Adri Priadana Kanghyun Jo Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan Korea Republic of Hyundai Motor Technology & Engineering Center (China) LTD. Department of Mechanical and Automotive Engineering University of Ulsan Ulsan Korea Republic of Data Science Group INTERX Ulsan Korea Republic of

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

With the advent of new energy vehicles, enhanced energy storage capabilities have provided a robust operating environment for complex intelligent systems, which rely heavily on onboard computers and a multitude of sensors. This paper introduces a vision-based intelligent system in vehicles that utilizes onboard vehicle cameras as input sensors and leverages multiple neural network architectures along with meticulously designed algorithms to facilitate human-vehicle interaction. Within this intelligent system, we sequentially employ a human detection model and a human keypoints recognition model to predict the position of target keypoints, marking their trajectories in real-time video streams for use in critical command recognition algorithms. Furthermore, this study explores the computational resource consumption and the processing power of onboard computers, affirming that the system can be applied in real time to the majority of current onboard computing systems.

关键词： computers Target recognition Computational modeling Neural networks Predictive models Prediction algorithms Real-time systems Sensor systems Intelligent systems Intelligent sensors

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JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

arXiv

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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Towards Benchmarking Erasure Coding Schemes in Object Storage System

SSRN

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SSRN 2023年

作者： Upoma, Rezuana Imtiaz Afrin, Shaima Hossain, Mahanaj Noor, Khandaker Ishrak Noor, Yeasif Bin Sakif, Md. Sadiqul Islam Noor, Jannatun Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Research Group School of Data and Sciences BRAC University Dhaka Bangladesh mPower-Social Enterprise Dhaka Bangladesh

Erasure Coding (EC) is a security measure that eliminates the need for complete data replication by allowing data to be reconstructed from parity pieces. This method offers increased data redundancy, efficiency, lowered storage costs, and enhanced fault tolerance making it a preferred alternative to replication in Object Storage Systems. The fundamental concept behind EC is to encrypt a specific amount of data in a manner that guarantees the transfer of all coded pieces without any loss. As data volumes continue to rapidly increase, the time efficiency of the EC method becomes crucial in ensuring optimal system performance. Various variables, including the algorithm employed, data size, number of storage nodes, hardware resources, and network conditions, can influence the speed of EC operations. Hence, we inspect the Erasure Coding algorithm known as Reed-Solomon Codes (RS). We explore the algorithm's encoding speed, taking into account factors such as data size and the generation of parity blocks. By evaluating and enhancing existing mechanisms, our research aims to address time efficiency and fault tolerance challenges in cloud-based object storage systems. To enhance data retrieval and storage processes, we comprehensively examine time efficiency mechanisms, such as data placement policies and scheduling algorithms. Additionally, we investigate the time taken for an open-source cloud storage system, OpenStack Swift, to store data by analyzing two datasets and determining the duration required to store these files. Furthermore, our study assesses fault tolerance mechanisms, including redundancy schemes, error correction codes, and distributed data placement strategies, to bolster system resilience. We propose different approaches to minimize access latency, improve overall time efficiency, and ensure data availability, even in the presence of failures. © 2023, The Authors. All rights reserved.

关键词： Reed-Solomon codes

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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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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