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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Tian, Dongxin Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798400709449

This study focuses on the application of Deep Q-Networks (DQN) to train AI agents to play bullet hell games. We built a training environment and utilized ray casting to collect input data for the network. Two similar network model architectures were evaluated and compared to maximize the learning efficiency of our AI agent. The trained AI demonstrates commendable performance and the ability to learn and adapt strategies into gameplay. However, while the AI agent displayed potential in mastering gameplay dynamics, there remain several challenges to integrating the agent to complete commercial bullet hell games. These challenges may provide directions for future research. © 2023 ACM.

关键词： Deep learning

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CKN: An Edge AI Distributed Framework 19

CKN: An Edge AI Distributed Framework

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19th IEEE International Conference on e-Science, e-Science 2023

作者： Withana, Sachith Plale, Beth Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798350322231

The edge-cloud-HPC continuum is transformative for AI processing at the edge. With greater availability of both edge and cloud resources, AI inference, training, and optimization can be distributed across the continuum. We target edge-cloud in particular where the workload at the Edge server can exhibit discrete changes, for instance, when motion is detected. We optimize for Quality of Experience (QoE) and utilize historical data from the Edge, graphs, and Deep Learning to infer the next action to take. Using a large synthetic workload and publicly profiled inference models, our results show that predictive guidance outperforms random choice or best guess in optimal QoE of the edge-cloud continuum. © 2023 IEEE.

关键词： Quality of service

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The Potential of Neural Speech Synthesis-Based Data Augmentation for Personalized Speech Enhancement 48

The Potential of Neural Speech Synthesis-Based Data Augmenta...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kuznetsova, Anastasia Sivaraman, Aswin Kim, Minje Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN United States

ISBN: (纸本)9781728163277

With the advances in deep learning, speech enhancement systems benefited from large neural network architectures and achieved state-of-the-art quality. However, speaker-agnostic methods are not always desirable, both in terms of quality and their complexity, when they are to be used in a resource-constrained environment. One promising way is personalized speech enhancement (PSE), which is a smaller and easier speech enhancement problem for small models to solve, because it focuses on a particular test-time user. To achieve the personalization goal, while dealing with the typical lack of personal data, we investigate the effect of data augmentation based on neural speech synthesis (NSS). In the proposed method, we show that the quality of the NSS system's synthetic data matters, and if they are good enough the augmented dataset can be used to improve the PSE system that outperforms the speaker-agnostic baseline. The proposed PSE systems show significant complexity reduction while preserving the enhancement quality. © 2023 IEEE.

关键词： Speech synthesis

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Bridging Fashion and Technology: Synthetic Human Models for an Enhanced E-Commerce Experience 23rd

Bridging Fashion and Technology: Synthetic Human Models for...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Duarte, Ana Rita Conceição, Luís Department of Informatics Engineering School of Engineering Polytechnic of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development School of Engineering Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031734960

The fashion industry is undergoing a significant transformation, driven by advancements in digitalization and artificial intelligence (AI). This paper explores the integration of Stable Diffusion Models (SDMs) and AI to create high-quality images of synthetic models wearing a target cloth in multiple poses, enhancing the users’ experience on e-commerce websites and addressing the fast-paced demands of fashion trends. The proposed pipeline includes multiple steps: face generation, pose estimation, cloth warping, human synthesis, and refinement networks, each designed to enhance the realism and quality of the final images for e-commerce platforms. Experimental results on a small VITON-HD dataset demonstrate this approach’s overall success. BRISQUE, NIQE, and entropy were used for objective evaluation, scoring 14.1449, 4.1354, and 7.1238 respectively, indicating a high level of detail, naturalness, and complexity on the generated images. Future work should focus on enhancing the representation of clothing with complex patterns and lower and full garments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clothes

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CryptoQFL: Quantum Federated Learning on Encrypted Data 4

CryptoQFL: Quantum Federated Learning on Encrypted Data

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4th IEEE International Conference on Quantum computing and engineering, QCE 2023

作者： Chu, Cheng Jiang, Lei Chen, Fan Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798350343236

Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum homomorphic encryption before sending them to the central quantum server;(2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation;and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches. © 2023 IEEE.

关键词： Data privacy

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FazBoard: An AI-Educational Hybrid Teaching and Learning System 8th

FazBoard: An AI-Educational Hybrid Teaching and Learning Sys...

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8th Future Technologies Conference, FTC 2023

作者： BenMessaoud, Fawzi Bolchini, Davide Ash, Eliza Tseng, Ching-Min IU Luddy School of Informatics Computing and Engineering IUPUI Bloomington United States

ISBN: (纸本)9783031474538

FazBoard, an avant-garde educational platform, seamlessly integrates artificial intelligence with contemporary educational methodologies to foster a dynamic, adaptive, and collaborative learning ecosystem. The platform’s linchpin comprises two components: an agile digital canvas that simulates interactive teaching and learning spaces, and an AI Assistant, incarnated as a digital humanoid, available round-the-clock for responsive academic support. The digital canvas is designed to cultivate an immersive and versatile environment, emulating the interactions of traditional classrooms without the constraints of time or location. The AI Assistant excels in providing instantaneous responses to queries, aggregating valuable learning analytics, and streamlining administrative tasks - all of which contribute to curriculum refinement and enhanced pedagogical efficacy. Furthermore, FazBoard is aimed at bolstering student engagement by creating an inclusive learning milieu, through adaptive learning strategies that cater to the diverse educational needs of its users. This paper delves into the architecture, functionalities, and far-reaching applications of FazBoard, heralding it as a quintessential model of integrating AI into education, thereby shaping the trajectory of 21st-century educational practices. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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From Screens to Projector, Wall, and TVs: Conceptualizing Livestreams as Design Material for Direct and Indirect Viewership Experiences

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Proceedings of the ACM on Human-Computer Interaction 2023年第CSCW1期7卷 1-22页

作者： Sher, Stephen Tsung-Han Su, Norman Makoto Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States University of California Santa CruzCA United States

Livestreams are now ubiquitous-moving beyond web and mobile-based platforms into products such as fitness machines and smart home assistants. As such, viewership practices have also become varied and diverse: livestreams are used for interactive entertainment, social companionship, and multi-perspective spectatorship for music festivals and sporting events. At the premier in-person gaming livestream event, Games Done Quick (GDQ), attendees engage in a variety of different viewership practices across different event spaces where the livestream is projected on large stage screens, onto walls as peripheral displays, and routed to televisions in hotel venue rooms. This study takes GDQ as an information-rich fieldsite to examine how livestream viewership shifts, changes, and interleaves across physical spaces to provide an expanded livestream experience. From 11 semi-structured interviews with GDQ attendees and participant observations at three GDQ events, we found diverse practices of both direct and indirect viewership: the livestream is viewed in collocation with an audience and stage, but is also used to establish viewership continuity across disjoint spaces, tangentially to shop for interesting moments, as a timekeeping device, and even as a means of auditory obfuscation. To unpack these forms of viewership, we conceptualize livestreams as design material and identify its two key qualities of focality and metacontent. We illustrate how examining livestreams' focality and metacontent qualities allow us to understand the relationship between different livestream configurations and different viewership behaviors, as well as chart out new under-explored design directions for livestreaming that blend physical and digital spaces. © 2023 ACM.

关键词： Automation

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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Computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Automation of interaction—interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

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Personal and Ubiquitous computing 2023年第6期27卷 2281-2290页

作者： Wiberg, Mikael Stolterman Bergqvist, Erik Interaction Design Division Chalmers University of Technology Gothenburg Sweden Department of Informatics Umeå University Umeå Sweden Computing and Engineering Luddy School of Informatics Indiana University Bloomington United States

Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials. © 2023, The Author(s).

关键词： Automation

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COMBINING HAND POSE ESTIMATION WITH AUDIO FOR NOISE-ROBUST PIANO SCORE FOLLOWING 21

COMBINING HAND POSE ESTIMATION WITH AUDIO FOR NOISE-ROBUST P...

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21st Sound and Music computing Conference, SMC 2024

作者： Pet, Kaitlin Maezawa, Akira Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Yamaha Corpration Hamamatsu Japan

ISBN: (纸本)9789893520758

Tracking the musical score position from a user's piano performance is difficult in noisy environments and other cases when the audio signal is unreliable. We present a multi-modal score following method that seamlessly fuses audio and hand tracking from top-down video of a pianist's hands on the keyboard, using a probabilistic model and a feature-weighing mechanism. Experiments demonstrate our method's robustness under noisy environments and when pieces contain mistakes, compared to a score follower based only on audio input. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

关键词： Palmprint recognition

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