ArXiv最新文献_第5页

ArXiv Pub Date : 2024-03-10 DOI: 10.1609/aaai.v38i15.29675

Pedro Zuidberg Dos Martires

引用次数: 0

Enhancing Expressiveness in Dance Generation via Integrating Frequency and Music Style Information 通过整合频率和音乐风格信息增强舞蹈生成的表现力

ArXiv Pub Date : 2024-03-09 DOI: 10.1109/icassp48485.2024.10448469

Qiaochu Huang, Xu He, Boshi Tang, Hao-Wen Zhuang, Liyang Chen, Shuochen Gao, Zhiyong Wu, Haozhi Huang, Helen M. Meng

引用次数: 1

UniSparse: An Intermediate Language for General Sparse Format Customization UniSparse：通用稀疏格式定制的中间语言

ArXiv Pub Date : 2024-03-09 DOI: 10.1145/3649816

Jie Liu, Zhongyuan Zhao, Zijian Ding, Benjamin Brock, Hongbo Rong, Zhiru Zhang

{"title":"UniSparse: An Intermediate Language for General Sparse Format Customization","authors":"Jie Liu, Zhongyuan Zhao, Zijian Ding, Benjamin Brock, Hongbo Rong, Zhiru Zhang","doi":"10.1145/3649816","DOIUrl":"https://doi.org/10.1145/3649816","url":null,"abstract":"The ongoing trend of hardware specialization has led to a growing use of custom data formats when processing sparse workloads, which are typically memory-bound. These formats facilitate optimized software/hardware implementations by utilizing sparsity pattern- or target-aware data structures and layouts to enhance memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Additionally, because these frameworks represent formats using a limited set of per-dimension attributes, they lack the flexibility to accommodate numerous new variations of custom sparse data structures and layouts. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. Unlike the existing attribute-based frameworks, UniSparse decouples the logical representation of the sparse tensor (i.e., the data structure) from its low-level memory layout, enabling the customization of both. As a result, a rich set of format customizations can be succinctly expressed in a small set of well-defined query, mutation, and layout primitives. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats, and automatic code generation of format conversion and compute operations for heterogeneous architectures. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with specialized formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, an AMD Xilinx FPGA, and a simulated processing-in-memory (PIM) device.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Content Moderation Justice and Fairness on Social Media: Comparisons Across Different Contexts and Platforms 社交媒体上的内容审核公正与公平：不同语境和平台的比较

ArXiv Pub Date : 2024-03-09 DOI: 10.1145/3613905.3650882

Jie Cai, Aashka Patel, Azadeh Naderi, D. Y. Wohn

引用次数: 0

sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks sVAD：利用尖峰神经网络进行鲁棒、低功耗和轻量级语音活动检测

ArXiv Pub Date : 2024-03-09 DOI: 10.1109/icassp48485.2024.10446945

Qu Yang, Qianhui Liu, Nan Li, Meng Ge, Zeyang Song, Haizhou Li

引用次数: 0

A Preliminary Exploration of YouTubers' Use of Generative-AI in Content Creation 对优酷用户在内容创作中使用生成式人工智能的初步探索

ArXiv Pub Date : 2024-03-09 DOI: 10.1145/3613905.3651057

Yao Lyu, He Zhang, Shuo Niu, Jie Cai

引用次数: 1

Assessing User Apprehensions About Mixed Reality Artifacts and Applications: The Mixed Reality Concerns (MRC) Questionnaire 评估用户对混合现实人工制品和应用程序的疑虑：混合现实关注点（MRC）调查问卷

ArXiv Pub Date : 2024-03-09 DOI: 10.1145/3613904.3642631

Christopher Katins, Pawel W. Wo'zniak, Aodi Chen, Ihsan Tumay, Luu Viet Trinh Le, John Uschold, Thomas Kosch

引用次数: 0

Deciphering Crypto Twitter 解密加密推特

ArXiv Pub Date : 2024-03-09 DOI: 10.1145/3614419.3644026

In-Soon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma, Thilanka Munasinghe, Aparna Gupta, O. Seneviratne

{"title":"Deciphering Crypto Twitter","authors":"In-Soon Kang, Maruf Ahmed Mridul, Abraham Sanders, Yao Ma, Thilanka Munasinghe, Aparna Gupta, O. Seneviratne","doi":"10.1145/3614419.3644026","DOIUrl":"https://doi.org/10.1145/3614419.3644026","url":null,"abstract":"Cryptocurrency is a fast-moving space, with a continuous influx of new projects every year. However, an increasing number of incidents in the space, such as hacks and security breaches, threaten the growth of the community and the development of technology. This dynamic and often tumultuous landscape is vividly mirrored and shaped by discussions within Crypto Twitter, a key digital arena where investors, enthusiasts, and skeptics converge, revealing real-time sentiments and trends through social media interactions. We present our analysis on a Twitter dataset collected during a formative period of the cryptocurrency landscape. We collected 40 million tweets using cryptocurrency-related keywords and performed a nuanced analysis that involved grouping the tweets by semantic similarity and constructing a tweet and user network. We used sentence-level embeddings and autoencoders to create K-means clusters of tweets and identified six groups of tweets and their topics to examine different cryptocurrency-related interests and the change in sentiment over time. Moreover, we discovered sentiment indicators that point to real-life incidents in the crypto world, such as the FTX incident of November 2022. We also constructed and analyzed different networks of tweets and users in our dataset by considering the reply and quote relationships and analyzed the largest components of each network. Our networks reveal a structure of bot activity in Crypto Twitter and suggest that they can be detected and handled using a network-based approach. Our work sheds light on the potential of social media signals to detect and understand crypto events, benefiting investors, regulators, and curious observers alike, as well as the potential for bot detection in Crypto Twitter using a network-based approach.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CSCNET: Class-Specified Cascaded Network for Compositional Zero-Shot Learning CSCNET：用于合成零点学习的分类指定级联网络

ArXiv Pub Date : 2024-03-09 DOI: 10.1109/icassp48485.2024.10446756

Yanyi Zhang, Qi Jia, Xin Fan, Yu Liu, Ran He

引用次数: 0

Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera 利用递归网络消除劣化，实现欠显示摄像机的视频修复

ArXiv Pub Date : 2024-03-08 DOI: 10.1609/aaai.v38i4.28144

Chengxu Liu, Xuan Wang, Yuanting Fan, Shuai Li, Xueming Qian

{"title":"Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera","authors":"Chengxu Liu, Xuan Wang, Yuanting Fan, Shuai Li, Xueming Qian","doi":"10.1609/aaai.v38i4.28144","DOIUrl":"https://doi.org/10.1609/aaai.v38i4.28144","url":null,"abstract":"Under-display camera (UDC) systems are the foundation of full-screen display devices in which the lens mounts under the display. The pixel array of light-emitting diodes used for display diffracts and attenuates incident light, causing various degradations as the light intensity changes. Unlike general video restoration which recovers video by treating different degradation factors equally, video restoration for UDC systems is more challenging that concerns removing diverse degradation over time while preserving temporal consistency. In this paper, we introduce a novel video restoration network, called D2RNet, specifically designed for UDC systems. It employs a set of Decoupling Attention Modules (DAM) that effectively separate the various video degradation factors. More specifically, a soft mask generation function is proposed to formulate each frame into flare and haze based on the diffraction arising from incident light of different intensities, followed by the proposed flare and haze removal components that leverage long- and short-term feature learning to handle the respective degradations. Such a design offers an targeted and effective solution to eliminating various types of degradation in UDC systems. We further extend our design into multi-scale to overcome the scale-changing of degradation that often occur in long-range videos. To demonstrate the superiority of D2RNet, we propose a large-scale UDC video benchmark by gathering HDR videos and generating realistically degraded videos using the point spread function measured by a commercial UDC system. Extensive quantitative and qualitative evaluations demonstrate the superiority of D2RNet compared to other state-of-the-art video restoration and UDC image restoration methods.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140396710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0