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Follow-Me AI: Energy-Efficient User Interaction with Smart Environments

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arXiv 2024年

作者： Saleh, Alaa Donta, Praveen Kumar Morabito, Roberto Motlagh, Naser Hossein Lovén, Lauri Center for Ubiquitous Computing University of Oulu Finland Distributed Systems Group TU Wien Vienna Austria Department of Communication Systems EURECOM France Department of Computer Science University of Helsinki Finland

This article introduces Follow-Me AI, a concept designed to enhance user interactions with smart environments, optimize energy use, and provide better control over data captured by these environments. Through AI agents that accompany users, Follow-Me AI negotiates data management based on user consent, aligns environmental controls as well as user communication and computes resources available in the environment with user preferences, and predicts user behavior to proactively adjust the smart environment. The manuscript illustrates this concept with a detailed example of Follow-Me AI in a smart campus setting, detailing the interactions with the building's management system for optimal comfort and efficiency. Finally, this article looks into the challenges and opportunities related to Follow-Me AI. © 2024, CC BY-NC-ND.

关键词： Energy efficiency

Deep Spiking Neural Networks with High Representation Similarity Model visual Pathways of Macaque and Mouse

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arXiv 2023年

作者： Huang, Liwei Ma, Zhengyu Yu, Liutao Zhou, Huihui Tian, Yonghong National Engineering Research Center of Visual Technology School of Computer Science Peking University China Department of Networked Intelligence Peng Cheng Laboratory China

Deep artificial neural networks (ANNs) play a major role in modeling the visual pathways of primate and rodent. However, they highly simplify the computational properties of neurons compared to their biological counterparts. Instead, Spiking Neural Networks (SNNs) are more biologically plausible models since spiking neurons encode information with time sequences of spikes, just like biological neurons do. However, there is a lack of studies on visual pathways with deep SNNs models. In this study, we model the visual cortex with deep SNNs for the first time, and also with a wide range of state-of-the-art deep CNNs and ViTs for comparison. Using three similarity metrics, we conduct neural representation similarity experiments on three neural datasets collected from two species under three types of stimuli. Based on extensive similarity analyses, we further investigate the functional hierarchy and mechanisms across species. Almost all similarity scores of SNNs are higher than their counterparts of CNNs with an average of 6.6%. Depths of the layers with the highest similarity scores exhibit little differences across mouse cortical regions, but vary significantly across macaque regions, suggesting that the visual processing structure of mice is more regionally homogeneous than that of macaques. Besides, the multi-branch structures observed in some top mouse brain-like neural networks provide computational evidence of parallel processing streams in mice, and the different performance in fitting macaque neural representations under different stimuli exhibits the functional specialization of information processing in macaques. Taken together, our study demonstrates that SNNs could serve as promising candidates to better model and explain the functional hierarchy and mechanisms of the visual system. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurons

Quantifying OpenMP: Statistical Insights into Usage and Adoption

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arXiv 2023年

作者： Kadosh, Tal Hasabnis, Niranjan Mattson, Timothy Pinter, Yuval Oren, Gal Department of Computer Science Ben-Gurion University Israel Israel Atomic Energy Commission Intel Labs United States Scientific Computing Center Nuclear Research Center Negev Israel Department of Computer Science Technion - Israel Institute of Technology Israel

In high-performance computing (HPC), the demand for efficient parallel programming models has grown dramatically since the end of Dennard Scaling and the subsequent move to multi-core CPUs. OpenMP stands out as a popular choice due to its simplicity and portability, offering a directive-driven approach for shared-memory parallel programming. Despite its wide adoption, however, there is a lack of comprehensive data on the actual usage of OpenMP constructs, hindering unbiased insights into its popularity and evolution. This paper presents a statistical analysis of OpenMP usage and adoption trends based on a novel and extensive database, HPCORPUS, compiled from GitHub repositories containing C, C++, and Fortran code. The results reveal that OpenMP is the dominant parallel programming model, accounting for 45% of all analyzed parallel APIs. Furthermore, it has demonstrated steady and continuous growth in popularity over the past decade. Analyzing specific OpenMP constructs, the study provides in-depth insights into their usage patterns and preferences across the three languages. Notably, we found that while OpenMP has a strong "common core" of constructs in common usage (while the rest of the API is less used), there are new adoption trends as well, such as simd and target directives for accelerated computing and task for irregular parallelism. Overall, this study sheds light on OpenMP's significance in HPC applications and provides valuable data for researchers and practitioners. It showcases OpenMP's versatility, evolving adoption, and relevance in contemporary parallel programming, underlining its continued role in HPC applications and beyond. These statistical insights are essential for making informed decisions about parallelization strategies and provide a foundation for further advancements in parallel programming models and techniques. HPCORPUS, as well as the analysis scripts and raw results, are available at: https://***/Scientific-computing-Lab-NRCN/HP

关键词： Parallel programming

FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling

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arXiv 2024年

作者： Ye, Hang Ma, Xiaoxuan Ci, Hai Zhu, Wentao Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology China State Key Laboratory of General Artificial Intelligence Peking University China

Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, they struggle to handle loose clothing, such as long dresses, where the canonicalization process becomes ill-defined when the clothing is far from the body, leading to disjointed and fragmented results. To overcome this limitation, we propose FreeCloth, a novel hybrid framework to model challenging clothed humans. Our core idea is to use dedicated strategies to model different regions, depending on whether they are close to or distant from the body. Specifically, we segment the human body into three categories: unclothed, deformed, and generated. We simply replicate unclothed regions that require no deformation. For deformed regions close to the body, we leverage LBS to handle the deformation. As for the generated regions, which correspond to loose clothing areas, we introduce a novel free-form, part-aware generator to model them, as they are less affected by movements. This free-form generation paradigm brings enhanced flexibility and expressiveness to our hybrid framework, enabling it to capture the intricate geometric details of challenging loose clothing, such as skirts and dresses. Experimental results on the benchmark dataset featuring loose clothing demonstrate that FreeCloth achieves state-of-the-art performance with superior visual fidelity and realism, particularly in the most challenging cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Human form models

MUCIC at CheckThat! 2021: FaDo-fake news detection and domain identification using transformers ensembling

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MUCIC at CheckThat! 2021: FaDo-fake news detection and domai...

2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

作者： Balouchzahi, Fazlourrahman Shashirekha, Hosahalli Lakshmaiah Sidorov, Grigori Center for Computing Research Instituto Politécnico Nacional CDMX Mexico Department of Computer Science Mangalore University Mangalore India

Since the beginning of Covid-19 era in November 2019, the patient growth curve is closely accompanied by the growth of fake news. Therefore, developing tools and models for the detection of fake news from real ones in various domains have become more significant than the earlier days. To address the detection of fake news, in this paper, we, team MUCIC, describe the models submitted to 'Fake News Detection', a shared task organized by CLEF-2021-CheckThat! Lab. This shared task contains two subtasks namely;Fake News Detection of News Articles (Subtask 3A) and Topical Domain Classification of News Articles (Subtask 3B) and both are multi-class text classification tasks. The proposed models have been developed by fine-tuning the three transformer-based language models namely;Roberta, Distilbert, and BERT from HuggingFace using training data and then ensembling them as estimators with majority voting. The proposed models performances evaluated through the evaluation script provided by organizers obtained F1-scores of 0.5309 and 0.8550 for Subtask 3A and Subtask 3B respectively. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

Benchmarking and Extending SYCL Hierarchical Parallelism 2

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Benchmarking and Extending SYCL Hierarchical Parallelism

2nd Workshop on Hierarchical Parallelism for Exascale computing, HiPar 2021

作者： Deakin, Tom McIntosh-Smith, Simon Alpay, Aksel Heuveline, Vincent University of Bristol Department of Computer Science Bristol United Kingdom Universität Heidelberg Engineering Mathematics and Computing Lab and Interdisciplinary Center for Scientific Computing Heidelberg Germany

ISBN: (纸本)9781665411325

SYCL is an open-standard, parallel programming model for programming heterogeneous devices from Khronos. It allows single-source programming of diverse attached devices in a cross-platform manner in modern C++. SYCL provides different layers of parallel abstractions, including Same Instruction Multiple Thread (SIMT) kernels, data-parallel loop concurrency and hierarchical parallelism. We discuss Scoped Parallelism as an extension to the existing Hierarchical Parallelism in SYCL, and highlight the advantages and disadvantages of these models from the perspective of the programmer and an implementer of SYCL. In this paper, we compare writing benchmark programs using SIMT kernel, hierarchical parallelism and scoped parallelism paradigms, and present results running on a high-performance CPU and GPU. © 2021 IEEE.

关键词： Benchmarking

HSSD: Hate speech spreader detection using N-Grams and voting classifier

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HSSD: Hate speech spreader detection using N-Grams and votin...

2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021

Profane or abusive speech with the intention of humiliating and targeting individuals, a specific community or groups of people is called Hate Speech (HS). Identifying and blocking HS contents is only a temporary solution. Instead, developing systems that are able to detect and profile the content polluters who share HS will be a better option. In this paper, we, team MUCIC, present the proposed Voting Classifier (VC) submitted to Hate Speech Spreader Detection shared task organized by PAN 2021. The task includes profiling HS spreaders for two languages, namely, English and Spanish from the text collected from Twitter. This task can be modeled as a binary text classification problem to classify an author (Twitter user) based on his/her tweets as 'Hate speech spreader' or 'Not'. The proposed models utilizes a combination of traditional char and word n-grams with syntactic ngrams as features extracted from the training set. These features are fed to a VC that employs three Machine Learning (ML) classifiers namely, Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) with hard and soft voting. The proposed models with accuracies of 73% and 83% for English and Spanish languages respectively, obtained second rank in the shared task. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

Adop fert-automatic detection of occupations and profession in medical texts using flair and bert

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Adop fert-automatic detection of occupations and profession ...

2021 Iberian Languages Evaluation Forum, IberLEF 2021

作者： Balouchzahia, Fazlourrahman Sidorovb, Grigori Shashirekhac, Hosahalli Lakshmaiah Center for Computing Research Instituto Politecnico Nacional CDMX Mexico Department of Computer Science Mangalore University Mangalore574199 India

Technological developments in healthcare industry are gener- ating lots of electronic health records as well as text data which is usually referred as medical text data. Processing medical text data in unstruc- tured form is not only challenging but also has lot of applications. Named entity recognition, the task of extracting named entities and classifying them into predefined categories is an important preprocessing step in the NLP pipeline. Extracting named entities from medical text is very useful for many applications and at the same time very challenging because of the characteristics of medical text data. Considering the gravity of medi- cal text processing, in this paper, we (Team MUCIC) describe the models submitted to "MEDical DOocu-ments PROFessions recognition" (MED- DOPROF), a first shared task consisting of three Tracks, namely: Track 1: MEDDOPROF-NER, Track 2: MEDDOPROF-CLASS, and Track 3: MEDDOPROF-NORM, in Spanish language. We participated in Track 1 and 2 and proposed two models based on fine-tuning BERT embed- dings using i) BertForTokenClassification from transformers and ii) Flair framework, for the automatic detection of Occupations and Professions in medical text. The model using BertForTokenClassification reported micro F1 scores of 0.629 and 0.598 for Track 1 and 2 respectively, while the Flair framework model reported micro F1 scores of 0.8 and 0.764 for Track 1 and 2 respectively. Further, the Flair framework model for MEDDOPROF-NER track became one among the best models. © 2021 CEUR-WS. All rights reserved.

关键词： Text processing

Learning Photometric Feature Transform for Free-form Object Scan

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arXiv 2023年

作者： Feng, Xiang Kang, Kaizhang Pei, Fan Ding, Huakeng You, Jinjiang Tan, Ping Zhou, Kun Wu, Hongzhi The State Key Lab of CAD & CG Zhejiang University Hangzhou310058 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Visual Computing Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We propose a novel framework to automatically learn to aggregate and transform photometric measurements from multiple unstructured views into spatially distinctive and view-invariant low-level features, which are subsequently fed to a multi-view stereo pipeline to enhance 3D reconstruction. The illumination conditions during acquisition and the feature transform are jointly trained on a large amount of synthetic data. We further build a system to reconstruct both the geometry and anisotropic reflectance of a variety of challenging objects from hand-held scans. The effectiveness of the system is demonstrated with a lightweight prototype, consisting of a camera and an array of LEDs, as well as an off-the-shelf tablet. Our results are validated against reconstructions from a professional 3D scanner and photographs, and compare favorably with state-of-the-art techniques. Copyright © 2023, The Authors. All rights reserved.

关键词： Stereo image processing