Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.
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The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.
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深度学习属于人工智能领域,机器执行通常需要某种人类智能的任务。类似于大脑的基本结构,深度学习算法包括一种人工神经网络,其类似于生物脑结构。利用他们的感官模仿人类的学习过程,深入学习网络被送入(感官)数据,如文本,图像,视频或声音。这些网络在不同的任务中优于最先进的方法,因此,整个领域在过去几年中看到了指数增长。这种增长在过去几年中每年超过10,000多种出版物。例如,只有在医疗领域中的所有出版物中覆盖的搜索引擎只能在Q3 2020中覆盖所有出版物的子集,用于搜索术语“深度学习”,其中大约90%来自过去三年。因此,对深度学习领域的完全概述已经不可能在不久的将来获得,并且在不久的将来可能会难以获得难以获得子场的概要。但是,有几个关于深度学习的综述文章,这些文章专注于特定的科学领域或应用程序,例如计算机愿景的深度学习进步或在物体检测等特定任务中进行。随着这些调查作为基础,这一贡献的目的是提供对不同科学学科的深度学习的第一个高级,分类的元调查。根据底层数据来源(图像,语言,医疗,混合)选择了类别(计算机愿景,语言处理,医疗信息和其他工程)。此外,我们还审查了每个子类别的常见架构,方法,专业,利弊,评估,挑战和未来方向。
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预训练在机器学习的不同领域表现出成功,例如计算机视觉,自然语言处理(NLP)和医学成像。但是,尚未完全探索用于临床数据分析。记录了大量的临床记录,但是对于在小型医院收集的数据或处理罕见疾病的数据仍可能稀缺数据和标签。在这种情况下,对较大的未标记临床数据进行预训练可以提高性能。在本文中,我们提出了专为异质的多模式临床数据设计的新型无监督的预训练技术,用于通过蒙版语言建模(MLM)启发的患者预测,通过利用对人群图的深度学习来启发。为此,我们进一步提出了一个基于图形转换器的网络,该网络旨在处理异质临床数据。通过将基于掩盖的预训练与基于变压器的网络相结合,我们将基于掩盖的其他域中训练的成功转化为异质临床数据。我们使用三个医学数据集Tadpole,Mimic-III和一个败血症预测数据集,在自我监督和转移学习设置中展示了我们的预训练方法的好处。我们发现,我们提出的培训方法有助于对患者和人群水平的数据进行建模,并提高所有数据集中不同微调任务的性能。
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Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.
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The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.
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在本文中,我们试图通过引入深度学习模型的句法归纳偏见来建立两所学校之间的联系。我们提出了两个归纳偏见的家族,一个家庭用于选区结构,另一个用于依赖性结构。选区归纳偏见鼓励深度学习模型使用不同的单位(或神经元)分别处理长期和短期信息。这种分离为深度学习模型提供了一种方法,可以从顺序输入中构建潜在的层次表示形式,即更高级别的表示由高级表示形式组成,并且可以分解为一系列低级表示。例如,在不了解地面实际结构的情况下,我们提出的模型学会通过根据其句法结构组成变量和运算符的表示来处理逻辑表达。另一方面,依赖归纳偏置鼓励模型在输入序列中找到实体之间的潜在关系。对于自然语言,潜在关系通常被建模为一个定向依赖图,其中一个单词恰好具有一个父节点和零或几个孩子的节点。将此约束应用于类似变压器的模型之后,我们发现该模型能够诱导接近人类专家注释的有向图,并且在不同任务上也优于标准变压器模型。我们认为,这些实验结果为深度学习模型的未来发展展示了一个有趣的选择。
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连接视觉和语言在生成智能中起着重要作用。因此,已经致力于图像标题的大型研究工作,即用句法和语义有意义的句子描述图像。从2015年开始,该任务通常通过由Visual Encoder组成的管道和文本生成的语言模型来解决任务。在这些年来,两种组件通过对象区域,属性,介绍多模态连接,完全关注方法和伯特早期融合策略的利用而显着发展。但是,无论令人印象深刻的结果,图像标题的研究还没有达到结论性答案。这项工作旨在提供图像标题方法的全面概述,从视觉编码和文本生成到培训策略,数据集和评估度量。在这方面,我们量化地比较了许多相关的最先进的方法来确定架构和培训策略中最有影响力的技术创新。此外,讨论了问题的许多变体及其开放挑战。这项工作的最终目标是作为理解现有文献的工具,并突出显示计算机视觉和自然语言处理的研究领域的未来方向可以找到最佳的协同作用。
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组织病理学图像包含丰富的表型信息和病理模式,这是疾病诊断的黄金标准,对于预测患者预后和治疗结果至关重要。近年来,在临床实践中迫切需要针对组织病理学图像的计算机自动化分析技术,而卷积神经网络代表的深度学习方法已逐渐成为数字病理领域的主流。但是,在该领域获得大量细粒的注释数据是一项非常昂贵且艰巨的任务,这阻碍了基于大量注释数据的传统监督算法的进一步开发。最新的研究开始从传统的监督范式中解放出来,最有代表性的研究是基于弱注释,基于有限的注释的半监督学习范式以及基于自我监督的学习范式的弱监督学习范式的研究图像表示学习。这些新方法引发了针对注释效率的新自动病理图像诊断和分析。通过对130篇论文的调查,我们对从技术和方法论的角度来看,对计算病理学领域中有关弱监督学习,半监督学习以及自我监督学习的最新研究进行了全面的系统综述。最后,我们提出了这些技术的关键挑战和未来趋势。
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本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片(Sota)。它填补了空白,因为现有的调查文章在其范围内或被约会。我们包括两个重要方面,目前正在挖掘和建模社交媒体的重要性:动态和网络。社会动态对于了解影响影响或疾病的传播,友谊的形成,友谊的形成等,另一方面,可以捕获各种复杂关系,提供额外的洞察力和识别否则将不会被注意的重要模式。
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Pre-publication draft of a book to be published byMorgan & Claypool publishers. Unedited version released with permission. All relevant copyrights held by the author and publisher extend to this pre-publication draft.
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近年来,随着新颖的策略和应用,神经网络一直在迅速扩展。然而,尽管不可避免地会针对关键应用程序来解决这些挑战,例如神经网络技术诸如神经网络技术中仍未解决诸如神经网络技术的挑战。已经尝试通过用符号表示来表示和嵌入域知识来克服神经网络计算中的挑战。因此,出现了神经符号学习(Nesyl)概念,其中结合了符号表示的各个方面,并将常识带入神经网络(Nesyl)。在可解释性,推理和解释性至关重要的领域中,例如视频和图像字幕,提问和推理,健康信息学和基因组学,Nesyl表现出了有希望的结果。这篇综述介绍了一项有关最先进的Nesyl方法的全面调查,其原理,机器和深度学习算法的进步,诸如Opthalmology之类的应用以及最重要的是该新兴领域的未来观点。
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从自然语言嵌入中汲取灵感,我们提出了Astromer,这是一种基于变压器的模型,以创建光曲线的表示。Astromer接受了数以百万计的Macho R波段样品的培训,并且很容易对其进行微调以匹配与下游任务相关的特定域。例如,本文显示了使用预训练的表示形式对变量恒星进行分类的好处。此外,我们还提供了一个Python库,其中包括这项工作中使用的所有功能。我们的图书馆包括预先培训的模型,可用于增强深度学习模型的性能,减少计算资源,同时获得最新的结果。
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越来越多的工作已经认识到利用机器学习(ML)进步的重要性,以满足提取访问控制属性,策略挖掘,策略验证,访问决策等有效自动化的需求。在这项工作中,我们调查和总结了各种ML解决不同访问控制问题的方法。我们提出了ML模型在访问控制域中应用的新分类学。我们重点介绍当前的局限性和公开挑战,例如缺乏公共现实世界数据集,基于ML的访问控制系统的管理,了解黑盒ML模型的决策等,并列举未来的研究方向。
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恶意软件检测在网络安全中起着至关重要的作用,随着恶意软件增长的增加和网络攻击的进步。以前看不见的恶意软件不是由安全供应商确定的,这些恶意软件通常在这些攻击中使用,并且不可避免地要找到可以从未标记的样本数据中自学习的解决方案。本文介绍了Sherlock,这是一种基于自学的深度学习模型,可根据视觉变压器(VIT)体系结构检测恶意软件。 Sherlock是一种新颖的恶意软件检测方法,它可以通过使用基于图像的二进制表示形式来学习独特的功能,以区分恶意软件和良性程序。在47种类型和696个家庭的层次结构中使用120万个Android应用的实验结果表明,自我监督的学习可以达到97%的恶意软件分类,而恶意软件的二进制分类比现有的最新技术更高。我们提出的模型还能够胜过针对多级恶意软件类型和家庭的最先进技术,分别为.497和.491。
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海洋生态系统及其鱼类栖息地越来越重要,因为它们在提供有价值的食物来源和保护效果方面的重要作用。由于它们的偏僻且难以接近自然,因此通常使用水下摄像头对海洋环境和鱼类栖息地进行监测。这些相机产生了大量数字数据,这些数据无法通过当前的手动处理方法有效地分析,这些方法涉及人类观察者。 DL是一种尖端的AI技术,在分析视觉数据时表现出了前所未有的性能。尽管它应用于无数领域,但仍在探索其在水下鱼类栖息地监测中的使用。在本文中,我们提供了一个涵盖DL的关键概念的教程,该教程可帮助读者了解对DL的工作原理的高级理解。该教程还解释了一个逐步的程序,讲述了如何为诸如水下鱼类监测等挑战性应用开发DL算法。此外,我们还提供了针对鱼类栖息地监测的关键深度学习技术的全面调查,包括分类,计数,定位和细分。此外,我们对水下鱼类数据集进行了公开调查,并比较水下鱼类监测域中的各种DL技术。我们还讨论了鱼类栖息地加工深度学习的新兴领域的一些挑战和机遇。本文是为了作为希望掌握对DL的高级了解,通过遵循我们的分步教程而为其应用开发的海洋科学家的教程,并了解如何发展其研究,以促进他们的研究。努力。同时,它适用于希望调查基于DL的最先进方法的计算机科学家,以进行鱼类栖息地监测。
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近年来,随着传感器和智能设备的广泛传播,物联网(IoT)系统的数据生成速度已大大增加。在物联网系统中,必须经常处理,转换和分析大量数据,以实现各种物联网服务和功能。机器学习(ML)方法已显示出其物联网数据分析的能力。但是,将ML模型应用于物联网数据分析任务仍然面临许多困难和挑战,特别是有效的模型选择,设计/调整和更新,这给经验丰富的数据科学家带来了巨大的需求。此外,物联网数据的动态性质可能引入概念漂移问题,从而导致模型性能降解。为了减少人类的努力,自动化机器学习(AUTOML)已成为一个流行的领域,旨在自动选择,构建,调整和更新机器学习模型,以在指定任务上实现最佳性能。在本文中,我们对Automl区域中模型选择,调整和更新过程中的现有方法进行了审查,以识别和总结将ML算法应用于IoT数据分析的每个步骤的最佳解决方案。为了证明我们的发现并帮助工业用户和研究人员更好地实施汽车方法,在这项工作中提出了将汽车应用于IoT异常检测问题的案例研究。最后,我们讨论并分类了该领域的挑战和研究方向。
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Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional feature encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works. We hope this effort will ignite further interest in the community to solve current challenges towards the application of transformer models in computer vision.
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The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts (e.g., source code or executable code) into a form that is suitable for learning. Many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable representations. Yet, in the context of Android problems, existing models are either limited to coarse-grained whole-app level (e.g., apk2vec) or conducted for one specific downstream task (e.g., smali2vec). Our work is part of a new line of research that investigates effective, task-agnostic, and fine-grained universal representations of bytecode to mitigate both of these two limitations. Such representations aim to capture information relevant to various low-level downstream tasks (e.g., at the class-level). We are inspired by the field of Natural Language Processing, where the problem of universal representation was addressed by building Universal Language Models, such as BERT, whose goal is to capture abstract semantic information about sentences, in a way that is reusable for a variety of tasks. We propose DexBERT, a BERT-like Language Model dedicated to representing chunks of DEX bytecode, the main binary format used in Android applications. We empirically assess whether DexBERT is able to model the DEX language and evaluate the suitability of our model in two distinct class-level software engineering tasks: Malicious Code Localization and Defect Prediction. We also experiment with strategies to deal with the problem of catering to apps having vastly different sizes, and we demonstrate one example of using our technique to investigate what information is relevant to a given task.
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