执法和城市安全受到监视系统中的暴力事件的严重影响。尽管现代(智能)相机广泛可用且负担得起,但在大多数情况下,这种技术解决方案无能为力。此外,监测CCTV记录的人员经常显示出迟来的反应,从而导致对人和财产的灾难。因此,对迅速行动的暴力自动检测至关重要。拟议的解决方案使用了一种新颖的端到端深度学习视频视觉变压器(Vivit),可以在视频序列中熟练地辨别战斗,敌对运动和暴力事件。该研究提出了利用数据增强策略来克服较弱的电感偏见的缺点,同时在较小的培训数据集中训练视觉变压器。评估的结果随后可以发送给当地有关当局,可以分析捕获的视频。与最先进的(SOTA)相比,所提出的方法在某些具有挑战性的基准数据集上实现了吉祥的性能。
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A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SUMREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.
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Syntax is a latent hierarchical structure which underpins the robust and compositional nature of human language. An active line of inquiry is whether large pretrained language models (LLMs) are able to acquire syntax by training on text alone; understanding a model's syntactic capabilities is essential to understanding how it processes and makes use of language. In this paper, we propose a new method, SSUD, which allows for the induction of syntactic structures without supervision from gold-standard parses. Instead, we seek to define formalism-agnostic, model-intrinsic syntactic parses by using a property of syntactic relations: syntactic substitutability. We demonstrate both quantitative and qualitative gains on dependency parsing tasks using SSUD, and induce syntactic structures which we hope provide clarity into LLMs and linguistic representations, alike.
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We explore unifying a neural segmenter with two-pass cascaded encoder ASR into a single model. A key challenge is allowing the segmenter (which runs in real-time, synchronously with the decoder) to finalize the 2nd pass (which runs 900 ms behind real-time) without introducing user-perceived latency or deletion errors during inference. We propose a design where the neural segmenter is integrated with the causal 1st pass decoder to emit a end-of-segment (EOS) signal in real-time. The EOS signal is then used to finalize the non-causal 2nd pass. We experiment with different ways to finalize the 2nd pass, and find that a novel dummy frame injection strategy allows for simultaneous high quality 2nd pass results and low finalization latency. On a real-world long-form captioning task (YouTube), we achieve 2.4% relative WER and 140 ms EOS latency gains over a baseline VAD-based segmenter with the same cascaded encoder.
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This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.
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Estimating treatment effects from observational data is a central problem in causal inference. Methods to solve this problem exploit inductive biases and heuristics from causal inference to design multi-head neural network architectures and regularizers. In this work, we propose to use neurosymbolic program synthesis, a data-efficient, and interpretable technique, to solve the treatment effect estimation problem. We theoretically show that neurosymbolic programming can solve the treatment effect estimation problem. By designing a Domain Specific Language (DSL) for treatment effect estimation problem based on the inductive biases used in literature, we argue that neurosymbolic programming is a better alternative to treatment effect estimation than traditional methods. Our empirical study reveals that our method, which implicitly encodes inductive biases in a DSL, achieves better performance on benchmark datasets than the state-of-the-art methods.
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Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks. Fine-tuning separate foundation models for many downstream tasks are expensive since the foundation model is usually very big. Parameter-efficient fine-tuning methods (e.g. adapter, sparse update methods) offer an alternative paradigm where a small set of parameters are updated to adapt the foundation model to new tasks. However, these methods still suffer from a high computational memory cost and slow training speed because they require backpropagation through the entire neural network at each step. In the paper, we analyze the performance of features at different layers of a foundation model on the speech recognition task and propose a novel hierarchical feature fusion method for resource-efficient transfer learning from speech foundation models. Experimental results show that the proposed method can achieve better performance on speech recognition task than existing algorithms with fewer number of trainable parameters, less computational memory cost and faster training speed. After combining with Adapters at all layers, the proposed method can achieve the same performance as fine-tuning the whole model with $97\%$ fewer trainable encoder parameters and $53\%$ faster training speed.
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赤道等离子体气泡(EPB)是低密度血浆的羽毛,它们从F层的底部升至Exosphere。 EPB是无线电波闪烁的已知原因,可以降低与航天器的通信。我们构建了一个随机的森林回归剂,以预测和预测IBI处理器在船上检测到的EPB [0-1]的可能性。我们使用从2014年到2021年的8年群数据,并将数据从时间序列转换为5维空间,该空间包括纬度,经度,MLT,年份和年度。我们还增加了KP,F10.7厘米和太阳风速。关于地理位置,当地时间,季节和太阳活动的EPB的观察主要与现有工作一致,而链接的地磁活动尚不清楚。该预测的精度为88%,并且在EPB特异性时空尺度上的性能很好。这证明了XGBoost方法能够成功捕获群EPB的气候和每日变异性。由于电离层内的局部和随机特征,捕获每日方差长期以来一直逃避研究人员。我们利用Shapley值来解释该模型并深入了解EPB的物理学。我们发现,随着太阳能速度的增加,EPB的概率降低。我们还确定了EPB概率周围的尖峰。这两个见解直接源自XGBoost和Shapley技术。
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该软件随着先进技术和方法论的发明而迅速变化。响应不断变化的业务需求而快速,成功升级软件的能力比以往任何时候都重要。对于软件产品的长期管理,测量软件可维护性至关重要。通过提供软件可维护性的准确预测,将软计算技术用于软件可维护性预测,在软件维护过程中表现出了巨大的希望。为了更好地了解软计算技术在软件可维护性预测中的作用,我们旨在为软件可维护性预测提供对软计算技术的系统文献综述。首先,我们提供了软件可维护性的详细概述。之后,我们探讨了软件可维护性的基本原理以及采用软计算方法来预测软件可维护性的原因。后来,我们检查了软件可维护预测过程中采用的软计算方法。此外,我们讨论了与使用软计算技术预测软件可维护性相关的困难和潜在解决方案。最后,我们以一些有希望的未来方向来结束审查,以推动这一有前途的领域的进一步研究创新和发展。
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非参考语音质量模型对于越来越多的应用程序很重要。 VoiceMos 2022挑战提供了一个带有主观标签的合成语音转换和文本到语音样本的数据集。这项研究着眼于在元数据的主观语音质量和数据集的分布不平衡的主观评级中可以解释的差异。使用WAV2VEC 2.0构建语音质量模型,具有其他元数据功能,其中包括评估者组和系统标识符,并获得了竞争性指标,包括Spearman等级相关系数(SRCC)为0.934,MSE为0.088,在系统级别和0.877和0.198和0.198和0.198的MSE和0.198话语级别。使用数据限制或盲目的数据和元数据进一步改善了指标。元数据分析表明,由于验证和测试数据集中每个系统使用的话语数量的广泛变化,系统级指标并不代表模型的系统级预测。我们得出的结论是,通常,条件在测试集中应具有足够的话语以绑定样本平均误差,并且在系统之间的话语计数中相对平衡,否则话语级别的指标可能更可靠和可解释。
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