知识驱动的对话世代最近取得了非凡的突破。与一般的对话系统相比，卓越的知识对话系统可以通过预先提供的知识产生更多信息和知识渊博的响应。但是，在实际应用中，对话系统无法事先提供相应的知识。为了解决该问题，我们设计了一个名为DRKQG的知识驱动的对话系统（\ emph {通过查询生成动态检索知识，以获取信息性对话响应}）。具体而言，系统可以分为两个模块：查询生成模块和对话生成模块。首先，利用时间感知机制来捕获上下文信息，并可以生成查询以检索知识。然后，我们集成了复制机制和变压器，该机制允许响应生成模块产生从上下文和检索知识中得出的响应。 LIC2022，语言和情报技术竞赛的实验结果表明，我们的模块在自动评估指标上的大幅度优于基线模型，而BAIDU语言学团队的人类评估表明，我们的系统在事实上取得了令人印象深刻的结果，实际上是正确的，知识渊博。

For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new distributed inference acceleration method HALP using three raspberry Pi 4. HALP accelerates inference by designing a seamless collaboration among edge devices (EDs) in Edge Computing. We maximize the parallelization between communication and computation among the collaborative EDs by optimizing the task partitioning ratio based on the segment-based partitioning. Experimental results show that the distributed inference HALP achieves 1.7x inference acceleration for VGG-16. Then, we combine distributed inference with conventional neural network model compression by setting up different shrinking hyperparameters for MobileNet-V1. In this way, we can further accelerate inference but at the cost of inference accuracy loss. To strike a balance between latency and accuracy, we propose dynamic model selection to select a model which provides the highest accuracy within the latency constraint. It is shown that the model selection with distributed inference HALP can significantly improve service reliability compared to the conventional stand-alone computation.

有两种流行的损失功能用于视觉检索，即三胞胎损失和对比度学习损失，这两者本质上都可以最大程度地减少负对和正对的相似性之间的差异。更具体地说，在现有的检索模型中广泛使用的硬采矿（三重态HN）的三胞胎损失很容易落入训练中的局部最小值。另一方面，广泛用于视觉的预训练中的视觉对比学习损失（VLC）已被证明可以在视觉语言检索上获得显着的性能提高，但通过使用微调的性能来实现。小型数据集上的VLC并不令人满意。本文提出了对视觉语言检索的统一损失相似性优化，为理解现有的损失功能提供了强大的工具。我们的统一损失包括VLC的硬样品挖掘策略，并引入了三胞胎损失使用的边距，以获得更好的相似性分离。结果表明，三重态HN和VLC都是我们统一损失的特殊形式。与三胞胎-HN相比，我们的统一损失具有快速的收敛速度。与VLC相比，我们的统一损失更具歧视性，可以在下游微调任务中更好地概括。图像文本和视频检索基准测试的实验表明，我们的统一损失可以显着提高最新检索模型的性能。

与自然语言处理的XAI旨在产生可读的解释，作为AI决策的证据，以解决解释性和透明度。但是，从HCI的角度来看，当前的方法仅着眼于提供单一的解释，该解释无法解决人类思想和语言经验的多样性。因此，本文通过提出一个生成XAI框架，交互来解决此差距（解释并预测与上下文条件变分自动编码器查询）。我们的新框架分为两个步骤提供了解释：（一步）解释和标签预测； （第二步）各种证据生成。我们在基准数据集E-SNLI上对变压器体系结构进行密集实验。我们的方法在第一步中，针对解释生成（BLEU的增长率高达4.7％）的最先进基线模型的竞争性或更好的表现；它还可以在第二步中产生多种不同的解释。

巨大的开放在线课程（MooCs）已成为电子学习的热门选择，因为他们的灵活性很大。但是，由于大量的学习者及其多样化的背景，它征税，以提供实时支持。学习者可能会在各自的MooC论坛上发布他们的混乱和斗争，但随着MooC教师的大量员额和高工作量，教师不太可能识别所有需要干预的学习者。由于数据的不平衡和任务的复杂性，已被研究是一种自然语言处理（NLP）问题的研究，并且已知是具有挑战性的。在本文中，我们探讨了贝叶斯的第一次对学习者的文本帖子进行了两种方法：蒙特卡罗辍学和变分推论，作为评估学习者帖子的教师干预需求的新解决方案。我们基于在类似情况下基于概率模型的基于概率模型的概率模型进行比较模型，对于应用预测的不同情况。结果表明，贝叶斯深度学习提供了传统神经网络未提供的批判性不确定性措施。这增加了对AI的说明，信任和稳健性，这在基于教育的应用中至关重要。另外，与非概率神经网络相比，它可以实现类似或更好的性能，以及较低的方差。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Decompilation aims to transform a low-level program language (LPL) (eg., binary file) into its functionally-equivalent high-level program language (HPL) (e.g., C/C++). It is a core technology in software security, especially in vulnerability discovery and malware analysis. In recent years, with the successful application of neural machine translation (NMT) models in natural language processing (NLP), researchers have tried to build neural decompilers by borrowing the idea of NMT. They formulate the decompilation process as a translation problem between LPL and HPL, aiming to reduce the human cost required to develop decompilation tools and improve their generalizability. However, state-of-the-art learning-based decompilers do not cope well with compiler-optimized binaries. Since real-world binaries are mostly compiler-optimized, decompilers that do not consider optimized binaries have limited practical significance. In this paper, we propose a novel learning-based approach named NeurDP, that targets compiler-optimized binaries. NeurDP uses a graph neural network (GNN) model to convert LPL to an intermediate representation (IR), which bridges the gap between source code and optimized binary. We also design an Optimized Translation Unit (OTU) to split functions into smaller code fragments for better translation performance. Evaluation results on datasets containing various types of statements show that NeurDP can decompile optimized binaries with 45.21% higher accuracy than state-of-the-art neural decompilation frameworks.

Driven by improved architectures and better representation learning frameworks, the field of visual recognition has enjoyed rapid modernization and performance boost in the early 2020s. For example, modern ConvNets, represented by ConvNeXt, have demonstrated strong performance in various scenarios. While these models were originally designed for supervised learning with ImageNet labels, they can also potentially benefit from self-supervised learning techniques such as masked autoencoders (MAE). However, we found that simply combining these two approaches leads to subpar performance. In this paper, we propose a fully convolutional masked autoencoder framework and a new Global Response Normalization (GRN) layer that can be added to the ConvNeXt architecture to enhance inter-channel feature competition. This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation. We also provide pre-trained ConvNeXt V2 models of various sizes, ranging from an efficient 3.7M-parameter Atto model with 76.7% top-1 accuracy on ImageNet, to a 650M Huge model that achieves a state-of-the-art 88.9% accuracy using only public training data.