We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Dialogue systems can leverage large pre-trained language models and knowledge to generate fluent and informative responses. However, these models are still prone to produce hallucinated responses not supported by the input source, which greatly hinders their application. The heterogeneity between external knowledge and dialogue context challenges representation learning and source integration, and further contributes to unfaithfulness. To handle this challenge and generate more faithful responses, this paper presents RHO ($\rho$) utilizing the representations of linked entities and relation predicates from a knowledge graph (KG). We propose (1) local knowledge grounding to combine textual embeddings with the corresponding KG embeddings; and (2) global knowledge grounding to equip RHO with multi-hop reasoning abilities via the attention mechanism. In addition, we devise a response re-ranking technique based on walks over KG sub-graphs for better conversational reasoning. Experimental results on OpenDialKG show that our approach significantly outperforms state-of-the-art methods on both automatic and human evaluation by a large margin, especially in hallucination reduction (17.54% in FeQA).

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

基于深度学习的单图像超分辨率（SISR）方法引起了人们的关注，并在现代高级GPU上取得了巨大的成功。但是，大多数最先进的方法都需要大量参数，记忆和计算资源，这些参数通常会显示在当前移动设备CPU/NPU上时显示出较低的推理时间。在本文中，我们提出了一个简单的普通卷积网络，该网络具有快速最近的卷积模块（NCNET），该模块对NPU友好，可以实时执行可靠的超级分辨率。提出的最近的卷积具有与最近的UP采样相同的性能，但更快，更适合Android NNAPI。我们的模型可以很容易地在具有8位量化的移动设备上部署，并且与所有主要的移动AI加速器完全兼容。此外，我们对移动设备上的不同张量操作进行了全面的实验，以说明网络体系结构的效率。我们的NCNET在DIV2K 3X数据集上进行了训练和验证，并且与其他有效的SR方法的比较表明，NCNET可以实现高保真SR结果，同时使用更少的推理时间。我们的代码和预估计的模型可在\ url {https://github.com/algolzw/ncnet}上公开获得。

现实世界中的数据通常显示出长尾巴的开放式（带有看不见的类）分布。实践识别系统必须在多数（头）和少数族裔（尾巴）阶级之间取得平衡，在整个分布中进行概括，并承认新颖的阶级（公开阶级）。我们将开放的长尾识别++（OLTR ++）定义为从这种自然分布的数据中学习，并优化了包括已知和开放类的平衡测试集的分类精度。 OLTR ++在一种集成算法中处理不平衡的分类，很少的学习，开放式识别和积极学习，而现有的分类方法通常仅着眼于一个或两个方面，并且在整个频谱中交付不佳。关键挑战是：1）如何在头和尾巴之间共享视觉知识，2）如何减少尾巴和开放式阶级之间的混淆，以及3）如何用学习知识积极地探索开放的课程。我们的算法OLTR ++将图像映射到特征空间，以便视觉概念可以通过记忆关联机制和学识渊博的指标（动态元元素）相互关联，这两者都尊重所封闭的见解类别的封闭世界分类并承认的新颖性打开课程。此外，我们提出了一个基于视觉记忆的主动学习方案，该方案学会以数据效率的方式识别未来扩展的开放类。在三个大规模开放的长尾数据集中，我们从Imagenet（以对象为中心），位置（以场景为中心）和MS1M（面部为中心）数据策划了三个标准基准（CIFAR-10-LT，CIFAR，CIFAR，CIFAR） -100-LT和Inaturalist-18），我们作为统一框架的方法始终展示竞争性能。值得注意的是，我们的方法还显示出积极探索开放阶级和对少数群体的公平分析的强大潜力。

在本文中，我们介绍了DA $^2 $，这是第一个大型双臂灵敏性吸引数据集，用于生成最佳的双人握把对，用于任意大型对象。该数据集包含大约900万的平行jaw grasps，由6000多个对象生成，每个对象都有各种抓紧敏度度量。此外，我们提出了一个端到端的双臂掌握评估模型，该模型在该数据集的渲染场景上训练。我们利用评估模型作为基准，通过在线分析和真实的机器人实验来显示这一新颖和非平凡数据集的价值。所有数据和相关的代码将在https://sites.google.com/view/da2dataset上开源。

大规模数据集在计算机视觉中起着至关重要的作用。但是当前的数据集盲目注释而没有与样品区分的区分，从而使数据收集效率低下且不计。开放的问题是如何积极地构建大型数据集。尽管先进的主动学习算法可能是答案，但我们在实验上发现它们在分发数据广泛的现实注释方案中是la脚的。因此，这项工作为现实的数据集注释提供了一个新颖的主动学习框架。配备了此框架，我们构建了一个高质量的视觉数据集 - 竹子，由69m的图像分类注释，带有119K类别，带有809个类别的28m对象边界框注释。我们通过从几个知识库中整合的层次分类法来组织这些类别。分类注释比Imagenet22K大四倍，检测的注释比Object365大三倍。与ImagEnet22K和Objects365相比，预先训练的竹子在各种下游任务中实现了卓越的性能（分类的6.2％增长，检测到2.1％的增长）。我们认为，我们的积极学习框架和竹子对于将来的工作至关重要。

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

本文从单个RGB图像中解决了人手的3D点云重建和3D姿势估计。为此，我们在学习姿势估计的潜在表示时，我们展示了一个用于本地和全球点云重建的新型管道，同时使用3D手模板。为了展示我们的方法，我们介绍了一个新的多视图手姿势数据集，以获得现实世界中的手的完整3D点云。我们新拟议的数据集和四个公共基准测试的实验展示了模型的优势。我们的方法优于3D姿势估计中的竞争对手，同时重建现实看的完整3D手云。

现在，推荐系统已经变得繁荣，旨在通过学习嵌入来预测用户对项目的潜在兴趣。图形神经网络的最新进展〜（GNNS）还提供带有强大备份的推荐系统，从用户项图中学习嵌入。但是，由于数据收集困难，仅利用用户项交互遭受冷启动问题。因此，目前的努力建议将社交信息与用户项目相互作用融合以缓解它，这是社会推荐问题。现有工作使用GNNS同时聚合两个社交链接和用户项交互。但是，它们都需要集中存储的社交链接和用户的互动，从而导致隐私问题。此外，根据严格的隐私保护，在一般数据保护规则下，将来可能不可行的数据存储可能是不可行的，敦促分散的社会建议框架。为此，我们设计了一个小说框架\ textbf {fe} delated \ textbf {so} cial推荐与\ textbf {g} raph神经网络（fesog）。首先，FeSog采用关系的关注和聚集来处理异质性。其次，Fesog Infers使用本地数据来保留个性化的用户嵌入。最后但并非最不重要的是，所提出的模型采用伪标签技术，其中包含项目采样，以保护隐私和增强培训。三个现实世界数据集的广泛实验可以证明FeSog在完成社会建议和隐私保护方面的有效性。我们是为我们所知，为社会建议提供联邦学习框架的第一项工作。