The rapid development of technology has brought unmanned aerial vehicles (UAVs) to become widely known in the current era. The market of UAVs is also predicted to continue growing with related technologies in the future. UAVs have been used in various sectors, including livestock, forestry, and agriculture. In agricultural applications, UAVs are highly capable of increasing the productivity of the farm and reducing farmers' workload. This paper discusses the application of UAVs in agriculture, particularly in spraying and crop monitoring. This study examines the urgency of UAV implementation in the agriculture sector. A short history of UAVs is provided in this paper to portray the development of UAVs from time to time. The classification of UAVs is also discussed to differentiate various types of UAVs. The application of UAVs in spraying and crop monitoring is based on the previous studies that have been done by many scientific groups and researchers who are working closely to propose solutions for agriculture-related issues. Furthermore, the limitations of UAV applications are also identified. The challenges in implementing agricultural UAVs in Indonesia are also presented.
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Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.
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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.
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The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages. To improve its zero-shot performance on unseen languages, it is desirable to adapt BLOOM, but previous works have only explored adapting small language models. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at \url{https://github.com/bigscience-workshop/multilingual-modeling/}.
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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在阻止印尼自然语言处理(NLP)研究进步的基本问题的中心,我们发现数据稀缺。印尼语言,尤其是当地语言的资源极为稀缺和代表性不足。许多印尼研究人员没有发布其数据集。此外,我们拥有的少数公共数据集散布在不同的平台上,因此使印尼NLP的可重复性和以数据为中心的研究更加艰巨。面对这一挑战,我们开始了第一个印尼NLP众包努力,Nusacrowd。Nusacrowd努力为所有印尼语言中的NLP任务提供标准化数据加载,以提供最大的数据表聚合。通过使印尼NLP资源的开放式和集中式访问能力,我们希望Nusacrowd可以解决阻碍印度尼西亚NLP进展的数据稀缺问题,并将NLP从业者带来合作。
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本文介绍了一个多模式的室内轨道图数据集,Odombeyondvision,具有不同频谱的多个传感器,并使用不同的移动平台收集。Odombeyondvision不仅包含传统的导航传感器,例如IMUS,机械激光镜,RGBD摄像头,还包括几个新兴传感器,例如单芯片MMWave Radar,LWIR热相机和固态激光雷达。在无人机,UGV和手持式平台上的上述传感器中,我们分别记录了各种室内场景和不同照明条件的多模式探光数据及其运动轨迹。我们释放了示例雷达,雷达惯性和热惯性循环仪的实现,以证明其未来工作的结果,以对其进行比较和改进。包括工具包和文档在内的完整数据集可公开可用:https://github.com/maps-lab/odombeyondvision。
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Several solutions for lightweight TTS have shown promising results. Still, they either rely on a hand-crafted design that reaches non-optimum size or use a neural architecture search but often suffer training costs. We present Nix-TTS, a lightweight TTS achieved via knowledge distillation to a high-quality yet large-sized, non-autoregressive, and end-to-end (vocoder-free) TTS teacher model. Specifically, we offer module-wise distillation, enabling flexible and independent distillation to the encoder and decoder module. The resulting Nix-TTS inherited the advantageous properties of being non-autoregressive and end-to-end from the teacher, yet significantly smaller in size, with only 5.23M parameters or up to 89.34% reduction of the teacher model; it also achieves over 3.04x and 8.36x inference speedup on Intel-i7 CPU and Raspberry Pi 3B respectively and still retains a fair voice naturalness and intelligibility compared to the teacher model. We provide pretrained models and audio samples of Nix-TTS.
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我们从任务特定的BERT基教师模型执行知识蒸馏(KD)基准到各种学生模型:Bilstm,CNN,Bert-Tiny,Bert-Mini和Bert-small。我们的实验涉及在两个任务中分组的12个数据集:印度尼西亚语言中的文本分类和序列标记。我们还比较蒸馏的各个方面,包括使用Word Embeddings和未标记的数据增强的使用。我们的实验表明,尽管基于变压器的模型的普及程度不断上升,但是使用Bilstm和CNN学生模型,与修剪的BERT模型相比,使用Bilstm和CNN学生模型提供了性能和计算资源(CPU,RAM和存储)之间的最佳权衡。我们进一步提出了一些快速胜利,通过涉及涉及丢失功能,Word Embeddings和未标记的数据准备的简单选择的高效KD培训机制来生产小型NLP模型。
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对不利环境中的行人无处不在的定位服务了很长的挑战。尽管深入学习的戏剧性进展,但多传感器深度测量系统却带来了高计算成本并随着时间的推移遭受累积漂移的错误。由于边缘设备的计算能力越来越多,我们通过在边缘与EKF(扩展卡尔曼滤波器) - 欧拉后端集成了最新的深径测量模型,提出了一种新的无处不在的定位解决方案。我们仔细比较并选择三个传感器模式,即惯性测量单元(IMU),毫米波(MMWAVE)雷达和热红外摄像机,并实现实时运行的深度内径推理引擎。提出了考虑精度,复杂性和边缘平台的深度径流的管道。我们设计一个Lora链接,用于定位数据回程,并将深度内径仪的聚合位置投影到全局框架中。我们发现简单的基于EKF的融合模块足以用于通用定位校准,具有超过34%的精度增长,针对任何独立的深径测量系统。不同环境的广泛测试验证了我们所提出的定位系统的效率和功效。
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