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2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)最新文献

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Using the MQTT Broker as a Speech-Activated Medium to Control the Operation of Devices in the Smart Office 利用MQTT代理作为语音激活介质控制智能办公设备的运行
K. Namee, Rudsada Kaewsaeng-On, J. Polpinij, G. Albadrani, Kavin Rueagraklikhit, A. Meny
This research is applying the MQTT broker protocol as a medium for various work orders in smart office management. It is an experiment and development of all functions of MQTT broker whether publishing, chatting and subscribing both globally and locally. The results are able to perform all commands correctly. In addition, in this research, the command procedure was added. This is a human speech command to operate all MQTT Brokers functions. However, there are still some weaknesses in the matter of voice commands are delayed response. It might not be a very good user experience. In this experiment, many functions were woven into the smart office. Regardless of whether the bulb acts as an IoT bulb internally connected to the MQTT broker, the camera performs the function of recognizing a person's face which is internally connected to MQTT broker. Speech also serves voice commands, lamp and feedback are connected to MQTT broker. Air conditioner acts as IoT air conditioner switch externally connected to cloud server. In addition, dashboard It also acts as an IoT visual light switch that connects externally to the cloud.
本研究将MQTT代理协议作为智能办公管理中各种工单的媒介。它是MQTT代理的所有功能的实验和开发,包括全局和本地的发布、聊天和订阅。结果能够正确执行所有命令。此外,本研究还增加了命令程序。这是一个人工语音命令,用于操作所有MQTT broker功能。然而,在语音指令响应延迟问题上仍然存在一些弱点。这可能不是一个很好的用户体验。在这个实验中,许多功能被编织到智能办公室中。无论灯泡是否作为内部连接到MQTT代理的物联网灯泡,摄像头都执行识别内部连接到MQTT代理的人脸的功能。语音还提供语音命令,灯和反馈都连接到MQTT代理。空调作为物联网空调交换机,对外连接云服务器。此外,仪表板还可以作为物联网视觉灯开关,从外部连接到云。
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引用次数: 0
Development of Internet of Things System for Environment Control in Niam Hom (Strobilanthesnivea Craib) House Niam Hom (Strobilanthesnivea crab) House环境控制物联网系统的开发
Sancha Panpaeng, Natawut Payakkhin, Pipop Maneejamnong
This research aims to 1) Study the use of IoT technology to measure soil moisture and air humidity and control water spraying and brightness values in Niam Hom Houses, and 2) Develop systems and tools for users to monitor and record the house's temperature, soil moisture, air humidity, and brightness values. The development tool uses Arduino MEGA and NodeMCU ESP8266 to connect the sensors to obtain data from a specific environment. Design and control the measurement circuit system in the farmhouse with a size of 4 x 6 meters using black shading nets of 50% and 70%. The IoT system helps to control soil moisture, and the air humidity is good, making onion trees grow well. Good yield and different physiology in black shading net 50% in combination with chemical fertilizer application.
本研究旨在1)研究使用物联网技术测量Niam Hom房屋的土壤湿度和空气湿度,并控制喷水和亮度值;2)为用户开发系统和工具,以监测和记录房屋的温度,土壤湿度,空气湿度和亮度值。开发工具使用Arduino MEGA和NodeMCU ESP8266连接传感器,从特定环境中获取数据。设计和控制农舍的测量电路系统,尺寸为4 × 6米,使用50%和70%的黑色遮阳网。物联网系统有助于控制土壤湿度,空气湿度良好,使洋葱树生长良好。黑色遮阳网产量好,生理机能不同,50%配施化肥。
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引用次数: 0
Source Code Plagiarism Detection Based on Abstract Syntax Tree Fingerprintings 基于抽象语法树指纹的源代码抄袭检测
V. Suttichaya, Niracha Eakvorachai, Tunchanok Lurkraisit
Syntax Tree (AST) is an abstract logical structure of source code represented as a tree. This research utilizes information of fingerprinting with AST to locate the similarities between source codes. The proposed method can detect plagiarism in source codes using the number of duplicated logical structures. The structural information of program is stored in the fingerprints format. Then, the fingerprints of source codes are compared to identify number of similar nodes. The final output is calculated from number of similar nodes known as similarities scores. The result shows that the proposed method accurately captures the common modification techniques from basic to advance.
语法树(AST)是用树表示的源代码的抽象逻辑结构。本研究利用AST的指纹信息来定位源代码之间的相似性。该方法可以利用重复逻辑结构的数量来检测源代码中的抄袭行为。程序的结构信息以指纹格式存储。然后,比较源代码的指纹来识别相似节点的数量。最终的输出是根据被称为相似性分数的相似节点的数量来计算的。结果表明,该方法准确地捕捉了常用的从基本到高级的改性技术。
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引用次数: 0
Simulation of Homogenous Fish Schools in the Presence of Food and Predators using Reinforcement Learning 利用强化学习模拟食物和捕食者存在下的同质鱼群
Ravipas Wangananont, Norapat Buppodom, Sanpat Chanthanuraks, Vishnu Kotrajaras
We utilized Deep Reinforcement Learning to incor-porate schooling, foraging, and predator avoidance behaviors into a single fish behavior model. We used Proximal Policy Optimization (PPO) with Intrinsic Curiosity Reward (ICR) to make fish agents learn in our Unity Environment. We created an interactive control system on Unity that allows users to visualize and manipulate the simulation using only a mouse and keyboard. We compared our model with three variations: one without schooling reward, one without foraging reward, and one without predator avoidance reward. Our original model (schooling, foraging, and predator avoidance) clearly illustrated the unification of all three behaviors.
我们利用深度强化学习将鱼群、觅食和捕食者躲避行为整合到一个单一的鱼类行为模型中。我们使用带有内在好奇心奖励(ICR)的近端策略优化(PPO)来使鱼代理在Unity环境中学习。我们在Unity上创建了一个交互式控制系统,允许用户仅使用鼠标和键盘就可以可视化和操纵模拟。我们将我们的模型与三种变体进行了比较:一种没有学校奖励,一种没有觅食奖励,还有一种没有捕食者躲避奖励。我们最初的模型(学习、觅食和躲避捕食者)清楚地说明了这三种行为的统一。
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引用次数: 1
A Comparative Study of Noise Augmentation and Deep Learning Methods on Raman Spectral Classification of Contamination in Hard Disk Drive 噪声增强与深度学习方法在硬盘污染拉曼光谱分类中的比较研究
S. Gulyanon, Somrudee Deepaisam, Chayud Srisumarnk, Nattapol Chiewnawintawat, Angkoon Anzkoonsawaenasuk, Seksan Laitrakun, Pakorn Ooaorakasit, P. Rakpongsiri, Thawanpat Meechamnan, D. Sompongse
Deep neural networks have become state-of-the-art for many tasks in the past decade, especially Raman spectral classification. However, these networks heavily rely on a large collection of labeled data to avoid overfitting. Although labeled data is scarce in many application domains, there are techniques to help alleviate the problem, such as data augmentation. In this paper, we investigate one particular kind of data augmentation, noise augmentation that simply adds noise to input samples, for the Raman spectra classification task. Raman spectra yield fingerprint-like information about all chemical components but are prone to noise when the material's particles are small. We study the effectiveness of three noise models for noise augmen-tation in building a robust classification model, including noise from the background chemicals, extended multiplicative signal augmentation (EMSA), and statistical noises. In the experiments, we compared the performance of 11 popular deep learning models with the three noise augmentation techniques. The results suggest that RNN-based models perform relatively well with the increase in augmented data size compared to CNN-based models and that robust noise augmentation methods require characteristics of random variations. However, hyperparameter optimization is crucial for taking optimal advantage of noise augmentation.
在过去的十年中,深度神经网络在许多任务中已经成为最先进的技术,特别是拉曼光谱分类。然而,这些网络严重依赖于大量标记数据来避免过拟合。尽管标记数据在许多应用程序领域是稀缺的,但是有一些技术可以帮助缓解这个问题,例如数据增强。在本文中,我们研究了一种特殊类型的数据增强,即简单地向输入样本添加噪声的噪声增强,用于拉曼光谱分类任务。拉曼光谱可以产生关于所有化学成分的类似指纹的信息,但当材料的颗粒很小时,它容易产生噪声。本文研究了背景化学物质噪声、扩展乘法信号增强(EMSA)和统计噪声三种噪声增强模型在建立鲁棒分类模型中的有效性。在实验中,我们比较了11种流行的深度学习模型与三种噪声增强技术的性能。结果表明,与基于cnn的模型相比,基于rnn的模型在增强数据量增加时表现相对较好,并且稳健的噪声增强方法需要随机变化的特征。然而,超参数优化是实现噪声增强的最优优势的关键。
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引用次数: 0
ThEconSum: an Economics-domained Dataset for Thai Text Summarization and Baseline Models consum:一个经济学领域的数据集,用于泰国文本摘要和基线模型
Sawittree Jumpathong, Akkharawoot Takhom, P. Boonkwan, Vipas Sutantayawalee, Peerachet Porkaew, Sitthaa Phaholphinyo, Charun Phrombut, T. Supnithi, Khemarath Choke-Mangmi, Saran Yamasathien, Nattachai Tretasayuth, Kasidis Kanwatchara, Atiwat Aiemleuk
Language resources as datasets are an essential component in developing an effective automatic text summarization (ATS) system. Some public datasets are relatively uncommon when compared with popular languages, due to the complexity of language preprocessing resulting in a labor-intensive annotation by linguists. ATS techniques are to condense the size of text into a shorter output and reduce the time for finding the information from the huge textual data. This paper presents the Thai ATS construction with Economics-domain data, called ThEconSum, which manifests some linguistic challenges for Thai summarization. Existing public public datasets were employed for developing the ATS system in Thai economic news articles.
语言资源作为数据集是开发有效的自动文本摘要(ATS)系统的重要组成部分。与流行语言相比,一些公共数据集相对不常见,这是由于语言预处理的复杂性导致语言学家的劳动密集型注释。ATS技术是将文本的大小压缩为更短的输出,减少从庞大的文本数据中查找信息的时间。本文介绍了使用经济领域数据构建的泰国语自动统计系统,称为consum,这显示了泰国语摘要在语言上的一些挑战。现有的公共数据集被用于开发泰国经济新闻文章的ATS系统。
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引用次数: 0
Visual-based Musical Data Representation for Composer Classification 基于视觉的作曲家分类音乐数据表示
S. Deepaisarn, Suphachok Buaruk, Sirawit Chokphantavee, Sorawit Chokphantavee, Phuriphan Prathipasen, Virach Sornlertlamvanich
Automated classification for musical genres and composers is an artificial intelligence research challenge insofar as music lacks a rigidly defined structure and may result in varied interpretations by individuals. This research collected acoustic features from a sizable musical database to create an image dataset for formulating a classification model. Each image was constructed by combining pitch, temporal index length, and additional incorporated features of velocity, onset, duration, and a combination of the three. Incorporated features underwent Sigmoid scaling, creating a novel visual-based music representation. A deep learning framework, fast.ai, was used as the primary classification instrument for generated images. The results were that using velocity solely as an incorporated feature provides optimal performance, with an F1-score of 0.85 using the ResN$e$t34 model. These findings offer preliminary insight into composer classification for heightening understanding of music composer signature characterizations.
音乐流派和作曲家的自动分类是一项人工智能研究挑战,因为音乐缺乏严格定义的结构,可能导致个人的不同解释。本研究从一个相当大的音乐数据库中收集声学特征,以创建一个图像数据集,用于制定分类模型。每张图像都是通过结合间距、时间指数长度以及速度、开始、持续时间和三者的组合等附加特征来构建的。合并的特征经历了Sigmoid缩放,创造了一种新颖的基于视觉的音乐表现。一个深度学习框架,快。Ai,作为生成图像的主要分类工具。结果表明,单独使用速度作为合并特征提供了最佳性能,使用ResN$e$t34模型的f1得分为0.85。这些发现为作曲家分类提供了初步的见解,以加深对音乐作曲家签名特征的理解。
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引用次数: 1
Fault Prediction Model for Motor and Generative Adversarial Networks for Acceleration Signal Generation 电机故障预测模型及生成对抗网络加速度信号生成
Saran Deeluea, C. Jeenanunta, Apinun Tunpun
The manufacturing process must continuously be improved. One of the most efficient strategies is maintenance scheduling by predictive maintenance for early fault detection and assisting with real-time decisions. The major concern of developing a predictive maintenance system is the lack of abnormal data and the cost of a high-specification sensor device for collecting data. This paper introduces the unsupervised learning model called Generative Adversarial Networks (GANs) for generating abnormal data in the form of acceleration signals to provide a dataset for developing an early fault prediction model and assisting a real-time decision on a low-frequency sensor device. The prediction model dataset is labeled on IS010816 to classify the label of data by Velocity Vibration (mm/s). The machine learning classifier model implements a hyperparameters optimization framework called OPTUNA to provide the best model performance. The proposed system aims to assist in real-time decision and maintenance schedules for the injection molding machine and offer the prediction model based on low-frequency sensor data from a drive motor.
制造工艺必须不断改进。最有效的策略之一是通过预测性维护进行维护计划,以便及早发现故障并协助进行实时决策。开发预测性维护系统的主要问题是缺乏异常数据和用于收集数据的高规格传感器设备的成本。本文介绍了一种无监督学习模型——生成式对抗网络(GANs),用于生成加速度信号形式的异常数据,为低频传感器设备的早期故障预测模型和实时决策提供数据集。采用iso10816标准对预测模型数据集进行标注,按速度振动(mm/s)对数据进行标注。机器学习分类器模型实现了一个称为OPTUNA的超参数优化框架,以提供最佳的模型性能。该系统旨在协助注塑机的实时决策和维护计划,并提供基于驱动电机低频传感器数据的预测模型。
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引用次数: 0
Improving Neural Machine Translation for Low-resource English-Myanmar-Thai Language Pairs with SwitchOut Data Augmentation Algorithm 基于SwitchOut数据增强算法的低资源英缅泰语对神经机器翻译
Mya Ei San, Ye Kyaw Thu, T. Supnithi, Sasiporn Usanavasin
To improve the data resource of low-resource English- Myanmar- Thai language pairs, we build the first parallel medical corpus, named as En-My- Th medical corpus which is composed of total 14,592 parallel sentences. In our paper, we make experiments on the English-Myanmar language pair of new En-My-Th medical corpus and in addition, English-Thai and Thai-Myanmar language pairs from the existing ASEAN- MT corpus. The experiments of SwitchOut data augmentation algorithm and the baseline attention-based sequence to sequence model are trained on the aforementioned language pairs in both directions. Experimental results show that combination of Switch Out algorithm with the baseline model outperforms the baseline only model in the translation of most language pairs for both corpora. Furthermore, we investigate the performance of the baseline model and baseline+SwitchOut model by adding or removing word dropout at the recurrent layers, at which baseline+SwitchOut model with the dropout increases around (+1.0) BLEU4 and GLEU scores in some of language nairs.
为了完善低资源的英缅泰语对数据资源,我们构建了首个平行医学语料库En-My- Th,共包含14592个平行句。在本文中,我们对新En-My-Th医学语料库中的英缅语对以及现有ASEAN- MT语料库中的英泰语对和泰缅语对进行了实验。SwitchOut数据增强算法和基于基线注意力的序列到序列模型实验在上述语言对上进行了两个方向的训练。实验结果表明,在两种语料库的大多数语言对翻译中,Switch Out算法与基线模型相结合的翻译效果优于仅基线模型。此外,我们通过在循环层添加或删除单词dropout来研究基线模型和基线+SwitchOut模型的性能,在一些语言问题中,基线+SwitchOut模型的dropout增加了大约(+1.0)BLEU4和GLEU分数。
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引用次数: 1
Image Captioning for Thai Cultures 泰国文化图片说明
S. Watcharabutsarakham, S. Marukatat, K. Kiratiratanapruk, Pitchayagan Temniranrat
Before each trip, tourists generally gather information or photos from different places. This work aims at providing additional information about touristic sites in Thailand via automatic image captioning. Image captioning is the process of generating a textual description for given images. In recent years, the development of Artificial Intelligence in combining image processing and natural language processing has gained attention worldwide. Image captioning can be regarded as a sequence-to-sequence modeling problem, as it converts images, which are considered a sequence of pixels, to a sequence of words. This work proposed a finetuned model that combined CNNs and LSTM to generate the image description. In the experiment part, we use BLEU to evaluate the model.
在每次旅行之前,游客通常会从不同的地方收集信息或照片。这项工作旨在通过自动图像字幕提供有关泰国旅游景点的额外信息。图像字幕是为给定图像生成文本描述的过程。近年来,将图像处理与自然语言处理相结合的人工智能的发展受到了全世界的关注。图像字幕可以看作是一个序列到序列的建模问题,因为它将图像(被认为是一个像素序列)转换为一个单词序列。本文提出了一种结合cnn和LSTM的微调模型来生成图像描述。在实验部分,我们使用BLEU对模型进行评价。
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引用次数: 0
期刊
2022 17th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)
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