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Comparative Analysis of Time Series and Machine Learning Models for Air Quality Prediction Utilizing IoT Data 13th

Comparative Analysis of Time Series and Machine Learning M...

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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Vonitsanos, Gerasimos Panagiotakopoulos, Theodor Kameas, Achilles Computer Engineering and Informatics Department University of Patras Patras Greece School of Technology and Science Hellenic Open University Patras Greece School of Business University of Nicosia Nicosia Cyprus

ISBN: (纸本)9783031632266

Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LSTM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10). © IFIP International Federation for Information Processing 2024.

关键词： Air quality

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On the Quantization of Recurrent Neural Networks for Smiles Generation 48

On the Quantization of Recurrent Neural Networks for Smiles ...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Durao, Adriano Arrais, Joel P. Ribeiro, Bernardete Falcao, Gabriel Instituto de Telecomunicações Portugal University of Coimbra Dept. of Electrical and Computer Engineering Portugal Cisuc Portugal University of Coimbra Dept. of Informatics Engineering Portugal

ISBN: (纸本)9781728163277

This paper focuses on the effects of applying quantization during training to Recurrent Neural Networks (RNNs) used in Simplified Molecular-Input Line-Entry System (SMILES) generation, a form of line notation for molecular information used in the development of pharmaceutical drugs, from the PubChem database. It offers the flexibility to choose the precision used by the model, by defining the number of bits at each layer. The RNNs are the focus of the current study, by comparing the performance of three of the most used algorithms, Simple RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The models were trained on a selection of SMILES. By exploiting the QK-eras library, quantization performance was compared to their floating-point equivalent for several combinations of parameters. The goal of the testing program developed is to generate a large number of novel SMILES, facilitating the process of Drug Discovery which is traditionally long, and thus very expensive and difficult. By understanding how the behavior of quantized networks deviates from the regular model, in relation to the parameters used, we are able to control the process of choosing whether to quantize a model and to which degree it becomes more or less efficient. In this study, we observed good performance even for 4-bit models making use of LSTM and GRU layers, the same way we concluded that Simple RNN quantization does not compensate the effort. © 2023 IEEE.

关键词： Drug Discovery Gated Recurrent Unit Long Short-Term Memory Quantization Recurrent Neural Networks

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Augmented Reality-Based Navigation: Bridging Indoor and Outdoor Environments through Immersal Integration 1

Augmented Reality-Based Navigation: Bridging Indoor and Outd...

引用

1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Maulana Achmad, Zacky Sukaridhoto, Sritrusta Agus Zainuddin, Muhammad Savila, Aurent Firmansyah, Ardian Mega Saputra, Septa Politeknik Elektronika Negeri Surabaya Dept. of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Multimedia Creative Technology Surabaya Indonesia

ISBN: (纸本)9798350365191

A navigation system is an essential tool designed to assist users in determining and following a route from one location to another. Navigation systems are typically categorized into two types: outdoor navigation systems designed for open areas and indoor navigation systems used within enclosed spaces. However, transitioning between indoor and outdoor environments has limitations, often requiring users to switch applications, such as using GPS for outdoor navigation and a different application for indoor navigation. Therefore, we propose the integration of indoor and outdoor navigation based on Augmented Reality. The development of AR navigation system begins with the creation of 3D assets of the Politeknik Elektronika Negeri Surabaya (PENS) campus, which is the site of our research including three buildings for indoor navigation and the connecting roads between the buildings for outdoor navigation. The development of this navigation system uses the Immersal SDK as a library for Spatial Mapping, Localization and System Integration. Several features are included, such as indoor-outdoor navigation, multilevel floor navigation, and zero additional devices. The system testing results are based on user testing, integration testing, and multilevel floor testing. From User Testing with PIECES Framework, 32 respondents expressed satisfaction with the proposed system. Integration Testing showed that the system could navigate between indoor and outdoor environments. And Multilevel Floor Testing demonstrated that the system could navigate within buildings with multiple floors. © 2024 IEEE.

关键词： Augmented reality

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Actively Learning Ontologies from LLMs: First Results (Extended Abstract) 37

Actively Learning Ontologies from LLMs: First Results (Exten...

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37th International Workshop on Description Logics, DL 2024

作者： Magnini, Matteo Ozaki, Ana Squarcialupi, Riccardo Department of Computer Science and Engineering University of Bologna Via dell’Università 50 Cesena Italy Department of Informatics University of Oslo Gaustadalléen 23B Oslo Norway Department of Informatics University of Bergen Thormøhlensgate 55 Bergen Norway

In active learning a learner attempts to acquire some kind of knowledge by posing questions to a teacher. Here we consider that the teacher is a language model and study the case in which the knowledge is expressed as an ontology. To evaluate the approach, we present first results testing logical consistency and the performance of GPT and other language models when answering whether concept inclusions from existing EL ontologies are ‘true’ or ‘false’. © 2024 Copyright for this paper by its authors.

关键词： Active learning

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International Research Conference on Smart Computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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Optimization of SVM Classification Accuracy with Bayesian Optimization Utilizing Data Augmentation 6

Optimization of SVM Classification Accuracy with Bayesian Op...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Lase, Yuyun Yusnida Darwis, R.A. Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Politeknik Negeri Medan Department of Mechanical Engineering Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

Optimizing the accuracy of SVM classification is one of the most important things in the world of machine learning. This study will use the SVM (Support Vector Machine) algorithm in classifying. This study aims to improve the classification accuracy of the Support Vector Machine (SVM) model by utilizing Bayesian Optimization techniques and data augmentation. The Bayesian Optimization technique will be used to find the most optimal values for the kernel and C parameters in the SVM model. While data augmentation is done to improve the quality of the training data by adding variations and data features. This research uses a synonym replacement technique in data augmentation. The data used was obtained from social media Twitter. The results of this study are in the form of a model with a combination of Bayesian Optimization and augmentation. The accuracy obtained with the SVM model using Bayesian Optimization and data augmentation is 89%. However, the conventional SVM model without Bayesian Optimization only produces 82.19% accuracy so the SVM model with Bayesian Optimization increases the accuracy by 7%. © 2023 IEEE.

关键词： Bayesian Optimization Data augmentation Media Social SVM

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eduARM: Web Platform to Support the Teaching and Learning of the ARM Architecture 15

eduARM: Web Platform to Support the Teaching and Learning of...

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15th International Conference on computer Supported Education, CSEDU 2023

作者： Alves, Maria Inês Araújo, António Duarte Lima, Bruno Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal INESC TEC Department of Informatics Engineering Faculty of Engineering University of Porto Porto Portugal

ISBN: (纸本)9789897586415

computer architecture is a prevalent topic of study in informatics and Electrical engineering courses, though students’ overall grasp of this subject’s concepts is many times hampered, mainly due to the lack of educational tools that can intuitively represent the internal behaviour of a CPU. With the evolution of the ARM architecture and its adoption in higher education institutions, the demand for this sort of tool has increased. Educational tools, specifically developed for the ARMv8 processor, are scarce and inadequate for what is necessary in an academic context. In order to contribute towards solving this problem, eduARM, a practical and interactive web platform that simulates how a ARMv8 CPU functions, was developed and is presented through this paper. Since this tool’s main purpose is to aid computer architecture students, contributing to an improvement in their learning experience, it comprises varied concepts of computer architecture and organization in a simple and intuitive manner, such as the internal structure of a CPU, in both its unicycle and pipelined versions, and the effects of executing a set of instructions. As to better understand its value, the developed tool was then validated through a case study with the participation of computer architecture students. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Students

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Arabic Named Entity Recognition Using Clustered Word Embedding 19th

Arabic Named Entity Recognition Using Clustered Word Embeddi...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Sabty, Caroline Elmahdy, Mohamed Abdennadher, Slim Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Informatics and Computer Science German International University Cairo Egypt

ISBN: (纸本)9783031237928

Named Entity Recognition in Arabic is a challenging topic because of morphological and lexical richness of Arabic. In this paper, we propose an Arabic NER system that is based on word embedding. Word embedding hold semantic information about the context of the words. We hypothesized that the integration of word embedding features to the conventional lexical and contextual features could improve Arabic NER performance. The Conditional Random Field (CRF) sequence classifier was used. Since most CRF implementations only support categorical features, continuous word embedding vectors are clustered. In this paper, we are mainly investigating the effect of the number of clusters on NER performance. Moreover, the combination of fine and coarse grained clusters has resulted in further recognition improvement. © 2023, Springer Nature Switzerland AG.

关键词： Embeddings

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Cooperative Vehicle Identification for Safe Connected Autonomous Driving

Cooperative Vehicle Identification for Safe Connected Autono...

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2023 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2023

作者： Tang, Zuoyin He, Jianhua Zheng, Jiawei Aston University School of Informatics and Digital Engineering United Kingdom Essex University School of Computer Science and Electronic Engineering United Kingdom Beijing Post and Telecommunications University China

ISBN: (纸本)9798350345407

5G connected autonomous vehicles (CAVs) is a key to address the challenges faced by autonomous driving with enhanced perception and cooperation on driving. To achieve reliable cooperation for safety critical CAV driving applications, an important but rarely studied issue is identifying communication vehicles of interests from sensed vehicles (ICSV). Wrong vehicle identification may cause unsafe driving decisions and lead to potential accidents. In this paper we study the ICSV problem for safe cooperative autonomous driving. We present a location based baseline method for ICSV and discuss its potential problems. Then we propose a cooperative method to improve reliability and accuracy. In the proposed method vehicle registration number (VRN) is used for vehicle identification. And multiple CAVs can cooperate on both sensing and identifying communication vehicles from their detected ones. VRNs can be hashed before sharing to protect privacy, and are compared to the shared ones for vehicle identification. Experiment results show that the approach is feasible and can have a very low false positive rate. © 2023 IEEE.

关键词： Autonomous vehicles

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