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The Problem of Pos Tagging and Stemming for Agglutinative Languages (Turkish, Uyghur, Uzbek Languages)

The Problem of Pos Tagging and Stemming for Agglutinative La...

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International Conference on computer Science and engineering (UBMK)

作者： Elov Botir Boltayevich Eşref Adalι Khamroeva Shahlo Mirdjonovna Abdullayeva Oqila Xolmo'Minovna Xusainova Zilola Yuldashevna Xudayberganov Nizomaddin Uktamboy O'g'li Dept. of Computational Linguistics and Digital Technologies Tashkent State University of Uzbek Language and Literature named Alisher Navo'i Tashkent Uzbekistan Computer Engineering and Informatics Faculty Istanbul Technical University Istanbul Türkiye

The number of possible word forms in agglutinative languages is theoretically unlimited. This, in turn, creates the problem of POS tagging (part-of-speech) of out-of-vocabulary (OOV) words in agglutinative languages. In agglutinative languages, words are formed by adding suffixes to the stem. Due to the occurrence of phonetic harmony and disharmony while adding suffixes to the stem, it is necessary to analyze both phonetic and morphological changes. When solving many NLP tasks, it is necessary to reduce word forms to the stem (stemming). Removing all inflectional affixes from a word and lemmatizing the rest of the word is considered one of the important tasks of natural language processing (NLP), and this process is called stemming. The stemming process is important in information retrieval (IR) systems.

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RAN Explainable Anomaly Prediction for 6G Networks

RAN Explainable Anomaly Prediction for 6G Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Panagiotis Marantis Kostas Ramantas Christos Verikoukis Dept. of Signal Theory and Communications Universitat Politècnica de Catalunya Barcelona Spain Industrial Systems Institute Patras Greece Iquadrat Informatica S.L. Barcelona Spain Dept. of Computer Engineering and Informatics University of Patras Patras Greece Athena Research Center Athens Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly.

关键词： 6G mobile communication Time series analysis Key performance indicator Open RAN Predictive models Network architecture Prediction algorithms Forecasting Anomaly detection Long short term memory

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Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

Õ(n+poly(k))-time Algorithm for Bounded Tree Edit Distance

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Annual IEEE Symposium on Foundations of computer Science

作者： Debarati Das Jacob Gilbert MohammadTaghi Hajiaghayi Tomasz Kociumaka Barna Saha Hamed Saleh Dept. of Computer Science & Engineering Pennsylvania State University State College PA United States Dept. of Computer Science University of Maryland College Park MD United States Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science & Engineering Halıcıoğlu Data Science Institute University of California San Diego San Diego CA United States

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Computing the edit distance of two strings is one of the most basic problems in computer science and combinatorial optimization. Tree edit distance is a natural generalization of edit distance in which the task is to compute a measure of dissimilarity between two (unweighted) rooted trees with node labels. Perhaps the most notable recent application of tree edit distance is in NoSQL big databases, such as MongoDB, where each row of the database is a JSON document represented as a labeled rooted tree and finding dissimilarity between two rows is a basic operation. Until recently, the fastest algorithm for tree edit distance ran in cubic time (Demaine, Mozes, Rossman, Weimann; TALG’10); however, Mao (FOCS’21) broke the cubic barrier for the tree edit distance problem using fast matrix *** a parameter k as an upper bound on the distance, an $\mathcal{O}(n+k^{2})$-time algorithm for edit distance has been known since the 1980s due to works of Myers (Algorithmica’86) and Landau and Vishkin (JCSS’88). The existence of an $\tilde{\mathcal{O}}(n+poly(k))$-time algorithm for tree edit distance has been posed as open question, e.g., by Akmal and Jin (ICALP’21), who give a stateof-the-art $O(nk^{2})$-time algorithm. In this paper, we answer this question positively.

关键词： computer science Upper bound Costs Databases Task analysis Optimization

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

An Investigation of Running Load Comparisons of ARCore on Na...

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Vocational Education and Electrical engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

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An Energy-Autonomous SWIPT RFID Tag for Communication in the 2.4 GHz ISM Band 3

An Energy-Autonomous SWIPT RFID Tag for Communication in the...

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3rd URSI Atlantic and Asia Pacific Radio Science Meeting, AT-AP-RASC 2022

作者： Paolini, Giacomo Quddious, Abdul Chatzichristodoulou, David Masotti, Diego Nikolaou, Symeon Costanzo, Alessandra University of Bologna Dei 'Guglielmo Marconi' Bologna Italy Kios Research and Innovation Center of Excellence Nicosia Cyprus Frederick Research Center Nicosia Cyprus Rf and Microwave Solutions Ltd Cyprus Frederick University Dept. Electrical Computer Engineering and Informatics Nicosia Cyprus University of Bologna Dei 'Guglielmo Marconi' Cesena Italy

ISBN: (纸本)9789463968058

This paper presents the design of an energy autonomous radiofrequency identification (RFID) tag for communication and/or indoor localization. It makes use of the energy harvesting (EH) technique in the 915 MHz ultra-high frequency (UHF) band for the feeding of a system-on-a-chip (SoC), working also as a radio transceiver, in order to communicate data to a reader, or a gateway, at 2.4 GHz. First, the design of a multiple-input multiple-output (MIMO) antenna is described, with the dimensions of the whole tag limited to 47 × 100 mm2. Subsequently, the description and the performance of the UHF rectifier are reported, with its connection to the related power management unit (PMU). The PMU is then connected to a transceiver that can send information about its identification number (ID) and the received signal strength indicator (RSSI) without the need of a battery. © 2022 URSI.

关键词： MIMO systems

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MRIGAT: Many-to-many Recommendation using Interaction based Knowledge Graph Attention 22

MRIGAT: Many-to-many Recommendation using Interaction based ...

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3rd International Symposium on Artificial Intelligence for Medical Sciences, ISAIMS 2022

作者： Gao, Feng Yuan, Kai Gu, Jinguang Liu, Yun Department of Computer Science and Technology Wuhan University of Science and Technology China The Key Laboratory of Rich-Media Knowledge Organization Service of Digital Publishing Content Institute of Scientific and Technical Information of China China Guangxi Eco-engineering Vocational & Technical College China 1st Affiliated Hospital Dept. of Medical Informatics School of Biomedical Engineering & Informatic Nanjing Medical University China

ISBN: (纸本)9781450398442

Recommendation systems combining Graph Neural Networks and Knowledge Graphs have been successfully applied in various domains. However, most existing approaches only consider one-to-one or one-to-many user-item interactions and cannot cater to many-to-many recommendation scenarios, such as providing prescriptions based on clinical diagnosis and medical history. Providing such a recommendation requires considering the implicit interaction within input user features as well as output candidates. In this paper, we propose a two-stage knowledge graph attention aggregation mechanism that helps recommend drug combinations for a patient based on his conditions. First, a disease-drug interaction graph is constructed and integrated with the medical domain knowledge, forming a collaborative knowledge graph. Secondly, an intra-feature attention aggregation is performed to obtain the representations of diseases and drugs based on drug-drug and disease-disease interactions, respectively. Thirdly, an inter-feature attention aggregation is performed using the disease-drug interaction to better represent a user's condition. Finally, the user's condition representation is concatenated with other user features to generate the final user representation for the prescription recommendation. Experiments with realistic datasets show that our approach can outperform existing recommendation systems by 4.3% and 6.1% in precision and recall, respectively. © 2022 ACM.

关键词： Knowledge graph

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease

Machine Vision Approach Using Multi Features for Detection o...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Anindita Septiarini Hamdani Hamdani Encik Akhmad Syaifudin Emy Setyaningsih Damar Nurcahyono Noor Alam Hadiwijaya Dept. of Informatics Mulawarman University Samarinda Indonesia Faculty of Agriculture Mulawarman University Samarinda Indonesia Department of Computer System Engineering Institut Sains Teknologi AKPRIND Yogyakarta Indonesia Dept. of Information Technology Politeknik Negeri Samarinda Samarinda Indonesia

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%.

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Using Natural Language Processing to Predict Costume Core Vocabulary of Historical Artifacts

Using Natural Language Processing to Predict Costume Core Vo...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Madhuvanti Muralikrishnan Amr Hilal Chreston Miller Dina Smith-Glaviana Computer Science Dept Virginia Tech Blacksburg VA Informatics Lab Virginia Tech Blacksburg VA Department of Computer and Systems Engineering Alexandria University Egypt Apparel Housing and Resource Management Dept Virginia Tech Blacksburg VA

Historic dress artifacts are a valuable source for human studies. In particular, they can provide important insights into the social aspects of their corresponding era. These insights are commonly drawn from garment pictures as well as the accompanying descriptions and are usually stored in a standardized and controlled vocabulary that accurately describes garments and costume items, called the Costume Core Vocabulary. Building an accurate Costume Core from garment descriptions can be challenging because the historic garment items are often donated, and the accompanying descriptions can be based on untrained individuals and use a language common to the period of the items. In this paper, we present an approach to use Natural Language Processing (NLP) to map the free-form text descriptions of the historic items to that of the controlled vocabulary provided by the Costume Core. Despite the limited dataset, we were able to train an NLP model based on the Universal Sentence Encoder to perform this mapping with more than 90% test accuracy for a subset of the Costume Core vocabulary. We describe our methodology, design choices, and development of our approach, and show the feasibility of predicting the Costume Core for unseen descriptions. With more garment descriptions still being curated to be used for training, we expect to have higher accuracy for better generalizability.

关键词： Training Vocabulary Design methodology Clothing Buildings Machine learning Natural language processing

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Lossy Source Coding of Binary Memoryless Source by Trained LDGM Encoder

Lossy Source Coding of Binary Memoryless Source by Trained L...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Kazuki Ogawa Hiromu Takata Motohiko Isaka Dept. of Informatics Graduate School of Science and Technology Kwansei Gakuin University Sanda Japan Dept. of Computer Science School of Engineering Kwansei Gakuin University Sanda Japan

ISBN: (数字)9784885523410

ISBN: (纸本)9781665499071

Use of low-density generator matrix codes as codebook and encoding with message-passing algorithm is an efficient approach for lossy coding of the i.i.d. binary source. We propose lossy source encoder which is trained on the associated unfolded Tanner graph for improving the rate-distortion performance and/or reducing computational cost.

关键词： Codes Source coding Rate-distortion Generators Computational efficiency

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