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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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Optimization and Benchmarking of Convolutional Networks with Quantization and OpenVINO in Baggage Image Recognition 8

Optimization and Benchmarking of Convolutional Networks with...

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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Andriyanov, Nikita Papakostas, George Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia International Hellenic University Department of Computer Science Thessaloniki Greece

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The paper is devoted to the study of the neural networks inference acceleration using the weights quantization and Intel OpenVINO Toolkit. At the same time, the study considers block architecture convolutional networks trained from scratch. In addition, it is shown that transfer learning makes it possible to obtain higher model accuracy. And their implementation on OpenVINO can significantly increase the processing performance. It is also shown that the use of OpenVINO provides significant acceleration without loss of performance for such networks, while quantization leads to significant loss of quality. © 2022 IEEE.

关键词： computer vision

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Transfer Learning for Pediatric Glucose Forecasting 4

Transfer Learning for Pediatric Glucose Forecasting

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4th Machine Learning for Health Symposium, ML4H 2024

作者： Ryser, Alain Feng, Chuhao Scheithauer, Tobias Pfister, Marc Burckhardt, Marie-Anne Bachmann, Sara Marx, Alexander Vogt, Julia E. Department of Computer Science ETH Zurich Zürich Switzerland Department of Statistics Research Center Trustworthy Data Science and Security of the University Alliance Ruhr TU Dortmund University Dortmund Germany Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland Pediatric Pharmacology and Pharmacometrics University Children's Hospital Basel Basel Switzerland Department of Clinical Research University of Basel Basel Switzerland Pediatric Endocrinology and Diabetology University Children's Hospital Basel Basel Switzerland SIB Swiss Institute of Bioinformatics Ecublens Switzerland

Effective blood glucose forecasting is crucial for detecting events such as hypo- or hyperglycemia in people with diabetes, yet remains challenging in domains with only small, heterogeneous datasets, such as in the pediatric field. We present GluTFT, a novel transfer learning approach that allows leveraging models pretrained on publicly available adult diabetes datasets for pediatric glucose forecasting. We systematically evaluate multiple transfer learning strategies, including zero-shot prediction and fine-tuning across the entire dataset as well as specific subgroups of participants. Our extensive experiments reveal that GluTFT excels on the pretraining datasets and significantly outperforms baseline methods when fine-tuned. To validate the clinical relevance of our approach, we evaluate Parkes Error Grids, demonstrating the quality of GluTFT's blood glucose forecasts and its potential for enhancing clinical decision-making for pediatric diabetes. © 2024 A. Ryser, C. Feng, T. Scheithauer, M. Pfister, M.-A. Burckhardt, S. Bachmann, A. Marx & J.E. Vogt.

关键词： Zero-shot learning

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The Emergent Aging Model: Aging as an Emergent Property of Biological Systems

arXiv

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arXiv 2024年

作者： Qin, Hong Department of Computer Science and Engineering Department of Biology Geology and Environmental Sciences University of Tennessee at Chattanooga ChattanoogaTN United States Department of Computer Science School of Data Sciences Old Dominion University NorfolkVA United States

Based on the study of cellular aging using the single cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent property of complex biological systems, exemplified by biological networks such as gene networks. An emergent property refers to traits that a system has at the system level but which its low-level components do not. EAM is based on a quantitative definition of aging using the mortality rate. A biological entity with a constant mortality rate is considered non-aging which is equivalent to a first-order chemical reaction. Aging can be quantitatively defined an increasing mortality rate over time, corresponding to an organism’s increasing chance of dying over time. EAM posits that biological aging can arise at the system level of an organism, even if the system is composed of only non-aging components. EAM is consistent with the observation that aging is largely stochastic, influenced by numerous genes and epigenetic factors, with no single gene or factor known as the bone fide cause of aging. A parsimonious version of EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan correlation, and the trade-off between initial reproductive fitness (asexual reproductive fitness) and late-life survival. EAM has been applied to experimental results of the replicative lifespan of the budding yeast and can potentially offer new insights into the aging process of other biological species. © 2024, CC BY.

关键词： Population statistics

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Blockchain and API Development to Improve Relational database Integrity and System Interoperability

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Procedia computer science 2023年 216卷 151-160页

作者： Andhika Naafi Ramadhan Kharisma Naufal Pane Kun Rifki Wardhana Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.

关键词： Blockchai Cybercrim database Interoperability

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MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles

arXiv

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arXiv 2024年

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciarán Department of Electronic & Computer Engineering University of Limerick Ireland SFI CRT Foundations in Data Science University of Limerick Ireland

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles. © 2024, CC BY.

关键词： Trajectories

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A Sequential Perturbation Framework for Solving Laplacian Systems

A Sequential Perturbation Framework for Solving Laplacian Sy...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Tse-Yu Lin Yen-Lung Tsai Data Science Degree Program National Taiwan University Taipei Taiwan Department of Mathematical Sciences National Chengchi University Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

In this paper, we investigate the iterative solution of a sequence of graph Laplacian systems by leveraging the (relative) continuity of the generalized inverse operator. Our primary objective is to extend traditional matrix perturbation techniques specifically to the context of graph Laplacian systems. We achieve this by modeling the evolution of these systems starting from a given initial graph Laplacian and applying a series of systematic updates. We propose a novel framework that formulates an iterative method for solving the targeted graph Laplacian system based on the solutions of simpler, earlier Laplacian systems. By establishing a relationship between the solutions of two Laplacian systems, we derive a recursive formula that enables efficient computation of the updated solutions. This approach not only enhances computational efficiency but also maintains accuracy, as it utilizes the inherent structure of the graph Laplacian, including its positive semi-definiteness and the zero-mean condition of the right-hand side.

关键词： Electric potential Laplace equations Systematics Accuracy Perturbation methods Computational efficiency Iterative methods Consumer electronics

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Semi-Supervised Deep Learning Based Method for Abnormality Detection in Videos

Semi-Supervised Deep Learning Based Method for Abnormality D...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Deevesh Chaudhary Sunil Kumar Vijaypal Singh Dhaka Department of Data Science and Engineering Manipal University Jaipur Jaipur India Department of Computer Communication and Engineering Manipal University Jaipur Jaipur India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Anomaly detection refers to recognition of events different from normal ones for example road accident, fight, robbery, arsenal etc. Anomaly identification in real world surveillance videos is an important application of computer vision. The work proposed in the paper detects anomalous events in surveillance videos dataset and is based upon semi supervised deep learning model. We trained the model using UCF Crime dataset that consists of 950 normal videos and 950 anomalous videos. The anomaly videos in the dataset consists of 13 different types of anomalies such as Abuse, arrest, explosion, fight etc. that generally occur in real life. The anomaly in the dataset is labelled at video level and not at a specific frame in a video to define the semi supervised nature of learning paradigm. The extracted 3D features from dataset are fed into the multilayered deep learning model. Experimental results show that our approach has significant improvement over state-of-the-art approaches for anomaly detection in surveillance videos. The accuracy of model comes out to be 83.96 percent, that is improvement over other methods.

关键词： Deep learning Solid modeling Three-dimensional displays Road accidents Annotations Surveillance Computational modeling Feature extraction Anomaly detection Videos

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Analyse The Energy Consumption by Integrating the IOT and Pattern Recognition Technique

Analyse The Energy Consumption by Integrating the IOT and Pa...

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Disruptive Technologies (ICDT), International Conference on

作者： Subba Rao Polamuri Lakshmi Nalla A Durga Madhuri Surendranath Kalagara Bonthu Subrahmanyam Peyyeti.B.L. Aparna Department of CSE (Artificial Intelligence & Data Science) Bonam Venkata Chalamayya Engineering College (Autonomous) Odalarevu India Department of Computer Science and Engineering Bonam Venkata Chalamayya Engineering College (Autonomous) Odalarevu India Department of Science and Humanities Bonam Venkata Chalamayya Engineering College (Autonomous) Odalarevu India Department of Computer Science and Engineering Bonam Venkata Chalamayya College of Engineering Palacharla Rajahmundry Department of Computer Science and Engineering Bonam Venkata Chalamayya Institute of Technology & Science Batlapalem

High power costs and end-use electricity usage are driving energy consumption optimization and data-driven demand-side management. The consumer action and the approach used to energy management have a significant impact on how well these systems work. It is crucial to provide comprehensive energy-consumption information models down to the appliance level to educate the public and encourage them to adopt more efficient energy habits. The Home Energy Management System's (HEMS) primary objective is to cultivate an energy-efficient environment that controls IoT devices via its network. With HEMS, customers may adjust their use to day-to-day price fluctuations, thus reducing energy expenses. expressive data-mining methods to create a numerical model that provides users with data about their home appliances. This data includes the number and length of operations, energy consumption over different periods based on 15-minute monitoring data, and the ability to disaggregate cycles for appliances with cyclic operation. Calibration and validation of the model were performed on two datasets acquired by ENEA via actual monitoring of the Italian home. Subsequent testing across many appliances demonstrated successful analysis of energyingesting decorations. Consequently, it has been included in the ENEA-developed DHOMUS IoT stage, which tracks and analyses residential energy use to raise public participation and knowledge about this issue. Encourage end users to make more ethical and sustainable energy use decisions and meet the current European Council Regulation (EU) 2021/1853 requirements by reducing energy consumption, according to the findings it shows that the model built is sufficiently accurate.

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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ashita Batra Mannas Narang Neeraj Kumar Sharma Pradip K. Das Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development.

关键词： Tongue Annotations Open Access Event detection Manuals Signal processing data collection Acoustics Multilingual Speech processing

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