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Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Ihdi Syahputra Ritonga Ade Candra Mohammad Andri Budiman Master of Data Science and Artificial Intelligence Program Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This research aims to optimize library services through user segmentation analysis and to determine the correlation between user activities and student graduation levels using the K-Means clustering algorithm. By analyzing this relationship, the study seeks to enhance both library service effectiveness and student academic success. The methodology involves several stages: data collection, preprocessing (including data merging and cleaning), and data modeling. The K-Means clustering method is specifically used to segment library users based on various factors and to identify patterns that correlate with student graduation levels. The dataset comprises circulation data and student graduation data from the UIN Padangsidimpuan Academic Information System (SIAKAD), including variables such as study duration, Grade Point Average (GPA), and loan levels. The analysis resulted in the identification of three distinct clusters: Cluster 0, Cluster 1, and Cluster 2. These clusters are characterized by different lengths of study, GPA, and library loan levels, providing insights into the preferences and needs of library users. The findings suggest that tailored library services, informed by these clusters, can potentially enhance student engagement and academic performance. For example, libraries could develop specialized discussion services related to student research, thereby improving thesis quality and research outcomes. Additionally, optimizing digital-based library services can further support student success.

关键词： Technological innovation Correlation Clustering methods Merging Clustering algorithms data collection Libraries data models Cleaning Information systems

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Implementation of Computer Vision of Jakarta Weather Image Categorization Using ResNet 8

Implementation of Computer Vision of Jakarta Weather Image C...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Purwandari, Kartika Sigalingging, Join W.C. Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta10720 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer vision data augmentation deep transfer learning residual network weather image categorization

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Consideration of Constructing a Weed Detection Method for an Agricultural Robot in Perilla Fields 23

Consideration of Constructing a Weed Detection Method for an...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Sugie, Tatsuya Takagi, Noboru Motoyoshi, Tatsuo Sawai, Kei Masuta, Hiroyuki Graduate School of Intelligent Robotics Program Toyama Prefectural University Toyama Japan Toyama Prefectural University Department of Data Science Toyama Japan Toyama Prefectural University Department of Intelligent Robotics Toyama Japan

ISBN: (纸本)9798331528041

In recent years, the number of farmers in Japan has been decreasing, and the automation of agriculture using robots, AI, and IoT, known as 'smart agriculture,' is being promoted. In this context, Toyama city is promoting a project to build a robot for automatic weeding between plants in a perilla fields. In this research, we are in charge of the system part that detects weeding regions and informs the weeding mechanism. In the field, a wide variety of weeds form vegetation, and it is necessary to prevent food crops from being cut. Therefore, we detect crop regions and the rest of the area should be the weeding regions. In this study, we construct a system to detect crop regions by transfer learning the object detection model YOLO-v8, which is a convolutional neural network (CNN), and evaluate the system in terms of detection accuracy and processing speed. © 2024 IEEE.

关键词： Convolutional neural networks

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Investigation of Robot's Eye Gaze Affecting Human Pedestrian Behavior by Robot in Virtual Space 23

Investigation of Robot's Eye Gaze Affecting Human Pedestrian...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Takata, R.Y.O. Fuse, Yotaro Takagi, Noboru Myagmardulam, Bilguunmaa Motoyoshi, Tatsuo Sawai, K.E.I. Masuta, Hiroyuki Graduate School of Intelligent Robotics Program Toyama Prefectural University Toyama Japan Toyama Prefectural University Department of Data Science Toyama Japan Toyama Prefectural University Department of Intelligent Robotics Toyama Japan

ISBN: (纸本)9798331528041

We investigate the effects of robot's gaze on human behavior when a human and a robot pass each other in a virtual reality space (VR). Nonverbal communication, particularly eye contact, influences physical proximity between individuals in human-to-human interaction. Results of a study indicate that the robot's gaze affects the physical distance between a human and a robot in human-robot communication. Studies on robot's gaze tend to focus on experimental scenarios in which robots and humans communicate. However, robot's gaze in environments with interactions, such as crossing paths, has not been extensively investigated. Therefore, we hypothesize that the robot's gaze exhibits an influence on the physical distance between humans and robots during encounters. In our experiment, we compare three conditions: a robot with a rotating neck and gaze directed at the subject, a faceless robot with a rotating neck, and a faceless robot with a rotating neck and gaze directed at the subject. We analyze the subject's paths of movement and physical distance between the subjects and the robot. Results suggest that the hypothesis that 'intimacy equilibrium model' works when humans and robot's cross paths in the VR environment is not supported. © 2024 IEEE.

关键词： Human robot interaction

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Enhancing Information Freshness: An AoI Optimized Markov Decision Process Dedicated in The Underwater Task

arXiv

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arXiv 2024年

作者： Xu, Jingzehua Ding, Yimian Yang, Yiyuan Xie, Guanwen Zhang, Shuai MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Computer Science University of Oxford United Kingdom Department of Data Science New Jersey Institute of Technology United States

Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly failed due to the observation delay caused by acoustic communication in the Internet of underwater things. In this study, we present an AoI optimized Markov decision process (AoI-MDP) to improve the performance of underwater tasks. Specifically, AoI-MDP models observation delay as signal delay through statistical signal processing, and includes this delay as a new component in the state space. Additionally, we introduce wait time in the action space, and integrate AoI with reward functions to achieve joint optimization of information freshness and decision-making for AUVs leveraging RL for training. Finally, we apply this approach to the multi-AUV data collection task scenario as an example. Simulation results highlight the feasibility of AoI-MDP, which effectively minimizes AoI while showcasing superior performance in the task. To accelerate relevant research in this field, we have made the simulation codes available as open-source © 2024, CC BY.

关键词： Markov processes

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RelMobNet: End-to-End Relative Camera Pose Estimation Using a Robust Two-Stage Training 17th

RelMobNet: End-to-End Relative Camera Pose Estimation Using ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Rajendran, Praveen Kumar Mishra, Sumit Vecchietti, Luiz Felipe Har, Dongsoo Division of Future Vehicle KAIST Daejeon Korea Republic of The Robotics Program KAIST Daejeon Korea Republic of Data Science Group Institute for Basic Science Daejeon Korea Republic of The CCS Graduate School of Mobility KAIST Daejeon Korea Republic of

ISBN: (纸本)9783031250743

Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmented reality and robotics. In this paper, we present an end-to-end relative camera pose estimation network using a siamese architecture that is independent of camera parameters. The network is trained using the Cambridge Landmarks data with four individual scene datasets and a dataset combining the four scenes. To improve generalization, we propose a novel two-stage training that alleviates the need of a hyperparameter to balance the translation and rotation loss scale. The proposed method is compared with one-stage training CNN-based methods such as RPNet and RCPNet and demonstrate that the proposed model improves translation vector estimation by 16.11%, 28.88%, and 52.27% on the Kings College, Old Hospital, and St Marys Church scenes, respectively. For proving texture invariance, we investigate the generalization of the proposed method augmenting the datasets to different scene styles, as ablation studies, using generative adversarial networks. Also, we present a qualitative assessment of epipolar lines of our network predictions and ground truth poses. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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Optimal Tree-Based Mechanisms for Differentially Private Approximate CDFs

arXiv

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arXiv 2024年

作者： Rameshwar, V. Arvind Tandon, Anshoo Sharma, Abhay India Urban Data Exchange Program Unit Indian Institute of Science Bengaluru India

This paper considers the Ε-differentially private (DP) release of an approximate cumulative distribution function (CDF) of the samples in a dataset. We assume that the true (approximate) CDF is obtained after lumping the data samples into a fixed number K of bins. In this work, we extend the well-known binary tree mechanism to the class of level-uniform tree-based mechanisms and identify Ε-DP mechanisms that have a small 2-error. We identify optimal or close-to-optimal tree structures when either of the parameters, which are the branching factors or the privacy budgets at each tree level, are given, and when the algorithm designer is free to choose both sets of parameters. Interestingly, when we allow the branching factors to take on real values, under certain mild restrictions, the optimal level-uniform tree-based mechanism is obtained by choosing equal branching factors independent of K, and equal privacy budgets at all levels. Furthermore, for selected K values, we explicitly identify the optimal integer branching factors and tree height, assuming equal privacy budgets at all levels. Finally, we describe general strategies for improving the private CDF estimates further, by combining multiple noisy estimates and by post-processing the estimates for consistency. © 2024, CC BY.

关键词： Distribution functions

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Large Language Models as Efficient Reward Function Searchers for Custom-Environment Multi-Objective Reinforcement Learning

arXiv

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arXiv 2024年

作者： Xie, Guanwen Xu, Jingzehua Yang, Yiyuan Ding, Yimian Zhang, Shuai MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Computer Science University of Oxford United Kingdom Department of Data Science New Jersey Institute of Technology United States

Achieving the effective design and improvement of reward functions in reinforcement learning (RL) tasks with complex custom environments and multiple requirements presents considerable challenges. In this paper, we propose ERFSL, an efficient reward function searcher using LLMs, which enables LLMs to be effective white-box searchers and highlights their advanced semantic understanding capabilities. Specifically, we generate reward components for each numerically explicit user requirement and employ a reward critic to identify the correct code form. Then, LLMs assign weights to the reward components to balance their values and iteratively adjust the weights without ambiguity and redundant adjustments by flexibly adopting directional mutation and crossover strategies, similar to genetic algorithms, based on the context provided by the training log analyzer. We applied the framework to an underwater data collection RL task without direct human feedback or reward examples (zero-shot learning). The reward critic successfully corrects the reward code with only one feedback instance for each requirement, effectively preventing unrectifiable errors. The initialization of weights enables the acquisition of different reward functions within the Pareto solution set without the need for weight search. Even in cases where a weight is 500 times off, on average, only 5.2 iterations are needed to meet user requirements. The ERFSL also works well with most prompts utilizing GPT-4o mini, as we decompose the weight searching process to reduce the requirement for numerical and long-context understanding capabilities1 © 2024, CC BY.

关键词： Reinforcement learning

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Viral Melodies: Exploring the Factors Influencing Music Virality in TikTok Engagement

Viral Melodies: Exploring the Factors Influencing Music Vira...

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science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Evelyn Zefanya Rahardjo Jasmine Mutia Alifa Sharon Zefanya Setiawan Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department School of Computer Science Data Science Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

In the digital era and the evolution of social media platforms like TikTok, understanding the factors influencing content virality has become increasingly crucial. Therefore, this research aims to delve into the musical variables contributing to the popularity of content on TikTok, including aspects such as artist, beat of music, and the number of shares. Focusing on content that successfully garnered over $\mathbf{5, 0 0 0}$ likes, this study employed a data analysis approach involving the librosa library for music tempo extraction and logistic regression to identify relationships among these variables. The research findings indicate that the most significant variable influencing the virality of music content on TikTok is the number of shares. This highlights that the phenomenon of virality is not solely dependent on musical characteristics like artist or beat but is more influenced by social interactions through video sharing among users. The findings suggest that when someone shares a video with others who may have similar music preferences, the video is likely to receive likes, even if it does not appear on the user’s For You Page (FYP). Thus, the virality of content on TikTok can be explained by the existence of social connections among friends that trigger the viral spread of the video. These results provide valuable insights into the mechanisms behind the success of conten/t on this platform.

关键词： Logistic regression Video on demand Social networking (online) Refining Music Focusing Libraries Web sites data mining Information technology

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Analyzing Nickel Stock Price Prediction: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models

Analyzing Nickel Stock Price Prediction: A Comparative Study...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Ferry Irwanto Muhammad Farrell Hafizh Sasongko Stevanus Febrian Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

This research delves to predict PT Vale Indonesia Tbk stock price as an experiment on Indonesian stock using three models: naïve, LSTM, and 1D-CNN. Our analysis emphasizes the importance of matching model architectures to data properties. We compare models' performance with fiveday window for predict one-day prediction output. Interestingly, the single-layer LSTM outperforms the 1D-CNN even with similar hyperparameters, showcasing its strength in capturing long-term temporal dependencies crucial for nickel prices. While the 1D-CNN excels at identifying short-term patterns, its limited receptive field hinders long-term dependence. Recognizing the potential of both models, we encourage exploring hybrid architectures combining LSTM and CNN strengths for further improvement in financial forecasting. The experiment result shows single-layer LSTM outperforms a 1D-CNN with similar settings.

关键词： Analytical models Predictive models Nickel data models Communications technology Fourth Industrial Revolution Convolutional neural networks

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