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computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Raymond Sunardi Oetama Yanfi Yanfi Masagus M. Ikhsan Assiddiq U.P Sani Muhamad Isa Information System DepartmentFaculty of Engineering and Informatics Universitas Multimedia Nusantara Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%.

关键词： Support vector machines Training Sentiment analysis Vocabulary Dictionaries Social networking (online) Blogs

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ONE EXPLANATION DOES NOT FIT XIL

arXiv

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arXiv 2023年

作者： Friedrich, Felix Steinmann, David Kersting, Kristian Department of Computer Science TU Darmstadt Germany Hessian.AI Centre for Cognitive Science TU Darmstadt Germany Germany

Current machine learning models produce outstanding results in many areas but, at the same time, suffer from shortcut learning and spurious correlations. To address such flaws, the explanatory interactive machine learning (XIL) framework has been proposed to revise a model by employing user feedback on a model's explanation. This work sheds light on the explanations used within this framework. In particular, we investigate simultaneous model revision through multiple explanation methods. To this end, we identified that one explanation does not fit XIL and propose considering multiple ones when revising models via XIL. © 2023, CC BY.

关键词： Machine learning

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Handling Problems of Credit Data for Imbalanced Classes using SMOTEXGBoost 3

Handling Problems of Credit Data for Imbalanced Classes usin...

引用

3rd International Conference on Computational science and Information Management, ICoCSIM 2019

作者： Mardiansyah, Heru Widia Sembiring, Rahmat Efendi, Syahril Graduate Program of Computer Science Department of Information Technology Department of Computer Science Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medam Indonesia

Some researchers find data with imbalanced class conditions, where there are data with a number of minorities and a majority. SMOTE is a data approach for an imbalanced classes and XGBoost is one algorithm for an imbalanced data problems. This research uses SMOTE and XGBoost or abbreviated as SMOTEXGBoost for handling data with an imbalanced classes. The results showed almost the same accuracy value between SMOTE and SMOTEXGBoost at 99%. While the value of AUC SMOTEXBoost has a more stable value than SMOTE that is equal to 99.89% for training and 98.51% for testing. © Published under licence by IOP Publishing Ltd.

关键词： Data handling

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FROM IMAGES TO CONNECTIONS: CAN DQN WITH GNNS LEARN THE STRATEGIC GAME OF HEX?

arXiv

引用

arXiv 2023年

作者： Keller, Yannik Blüml, Jannis Sudhakaran, Gopika Kersting, Kristian Computer Science Department TU Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany

The gameplay of strategic board games such as chess, Go and Hex is often characterized by combinatorial, relational structures—capturing distinct interactions and non-local patterns—and not just images. Nonetheless, most common self-play reinforcement learning (RL) approaches simply approximate policy and value functions using convolutional neural networks (CNN). A key feature of CNNs is their relational inductive bias towards locality and translational invariance. In contrast, graph neural networks (GNN) can encode more complicated and distinct relational structures. Hence, we investigate the crucial question: Can GNNs, with their ability to encode complex connections, replace CNNs in self-play reinforcement learning? To this end, we do a comparison with Hex—an abstract yet strategically rich board game—serving as our experimental platform. Our findings reveal that GNNs excel at dealing with long range dependency situations in game states and are less prone to overfitting, but also showing a reduced proficiency in discerning local patterns. This suggests a potential paradigm shift, signaling the use of game-specific structures to reshape self-play reinforcement learning. © 2023, CC BY-SA.

关键词： Reinforcement learning

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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Boundary Conditions in Geodesic Motion Planning for Manipulators

Boundary Conditions in Geodesic Motion Planning for Manipula...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mario Laux Andreas Zell Department of Computer Science Chair of Cognitive Systems University of Tübingen Tübingen Germany

In dynamic environments, robotic manipulators and especially cobots must be able to react to changing circumstances while in motion. This substantiates the need for quick trajectory planning algorithms that are able to cope with arbitrary velocity and acceleration boundary conditions. Apart from dynamic re-planning, being able to seamlessly join trajectories together opens the door for divide-and-conquer-type algorithms to focus on the individual parts of a motion separately. While geodesic motion planning has proven that it can produce very smooth and efficient actuator movement, the problem of incorporating non-zero boundary conditions has not been addressed yet. We show how a set of generalized coordinates can be used to transition between boundary conditions and free movement in an optimal way while still retaining the known advantages of geodesic planners. We also outline, how our approach can be combined with the family of time-scaling algorithms for further improvement of the generated trajectories.

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DARNet: dual attention refinement network with spatiotemporal construction for auditory attention detection 24

DARNet: dual attention refinement network with spatiotempora...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Sheng Yan Cunhang Fan Hongyu Zhang Xiaoke Yang Jianhua Tao Zhao Lv Anhui Province Key Laboratory of Multimodal Cognitive Computations School of Computer Science and Technology Anhui University Department of Automation Tsinghua University

ISBN: (纸本)9798331314385

At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model's ability to decode brain activity. To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion & classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model's ability to capture long-range latent dependencies. The feature fusion & classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results. The experimental results indicate that DARNet achieved excellent classification performance, particularly under short decision windows. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91%. Code is available at: https://***/fchest/***.

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Maternal Health Risk Classification - A Comparison between Algorithm Decision Tree and k-Nearest Neighbor (kNN)

Maternal Health Risk Classification - A Comparison between A...

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2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology, ICSEIET 2023

作者： Fatmawati, Maylia Novita, Mega Saputro, Nugroho Dwi Chauhan, Alok Singh Nada, Noora Qotrun Sinha, Amit Herlambang, Bambang Agus Gupta, Shruti Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Informatics Semarang Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Semarang Indonesia School of Computing Science & Engineering Galgotias University Greater Noida India ABES Engineering College Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

Maternal health is a critical concern, particularly for individuals who are pregnant and will shape the future generations. However, not all expectant mothers receive tailored attention and care for their unique health needs. Consequently, many mothers face high levels of health risks during pregnancy, highlighting the importance of classifying maternal health risks. Classification can help identify influential factors affecting maternal health and predict risk levels for individual mothers. In the field of data mining, various classification methods exist for analyzing complex datasets. This study focuses on two widely used algorithms: decision tree and k-Nearest Neighbor (kNN). By employing these algorithms, we aim to classify a dataset comprising 1,014 records of maternal health risk levels. Through cross-validation and T-tests, we rigorously evaluate the performance of the decision tree and kNN algorithms in this context. Our findings indicate that the kNN algorithm is more suitable for classifying maternal health risks, achieving a significantly higher accuracy of 71.50% compared to the decision tree algorithm's accuracy of 70.71%. This result establishes the superiority of the kNN algorithm in accurately classifying maternal health risks. These findings have implications for developing targeted interventions and tailored healthcare strategies to mitigate identified risks. © 2023 IEEE.

关键词： Health risks

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LLAVAGUARD: An Open VLM-based Framework for Safeguarding Vision Datasets and Models

arXiv

引用

arXiv 2024年

作者： Helff, Lukas Friedrich, Felix Brack, Manuel Kersting, Kristian Schramowski, Patrick Department of Computer Science Technical University of Darmstadt Germany hessian.AI Darmstadt Germany DFKI Germany Centre for Cognitive Science Darmstadt Germany Ontocord Germany

This paper introduces LlavaGuard, a suite of VLM-based vision safeguards that address the critical need for reliable guardrails in the era of large-scale data and models. To this end, we establish a novel open framework, describing a customizable safety taxonomy, data preprocessing, augmentation, and training setup. For teaching a VLM safeguard on safety, we further create a multimodal safety dataset with high-quality human expert annotations, where each image is labeled with a safety rating, category and rationale. We also employ advanced augmentations to support context-specific assessments. The resulting LlavaGuard models, ranging from 0.5B to 7B, serve as a versatile tool for evaluating the safety compliance of visual content against flexible policies. In comprehensive experiments, LlavaGuard outperforms both state-of-the-art safeguards and VLMs in accuracy and in flexibly handling different policies. Additionally, we demonstrate LlavaGuard’s performance in two real-world applications: large-scale dataset annotation and moderation of text-to-image models. We make our entire framework publicly available1, including the dataset and model weights. © 2024, CC BY.

关键词： Guards (shields)

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Deciphering the Complex Characterization of Coding LncRNA

Deciphering the Complex Characterization of Coding LncRNA

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Tianyuan Liu Xiucai Ye Tetsuya Sakurai Tsukuba Life Science Innovation Program University of Tsukuba Tsukuba Japan Department of Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This research addresses the intricate nature of coding long non-coding RNAs (lncRNAs), challenging the traditional view of these molecules as merely non-coding elements. By analyzing sequence, physicochemical, and structural features, we have identified distinct characteristics of mRNA, coding lncRNA, and untranslated lncRNA. The CodLncPred model, developed using the XGBoost model, outperforms existing tools in classifying coding lncRNAs. Furthermore, our study evaluates the computational efficiency of various algorithms, including the cost of time and memory, underscoring the practical implications. Our findings offer a new perspective on coding lncRNAs, providing a robust framework for future exploration in genome biology and disease research.

关键词： Drugs Accuracy Computational modeling RNA Genomics Encoding Classification algorithms

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