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arXiv 2024年

作者： Perera-Lago, Javier Toscano-Durán, Víctor Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutierrez-Naranjo, Miguel Ángel Rucco, Matteo Department of Applied Mathematics I University of Seville Seville Spain Department of Quantitative Methods University of Loyola Campus Sevilla Dos Hermanas Seville Spain Department of Computer Science and Artificial Intelligence University of Seville Seville Spain Data Science Department Biocentis Milan Italy

In recent years, Deep Learning has gained popularity for its ability to solve complex classification tasks, increasingly delivering better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure how similar are the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model. © 2024, CC BY.

关键词： Energy efficiency

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COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data

arXiv

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arXiv 2022年

作者： Ditz, Jonas C. Reuter, Bernhard Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen Germany

Motivation: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate as black boxes. In high stakes scenarios, like healthcare, using a black-box model poses safety and security issues. Without an explanation about molecular factors and phenotypes that affected the prediction, healthcare providers are left with no choice but to blindly trust the models. We propose a new type of artificial neural network, named Convolutional Omics Kernel Network (COmic). By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples. Furthermore, COmic can be easily adapted to utilize multi-omics data. Results: We evaluated the performance capabilities of COmic on six different breast cancer cohorts. Additionally, we trained COmic models on multi-omics data using the METABRIC cohort. Our models performed either better or similar to competitors on both tasks. We show how the use of pathway-induced Laplacian kernels opens the black-box nature of neural networks and results in intrinsically interpretable models that eliminate the need for post-hoc explanation models. Availability: Datasets, labels, and pathway-induced graph Laplacians used for the single-omics tasks can be downloaded here. While datasets and graph Laplacians for the METABRIC cohort can be downloaded from the above mentioned repository, the labels have to be downloaded from cBioPortal. COmic source code as well as all scripts necessary to reproduce the experiments and analysis are publicly available at https://***/jditz/comics. © 2022, CC BY.

关键词： Large dataset

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VAD-Net: Multidimensional Emotion Recognition from Facial Expression Images

VAD-Net: Multidimensional Emotion Recognition from Facial Ex...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yi Huo Yun Ge Educational Information Technology Teachers’ College Beijing Union University Beijing China Department of Computer Teaching and Research University of Chinese Academy of Social Science Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Current FER (Facial Expression Recognition) dataset is mostly labeled by emotion categories, such as happy, angry, sad, fear, disgust, surprise, and neutral which are limited in expressiveness. However, future affective computing requires more comprehensive and precise emotion metrics which could be measured by VAD(Valence-Arousal-Dominance) multidimension parameters. To address this, AffectNet has tried to add VA (Valence and Arousal) information, but still lacks D(Dominance). Thus, the research introduces VAD annotation on FER2013 dataset, takes the initiative to label D(Dominance) dimension. Then, to further improve VAD prediction accuracy, it enforces orthogonalized convolution on regression network to extract more diverse and expressive features. Experiment results show that D dimension could be measured but is difficult to obtain compared with V and A dimension, no matter in manual annotation or regression model prediction. Furthermore, the ablation test is carried out by introducing orthogonal convolution whose results verifies that better VAD prediction could be achieved under the configuration of orthogonalized convolution. Therefore, the research provides an initial annotation work for D(Dominance) dimension on FER dataset, and proposes a better regression network for VAD prediction through orthogonalized operation. The newly built VAD annotated FER2013 dataset could act as a benchmark to measure VAD multidimensional emotions, while the orthogonalized regression network could act as the baseline for VAD facial expression recognition. The newly labeled VAD dataset and prediction baseline code is publicly available on Github: https://***/YeeHoran/VAD-Net.

关键词： Emotion recognition Convolution Annotations Face recognition Current measurement Education Benchmark testing

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Machine Learning for Energy Load Prediction and its Interpretation 11

Machine Learning for Energy Load Prediction and its Interpre...

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11th IEEE International Conference on Intelligent Systems, IS 2022

作者： Charytanowicz, Malgorzata Olwert, Anna Radziszewska, Weronika Jarnicka, Jolanta Gajowniczek, Krzysztof Zabkowski, Tomasz Brozyna, Jacek Mentel, Grzegorz Matejko, Grzegorz Research Institute Pas Center for Methods of Data Analysis Systems Warsaw Poland Research Institute Pas Department of Computer Science Systems Warsaw Poland Research Institute Pas Center for Computer Modelling Systems Warsaw Poland Warsaw University of Life Sciences Institute of Information Technology Warsaw Poland Rzeszow University of Technology Department of Quantitative Methods Rzeszów Poland Polskie Towarzystwo Cyfrowe Lublin Poland

ISBN: (纸本)9781665456562

This paper refers to real-life data on energy consumption from three companies, connected to a medium voltage grid. The companies use single-, two-, or three-zone tariffs and the general characteristic of their activity is comparable. The input dataset contains hourly electricity consumption recorded from January 2020 to December 2020. The independent variables used for the study include weather, time-dependent factors, and aggregated energy factors. We perform the prediction of consumption using three methods: naive, multiple regression, and extreme gradient boosting regression. The performance of the methods is compared based on the MSE, RMSE, MAE, and MAPE metrics. The study shows that the scores achieved for the extreme gradient boosting method are higher than for the multiple regression. To gain the interpretability of the energy load forecasting and feature analysis the Shapley additive explanations method is used for the best model. As a result, a ranking of the most important factors is created. It shows the importance of aggregated energy factors and weather conditions while placing daylight and season at the bottom of the ranking. © 2022 IEEE.

关键词： Energy utilization

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Automated Segmentation of Ischemic Stroke Lesions in Non-Contrast Computed Tomography Images for Enhanced Treatment and Prognosis

arXiv

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arXiv 2024年

作者： Musah, Toufiq Adjei, Prince Ebenezer Otoo, Kojo Obed Kwame Nkrumah University of Science and Technology Department of Computer Engineering Ghana Kumasi Centre for Collaborative Research in Tropical Medicine Ghana Komfo Anokye Teaching Hospital Radiology Department Ghana

Stroke is the second leading cause of death worldwide, and is increasingly prevalent in low- and middle-income countries (LMICs). Timely interventions can significantly influence stroke survivability and the quality of life after treatment. However, the standard and most widely available imaging method for confirming strokes and their sub-types, the NCCT, is more challenging and time-consuming to employ in cases of ischemic stroke. For this reason, we developed an automated method for ischemic stroke lesion segmentation in NCCTs using the nnU-Net framework, aimed at enhancing early treatment and improving the prognosis of ischemic stroke patients. We achieved Dice scores of 0.596 and Intersection over Union (IoU) scores of 0.501 on the sampled dataset. After adjusting for outliers, these scores improved to 0.752 for the Dice score and 0.643 for the IoU. Proper delineation of the region of infarction can help clinicians better assess the potential impact of the infarction, and guide treatment procedures. © 2024, CC BY.

关键词： computerized tomography

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Engineering Students' Attitudes and Perceptions towards Project-Based Learning: A Study from Kerala, Southern India 13

Engineering Students' Attitudes and Perceptions towards Proj...

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13th IEEE International Conference on teaching, Assessment and Learning for Engineering, TALE 2024

作者： Akshay, R.S. Sunny, Alviya Saranya, V.S. John, Aby Subheesh, N.P. Anzar, S.M. Government Engineering College Department of Electronics and Communication Engineering Trivandrum India Model Engineering College Department of Computer Science and Engineering India Accenture Solutions Pvt Ltd Coimbatore India Rudn University Faculty of Philology Moscow Russia Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India

ISBN: (纸本)9798350376234

The shift from teacher-centric to student-centric education has been a subject of debate, with traditional instruction often criticized for fostering rote learning and passive engagement. Due to its emphasis on student autonomy and real-world application of knowledge, the growing popularity of Project-Based Learning as a novel alternative to traditional classroom-based teaching pedagogy is attracting educational institutions all around the world. In engineering education, multifaceted skill development is paramount. PjBL offers a dynamic approach to cultivate essential competencies and it significantly improves engineering education. The National Education Policy 2020 (NEP 2020) of India lays a strong emphasis on Project-Based Learning as the cornerstone of educational reform. But, only smaller number of research studies are conducted in the country to explore the attitudes and perceptions of students towards Project-Based Learning in the engineering sector. It lays bare the imminent need for educationalists to focus on this research gap. This paper explores the attitudes and perceptions of engineering students towards Project-Based Learning from Kerala, a state in Southern India. A novel questionnaire entitled Project-Based Learning in Engineering Education in Kerala (PLEEK) is developed by the IEEE Education Society Kerala Chapter to conduct the survey. A total of 136 students from all over Kerala attended the survey. It is observed that the students have a positive perception of Project-Based Learning and its impact on enhancing their education. On the other hand, an abysmally low count of 11% of students believed that their current campus ecosystem supported this teaching pedagogy. They highlighted the need for a drastic transformation in the present academic infrastructure to support Project-Based Learning. The findings of the study underscore the potential of Project-Based Learning in enhancing learning outcomes, fostering critical skills, and preparing studen

关键词： Engineering education

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Heuristic Algorithm for Solving Travelling Salesman Problem: Adding the Endpoints of the Longest Edges

Heuristic Algorithm for Solving Travelling Salesman Problem:...

引用

International Conference on Problems of Cybernetics and Informatics (PCI)

作者： Fidan Nuriyeva Department of Computer Science Dokuz Eylul University Izmir Turkey Laboratory of Recognition Identification and Methods of Optimal Solutions Institute of Control Systems Baku Azerbaijan

This study proposes, a new heuristic algorithm based on insertion heuristic to solve the symmetric Travelling Salesman Problem. In the proposed algorithm, firstly the two longest edges are selected and the starting tour passing through their endpoints is constructed, then the endpoints of the next longest edges are added to this tour and the solution passing through all the vertices is found. This problem is known to be NP-Hard. The proposed method was successfully tested on different-sized problems. The experimental results show that the proposed algorithm outperforms many existing methods.

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It's a Match - Enhancing the Fit between Users and Phishing Training through Personalisation 25

It's a Match - Enhancing the Fit between Users and Phishing ...

引用

2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Schöni, Lorin Roch, Neele Sievers, Hannah Strohmeier, Martin Mayer, Peter Zimmermann, Verena Security Privacy & Society Eth Zurich Zurich Switzerland Armasuisse Thun Switzerland Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Department of Humanities Social and Political Sciences Eth Zürich Zürich Switzerland

ISBN: (纸本)9798400713941

Effective training is essential for enhancing users' ability to detect phishing attempts. Personalised training offers huge potential to more closely align training content with individuals' needs and skill levels. In an online study, we assigned N=342 participants to personalised training or a random training variant to compare their effectiveness. The personalisation was based on a phishing proficiency score calculated from factors such as detection ability, knowledge, and security attitude. After training, the participants demonstrated greater proficiency, with an increased ability to detect phishing emails and higher security attitudes. These effects were most pronounced in the personalised condition, demonstrating the potential of personalisation to improve training outcomes. Overall, personalised training levelled the playing field, efficiently bringing all groups, regardless of their initial proficiency, to a comparable and desired post-training phishing proficiency level. Finally, we derived recommendations for designing personalised phishing training content and assigning users to suitable training programmes. © 2025 Copyright held by the owner/author(s).

关键词： Driver training

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Pareto-Optimal Estimation and Policy Learning on Short-Term and Long-Term Treatment Effects

SSRN

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SSRN 2024年

作者： Wang, Yingrong Wu, Anpeng Li, Haoxuan Liu, Weiming Miao, Qiaowei Xiong, Ruoxuan Wu, Fei Kuang, Kun College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310058 China Center for Data Science Peking University Beijing Beijing100091 China Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States

This paper focuses on developing Pareto-optimal estimation and policy learn- ing to identify the most effective treatment that maximizes the total reward from both short-term and long-term effects, which might conflict with each other. For example, a higher dosage of medication might increase the speed of a patient’s recovery (short-term) but could also result in severe long-term side effects. Although recent works have investigated the problems about short-term or long-term effects or the both, how to trade-off between them to achieve optimal treatment remains an open challenge. Moreover, when multiple objectives are directly estimated using conventional causal repre- sentation learning, the optimization directions among various tasks can con- flict as well. In this paper, we systematically investigate these issues and introduce a Pareto-Efficient algorithm, comprising Pareto-Optimal Estima- tion (POE) and Pareto-Optimal Policy Learning (POPL), to tackle them. POE incorporates a continuous Pareto module with representation balanc- ing, enhancing estimation efficiency across multiple tasks. As for POPL, it involves deriving short-term and long-term outcomes linked with various treatment levels, facilitating an exploration of the Pareto frontier emanating from these outcomes. Results on both the synthetic and real-world datasets demonstrate the superiority of our method. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Paper Defects Classification Based on VGG16 and Transfer Learning

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Palpu Chongi Gisul/Journal of Korea Technical Association of the Pulp and Paper Industry 2021年第2期53卷 5-14页

作者： Yun-Hui, Qu Tang, Wei Feng, Bo Computer Teaching and Research Section Xi’an Medical University Xi’an Shaanxi710021 China Department of Electric and Control Engineering Shaanxi University of Science & Technology Xi’an Shaanxi710021 China Department of Electric and Control Engineering Shaanxi University of Science & Technology Xi’an Shaanxi710021 China

There are some problems in traditional paper defects classification, such as the poor generalization performance, less types of recognition, and insufficient recognition accuracy. The deep learning method provides a new scheme for paper defects classification. However, due to the small sample size of paper defect images set, the over fitting phenomenon is easy to appear in the training process. Aiming this problem, a transfer learning method based on convolutional neural network model is ***, freezing the first seven construction layers of VGG16 network which has been trained by ImageNet, and fine tune the rest convolution layers with the paper defect images set to complete the feature extraction;Secondly, the full connection layers for classification are improved to meet the needs of paper defects classification;Finally, transfer learning strategy is adopted in the training process to improve the efficiency. The experimental results demonstrate that the paper defects classification proposed in our approach can improve the efficiency and accuracy of paper defects recognition. The approach will beneficial for the web inspection process. © 2021 Korean Technical Assoc. of the Pulp and Paper Industry. All rights reserved.

关键词： Efficiency

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