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arXiv 2023年

作者： Bayram, Firas Aupke, Phil Ahmed, Bestoun S. Kassler, Andreas Theocharis, Andreas Forsman, Jonas Department of Mathematics and Computer Science Karlstad University Karlstad65188 Sweden Department of Engineering and Physics Karlstad University Karlstad University Karlstad65188 Sweden Advanced Analytics Solution CGI Karlstad65224 Sweden Faculty of Computer Science Deggendorf Institute of Technology Deggendorf94469 Germany Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Prague16627 Czech Republic

Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs. © 2023, CC BY.

关键词： Long short-term memory

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Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Xiao, Hui Hong, Yuting Dong, Li Yan, Diqun Zhuang, Jiayan Xiong, Junjie Liang, Dongtai Peng, Chengbin The Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Hangzhou Shenhao Technology Co. Ltd Hangzhou310000 China

Semi-supervised semantic segmentation relieves the reliance on large-scale labeled data by leveraging unlabeled data. Recent semi-supervised semantic segmentation approaches mainly resort to pseudo-labeling methods to exploit unlabeled data. However, unreliable pseudo-labeling can undermine the semi-supervision processes. In this paper, we propose an algorithm called Multi-Level Label Correction (MLLC), which aims to use graph neural networks to capture structural relationships in Semantic-Level Graphs (SLGs) and Class-Level Graphs (CLGs) to rectify erroneous pseudo-labels. Specifically, SLGs represent semantic affinities between pairs of pixel features, and CLGs describe classification consistencies between pairs of pixel labels. With the support of proximate pattern information from graphs, MLLC can rectify incorrectly predicted pseudo-labels and can facilitate discriminative feature representations. We design an end-to-end network to train and perform this effective label corrections mechanism. Experiments demonstrate that MLLC can significantly improve supervised baselines and outperforms state-of-the-art approaches in different scenarios on Cityscapes and PASCAL VOC 2012 datasets. Specifically, MLLC improves the supervised baseline by at least 5% and 2% with DeepLabV2 and DeepLabV3+ respectively under different partition protocols. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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About Time: Advances, Challenges, and Outlooks of Action Understanding

arXiv

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arXiv 2024年

作者： Stergiou, Alexandros Poppe, Ronald Faculty of Electrical Engineering Mathematics and Computer Science The University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands Department of Information and Computing Sciences Utrecht University Princetonplein 5 Utrecht3584 CC Netherlands

We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries, synthesize unobserved parts of videos, and predict context. This survey comprehensively reviews advances in uni- and multi-modal action understanding across a range of tasks. We focus on prevalent challenges, overview widely adopted datasets, and survey seminal works with an emphasis on recent advances. We broadly distinguish between three temporal scopes: (1) recognition tasks of actions observed in full, (2) prediction tasks for ongoing partially observed actions, and (3) forecasting tasks for subsequent unobserved action. This division allows us to identify specific action modeling and video representation challenges. Finally, we outline future directions to address current shortcomings. © 2024, CC BY.

关键词： Video analysis

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Creation of a Smart Energy Management System in the RTU Student Campus in Kipsala

Creation of a Smart Energy Management System in the RTU Stud...

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International Scientific Conference on Power and electrical engineering of Riga Technical University (RTUCON)

作者： Egīls Dzelzītis Pēteris Apse-Apsītis Edijs Luņģis RTU Institute of Heat Gas and Water Technology Riga Latvia RTU Institute of Industrial Electronics Electrical Engineering and Energy Riga Latvia Faculty Of Computer Science Information Tehnology And Energy RTU Riga Latvia

The task of the energy management system is to create conditions for maximizing the efficiency of electricity and heat consumption in RTU buildings, while ensuring a comfortable indoor climate and enabling continuous ... 详细信息

ISBN: (数字)9798350365771

ISBN: (纸本)9798350365788

关键词： Energy consumption Costs Three-dimensional displays ISO Standards Machine learning Digital twins Energy management systems Monitoring Meteorology Resistance heating

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PressHeart: A Two-Factor Authentication Mechanism via PPG Signals for Wearable Devices

PressHeart: A Two-Factor Authentication Mechanism via PPG Si...

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IEEE International Conference on Communications (ICC)

作者： Weihao Zhang Xinyan Zhou Haiming Chen Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China The Department of Artificial Intelligence Nankai University Tianjin China Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The integration of biometric-based user authentication into wearable devices has become increasingly important for protecting users' private information and property. In this paper, we propose a two-factor authentication mechanism, PressHeart, which utilizes widely-used Photoplethysmography (PPG) sensors embedded in wearable devices. Our observations reveal that PPG sensors can implicitly measure excitation press signals when the users press the skin or the device, which implies individual wearing and behavioral habits that can serve as reliable factors for user authentication. For better separating the press signals from PPG signals and extracting sufficient signals for user authentication, we introduce two adaptive segmentation methods and a specific feature set for feature extraction in PressHeart. To validate the performance of Press Heart, we develop a prototype with a PPG sensor and conduct experiments involving 14 participants. The experiment results demonstrate that PressHeart can achieve an average of 94.9 % accuracy with high authentication efficiency and security.

关键词： Performance evaluation Presses Accuracy Authentication Prototypes Feature extraction Skin

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The Child Factor in Child-Robot Interaction: Discovering the Impact of Developmental Stage and Individual Characteristics

arXiv

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arXiv 2024年

作者： Rudenko, Irina Rudenko, Andrey Lilienthal, Achim J. Arras, Kai O. Bruno, Barbara Freiburg im Breisgau Germany Robert Bosch GmbH Corporate Research Stuttgart Germany Germany Örebro University Sweden Faculty of Computer Science Electrical Engineering and Information Technology University of Stuttgart Germany Germany

Social robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily life. Child-Robot Interaction (CRI), as any domain involving children, inevitably faces the major challenge of designing generalized strategies to work with unique, turbulent and very diverse individuals. Addressing this challenging endeavor requires to combine the standpoint of the robot-centered perspective, i.e. what robots technically can and are best positioned to do, with that of the child-centered perspective, i.e. what children may gain from the robot and how the robot should act to best support them in reaching the goals of the interaction. This article aims to help researchers bridge the two perspectives and proposes to address the development of CRI scenarios with insights from child psychology and child development theories. To that end, we review the outcomes of the CRI studies, outline common trends and challenges, and identify two key factors from child psychology that impact child-robot interactions, especially in a long-term perspective: developmental stage and individual characteristics. For both of them we discuss prospective experiment designs which support building naturally engaging and sustainable interactions. © 2024, CC BY.

关键词： Machine design

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Literature Review of Quantum-dot Cellular Automata (QCA) Technology used in Cryptography

Literature Review of Quantum-dot Cellular Automata (QCA) Tec...

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Recent Trends in Information Technology (CSCTIT), College of science International Conference of

作者： Maha A. Sayal S.A. Albermany Esam Al AlKaldy Computer Science Department College of computer and mathematics Science University of Thi-Qar Thi-Qar Iraq Computer Department Faculty of Computer Science & Mathematics University of Kufa Kufa Iraq Electrical Engineering Collage of Engineering University of Kufa Kufa Iraq

Cellular quantum dot (QCA) technology is a recent technology. It is used to get rid of the limitations of complementary metal semiconductor (CMOS). This is why high-speed QCA circuits, ultra-low consumption, and less space are designed. to encrypt securely, the purpose is the continuous development of the encryption and decryption process. Therefore, we use electronic circuits and gates such as the majority gate, inverter gate, and MUX circuit in this paper. The cipher used here is stream.

关键词：

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Comparison Between Single-Input and Multi-Input Classification with the Application of Canny Feature Extraction and Classification Algorithms on the Toraja Buffalo Dataset

Comparison Between Single-Input and Multi-Input Classificati...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdul Rachman Manga' As'syahrin Nanda Anik Nur Handayani Heru Wahyu Herwanto Rosa Andrie Asmara Dirgahayu Lantara Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Department of Electrical Engineering Universitas Negeri Malang Malang Indonesia Information Technology Department State Polytechnic of Malang Malang Indonesia Faculty of Industrial Engineering Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements

关键词： Support vector machines Measurement Accuracy Nearest neighbor methods Feature extraction Hyperparameter optimization Data models Livestock Recording Information management

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Semantic Data Representation for Explainable Windows Malware Detection Models

arXiv

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arXiv 2024年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kľuka, Ján Bisták, Tomáš Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava Slovakia

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive domains of human interest. They are becoming increasingly important also in the areas that have been until very recently dominated by subsymbolic knowledge representation and machine-learning (ML) based data processing. One such area is information security, and specifically, malware detection. We thus propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE – the Windows binary format) malware files. This ontology is inspired by the structure of the EMBER dataset, which focuses on the static malware analysis of PE files. With this proposal, we hope to provide a unified semantic representation for the existing and future PE-malware datasets and facilitate the application of symbolic, neuro-symbolic, or otherwise explainable approaches in the PE-malware-detection domain, which may produce interpretable results described by the terms defined in our ontology. In addition, we also publish semantically treated EMBER data, including fractional datasets, to support the reproducibility of experiments on EMBER. We supplement our work with a preliminary case study, conducted using concept learning, to show the general feasibility of our approach. While we were not able to match the precision of the state-of-the-art ML tools, the learned malware discriminators were interesting and highly interpretable. Copyright © 2024, The Authors. All rights reserved.

关键词： Ontology

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Statistical Time Series Forecasting Models for Pandemic Prediction

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computer Systems science & engineering 2023年第10期47卷 349-374页

作者： Ahmed ElShafee Walid El-Shafai Abeer D.Algarni Naglaa F.Soliman Moustafa H.Aly Department of Electrical Engineering Faculty of EngineeringAhram Canadian University6th October CityGizaEgypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electronics and Communications Engineering Department College of Engineering and TechnologyArab Academy for ScienceTechnology and Maritime TransportAlexandria1029Egypt

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease *** this case,COVID-19 data is a time-series dataset that can be projected using different ***,this work aims to gauge the spread of the outbreak severity over ***,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus *** have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML *** of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive ***,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best ***,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

关键词： Forecasting COVID-19 predictive models medical viruses mathematical model market research diseases

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