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arXiv 2025年

作者： Bassan, Shahaf Gur, Shlomit Zeltyn, Sergey Mavrogiorgos, Konstantinos Eliav, Ron Kyriazis, Dimosthenis School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel IBM Research Haifa Israel Department of Digital Systems University of Piraeus Piraeus Greece Department of Computer Science Bar-Ilan University Giv'atayim Israel

Tasks in Predictive Business Process Monitoring (PBPM), such as Next Activity Prediction, focus on generating useful business predictions from historical case logs. Recently, Deep Learning methods, particularly sequence-to-sequence models like Long Short-Term Memory (LSTM), have become a dominant approach for tackling these tasks. However, to enhance model transparency, build trust in the predictions, and gain a deeper understanding of business processes, it is crucial to explain the decisions made by these models. Existing explainability methods for PBPM decisions are typically post-hoc, meaning they provide explanations only after the model has been trained. Unfortunately, these post-hoc approaches have shown to face various challenges, including lack of faithfulness, high computational costs and a significant sensitivity to out-of-distribution samples. In this work, we introduce, to the best of our knowledge, the first self-explaining neural network architecture for predictive process monitoring. Our framework trains an LSTM model that not only provides predictions but also outputs a concise explanation for each prediction, while adapting the optimization objective to improve the reliability of the explanation. We first demonstrate that incorporating explainability into the training process does not hurt model performance, and in some cases, actually improves it. Additionally, we show that our method outperforms post-hoc approaches in terms of both the faithfulness of the generated explanations and substantial improvements in efficiency. © 2025, CC BY.

关键词： Prediction models

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Development of an interactive web-based geovisual analytics platform for analysing crime data

Development of an interactive web-based geovisual analytics ...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Kaif, Mohammad Aman Samaiya, Sanidhya Purnima, Rapuri Alekhya Sivasankar, Thota Roy, Achintya Poojitha, Akula Niit University Computer Science and Engineering Neemrana India Niit University Geographical Information Systems Neemrana India Niit University Mathematics and Basic Sciences Neemrana India Tata Consultancy Services Geoinformatics Division Hyderabad India

ISBN: (纸本)9798350310955

This paper presents the development of an interactive web-based geovisual analytics platform for analyzing crime data. The platform integrates spatial criminology principles and GIS techniques to provide a user-friendly interface for exploring and analyzing crime patterns. District-wise annual crime data from the NCRB reports, with a specific focus on crimes against women between 2017 and 2021, is utilized. The system architecture incorporates React, JavaScript, Bootstrap, Express, GeoServer, and ***, while open-source tools like QGIS and OpenLayers enhance functionality. The platform offers a main dashboard with statistical information presented through doughnut and line charts, along with an interactive map for detailed exploration. It serves as a valuable resource for law enforcement agencies, offering a free and customizable solution for spatial crime data analysis. Future work includes improving the integration between the map-view and graph overview sections and expanding the platform's application to accommodate diverse geospatial datasets. © 2023 IEEE.

关键词： Crime

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A Machine Learning Approach to Uncertainty Analysis in Power System Planning: Insights and Pathways for Decarbonization

SSRN

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SSRN 2024年

作者： Jahangiri, Zahra Miri, Mohammad Moo Yi, Kwang McPherson, Madeleine Institute for Integrated Energy Systems University of Victoria VictoriaBC Canada Department of Engineering and Computer Science University of Victoria VictoriaBC Canada Department of Computer Science University of British Columbia VancouverBC Canada

This study introduces a methodology for the Uncertainty Analysis of the Canadian power system, leveraging ML techniques. Specifically, we seek to uncover nonlinear behaviors and potential bifurcations, which conventional modeling methods are unable to observe due to the computational burden. Thus, we overcome this burden by developing a surrogate model for capacity expansion model using neural networks, enabling extensive scenario exploration. We further utilize K-means clustering and statistical techniques to identify output variable correlations and corresponding distinct power system scenarios. With our method we investigate the effects of uncertainty in input parameters, including capital costs, demand, and carbon tax on output variables, revealing realistic carbon reduction targets, potential diversification of the power generation mix, and strategic investments in renewable energy. Key findings include the increased adaptability of gas combined cycle over gas simple cycle in response to carbon tax, highlighting its role in a low-carbon future. Variations in the modeled expansion of solar and wind capacity, influenced by capacity factors and location-specific costs, underscore the necessity of a flexible grid system. This analysis reveals important correlations, such as that observed between variable renewable energy capacity factors and transmission expansion, and increased reliance on gas combined cycle with wind and solar due to capacity factor limitations. This approach also highlights the insensitivity of hydroelectric power capacity, a significant contributor to Canada's generation mix, to changes in input parameters. To summarize, our work goes beyond the capabilities of existing models and allows extensive uncertainty analysis and provides key insights into system behavior, causative factors, and an expanded breadth of potential pathways, laying a foundation for strategic planning and policy formulation. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Reinforcement-Learning-Based Task Offloading in Edge Computing systems with Firm Deadlines

Reinforcement-Learning-Based Task Offloading in Edge Computi...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Khai Doan Wesley Araujo Evangelos Kranakis Ioannis Lambadaris Yannis Viniotis Department of Systems and Computer Engineering Carleton University Ottawa ON Canada Carleton University School of Computer Science Ottawa ON Canada Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Task offloading in mobile edge computing systems is subject to various random factors including the connection to external servers, new task requests from users, and the availability of local processing services. However, statistical information is often not available in practical scenarios. To tackle the issue, we adopt a Q-learning-based approach that learns the optimal task offloading policy through observations of random events. Traditional Q-learning methods may face challenges such as long training times and high memory usage due to the large state and action space. To overcome this problem, we propose a novel method that leverages the concept of adjacent state sequence. In this type of sequence, we can infer the optimal offloading decision of a system state from other states. This method aims to improve the convergence speed and memory efficiency of the learning model by reducing the number of parameters that need to be learned and stored. Those eliminated parameters instead can be computed via a derived linear expression. We conduct experiments to demonstrate the enhancement of our proposed method compared to the traditional $\mathbf{Q}-$ learning in the studied problem.

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FLOOR EXTRACTION AND DOOR DETECTION FOR VISUALLY IMPAIRED GUIDANCE

arXiv

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arXiv 2024年

作者： Berenguel-Baeta, Bruno Guerrero-Viu, Manuel de Nova, Alejandro Bermudez-Cameo, Jesus Perez-Yus, Alejandro Guerrero, Jose J. Instituto de Investigacion en Ingenieria de Aragon Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. The combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. This combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. The proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance. © 2024, CC BY.

关键词： Floors

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Design of an RF-CMOS Switched-Capacitor Power Amplifier for NB-IoT RF Transceivers

Design of an RF-CMOS Switched-Capacitor Power Amplifier for ...

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International Young Engineers Forum (YEF-ECE)

作者： Ana Isabel Santos João Pedro Oliveira Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal Department of Electrical and Computer Engineering Centre of Technology and Systems CTS – UNINOVA Caparica Portugal

The paper presents the implementation of a Switched Capacitor Power Amplifier (SCPA) to be integrated into a Narrowband Internet of Things (NB-IoT) Transceiver. The SCPA is designed to operate at a frequency of 0.9GHz and aiming the maximum output power allowed by the standard of 23dBm. All the blocks within the SCPA were developed using RF components of a standard 130nm CMOS process, with a 1.2V/2.4V supply voltage. Results show that the SCPA is able to produce a maximum output power of 15.61dBm with a maximum Power-Added Efficiency (PAE) of 26.52% and a Total Harmonic Distortion (THD) of 0.68%. The measured HD2 and HD3 are -70.23dBc and -43.41dBc, respectively. Additional Process, Voltage and Temperature (PVT) corners and Monte Carlo simulations indicate the SCPA operates properly when subject to different conditions and mismatches. Besides the SCPA, a 16 QAM modulation stage is designed in VerilogA in order to evaluate the performance of the RF PA when a sequence of symbols is transmitted.

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Computation of 2D Discrete Geometric Moments Through Inclusion-Exclusion 3rd

Computation of 2D Discrete Geometric Moments Through Inclusi...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Čomić, Lidija Magillo, Paola Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia Department of Computer Science Bioengineering Robotics and Systems Engineering University of Genova Genova Italy

ISBN: (纸本)9783031090363

We propose a new formula for computing discrete geometric moments on 2D binary images. The new formula is based on the inclusion-exclusion principle, and is especially tailored for images coming from computer art, characterized by a prevalence of horizontal and vertical lines. On the target class of images, our formula reduces the number of pixels where calculations are to be performed. © 2022, Springer Nature Switzerland AG.

关键词： Binary images

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Accessibility and Inclusion in the Game Industry: A position paper 20

Accessibility and Inclusion in the Game Industry: A position...

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20th International Conference on the Foundations of Digital Games, FDG 2025

作者： Westin, Thomas Brusk, Jenny Palmquist, Adam Department of Computer and Systems Sciences Stockholm University Kista Sweden Science Park Skövde Skövde University Skövde Sweden School of Innovation Design and Engineering Mälardalen University Eskilstuna Sweden

ISBN: (纸本)9798400718564

Accessibility removes barriers in the workplace, with game design processes being enhanced by better development tools, improved recruitment procedures, and an increased awareness of inclusive practices. This position paper describes accessibility within the field of digital games - with a special focus on game developers with disabilities - based on challenges and opportunities at workplaces described in a previously available summary of an industry roundtable in 2024. The aim is to find ways forward by raising awareness about issues of inclusion and accessibility based on views from people in the game industry and related research. The paper presents seven thematic concepts and related principles: 1) proactive, inclusive leadership;2) better structure at workplaces;3) adaptation with remote work;4) normalise and facilitate adaptations;5) inclusive employment processes;6) sustainable advocacy;7) accessible tools for inclusive game design. This paper also presents a tentative model where the needs of employees are the input while policy, methods, tools, advocacy and stakeholders provide support, and accountability provides control, which combined may lead to proactive inclusion. Our position is that while game accessibility has advanced significantly in the industry in the last decade, there is a need for more research about inclusion and accessibility in game design processes within game companies. © 2025 Copyright held by the owner/author(s).

关键词： accessible tools advocacy employment processes facilitated adaptations Inclusive leadership normalised adaptations remote work workplace policy workplace structure

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy Data analysis Computational modeling Predictive models Feature extraction Data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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