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Humanities and Social Sciences Communications 2025年第1期12卷 1-20页

作者： Kelsey Cotton Kıvanç Tatar Anna-Kaisa Kaila Petra Jääskeläinen André Holzapfel Data Science and AI Division Computer Science and Software Engineering Chalmers Institute of Technology and University of Gothenburg Gothenburg Sweden Division of Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden

The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology,matters of fact and concernand feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine theimplicit, the wider connections and entangled filaments in Human–AI musical interactions.

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Comparison of Missing Data Imputation Methods in Time Series Forecasting

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computers, Materials & Continua 2022年第1期70卷 767-779页

作者： Hyun Ahn Kyunghee Sun Kwanghoon Pio Kim Division of Software Convergence Hanshin UniversityGyeonggi18101Korea Contents Convergence Software Research Institute Kyonggi UniversityGyeonggi16227Korea Division of AI Computer Science and Engineering Kyonggi UniversityGyeonggi16227Korea

Time series forecasting has become an important aspect of data analysis and has many real-world ***,undesirable missing values are often encountered,which may adversely affect many forecasting *** this study,we evaluate and compare the effects of imputationmethods for estimating missing values in a time *** approach does not include a simulation to generate pseudo-missing data,but instead perform imputation on actual missing data and measure the performance of the forecasting model created *** an experiment,therefore,several time series forecasting models are trained using different training datasets prepared using each imputation ***,the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting *** results obtained from a total of four experimental cases show that the k-nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.

关键词： Missing data imputation method time series forecasting LSTM

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Scenario pattern matching in large sensor recordings with simulation models for cyber-physical systems

Scenario pattern matching in large sensor recordings with si...

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Summer computer Simulation Conference, SCSC 2014, Part of the 2014 Summer Simulation Multiconference, SummerSim 2014

作者： Berger, Christian Department of Computer Science and Engineering Division for Software Engineering University of Gothenburg Sweden

ISBN: (纸本)9781632667335

Today's cyber-physical systems (CPS) like advanced driver assistance systems (ADAS) in modern vehicles use a large variety of sensors to process data from their surroundings. Thereby, systems like lane departure warning and adaptive cruise control support the driver in tedious or critical traffic situations. During the development of such systems, engineers also use recorded sensor data in offline validations to complement simulations that exhibit optimal environmental conditions. Such recordings are an ever-growing data source, and thus, effective methods are needed to find proper recordings in databases to support the system validation. Textual annotations for these sensor recordings require a well-defined taxonomy and continuous maintenance. Instead of relying on such a manually maintained taxonomy, an automated method for identifying relevant scenarios from real world sensor recordings by using simulation models is described. The outlined approach is evaluated with real world data sets used by lane-detection algorithms from nine different projects. Results from these data sets of more than 2.3 GB show that finding relevant traffic scenarios is possible in less than 0.15s.

关键词： Big data

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Fast block mode decision algorithm in H.264/AVC video coding

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2008年第3期19卷 175-183页

作者： Kim, Jong-Ho Kim, Byung-Gyu [a]Real-time Multimedia Research Lab Embedded Software Division Electronics and Telecommunications Research Institute Republic of Korea [b]Computer Software & Engineering Department University of Science and Technology Republic of Korea

The recent video coding standard H.264/AVC show extremely higher coding efficiency compare to any other previous standards. H.264/AVC can achieve over 50% of bit rate saving with same quality using the rate-distortion process, but it brings high computational complexity. In this paper, we propose an algorithm that can reduce the complexity of the codec by reducing the block mode decision process adaptively. Block mode decision process in H.264/AVC consists of inter mode decision process and intra mode decision process. We deal with reduction method for inter and intra mode decision. In this paper an efficient method is proposed to reduce the inter mode decision complexity using the direct prediction methods based on block correlation and adaptive rate distortion cost threshold for early stopping. The fast intra mode reduction algorithm based on inter mode information is also proposed to reduce the computational complexity. The experimental results show that the proposed algorithm can achieve up to 63.34-77.39% speed up ratio with a little PSNR loss. Increment in bit requirement is also not much noticeable. (c) 2007 Elsevier Inc. All rights reserved.

关键词： video coding image compression image coding prediction methods video processing

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MangoLeafXNet: An Explainable Deep Learning Model for Accurate Mango Leaf Disease Classification

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IEEE Access 2025年

作者： Rayed, Md Eshmam Jim, Jamin Rahman Islam, Md Juniadul Mridha, M.F. Kabir, Md Mohsin Hossen, Md. Jakir American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh Mälardalens University Division of Computer Science and Software Engineering Västerås722 20 Sweden Multimedia University Faculty of Engineering and Technology Department of Robotics and Automation Melaka Malaysia

Addressing the global challenge of ensuring a consistent and abundant supply of fresh fruit, particularly in the context of fruit crops, is hindered by the prevalence of plant diseases. These diseases directly impact the quality of fruits, leading to a decline in overall agricultural production. Mango leaf diseases pose significant threats to global mango production, necessitating accurate and efficient classification techniques for timely disease management. Our study focuses on introducing MangoLeafXNet, a customized Convolutional Neural Network (CNN) architecture specifically tailored for the classification of mango leaf diseases, along with a healthy class. Our proposed model comprises six layers optimized to capture intricate disease patterns, demonstrating superior performance compared with prevalent pre-trained models. The model is trained and evaluated on three publicly available datasets: MangoLeafBD (4000 images across 8 classes), MangoPest (16 pest classes including healthy leaves), and MLDID (3000 high-resolution images across 5 classes). Our model demonstrated exceptional classification performance, attaining 99.8% accuracy, 99.62% recall, 99.5% precision, and an F1-score of 99.56%. Further validation on the MangoPest dataset and the Mango Leaf Disease Identification Dataset (MLDID) resulted in accuracies of 96.31% and 96.33%, respectively, confirming the robustness and adaptability of MangoLeafXNet across different datasets. Additionally, we incorporate Explainable AI techniques, including GRAD-CAM, Saliency Map, and LIME to enhance the interpretability of our model. We deployed Gradio web interface to create an interactive interface that allows users to upload images of mango leaves and get real-time classification and validation results along with confidence scores. This contribution not only advances the state-of-the-art in mango leaf disease classification but also offers promising prospects for real-time disease diagnosis and precision agriculture

关键词： Fruits

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A light-weight defect classification scheme for embedded automotive software and its initial evaluation

A light-weight defect classification scheme for embedded aut...

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2012 IEEE 23rd International Symposium on software Reliability engineering, ISSRE 2012

作者： Mellegård, Niklas Staron, Miroslaw Törner, Fredrik Software Engineering Division Department of Computer Science and Engineering Chalmers University of Technology Sweden Software Engineering Division Department of Computer Science and Engineering University of Gothenburg Sweden Volvo Car Corporation Sweden

ISBN: (纸本)9780769548883

Objective: Defect classification is an essential part of software development process models as a means of early identification of patterns in defect inflow profiles. Such classification, however, may often be a tedious task requiring analysis work in addition to what is necessary to resolve the issue. To increase classification efficiency, adapted schemes are needed. In this paper a light-weight defect classification scheme adapted for minimal process footprint - in terms of learning and classification effort - is proposed and initially evaluated. Method: A case study was conducted at Volvo Car Corporation to adapt the IEEE Std. 1044 for automotive embedded software. An initial evaluation was conducted by applying the adapted scheme to defects from an existing software product with industry professionals as subjects. Results: The results showed that the classification scheme was quick to learn and understand - required classification time stabilized around 5-10 minutes already after practicing on 3-5 defects. The results also showed that the patterns in the classified defects were interesting for the professionals, although in order to apply statistical methods more data was needed. Conclusions: We conclude that the adapted classification scheme captures what is currently tacit knowledge and has the potential of revealing patterns in the defects detected in different project phases. Furthermore, we were, in the initial evaluation, able to contribute with new information about the development process. As a result we are currently in the process of incorporating the classification scheme into the company's defect reporting system. © 2012 IEEE.

关键词： software reliability

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Improving scenario selection for simulations by run-time control-flow analysis

Improving scenario selection for simulations by run-time con...

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Summer computer Simulation Conference, SCSC 2013 and Work in Progress, WIP 2013, Part of the 2013 Summer Simulation Multiconference, SummerSim 2013

作者： Berger, Christian Chalmers / University of Gothenburg Department of Computer Science and Engineering Division for Software Engineering Sweden

ISBN: (纸本)9781627482769

Cyber-physical systems like active safety systems in recent vehicles are significantly driven by software and rely predominantly on data that is perceived by cameras, laser scanners, and the like from the system's environment. For example, these sensor-based systems realize pedestrian protection functionalities, which cannot be tested under simplified conditions on proving grounds only or by arbitrary test-runs on public roads anymore. Instead, simulative environments are used nowadays, which provide the virtual surroundings for such a system where its real input sources are replaced with simplified sensor models. Thus, interactive and hazard-free system tests and automated system evaluations can be carried out easily. However, the simple strategy to run all available modeled traffic scenarios in the simulation on any change of the implementation would consume too much computation time to provide effective and fast feedback for developers. In this article, an improved strategy for selecting scenarios that shall be run in a simulation based on run-time control-flow analysis is proposed, which resulted from the in-depth analysis of the revision history of the source code and their accompanying simulations for two self-driving vehicles. The outlined strategy is evaluated on a self-driving miniature vehicle.

关键词： Cyber Physical System

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Reducing simulation testing time by parallel execution of loosely coupled segments of a test scenario

Reducing simulation testing time by parallel execution of lo...

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Workshop on engineering Simulations for Cyber Physical Systems, ES4CPS 2014 - Held in Conjunction with the Conference Design, Automation and Test in Europe, DATE 2014

作者： Al Mamun, Md Abdullah Hansson, Jörgen University of Gothenburg Sweden Department of Computer Science and Engineering Division of Software Engineering Sweden

ISBN: (纸本)9781450326148

The automotive industry is going through a major transition where all of the main car OEMs (original equipment manufacturer) are putting serious efforts toward self-driving vehicles, and some of the OEMs have the vision to sell fully functional driver-less car by 2020. software is the primary driving force for implementing different functionalities of today's cyber-physical systems. Being a safety critical system, functionalities of an autonomous vehicle need to be rigorously tested in different driving conditions, for example, weather, traffic, road markings, *** order to get early feedback on the implemented functionalities, testing in the simulation environment has become common before testing them on the prototype vehicle. This paper presents an approach to reduce the testing time in the simulation environment by parallelly executing the loosely coupled segments of a test scenario. Reducing simulation testing time is also substantial when we need to run regression test scenarios following changes in the implementation. The preliminary investigation shows that by applying the proposed concept, we can significantly reduce the simulation testing time both in the cases of successful and failed run. © 2014 ACM.

关键词： Cyber Physical System

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Multiple-Object Tracking Using Histogram Stamp Extraction in CCTV Environments

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computers, Materials & Continua 2023年第12期77卷 3619-3635页

作者： Ye-Yeon Kang Geon Park Hyun Yoo Kyungyong Chung Division of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea Contents Convergence Software Research Institute Kyonggi UniversitySuwon16227Korea

Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an *** technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the *** tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same ***,this paper attempts to determine the same object present in different images using color information among the unique information of the ***,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television *** proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other *** this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile *** Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the *** the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different *** the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 *** this,it is possible to detect and track the same object in real time when using the proposed

关键词： Data mining deep learning object detection object tracking real-time object detection multiple object image processing

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Corrections to “Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine”

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IEEE Access 2025年 13卷 53717-53717页

作者： Abdul Razaq Louai A. Maghrabi Musheer Ahmad Farrah Aslam Wei Feng Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan Department of Software Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Information Sciences Division of Science and Technology University of Education Lahore Pakistan School of Mathematics and Computer Science Panzhihua University Panzhihua China

Presents corrections to the paper, Corrections to “Fuzzy Logic-Based Substitution-Box for Robust Medical Image Encryption in Telemedicine”.

关键词： Telemedicine Encryption Biomedical imaging

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