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Mediterranean Conference on Embedded Computing (MECO)

作者： Vasiliy Pinkevich Alexey Platunov Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

Modern embedded computing systems (ES) include heterogeneous components connected “horizontally” and “vertically”, and it demands developing individual quality control design flows for each project. This article discusses the problem of formalizing end-to-end testing and debugging processes in full-stack ES designs. An ontology of ES testing and debugging processes from the multi-level organization point of view and a number of models are proposed using a unified representation of testing, verification, validation and debugging problems at various levels of abstraction. This allows to formally specify a testing and debugging flow of an arbitrary ES design with transition systems describing different states of design process. The application of the proposed solutions provides: control of the coverage of system components by verification and validation processes; identification of gaps in the testing and debugging processes; monitoring of the completed verification and validation steps of system components at different levels.

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Verification of the CAD System for an Application-Specific Processor by Property-Based Testing

Verification of the CAD System for an Application-Specific P...

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Mediterranean Conference on Embedded Computing (MECO)

作者： Daniil Prohorov Aleksandr Penskoi Faculty of Software Engineering and Computer Systems ITMO University Saint-Petersburg Russia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

An application-specific processor CAD system is a complex software system with high dependability requirement. Verification of such system is a complicated process which involves many closely connected components, different computational process representation and tools, and requires much time and work. In the article, the authors describe their experience of usage property-based testing in the NITTA project (application-specific reconfigurable processor CAD for real-time application). Property-based testing allowed to significantly improve source code coverage and identify many hard to find bugs. The article will be interesting to CAD developers and as a real-world property-based testing application example.

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Alexander M. Boronakhin Alexandra V. Bolshakova Dmitry M. Klionskiy Daniil Yu. Larionov Roman V. Shalymov dept. of Laser Measurement and Navigational Systems faculty of Information Measurement and Biotechnical Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Laser Measurement and Navigational Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Information Systems dept. of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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QuIP: A P4 Quantum Internet Protocol Prototyping Framework

arXiv

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arXiv 2024年

作者： Kozlowski, Wojciech Kuipers, Fernando A. Smets, Rob Turkovic, Belma QuTech Delft University of Technology Delft2628 CD Netherlands The Networked Systems Group Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands SURF Utrecht3511 EP Netherlands TNO The Hague2595 DA Netherlands

Quantum entanglement is so fundamentally different from a network packet that several quantum network stacks have been proposed;one of which has even been experimentally demonstrated. Several simulators have also been developed to make up for limited hardware availability, and which facilitate the design and evaluation of quantum network protocols. However, the lack of shared tooling and community-agreed node architectures has resulted in protocol implementations that are tightly coupled to their simulators. Besides limiting their reusability between different simulators, it also makes building upon prior results and simulations difficult. To address this problem, we have developed QuIP: a P4-based Quantum Internet Protocol prototyping framework for quantum network protocol design. QuIP is a framework for designing and implementing quantum network protocols in a platform-agnostic fashion. It achieves this by providing the means to flexibly, but rigorously, define device architectures against which quantum network protocols can be implemented in the network programming language P416. QuIP also comes with the necessary tooling to enable their execution in existing quantum network simulators. We demonstrate its use by showcasing V1Quantum, a completely new device architecture, implementing a link- and network-layer protocol, and simulating it in the existing simulator NetSquid. © 2024, CC BY.

关键词： Network architecture

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IOT based prediction of rainfall forecast in coastal regions using deep reinforcement model

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Measurement: Sensors 2023年 29卷

作者： Nithyashri, J. Poluru, Ravi Kumar Balakrishnan, S. Ashok Kumar, M. Prabu, P. Nandhini, S. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpet District Kattankulathur603203 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad India Department of Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria Bengaluru560029 India Department of Computer Science and Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore India

This research proposes an IoT based technique for predicting rainfall forecast in coastal regions using a deep reinforcement learning model. The proposed technique utilizes Long Short-Term Memory (LSTM) networks to capture the temporal dependencies between the rainfall data collected from the coastal regions and the prediction model parameters. The proposed technique is evaluated on a dataset of rainfall data collected from the coastal regions of India and compared to traditional methods of rainfall forecasting. The accuracy and reliability of these models are evaluated by comparing them to prior models. Precipitation in coastal locations may be predicted with an average accuracy of 89% using the suggested model, as shown by the results. The suggested framework is computationally efficient and can be trained with little input. The results of this research give strong evidence that the proposed model is an effective tool for coastal precipitation forecasting. © 2023 The Authors

关键词： Internet of things

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Segmentation and Multi-facet Classification of Individual Logs in Wooden Piles 18th

Segmentation and Multi-facet Classification of Individual Lo...

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18th International Conference on computer Aided systems Theory, EUROCAST 2022

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany

ISBN: (纸本)9783031253119

The inspection of products and assessment of quality is connected with high costs and time effort in many industrial domains. This also applies to the forestry industry. Utilizing state-of-the-art deep learning models allows automizing the analysis of wooden piles in a vision-based manner. In this work, a parallel two-step approach is presented for the segmentation and multi-facet classification of individual logs, according to the wood type and quality. The present approach is based on a preliminary log localization step and like this allows determining the quality, volume and also the value of individual logs, respectively the whole wooden pile. Using a YOLOv4 model for wood species classification for douglas firs, pines and larches results in an accuracy of 74.53%, while a quality classification model for spruce logs reaches 86.58%. In addition to that, the trained U-NET segmentation model reaches an accuracy of 93%. In the future, the underlying data set and models will be further improved and integrated to a mobile application for the on site analysis of wooden piles by foresters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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Mining Student Information System Records to Predict Students’ Academic Performance 4th

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4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9783030141172

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance. © 2020, Springer Nature Switzerland AG.

关键词： Students

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