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arXiv 2023年

作者： Moguel, Enrique Garcia-Alonso, Jose Haghparast, Majid Murillo, Juan M. Computing and Advanced Technologies Foundation of Extremadura Carretera Nacional 521 Km 418 Cáceres10071 Spain Quercus Software Engineering Group Universidad de Extremadura Avda. de la Universidad s/n Cáceres10004 Spain Faculty of Information Technology University of Jyväskylä Jyväskylä40014 Finland

Early advances in the field of quantum computing have provided new opportunities to tackle intricate problems in areas as diverse as mathematics, physics, or healthcare. However, the technology required to construct such systems where different pieces of quantum and classical software collaborate is currently lacking. For this reason, significant advancements in quantum service-oriented computing are necessary to enable developers to create and operate quantum services and microservices comparable to their classical counterparts. Therefore, the core objective of this work is to establish the necessary technological infrastructure that enables the application of the benefits and lessons learned from service-oriented computing to the domain of quantum software engineering. To this end, we propose a pipeline for the continuous deployment of services. Additionally, we have validated the proposal by making use of a modification of the OpenAPI specification, the GitHub Actions, and AWS. Copyright © 2023, The Authors. All rights reserved.

关键词： Application programs

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Target-oriented Self Training for Heterogeneous Domain Adaptation

Target-oriented Self Training for Heterogeneous Domain Adapt...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： An Da Rui Zhang Shuang Li Weipeng Cao China Academy of Electronics and Information Technology China Electronics Technology Group Corporation Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Although the distributed machine learning platform is very successful in the application of structured data, it is still very challenging in the absence of labeled data and a variety of data forms. Therefore, heterogeneous domain adaptation (HDA) emerged at the historic moment, with the goal of transferring knowledge between domains of different features and different distributions. Existing HDA methods only focus on the better alignment of the target domain with the source domain, while ignoring the rich semantic structure information of the target domain data itself, thereby affecting the performance of knowledge transfer. In order to solve the above problems, we propose a simple and effective self-training method oriented to the target domain. Specifically, we use the clustering algorithm to find the prototype of the target domain clusters, and select confident unlabeled data through a novel method of finding pseudo-labels that we propose. Under two self-training mechanisms, that is, single-stage Self-training (TST-SS) and multi-stage self-training (TST-MS), without introducing any additional network parameters, gradually train the transferable model. We conducted extensive experiments on cross-domain and cross-feature tasks to prove that our method is far superior to the existing heterogeneous domain adaptation methods.

关键词： Training Adaptation models Semantics Distributed databases Prototypes Clustering algorithms Machine learning

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Show and Speak: Directly Synthesize Spoken Description of Images

Show and Speak: Directly Synthesize Spoken Description of Im...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xinsheng Wang Siyuan Feng Jihua Zhu Mark Hasegawa-Johnson Odette Scharenborg School of Software Engineering Xi’an Jiaotong University Xi’an China Multimedia Computing Group Delft University of Technology Delft The Netherlands University of Illinois at Urbana-Champaign IL USA

ISBN: (纸本)9781728176055;9781728176062

This paper proposes a new model, referred to as the show and speak (SAS) model that, for the first time, is able to directly synthesize spoken descriptions of images, bypassing the need for any text or phonemes. The basic structure of SAS is an encoder-decoder architecture that takes an image as input and predicts the spectrogram of speech that describes this image. The final speech audio is obtained from the predicted spectrogram via WaveNet. Extensive experiments on the public benchmark database Flickr8k demonstrate that the proposed SAS is able to synthesize natural spoken descriptions for images, indicating that synthesizing spoken descriptions for images while bypassing text and phonemes is feasible.

关键词： Databases Conferences Benchmark testing Acoustics Synthetic aperture sonar Speech processing Spectrogram

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Chances and Challenges of ChatGPT and Similar Models for Education in M&S

Chances and Challenges of ChatGPT and Similar Models for Edu...

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Simulation Winter Conference

作者： Andreas Tolk Philip Barry Margaret L. Loper Ghaith Rabadi William T. Scherer Levent Yilmaz Modeling and Analysis Innovation Center The MITRE Corporation Charlottesville VA USA Mission Analysis Group L3Harris Corporation Herndon VA USA Georgia Tech Research Institute Georgia Institute of Technology Atlanta GA USA University of Central Florida School of Modeling Simulation and Training Orlando FL USA Department of Systems and Information Engineering University of Virgina Charlottesville VA USA Samuel Ginn College of Engineering Computer Science and Software Engineering Auburn University Auburn AL USA

This position paper summarizes the inputs of a group of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within Modeling and Simulation education. The experts also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. Generally, the use of ChatGPT is encouraged, but it needs to be embedded into an updated curriculum with more emphasis on validity constraints, systems thinking, and ethics.

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Challenges and working solutions in agile adaptation: Experiences from the industry 30

Challenges and working solutions in agile adaptation: Experi...

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Joint 30th International Workshop on software Measurement and the 15th International Conference on software Process and Product Measurement, IWSM-Mensura 2020

作者： Özcan-Top, Özden Demirors, Onur McCaffery, Fergal Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Computer Engineering Izmir Institute of Technology Izmir Turkey Regulated Software Research Centre Dundalk Institute of Technology Dundalk Ireland STATSports Group Newry Ireland

Challenges in agile adaptation is inevitable in software development projects and have to be dealt with by software practitioners. The pathway to excellence in agility requires experience of challenges, failure of process scenarios;and the discovery of working solutions by software development teams. The major purpose of this study is to highlight both the challenges organizations faced when implementing agile techniques and the solutions adopted that proved successful. In order to specify these challenges and working solutions, we performed a multiple case study by using the software Agility Assessment Reference Model (AgilityMod). In this paper, we describe two cases that achieve the highest levels of agility among eight cases and describe their experiences in achieving a good adaptation through the challenges that they faced and the solutions that were found for these challenges. Additionally, we provide two challenges that have not been resolved yet and are subject to further discussions. Copyright © 2020 for this paper by its authors.

关键词： software design

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EEG Epileptic Seizure Detection Based on Fused Brain Functional Networks 2nd

EEG Epileptic Seizure Detection Based on Fused Brain Functio...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Zhou, Pengfei Pan, Yijie Zhang, Xun Shou, Jibin Guo, Jiayang Xu, Meiyan Gu, Peipei College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450000 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo China Zhejiang Moorgen Group Co. Ltd. Zhejiang310000 China Department of Hematology School of Medicine Xiamen University Xiamen361000 China National Institute for Data Science in Health and Medicine Xiamen University Xiamen361000 China College of Computer Science Minnan Normal University Zhangzhou363000 China

ISBN: (纸本)9789819619061

Epilepsy, characterized by abnormal neuronal discharges, is a widespread neurological disorder. Traditional detection methods primarily focus on extracting time-frequency features from raw electroencephalogram (EEG) signals, often overlooking the complex interactions between brain regions. To address this limitation, we propose an automated approach for epileptic seizure detection. We first construct a fused brain network (TFPP) by integrating the Pearson Correlation Coefficient (PCC) and Phase Lag Index (PLV) across both temporal and frequency domains, enhancing EEG signals by capturing synchronized activities across different brain regions. Next, we introduce the CNN_sk_BiLSTM model, which leverages Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks to sequentially extract spatial and temporal features. The SKAttention mechanism further refines feature extraction across multiple scales. Using the CHB-MIT scalp EEG dataset, the proposed method achieved an average sensitivity of 90.86%, specificity of 98.79%, and accuracy of 97.88%. These results confirm that fused brain functional networks effectively integrate the intra-channel characteristics of EEG signals, demonstrating the superior performance of the proposed approach in epileptic seizure detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Run, forest, run? On randomization and reproducibility in predictive software engineering

arXiv

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arXiv 2020年

作者： Liem, Cynthia C.S. Panichella, Annibale Multimedia Computing Group Delft University of Technology Delft Netherlands Software Engineering Research Group Delft University of Technology Delft Netherlands

Machine learning (ML) has been widely used in the literature to automate software engineering tasks. However, ML outcomes may be sensitive to randomization in data sampling mechanisms and learning procedures. To understand whether and how researchers in SE address these threats, we surveyed 45 recent papers related to three predictive tasks: defect prediction (DP), predictive mutation testing (PMT), and code smell detection (CSD). We found that less than 50% of the surveyed papers address the threats related to randomized data sampling (via multiple repetitions);only 8% of the papers address the random nature of ML;and parameter values are rarely reported (only 18% of the papers). To assess the severity of these threats, we conducted an empirical study using 26 real-world datasets commonly considered for the three predictive tasks of interest, considering eight common supervised ML classifiers. We show that different data resamplings for 10-fold cross-validation lead to extreme variability in observed performance results. Furthermore, randomized ML methods also show non-negligible variability for different choices of random seeds. More worryingly, performance and variability are inconsistent for different implementations of the conceptually same ML method in different libraries, as also shown through multi-dataset pairwise comparison. To cope with these critical threats, we provide practical guidelines on how to validate, assess, and report the results of predictive methods. © 2020, CC BY.

关键词： Random processes

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IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation

IoT-ID: Robust IoT Device Identification Based on Feature Dr...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Qi Chen Yubo Song Brendan Jennings Fan Zhang Bin Xiao Shang Gao Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Purple Mountain Laboratories Telecommunications Software & Systems Group Waterford Institute of Technology The Hong Kong Polytechnic University

ISBN: (纸本)9781728181059

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift— relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

关键词： Performance evaluation Protocols Fingerprint recognition Feature extraction Robustness Classification algorithms Object recognition

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An overview of domain-specific foundation model: key technologies, applications and challenges

arXiv

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arXiv 2024年

作者： Chen, Haolong Chen, Hanzhi Zhao, Zijian Han, Kaifeng Zhu, Guangxu Zhao, Yichen Du, Ying Xu, Wei Shi, Qingjiang Shenzhen Research Institute of Big Data Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China China Academy of Information and Communications Technology Beijing100191 China China Mobile Group Device Co. Ltd. Beijing100033 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China School of Software Engineering Tongji University Shanghai201804 China

The impressive performance of ChatGPT and other foundation-model-based products in human language understanding has prompted both academia and industry to explore how these models can be tailored for specific industries and application scenarios. This process, known as the customization of domain-specific foundation models, addresses the limitations of general-purpose models, which may not fully capture the unique patterns and requirements of domain-specific data. Despite its importance, there is a notable lack of comprehensive overview papers on building domain-specific foundation models, while numerous resources exist for general-purpose models. To bridge this gap, this article provides a timely and thorough overview of the methodology for customizing domain-specific foundation models. It introduces basic concepts, outlines the general architecture, and surveys key methods for constructing domain-specific models. Furthermore, the article discusses various domains that can benefit from these specialized models and highlights the challenges ahead. Through this overview, we aim to offer valuable guidance and reference for researchers and practitioners from diverse fields to develop their own customized foundation models. Copyright © 2024, The Authors. All rights reserved.

关键词： Building Information Model

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Correction to: The potential of an adaptive computerized dynamic assessment tutor in diagnosing and assessing learners’ listening comprehension

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Education and Information Technologies 2023年第6期29卷 7695-7695页

作者： Izadi, Mehri Izadi, Maliheh Heidari, Farrokhlagha Department of English Language and Literature University of Sistan and Baluchestan Zahedan Iran Software Engineering Research Group Delft University of Technology Delft The Netherlands

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