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11th international symposium on from data to models and back, datamod 2023

11th International Symposium on From Data to Models and Back...

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11th international symposium on from data to models and back, datamod 2023

ISBN: (纸本)9783031872167

the proceedings contain 8 papers. the special focus in this conference is on from data to models and back. the topics include: Extracting Cyber threat Intelligence from Social Media with Case Studies in Twitter/X and Reddit;attractor and Slicing Analysis of a T Cell Differentiation Model Based on Reaction Systems;preliminary Results on Shapley Value Notions and Propagation Methods for Boolean Networks;towards a Flexible Approach for Understanding and Comparing Traces;modelling and Verification of an Application for Managing Sensitive Health data;evaluating Large Language models and Prompt Variants on the Task of Detecting Cease and Desist Violations in German Online Product Descriptions.

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Integrated data Representation and Formal Analysis for Human and In Silico Experimentation in Cognitive Psychology 11th

Integrated Data Representation and Formal Analysis for Hum...

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11th international symposium on from data to models and back, datamod 2023

作者： Cerone, Antonio Pluck, Graham Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Faculty of Psychology Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9783031872167

this paper presents a novel framework aimed at enhancing the experimental methodology within cognitive psychology, integrating both informal and formal components to accommodate varied reasoning approaches. the framework supports the design and performing of experiments, and the structured storage of the information on outcomes in a way that is independent of specific interpretations and findings. this facilitates the replication and reproduction of the experiments and their interpretation under alternative theories. Moreover, the formal components of the information stored can be used for in silico simulation as well as within rigorous analytical approaches, such as formal methods and process mining. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： BRDL Cognitive psychology data storage Experiment builder Experimental variables Formal methods In silico simulation Process mining Real-Time Maude

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Modelling and Verification of an Application for Managing Sensitive Health data 11th

Modelling and Verification of an Application for Managin...

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11th international symposium on from data to models and back, datamod 2023

作者： Cuci, Almo Ozeer, Umar Salaün, Gwen University of Grenoble Alpes CNRS Grenoble INP Inria LIG Grenoble38000 France Euris Cloud Santé Paris France

ISBN: (纸本)9783031872167

the digitisation of personal health information (PHI) through electronic health record (EHR) is now widely adopted due to their efficiency in terms of cost, storage, processing, and the subsequent quality of delivering patient care. However, security concerns remain one of its major setback. In order to handle EHR, institutions need to comply with their local government security regulations. these regulations control to which extent health data can be processed, transmitted, and stored as well as define how misuses are addressed. φ-comp has been proposed as an industrial solution for monitoring, assessing, and evaluating the compliance of health applications with respect to defined security regulations. φ-comp is able to assess the level of security risk of an application at runtime and to automatically perform the required mitigation actions to recover a compliant environment. Since the risk associated to sensitive health data is critical, there is a need of guarantees in terms of correctness of the φ-comp approach. In this paper, we first present a formal specification of φ-comp representing all the components of the solution as well as their behaviour, that is, the way they all interact together to implement the whole approach from monitoring to mitigation. In a second step, some important properties of interest are formalised and analysed using model checking techniques on several realistic applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Evaluating Large Language models and Prompt Variants on the Task of Detecting Cease and Desist Violations in German Online Product Descriptions 11th

Evaluating Large Language Models and Prompt Variants on th...

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11th international symposium on from data to models and back, datamod 2023

作者： Lambert, Marian Döring, Nico Schuster, thomas Schmitz, Norbert XPACE GmbH Blücherstr. 32 Pforzheim75177 Germany Pforzheim University Tiefenbronner Street 65 Pforzheim75175 Germany

ISBN: (纸本)9783031872167

this paper compares different large language models (LLMs) on the task of detecting violations of cease and desist declarations in German online product descriptions as part of the KIVEDU project. We evaluate two proprietary LLMs by OpenAI (gpt-3.5-turbo-0301 and gpt-3.5-turbo-0613) and three open source LLMs by various organizations (LLaMA2, StableBeluga2, and Platypus2) using different variations in text input (prompt) on a dataset of 116 manually labeled pairs of cease and desist declarations and product descriptions. the evaluation aims to explore two research questions: 1) Which LLM is most adept at identifying violations? and 2) How do prompt variations impact model performance? the results show that StableBeluga2 performed best, achieving the highest accuracy and micro F1 score. It was also the most reliable model with minimal deviations in performance across prompt variants. the Platypus2 and gpt-3.5-turbo-0301 models also achieved good results though they displayed greater variability in their performance. the worst-performing model was LLaMA2. the results further show that prompting had a significant impact on model performance, with the presence of a step-by-step instruction generally decreasing performance and a "yes"/"no" output format leading to higher performance. However, this was highly dependent on the specific model. Role prompting and providing a longer vs. shorter instruction had a minimal impact on performance across models. Overall, the study demonstrates the potential of LLMs in automating the detection of cease and desist violations in online product descriptions. Further research is needed to evaluate other LLM models and prompt variations, as well as to explore approaches like LLM fine-tuning on domain-specific data to further improve performance. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Open source software

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Dependence Analysis of Indian Energy Sector Using Regular Vine Copula and Information Transfer Entropy 11th

Dependence Analysis of Indian Energy Sector Using Regular V...

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11th international symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2025

作者： Kumar, Pankaj Vijay, Vivek Indian Institute of Technology Jodhpur Rajasthan Jodhpur342030 India

ISBN: (纸本)9789819646029

In this paper, we analyse the dependence of the energy sector stocks using the vine copula models and understand the information transfer between them using the entropy method. An information spillover network is constructed using total transfer entropy, allowing for identifying the path of information flow. We have used the national stock exchange’s most prominent energy sector stocks. the top ten energy stocks were chosen based on the most valuable fundamental factors like market capitalization and net profit. We analyse the daily close price of ten energy stocks from 2015 to 2023 for eight-year periods. To count the serial dependence in data, we have used the GJR-GARCH(1,1) model with standardized student-t distribution for each of the ten energy stocks for marginal density. the vine copula approach is employed to model the dependence among these stocks. the R-Vine model with a joint maximum likelihood approach with all copula families outperforms for dependence. these findings provide insight into the tail dependence relationships among stocks in the energy sector. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Copula models Dependence GJR-GARCH model Information transfer entropy Regular(R) vine

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data Augmentation with Pseudo-Infrared Night-Vision Image Conversion for Improved Nighttime Object Detection 11

Data Augmentation with Pseudo-Infrared Night-Vision Image Co...

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11th international symposium on Computing and Networking Workshops, CANDARW 2023

作者： Ishizu, Ryoma Kitakaze, Hironori Matsumura, Ryo Oshima College National Insutitute of Technology Yamaguchi Japan Shunan University Yamaguchi Japan

ISBN: (纸本)9798350306941

this paper presents a novel data augmentation method with pseudo-infrared night-vision image conversion for RGB images captured under daylight conditions. Our proposed data augmentation method is to apply gamma correction after grayscale conversion to RGB images and composites them with a mask image that simulates infrared irradiation. Our goal is to improve the accuracy of nighttime object detection using infrared night-vision cameras. We exclusively utilized data from the "person"class in the PASCAL VOC dataset and conducted a nighttime detection performance comparison between models trained on this person dataset (original dataset) and models trained on the person dataset to which we applied our proposed data augmentation method (converted dataset). the experimental results showed that the Average Precision (AP) for the model trained on the original dataset is 74.9%. In contrast, the AP for the model trained on our converted dataset is 78.7%. © 2023 IEEE.

关键词： Object detection

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SEM: A Simple Yet Efficient Model-Agnostic Local Training Mechanism to Tackle data Sparsity and Scarcity in Federated Learning 11

SEM: A Simple Yet Efficient Model-Agnostic Local Training Me...

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11th international symposium on Computing and Networking, CANDAR 2023

作者： Pham, Quang Ha Nguyen, Nang Hung Nguyen, thanh Hung Pham, Huy Hieu Nguyen, Phi Le Nguyen, Truong thao Hanoi University of Science and Technology School of Information and Communication Technology Hanoi Viet Nam VinUniversity College of Engineering Computer Science and VinUni-Illinois Smart Health Center Viet Nam Japan

ISBN: (纸本)9798350306705

Recent years have witnessed the emergence of Feder-Ated Learning (FL) as a viable learning paradigm that permits the training of models without revealing sensitive data. FL systems typically consist of numerous clients that utilize their data to train models locally and an orchestration server responsible for combining local updates from the clients to produce a global model. thus, the performance of a Federated learning system is highly dependent on the client data and the local model trained on these data. In this study, we present an early attempt at addressing the sparsity and scarcity of client data, which may lead to the overfitting phenomenon of local models and substantially reduce the overall accuracy of the global model. Specifically, we propose a novel local training strategy that explores transfer learning and allows each local model to be trained using the data of two randomly paired clients. the proposed method is orthogonal to other Federated Learning algorithms and can be integrated into most Federated Learning systems. Extensive experiments in various settings on MNIST, CIFAR-10, and CIFAR-100 datasets showed that using our proposed method can relatively enhance the accuracy of the global model by up to 12.48%. Our work, for the first time, offers a simple yet effective solution that reduces the undesired effects of data sparsity and scarcity in FL. © 2023 IEEE.

关键词： Learning systems

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WHERE TO DIFFUSE, HOW TO DIFFUSE, AND HOW TO GET back: AUTOMATED LEARNING FOR MULTIVARIATE DIFFUSIONS 11

WHERE TO DIFFUSE, HOW TO DIFFUSE, AND HOW TO GET BACK: AUTOM...

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11th international Conference on Learning Representations, ICLR 2023

作者： Singhal, Raghav Goldstein, Mark Ranganath, Rajesh Courant Institute of Mathematical Sciences New York University United States Center for Data Science New York University United States

Diffusion-based generative models (DBGMs) perturb data to a target noise distribution and reverse this process to generate samples. the choice of noising process, or inference diffusion process, affects both likelihoods and sample quality. For example, extending the inference process with auxiliary variables leads to improved sample quality. While there are many such multivariate diffusions to explore, each new one requires significant model-specific analysis, hindering rapid prototyping and evaluation. In this work, we study Multivariate Diffusion models (MDMs). For any number of auxiliary variables, we provide a recipe for maximizing a lower-bound on the MDMs likelihood without requiring any model-specific analysis. We then demonstrate how to parameterize the diffusion for a specified target noise distribution;these two points together enable optimizing the inference diffusion process. Optimizing the diffusion expands easy experimentation from just a few well-known processes to an automatic search over all linear diffusions. To demonstrate these ideas, we introduce two new specific diffusions as well as learn a diffusion process on the MNIST, CIFAR10, and IMAGENET32 datasets. We show learned MDMs match or surpass bits-per-dims (BPDs) relative to fixed choices of diffusions for a given dataset and model architecture. © 2023 11th international Conference on Learning Representations, ICLR 2023. All rights reserved.

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A novel approach for selecting the participants to collect data in participatory sensing

A novel approach for selecting the participants to collect d...

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IEEE/INFORMATION PROCESSING SOCIETY JAPAN international symposium on Applications and the Internet

作者： Pham, Hong Nhat Sim, back Sun Youn, Hee Yong School of Information and Communication Engineering Sungkyunkwan University 440-746 Suwon Korea Republic of

ISBN: (纸本)9780769544236

this paper works on the participatory sensing in the area of sensor network. One of the most important challenges in participatory sensing is selecting the participants to collect their sensed data to monitor the environment. the goal here is to obtain the data of high quality with low payment from a predefined number of participants. To achieve the goal, this paper proposes a novel approach using the techniques of evolutionary algorithm. It models the goal as a multi-objective Knapsack problem, which is resolved by the hybrid scheme of Univariate Model Distribution Algorithm (UDMA) and the enhanced third Evolution step of Generalized Differential Evolution (EGDE3). the experiment results show the effectiveness of the proposed approach in terms of the ratio of the payment and data quality, and takeover time compared with the Genetic Algorithm (GA) and Reverse Auction based Dynamic Price (RADP) scheme. © 2011 IEEE.

关键词： Sensor networks

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Measurement models and Identification Methodology of Real Earnings Management- Empirical Research Based on the data of Chinese A-share Listed Companies 11

Measurement Models and Identification Methodology of Real Ea...

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11th international symposium on Project Management, ISPM 2023

作者： Sun, Qingwen Jin, Yu Zheng, Limei School of Business Administration Hebei University of Economics and Business Shijiazhuang050061 China Research Center for Corporate Governance and Enterprise Growth of Hebei University of Economics and Business Shijiazhuang050061 China Finance Department Hebei Institute of Architectural Design & Research Shijiazhuang050011 China

ISBN: (纸本)9781713877134

Studies have shown that with increased regulation, managers are more inclined to use real activities manipulation to whitewash financial reports. these activities will deviate from normal operational practices and seriously erode the future value of the enterprise, resulting in damage to the interests of investors. therefore, accurately measuring the degree of real earnings management and identifying the means of manipulation is of great significance for investors to correctly assess the firm value and effective supervision by regulators. In this paper, through literature research and mathematical analysis, a novel estimation model of inflated profits manipulated by real activities and an identification method of suspected samples are constructed based on previous research. the empirical results show that the metric model and quantile threshold adjustment method (QTAM) constructed in this paper can effectively identify the real activities manipulation behavior with high accuracy. © (2023) by Aussino Academic Publishing House All rights reserved.

关键词： Operations research

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