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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： V.S Anusuya Devi Prabhakar Agrawal R S Sengar Amandeep Nagpal Tamam Ali Abd Ulabbas Abedi Kathi Chandra Mouli A Sangeetha Departmento of Applied Sciences New Horizon College of Engineering Bangalore India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Department of Electrical & Electronics Engineering GL Bajaj Group of Institutions Mathura Lovely Professional University Phagwara India Medical Technology The Islamic University Najaf Iraq Department of CSE GRIET Bachupally Hyderabad Telangana India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The article “Designing Intuitive User Interfaces in Human-computer Interaction for Enhanced Digital Experience” describes a novel approach. By incorporating cutting-edge technologies and user-centric design principles into this innovative interface design, a significant advancement has been made. An inventive approach to creating user interfaces for any profession is suggested. It is the most amazing thing you have ever seen. Focusing on oneself clarifies the complexity of the mind. User-centered designs most likely represent their way of thinking. This improves the interface's usability by strengthening links. By showing how interfaces should be user extensions rather than just tools, this idea revolutionizes user interface design. The goal of this method is to respond to user input as soon as possible. Interfaces that combine mood analysis and real-time monitoring can potentially sense a user's emotions and actions in real time. Definitely not. Therefore, user interfaces could change depending on how the user interacts with the system. Because this approach allows for fully immersive experiences, virtual and augmented reality will be revolutionized. Integrating physical settings with virtual environments may trap people in experiences that are only available through a finite number of one-way paths. This quality attracts customers and makes it easier to create creative digital experiences. It is also recommended to use machine learning to allow for interface customization. Through personalization, screens could adapt, through reinforcement learning, to the words and actions of the user. The adaptive learning of the suggested approach might provide clients with a novel experience. To summarize, the proposed methodology reassesses cutting-edge technologies in relation to user-intuitive designs by prioritizing user-centric principles and adjusting based on feedback from users. These advancements have improved digital interactions, which has led to the expansion of co

关键词： Human computer interaction Adaptive learning Mood User centered design Virtual environments Reinforcement learning Real-time systems User experience Usability Augmented reality

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Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

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Empowering software Startups with Agile Methods and Practices: A Design science Approach

SSRN

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SSRN 2022年

作者： Gandomani, Taghi Javdani Nafchi, Mina Ziaei Parizi, Reza M. Department of Computer Science Shahrekord University Shahrekord Iran CHB Science and Technology Park Shahrekord Iran Data Science Research Group Department of Computer Science Shahrekord University Shahrekord Iran Department of Software Engineering and Game Development Kennesaw State University MariettaGA30060 United States

In recent years, the number of software startups is increasing rapidly and employing a proper software development methodology in these startups is a concern. Recently, these startups have been using agile software development methodologies and practices. However, due to the special conditions of these startups and the focus on producing the Minimum Viable Product, it seems that the use of these methodologies and practices is not done correctly. Accordingly, these startups face more severe challenges, making their success more difficult. The literature review shows that few studies have been done regarding the correct methods of using agile methods and practices in software startups. This research has used Design science Research Methodology (DSRM) to fill this research gap and develop agile adaptation guidelines in software startups. The developed guidelines were validated with the participation of 23 experts from 7 software startup teams through survey questionnaires and open discussion. Eventually, final guidelines containing 13 recommendations were developed in three categories: selection of agile methods and practices, preparation for adaptation, and application and adaptation of agile methods and practices. The evaluation results show the simplicity of understanding these guidelines, their usefulness, and their support for the expected agility of the software development process. © 2022, The Authors. All rights reserved.

关键词： software design

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On the Privacy of Mental Health Apps: An Empirical Investigation and its Implications for Apps Development

arXiv

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arXiv 2022年

作者： Iwaya, Leonardo Horn Ali Babar, M. Rashid, Awais Wijayarathna, Chamila Centre for Research on Engineering Software Technologies The University of Adelaide AdelaideSA5005 Australia Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden Bristol Cyber Security Group Department of Computer Science University of Bristol United Kingdom

An increasing number of mental health services are offered through mobile systems, a paradigm called mHealth. Although there is an unprecedented growth in the adoption of mHealth systems, partly due to the COVID-19 pandemic, concerns about data privacy risks due to security breaches are also increasing. Whilst some studies have analyzed mHealth apps from different angles, including security, there is relatively little evidence for data privacy issues that may exist in mHealth apps used for mental health services, whose recipients can be particularly vulnerable. This paper reports an empirical study aimed at systematically identifying and understanding data privacy incorporated in mental health apps. We analyzed 27 top-ranked mental health apps from Google Play Store. Our methodology enabled us to perform an in-depth privacy analysis of the apps, covering static and dynamic analysis, data sharing behaviour, server-side tests, privacy impact assessment requests, and privacy policy evaluation. Furthermore, we mapped the findings to the LINDDUN threat taxonomy, describing how threats manifest on the studied apps. The findings reveal important data privacy issues such as unnecessary permissions, insecure cryptography implementations, and leaks of personal data and credentials in logs and web requests. There is also a high risk of user profiling as the apps' development do not provide foolproof mechanisms against linkability, detectability and identifiability. Data sharing among third parties and advertisers in the current apps' ecosystem aggravates this situation. Based on the empirical findings of this study, we provide recommendations to be considered by different stakeholders of mHealth apps in general and apps developers in particular. We conclude that while developers ought to be more knowledgeable in considering and addressing privacy issues, users and health professionals can also play a role by demanding privacy-friendly apps. Copyright © 2022, The Authors. All r

关键词： Data privacy

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Incentivized Data Mules Based on State-Channels

Incentivized Data Mules Based on State-Channels

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Mirko Zichichi Luca Serena Stefano Ferretti Gabriele D’ Angelo Ontology Engineering Group Universidad Polit&#x00E9 cnica de Madrid Spain Department of Computer Science and Engineering University of Bologna Italy Department of Pure and Applied Sciences University of Urbino "Carlo Bo" Italy

Many services that are taken for granted in smart cities are not even remotely available in dislocated areas, i.e. "smart territories". With the aim to offer a practical and secure way to transport data in such constrained scenarios, we focus on the problem of incentivizing to Data Mules, i.e. devices dedicated to enable communication even in the absence of the Internet. We combine decentralized technologies and State-Channels to verify the correct behavior of participants in an offline scenario.

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Designing Intuitive User Interfaces in Human-computer Interaction for Enhanced Digital Experience

Designing Intuitive User Interfaces in Human-Computer Intera...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Devi, V.S Anusuya Agrawal, Prabhakar Sengar, R.S. Nagpal, Amandeep Abedi, Tamam Ali Abd Ulabbas Mouli, Kathi Chandra Sangeetha, A. New Horizon College of Engineering Departmento of Applied Sciences Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Gl Bajaj Group of Institutions Department of Electrical & Electronics Engineering Mathura India Lovely Professional University Phagwara India The Islamic University Najaf Iraq Griet Department of Cse Bachupally Telangana Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331527495

The article 'Designing Intuitive User Interfaces in Human-computer Interaction for Enhanced Digital Experience' describes a novel approach. By incorporating cutting-edge technologies and user-centric design principles into this innovative interface design, a significant advancement has been made. An inventive approach to creating user interfaces for any profession is suggested. It is the most amazing thing you have ever seen. Focusing on oneself clarifies the complexity of the mind. User-centered designs most likely represent their way of thinking. This improves the interface's usability by strengthening links. By showing how interfaces should be user extensions rather than just tools, this idea revolutionizes user interface design. The goal of this method is to respond to user input as soon as possible. Interfaces that combine mood analysis and real-time monitoring can potentially sense a user's emotions and actions in real time. Definitely not. Therefore, user interfaces could change depending on how the user interacts with the system. Because this approach allows for fully immersive experiences, virtual and augmented reality will be revolutionized. Integrating physical settings with virtual environments may trap people in experiences that are only available through a finite number of one-way paths. This quality attracts customers and makes it easier to create creative digital experiences. It is also recommended to use machine learning to allow for interface customization. Through personalization, screens could adapt, through reinforcement learning, to the words and actions of the user. The adaptive learning of the suggested approach might provide clients with a novel experience. To summarize, the proposed methodology reassesses cutting-edge technologies in relation to user-intuitive designs by prioritizing user-centric principles and adjusting based on feedback from users. These advancements have improved digital interactions, which has led to the expansion of co

关键词： User centered design

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Comprehensive Data Analysis and Machine Learning Models for Automatic Identification of Chemical Compounds Based on Terahertz spectra

Comprehensive Data Analysis and Machine Learning Models for ...

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International Conference on Infrared and Millimeter Waves

作者： Lim Zi Xi Josie Nan Zhang Phua Wei Ji Lijie Yu Jia Yi Kwang Angeline Tang Angeline Tiong Whei Yap Yee-Fun Lim Lin Ke Anor Technologies Pte Ltd Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Illicit Drugs Laboratory Applied Sciences Group Health Sciences Authority Singapore

The article describes a method for auto-identification of chemical compounds encountered in complicated real-world scenarios, with common issues such as complex spectra, mixtures, and masking of signals. This method comprised a sample treatment protocol, data processing technique, and machine learning algorithms. The data processing technique involved normalizing and filtering the chemical spectrum to isolate characteristic peaks. Principal Component Analysis and t-distributed Stochastic Neighbor Embedding were used to identify significant features and reduce data dimensionality. Machine learning models such as Support Vector Machine, Random Forest Classifier, Gradient Boosting Classifier, and Neural Network Classifier were implemented to classify the identified peaks. The accuracy of the models was tested using diluents, illicit drugs, and mixture chemicals, and over 90% accuracy were obtained across all machine learning models. This concluded that the method can potentially be incorporated into software for on-site testing and automatic identification of chemical compounds in real-world scenarios.

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Scenario-Assisted Deep Reinforcement Learning

arXiv

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arXiv 2022年

作者： Yerushalmi, Raz Amir, Guy Elyasaf, Achiya Harel, David Katz, Guy Marron, Assaf Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel School of Computer Science and Engineering The Hebrew University of Jerusalem Givat Ram Jerusalem91904 Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Generalised tangent stabilised nonlinear elasticity: An automated framework for controlling material and geometric instabilities

arXiv

引用

arXiv 2024年

作者： Poya, Roman Ortigosa, Rogelio Gil, Antonio J. Kim, Theodore Bonet, Javier Meshing Framework Simulation & Test Solutions Siemens Digital Industries Software Cambridge United Kingdom Multiphysics Simulation and Optimization Lab Technical University of Cartagena Cartagena Murcia Spain Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus United Kingdom Computer Graphics Group Yale School of Engineering & Applied Science Yale University New HavenCT United States Barcelona Spain

Tangent stabilised large strain isotropic elasticity was recently proposed by Poya et al. [1] wherein by working directly with principal stretches the entire eigenstructure of constitutive and geometric/initial stiffness terms were found in closed-form, giving fresh insights into exact convexity conditions of highly non-convex functions in discrete settings. Consequently, owing to these newly found tangent eigenvalues an analytic tangent stabilisation was proposed (for common non-convex strain energies that exhibit material and/or geometric instabilities) bypassing incumbent numerical approaches routinely used in nonlinear finite element analysis. This formulation appears to be extremely robust for quasi-static simulation of complex deformations even with no load increments and time stepping while still capturing instabilities (similar to dynamic analysis) automatically in ways that are infeasible for path-following techniques in practice. In this work, we generalise the notion of tangent stabilised elasticity to virtually all known invariant formulations of nonlinear elasticity. We show that, closed-form eigen-decomposition of tangents is easily available irrespective of invariant formulation or integrity basis. In particular, we work out closed-form tangent eigensystems for isotropic Total Lagrangian deformation gradient (F)-based and right Cauchy-Green (C)-based as well as Updated Lagrangian left Cauchy-Green (b)-based formulations and present their exact convexity conditions postulated in terms of their corresponding tangent and initial stiffness eigenvalues. In addition, we introduce the notion of geometrically stabilised polyconvex large strain elasticity for models that are materially stable but exhibit geometric instabilities for whom we construct their initial stiffness in a spectrally-decomposed form analytically. We further extend this framework to the case of transverse isotropy where once again, closed-form tangent eigensystems are found for common tran

关键词： Stiffness

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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