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International Conference on Machine Learning and Applications (ICMLA)

作者： Md Sultan Al Nahian Tasmia Tasrin Spencer Frazier Mark Riedl Brent Harrison Institute for Biomedical Informatics University of Kentucky Lexington KY USA School of Interactive Computing Georgia Institute of Technology Atlanta GA USA Department of Computer Science University of Kentucky Lexington KY USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact.

关键词： Training Ethics Natural languages Machine learning Standards

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A mobile application and system architecture for online speech training in Portuguese: design, development, and evaluation of SofiaFala

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Multimedia Tools and Applications 2024年 1-30页

作者： Macedo, Alessandra Alaniz Gonçalves, Vinícius de S. Mandrá, Patrícia P. Motti, Vivian Bulcão-Neto, Renato F. Rodrigues, Kamila Rios da Hora Av. Bandeirantes 3900 SP Ribeirão Preto14040-901 Brazil Av. Bandeirantes 3900 SP Ribeirão Preto14040-901 Brazil Department of Information Sciences and Technology - School of Computing George Mason University 4400 University Drive FairfaxVA22030 United States Institute of Informatics Federal University of Goiás Alameda Palmeiras Campus Samambaia GO Goiânia74690-900 Brazil Avenida Saocarlense 400 SP São Carlos13566-590 Brazil

Online Speech Therapy (OST) systems are assistive tools that provide online support for training, planning, and executing specific speech sounds. The most traditional OST systems are mobile applications, mainly supporting English and Spanish languages. This paper describes the development of a mobile assistive application for speech training –SofiaFala is freely available and provides support for Portuguese. The app builds upon computational modules that support speech therapy for people with neurodevelopmental disorders, including Down Syndrome. Specifically, the development of SofiaFala was iterative, involving target users actively. Speech-language pathologists as well as parents, caregivers, and children with speech disorders, mostly with Down Syndrome, contributed to the app development. This paper also describes the design of SofiaFala and the app functionalities. Also, we discuss usage workload and findings from an experimental study. In addition to analyzing the related work, we explain how we (i) elicited SofiaFala features;(ii) developed the software architecture of the app, seeking to connect speech-language pathologists’ and patients’ activities (including therapy session planning and home training exercises);(iii) evaluated SofiaFala through a field study involving target users;and (iv) addressed key challenges along the implementation process. SofiaFala provides integrated features that aim to maximize communication effectiveness, enhance language skills among users, and ultimately improve the quality of life of people with speech impairments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Application programs

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Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

Bootstrapping Simulation in Determining the Prognostic Facto...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Muhamad Jamil, Siti Afiqah Affendi Abdullah, Mohd Asrul Ibrahim, Nurain Mansor, Mahayaudin M. Md Ghani, Nor Azura Universiti Tun Hussein Onn Malaysia Faculty of Applied Sciences and Technology Pagoh Campus Johor84600 Malaysia School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia Kompleks Al-Khawarizmi Universiti Teknologi Mara Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798350302684

Big data analytics focuses on getting useful insights, trends and pattern out of complex and large data. Increasing the sample by resampling the data, in biostatistics expertise, can be employed using the bootstrapping techniques. The world of bootstrapping is very large and expanding where it does not only compute the confidence interval but also perform a standard resampling method. Nevertheless, survival analysis study mostly allows the data to be not normally distributed because of the censored observations. Small number of samples also one of the reasons why this study has to perform bootstrapping to overcome the issues of biasness. Bootstrapping method is said to be one of the best methods in handling skewed data. Thus, by considering bootstrapping method, this study aims to find the most significant prognostic factors of lung cancer disease that affect the survival times with the presence of censored observations by using the parametric survival analysis. Therefore, based on 100, 150, 250 and 600 number of sampling sizes, exponential distribution appeared to fit all the assigned sample sizes. Weibull and log-logistic distribution seems to fit the data only for 100 number of samples. Races and two of the interaction terms in the model appeared to be the most significant prognostic factors affecting the survival time of lung cancer. © 2023 IEEE.

关键词： Data Analytics

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Password Manager with Multi-Factor Authentication

Password Manager with Multi-Factor Authentication

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Networking and Communications (ICNWC), International Conference on

作者： R. Dhanalakshmi N. Vijayaraghavan S. Narasimhan Saleem Basha School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Mathematics KCG College of Technology Chennai India Computing and informatics department Mazoon University college muscat oman

Data breach is a serious issue as it leaks the personal information of more than billions of users and their privacy is compromised. More than 77% of organizations do not have a Cyber Security Incident Response plan. So, it is necessary to be informed of network security and ways to store and generate secured passwords. Having different and random passwords for ones digital accounts can exponentially increase the security of user’s data. The goal of this project is to build a password manager which can securely store and encrypt passwords and other data. The multifactor authentication will provide increased security to validate the user into password management system. Multifactor authentication system includes physical security key and graphical password authentication.

关键词： Privacy Data privacy Multi-factor authentication Computer bugs Passwords Organizations Network security

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning

DeepDect: A Facial Deepfake Video Detection Application usin...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Wan Xin Tay Hui Na Chua Muhammed Basheer Jasser Bayan Issa Richard T.K. Wong Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of ${8 4. 0 6 \%}$, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes.

关键词： Uniform resource locators Deepfakes Adaptation models Accuracy Computational modeling Feature extraction Data models Robustness Ensemble learning Overfitting

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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Computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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An Improved Casa Model for Estimating Crop Carbon Sinks from Remote Sensing Images

An Improved Casa Model for Estimating Crop Carbon Sinks from...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peng Wang Chong Niu Long Zhang Zhigang Yan Wenping Yin Botao He Pei Li Yong Xue School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China Shandong GEO-Surveying & Mapping Institute Jinan China School of Electronics Computing and Mathematics College of Engineering and Technology University of Derby Derby UK

Accurately estimating crop carbon sinks at large regional scales from the perspective of remote sensing is of great significance for carbon neutrality research, crop yield estimation, and scientific agriculture. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate Net primary productivity. The NEP is then further calculated by coupling the soil respiration model to represent the carbon sink at a regional scale. The main research contents: (1) The month-by-month net primary productivity of crops in Jiangsu Province in 2021. (2) The net ecosystem productivity of crops in Jiangsu Province month by month in 2021.

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding Yinong Chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of ***,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are *** order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word *** of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each *** integrating strategies are designed to obtain high quality contextual *** experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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Towards Effective and Efficient Error Handling Code Fuzzing Based on Software Fault Injection

Towards Effective and Efficient Error Handling Code Fuzzing ...

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IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Kang Chen Ming Wen Haoxiang Jia Rongxin Wu Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology (HRUST) Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Jin YinHu Laboratory Wuhan China School of Informatics Xiamen University Xiamen China School of Computer Science and Technology HUST Wuhan China Cluster and Grid Computing Lab

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

Software systems often encounter various errors or exceptions in practice, and thus proper error handling code is essential to ensure the reliability of software systems. Unfortunately, error handling code is often bug-prone, while sufficiently testing them is challenging as such code often cannot be triggered under normal conditions. Motivated by this, recent studies have proposed to leverage software fault injection (SFI) based fuzzing to discover potential bugs in complicated error handling code. Despite the promising results achieved, their effectiveness and efficiency are still compromised in practice due to the huge search space of error sites, inadequate fuzzing guidance, and the overhead induced by context-sensitive SFI. To achieve effective and efficient testing of error handling code, this study presents AFL-FI, which first utilizes a similarity-based method to identify suspicious error sites, and then incorporates the idea of error site coverage to guide the fuzzing process. Finally, the design of lightweight context-sensitive SFI enables AFL-FI to execute test cases efficiently. We evaluate AFL-FI on eight large-scale open-source projects, and the results show that it can outperform existing state-of-the-art fuzzing tools significantly in terms of branch code coverage. More importantly, AFL-FI has discovered 13 previously unknown bugs, and all of them have been confirmed while 12 of them have been fixed. Besides, our evaluation also demonstrates that all the key designs of AFL- F I are effective that contribute significantly to its overall performance.

关键词： Codes Computer bugs Fuzzing Software systems Software reliability Testing

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Stacking Classifier with Random Forest functioning as a Meta Classifier for Diabetes Diseases Classification 26

Stacking Classifier with Random Forest functioning as a Meta...

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Ali, Maria Haider, Muhammad Nasim Lashari, Saima Anwar Sharif, Wareesa Khan, Abdullah Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Faculty of Computing The Islamia University of Bahawalpur Pakistan School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Diabetes has been an offensive condition in recent years, and it can lead to major health problems. If diabetes is not addressed, it can lead to a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Diabetes condition must be addressed promptly to avoid a significant health risk. Machine learning algorithms can assist the doctor in identifying and diagnosing diabetes and other diseases. Different types of classifiers have been used to diagnose diabetes. To improve the performance of integrated flexible individual classifiers and lower the possibility of misclassifying a single instance, an ensemble approach named "Stacking Classifier" was developed. Several classifiers, such as Naïve Bayes, KNN, Linear regression, and decision tree (DT) were used but all these models have low accuracy. However, additional study is needed to detect diabetic condition due to a lack of major work and low accuracy. Therefore this study proposed an ensemble technique termed "Stacking Classifier" was designed to increase the performance of integrated flexible individual classifiers and reduce the probability of misclassifying a single instance. This study uses a variety of classifiers, including Naïve Bayes, KNN, Linear Discriminant Analysis, and Decision Tree, with Random Forest functioning as a Meta classifier. In terms of F-measure, Recall, Accuracy, and Precision, the proposed stacking classifier achieves a higher accuracy of 97.35 % when compared to current models such as Nave Naïve Bayes, KNN, Decision Tree, and Linear Discriminant Analysis, which are 74.60 %, 78.57 %, and 77.35 %, respectively. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision trees

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