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Advanced Information Scientific Development (ICAISD), International Conference on

作者： Leni Susanti Siti Hajar Mohamad Mila Andria Savitri Karen Etania Saputra Doni Purnama Alamsyah Ahmad Setiadi Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Business Management and Law Management and Science University Selangor Malaysia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Software Engineering Department Faculty of Information Technology Universitas Bina Sarana Informatika Jakarta Indonesia

ISBN: (数字)9798350392036

ISBN: (纸本)9798350392043

Today, financial technology is a part of consumer needs. Mobile payments are used to support daily transactions, so a study of factors that can support behavioral intention and mobile money use is conducted. This study examines the support of digital literacy, perceived security, and perceived compatibility of mobile payments in increasing behavioral intention and mobile money use. The research was conducted on mobile payment users in Bandung City (Indonesia), and 416 research participants were asked using a questionnaire. The research was conducted using a quantitative survey and was processed using the SmartPLS tool. Two tests, namely, the PLS Algorithm and Bootstrapping, were used to test the research model. Next, the research hypothesis testing was used to explain the results. The research results found positive support from digital literacy, perceived security, and perceived compatibility on mobile payment behavioral intention. However, only perceived compatibility can directly increase the use of mobile money. In line with the hypothesis test, behavioral intention can support increased mobile money use by digital payment users. The research findings explain the ability of behavioral intention to mediate support for digital literacy, perceived security, and perceived compatibility in mobile money use.

关键词： Surveys Online banking Fintech Urban areas Security Digital intelligence Testing

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Mitigating Insecure Outputs in Large Language Models(LLMs): A Practical Educational Module

Mitigating Insecure Outputs in Large Language Models(LLMs): ...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： MD Abdul Barek Md Mostafizur Rahman Shapna Akter A.B.M Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Computer Science and Software Engineering Auburn University USA Dept. of Computer Science Tuskegee University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Large Language Models (LLMs) have extensive ability to produce promising output. Nowadays, people are increasingly relying on them due to easy accessibility, rapid and outstanding outcomes. However, the use of these results without appropriate scrutiny poses serious security risks, particularly when they are integrated with other software, APIs, or plugins. This is because the LLM outputs are highly dependent on the prompts they receive. Therefore, it is essential to carefully clean these outputs before using them in additional software environments. This paper is designed to teach students about the potential dangers of contaminated LLM output within the context of web development through prelab, hands-on, and postlab experiences. Hands-on lab provides practical guidance on how to handle LLM vulnerabilities to make applications safe with some real-world examples in Python. This approach aims to provide students with a deeper understanding of the precautions necessary to ensure software against the vulnerabilities introduced by LLM output.

关键词： Training Navigation Large language models Computational modeling software Security software development management

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Systematizing the Meta-Analytical Process in software engineering 21

Systematizing the Meta-Analytical Process in Software Engine...

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Proceedings of the 2021 European Symposium on software engineering

作者： Letizia Jaccheri Zamira Kholmatova Giancarlo Succi Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer Science Lab for Industrializing the Production of Software Innopolis University Russia

ISBN: (纸本)9781450385060

The generalization of knowledge is a necessary part of every scientific field. Meta-analysis is already advocated as a tool for generalization in different areas such as medicine, psychology, business, and this process is already standardized for them. software engineering started using meta-analysis as a tool for aggregating results from families of experiments, but not so long for generalization of results coming from different studies, and for this purpose, the meta-analytical approach is not yet clarified. In this paper, we attempt to systematize the application of meta-analysis as a secondary study to the software engineering field suggesting our preliminary protocol. To see the reliability of the proposed protocol we conducted several studies using it. Following even uniform protocol with these studies, we identified the issues preventing the wide usage of meta-analysis in software engineering and proposed our solutions for them.

关键词： Meta-analysis in software engineering Evidence-based software engineering Research synthesis in software engineering

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Consumer and Farmer Centric Subscription Based Organic Vegetable/Fruit Delivery System 3

Consumer and Farmer Centric Subscription Based Organic Veget...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Karunarathna, T.M.N.P. Kumari, M.M.A. Kulasinghe, W.D.K.A. Parana Liyanage, T.L. Kodagoda, Nuwan Suriyanwsa, Kushnara Kumari, Disna Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Kings Hospital Colombo Sri Lanka

ISBN: (纸本)9781665408622

For centuries, the economy of Sri Lanka has been backed by the agricultural sector. Even though it has contributed to the development of the nation on a large scale, the economies and living standards of the farmers have not been improved much. Most of the farmers remain poor mainly due to not knowing the market demand and the low buying prices of the middleman. Meanwhile, the consumers in Sri Lanka are buying non-organic vegetables and fruits which are highly contaminated with dangerous chemicals which in turn has contributed to the rapidly increasing non-communicable disease rates in both consumers and farmers. The proposed system is an attempt to solve these problems faced by both farmers and consumers by reducing the farmers-consumer knowledge gap and providing consumers a trustworthy and personalized subscription for organic vegetables/fruits with a donation platform. © 2021 IEEE.

关键词： Crops

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Hybrid Semantic Conflict Prevention in Real-Time Collaborative Programming 17th

Hybrid Semantic Conflict Prevention in Real-Time Collaborati...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Xu, Wenhua Zhang, Yiteng Chiu, Brian Chen, Dong Jiang, Jinfeng Du, Bowen Fan, Hongfei School of Software Engineering Tongji University Shanghai China SAP Labs China Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom

ISBN: (纸本)9783030926373

Real-time collaborative programming allows a group of programmers to edit the same source code at the same time. To support semantic conflict prevention in real-time collaboration, a dependency-based automatic locking (DAL) approach was proposed in prior work. The DAL mechanism automatically detects programming elements with dependency relationships, and prohibits concurrent editing on the inter-dependent source code regions by locking. However, the prior DAL scheme is too restrictive, which leads to an unnecessarily large locking scope and seriously impacts the concurrent work. To address this issue, we propose a novel hybrid semantic conflict prevention (HSCP) scheme, to achieve a better balance between conflict prevention and concurrent work. The scheme enforces locking on the working and strongly-depended regions, while applies awareness highlight on weakly-depended regions. The depth of locking scope can be customized by each programmer in a fine-grained manner. A three-level awareness mechanism has been designed for programmers to intuitively distinguish working, strongly-depended and weakly-depended regions. In supporting the scheme, we have devised techniques and solutions, and implemented a prototype that supports programmers to enjoy hybrid semantic conflict prevention in real-time collaborative programming over Eclipse and IntelliJ IDEA platforms. Experimental evaluations have confirmed the satisfactory performance of the scheme in complex real-world scenarios. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Semantics

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Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning

arXiv

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arXiv 2024年

作者： Xu, Yongxin Zhang, Ruizhe Jiang, Xinke Feng, Yujie Xiao, Yuzhen Ma, Xinyu Zhu, Runchuan Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science School of Software & Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies Peking University Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by Large Language Models (LLMs) in hallucination generation and knowledge obsolescence by incorporating externally retrieved knowledge. However, due to potential conflicts between internal and external knowledge, as well as retrieval noise, LLMs often struggle to effectively integrate external evidence, leading to a decline in performance. Although existing methods attempt to tackle these challenges, they often struggle to strike a balance between model adherence and robustness, resulting in significant learning variance. Inspired by human cognitive processes, we propose Parenting, a novel framework that decouples adherence and robustness within the parameter space of LLMs. Specifically, Parenting utilizes a key parameter mining method based on forward activation gain to identify and isolate the crucial parameter units that are strongly linked to adherence and robustness. Then, Parenting employs a type-guided tailored tuning strategy, applying specific and appropriate fine-tuning methods to parameter units representing different capabilities, aiming to achieve a balanced enhancement of adherence and robustness. Extensive experiments on various datasets and models validate the effectiveness and generalizability of our methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Obsolescence

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ke Chen Xiaosong Han Xiaoran Li Yanchun Liang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai College of Science and Technology Zhuhai China Department of Electrical Engineering and Computer Science Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

关键词： Drugs Deep learning Adaptation models Visualization Biological system modeling Noise Knowledge graphs Predictive models Fingerprint recognition Transformers

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Deep Transfer Learning for Classification of Rabbit Behaviour Using Publicly Available Datasets

Deep Transfer Learning for Classification of Rabbit Behaviou...

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Advancements of Artificial Intelligence in African Context (AAIAC), International Conference on the

作者： Oluwadamisola Joshua Adedeji Adebayo Abayomi-Alli Oluwasefunmi ‘Tale Arogundade Olusola Abayomi-Alli Bayo Olushola Omoyiola Department of Computer Science Federal University of Agriculture Abeokuta Ogun State Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania University of the People Pasadena California United States

Deep learning has emerged successfully as a utilised technology for animal monitoring and classification. However, there exists limited research when it comes to rabbit monitoring and classification. This study presents real time models for detection and tracking of rabbits, along with a classification model for their behaviour. The detection model was trained on a dataset obtained from Robflow and YouTube, while the classification models utilised over 1100 curated images representing seven different physical behaviours of rabbits: digging, humping and mating, eating, periscoping, jumping and binkying, laying, and flopping and loafing. For object tracking, transfer learning was applied using a pretrained ResNet18 model for feature extraction, and YOLOv8 was employed for tracking, supported by ByteTRACK for consecutive frame tracking. Additionally, VisionTransformer, ResNet18, and EfficientNet models were finetuned to adapt their representations for behaviour classification. The experimental result demonstrated that the finetuned VisionTransformer model achieved an accuracy of 86%, outperforming ResNet18 and EfficientNet models with accuracies of 76% and 80% respectively. In the object detection experiment, the YOLOv8 model achieved a mean Average Precision (mAP50) score of 0.96. These results indicate the effectiveness of the proposed approach for accurate detection, tracking, and classification of rabbit behaviour in real-time.

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Facial Skincare Product Suggestion with Product Popularity and Post Recommendation Care

Facial Skincare Product Suggestion with Product Popularity a...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： H. M. W. T. C. B. Samarakoon A. U Ovitigala G. A. T. T. Wijesinghe K. M. L. P. Weerasinghe G. K. B. V. Karunathilake Ishara Weerathunga Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

Skin health significantly affects a person's overall wellbeing, which includes both physical and emotional aspects. The objective of this study is to develop an integrated system for recommending skincare products that integrates image processing, machine learning, sentiment analysis, and user feedback analysis to deliver individualized skincare advice and boost user satisfaction. Utilizing deep learning algorithms and face cues, the system uses user image analysis to detect important skin features, skin type, acne severity, and dark spots with extraordinary accuracy. Using sentiment analysis and clustering, the program identifies popular skincare products and tailors’ recommendations to user preferences. Recommendations are further personalized by collaborative filtering. By using image analysis to identify allergies and other negative effects, user safety is increased. The technique prioritizes data safety and includes model training, sentiment analysis, image processing, and user input analysis. Initial findings demonstrate the excellent accuracy of skin issue detection and severity prediction. By allowing users from a variety of user demographics to make informed skincare decisions, this complete framework has the potential to change the skincare industry.

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Comparing the Effect of Code Optimizations on Simulation Runtime Across Synchronous Cellular Automata Models of HIV

Comparing the Effect of Code Optimizations on Simulation Run...

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2021 Winter Simulation Conference, WSC 2021

作者： Li, Junjiang Giabbanelli, Philippe J. Koster, Till Miami University Department of Computer Science Software Engineering OH45056 United States Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Models developed by domain experts occasionally struggle to achieve a sufficient execution speed. Improving performances requires expertise in parallel and distributed simulations, hardware, or time to profile performances to identify bottlenecks. However, end-users in biological simulations of the Human Immunodeficiency Virus (HIV) have repeatedly demonstrated that these resources are either not available or not sought, resulting in models that are developed through user-friendly languages and platforms, then used on workstations. This situation becomes problematic when performances cannot cope with the salient characteristics of the phenomenon that is modeled, as is the case with cellular automata (CA) models of HIV. In this paper, we optimize the Python code of CA models of HIV to scale the number of cells handled by a simulation on a workstation commonly available to end-users. We demonstrate this scalability via five HIV CA models and compare these results to assess how modeling choices can impact runtime. © 2021 IEEE.

关键词： Viruses

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