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Focus-Shifting Attack: An Adversarial Attack That Retains Saliency Map Information and Manipulates Model Explanations

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IEEE Transactions on Reliability 2024年第2期73卷 808-819页

作者： Huang, Qi-Xian Chiang, Lin-Kuan Chiu, Min-Yi Sun, Hung-Min National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Tsing Hua University Department of Computer Science Hsinchu30013 Taiwan

With the increased use of deep learning in many fields, a question has been raised: 'How much should we trust the results generated by deep learning models?' Thus, there has been much research into the interpretations of model results, in order to open the black box of deep learning. The focus is more on interpretation than prediction in some fields such as medicine. Adversarial attacks are the most direct threats to deep learning models. They can add undetectable perturbations to the data to make the models give incorrect results, and model explanations are also susceptible to attacks. This leads to a loss of trust in explanations provided by the models, limiting the application and commercial value of deep learning. This research proposes a targeted adversarial attack algorithm that manipulates the interpretation of the model. Unlike other adversarial attacks on model interpretation, focus-shifting attack (FS Attack) can preserve the numerical depth of the original saliency map without specifying a perturbation budget. Experiments have shown that the FS Attack has a higher degree of image similarity and misleading interpretation than other adversarial attacks, and the property of preserving the numerical depth of the original saliency map makes it more difficult to detect. This study uses several common explanation methods as experimental subjects to investigate how these explanations can be manipulated and evaluate the effectiveness of the attack under different conditions. Under a particular interpretation, the FS Attack has a highly successful attack rate of 94.6%, which is a critical adversarial attack. © 1963-2012 IEEE.

关键词： Budget control

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Suicide risk assessment using word-level model with dictionary-based risky posts selection

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Multimedia Tools and applications 2024年第7期83卷 21435-21454页

作者： Tsai, Yun Sheng Chen, Arbee L. P. Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

Suicide is a serious issue around the world and is a leading cause of death in US. In the past 20 years, the suicide rate has seen a significant increase of 35%. With the rapid development of information technology, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find relevant suicide posts. This problem becomes more serious in the feature-based post embeddings since each post is converted into a single vector to serve as the input of the model, resulting in the loss of word-level information during training. In this paper, we addressed this problem by introducing a novel word-level model including a post-selectin layer as a solution. Firstly, we utilize a suicide keyword dictionary to identify risky posts that may be missed by the post-level attention mechanism. We then convert the words in the risky posts into word embeddings and use self-attention to generate the post embeddings for the risky posts. Finally, we pass the post embeddings to a multilayer perceptron to classify the suicide risk. We also demonstrate that the FScore used in previous studies can be reduced to a function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score a nearly 4% improvement compared to previous studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Embeddings

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Utilizing Multi-Objective Evolutionary Algorithms to Optimize Open Source Software Release Management

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IEEE Access 2023年 11卷 112248-112262页

作者： Yu, Chen-Yang Huang, Chin-Yu Institute of Information Systems and Applications National Tsing Hua University Hsinchu300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Scheduling and resource allocation for Open Source Software (OSS) product development pose crucial and challenging tasks due to program size and resource limitations. The properties of OSS further complicate product assessment and maintenance for developers. This paper proposes a model for the iterative and multi-release OSS development process. Unlike traditional methods that oversimplify the problem by reducing the multi-decision space into a single-objective optimization problem, our approach suggests employing Multi-objective Evolutionary Algorithms (MOEAs) to solve the Optimal Release Time Planning Problem, enabling the simultaneous maximization of reliability and minimization of cost. We consider testing cost and system reliability, two critical dimensions, as the primary objectives, while also incorporating testing resource consumption as the third objective. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is chosen as the primary method for its effectiveness in MOEAs, with a special scenario outlined in the paper where NSGA-II may not guarantee optimal solutions. Demonstrating the practicality of our proposed method, we utilize open source data to assess release time and illustrate its superiority to NSGA-II. Numerical examples further showcase the model's effectiveness. © 2013 IEEE.

关键词： Multiobjective optimization

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An AI-driven Approach for Tailoring Java Programming Instructions 3

An AI-driven Approach for Tailoring Java Programming Instruc...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Thiyagarajan, Gomathi Vijayalakshmi, V. Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331527549

AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but are increasingly gaining popularity in educationalsetting offering innovative approaches that transcend conventional methods. In this research we introduce ByteBuddy a virtual assistant which assists instructors on Java ***'s framework is designed in a way to integrate easily into educational context. This study reveals the architecture of ByteBuddy, the process model that captured the behavior of students in the learning environment and the observation of its application in java instructions. By applying this study, we provide profound insights on how the effective use of ByteBuddy supplements the teaching practices and measure learning outcomes. © 2025 IEEE.

关键词： Java programming language

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Thrips Detection and Monitoring System in Mango Greenhouses Based on Deep Learning Image Recognition

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-18页

作者： Wu, Ge-Han Huang, Nen-Fu Huang, Yu-Hsiang Chan, Pin-Cheng Bhat, Showkat Ahmad National Tsing Hua University Department Of Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Institute Of Information Systems And Applications Hsinchu1300044 Taiwan National Tsing Hua University Center Of Innovative Incubator Department Of Computer Science Hsinchu1300044 Taiwan

The ability of thrips to infect plants with viruses, oviposition, and feeding makes it one of the most damaging agricultural pests in the world. Due to their invasiveness, many pest species pose serious challenges to growers of a wide variety of crops especially Mango in both open fields and greenhouses globally. For detecting various pests, sticky paper traps have been widely used. It is essential to inspect sticky paper traps as part of an integrated pest management (IPM) process. Identification and counting the pests caught in the traps is a laborious process. A timely and effective strategy is therefore required to resolve the issue. This study proposes and develops a complete and feasible integrated pest detection and monitoring system (IPDMS) by combining an ultrahigh resolution pest monitoring device coupled with a deep learning-based object detection model. The developed system was able to collect sticky paper images with a resolution of 64 megapixels and an AWS database for analysis. Test results from two Mango greenhouses show that the model can detect thrips with 90% accuracy and an F1 -score of 0.95. Additionally, a web-based user interface was developed to provide visual image recognition results and thrip population analysis. Thus, the proposed IPDMS addresses the significant problems with pest detection systems (PDSs) by providing real-time information on pest occurrences in greenhouses. Thus, it makes pest detection and monitoring in agriculture efficient. © 1963-2012 IEEE.

关键词： Image recognition

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Leveraging Conflicts in Social Media Posts: Unintended Offense Dataset

Leveraging Conflicts in Social Media Posts: Unintended Offen...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Tsai, Che-Wei Huang, Yen-Hao Liao, Tsu-Keng Estrada, Didier Fernando Salazar Latifah, Retnani Chen, Yi-Shin Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan

ISBN: (纸本)9798891761643

In multi-person communications, conflicts often arise. Each individual may have their own perspective, which can differ. Additionally, commonly referenced offensive datasets frequently neglect contextual information and are primarily constructed with a focus on intended offenses. This study suggests that conflicts are pivotal in revealing a broader range of human interactions, including instances of unintended offensive language. This paper proposes a conflict-based data collection method to utilize inter-conflict cues in multi-person communications. By focusing on specific cue posts within conversation threads, our proposed approach effectively identifies relevant instances for analysis. Detailed analyses are provided to showcase the proposed approach, efficiently gathers data on subtly offensive content. The experimental results indicate that incorporating elements of conflict into data collection not only significantly enhances the comprehensiveness and accuracy of detecting offensive language but also enriches our understanding of conflict dynamics in digital communication. © 2024 Association for Computational Linguistics.

关键词： Data collection

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An Optimal Machine Learning Approach for Interpretation of Emotions and Prediction of School Students' Attitude towards Online Education as a Post-Effect of Covid-19 in India 15

An Optimal Machine Learning Approach for Interpretation of E...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pabreja, Kavita Mittal, Kanika Jinjharwal, Himanshu Department of Information Technology and Systems Lal Bahadur Shastri Institute of Management New Delhi India Department of Computer Applications Maharaja Surajmal Institute an affiliate of GGSIPU New Delhi India

ISBN: (纸本)9798331300579

The Indian education system was severely impacted during COVID-19 because of the unpreparedness of the entire education arrangements to manage the studies in an online mode. The students particularly senior secondary was deeply shocked when the schools closed down unprecedently and efforts were made by the schools to conduct classes and examinations in an online mode. This study aims to analyze the students' attitude towards the teaching-learning process considering the availability of digital devices and Internet facilities, before, during, and post covid times. Data from 113 school students have been collected and it comprises twenty independent features and one dependent feature. Exploratory data analysis followed by the application of a Support Vector Machine has been implemented to predict the dependent feature viz. the choice to adopt the online mode of learning. A grid search has been used to tune the threshold and locate the optimal value of the threshold for the ROC Curve. Python scikitlearn's univariate feature selection methods to select the top five features has resulted in the selection of just five independent features that are capable enough to predict the mentioned dependent features with an accuracy of 0.74 to predict the choice to adapt the online mode of learning with Support Vector machine learning classifier. The sentiments and emotions of the students pre-COVID, during-COVID and post-COVID have also been extracted. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Amalgamation of Transfer Learning and Explainable AI for Internet of Medical Things

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Recent Advances in computer Science and Communications 2024年第4期17卷 40-53页

作者： Murugan, Ramalingam Paliwal, Manish Patibandla, Rama Seetha Maha Lakshmi Shah, Pooja Balaga, Tarakeswara Rao Gurrammagari, Deepti Raj Singaravelu, Parvathavarthini Yenduri, Gokul Jhaveri, Rutvij School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India AP India Kallam Haranadhareddy Institute of Technology Andra Pradesh Guntur India Department of Computer Applications Kongu Engineering College Erode Perundurai India

The Internet of Medical Things (IoMT), a growing field, involves the interconnection of medical devices and data sources. It connects smart devices with data and optimizes patient data with real time insights and personalized solutions. It is mandatory to hold the development of IoMT and join the evolution of healthcare. This integration of Transfer Learning and Explainable AI for IoMT is considered to be an essential advancement in healthcare. By making use of knowledge transfer between medical domains, Transfer Learning enhances diagnostic accuracy while reducing data necessities. This makes IoMT applications more efficient which is considered to be a mandate in today’s healthcare. In addition, explainable AI techniques offer transparency and interpretability to AI driven medical decisions. This can foster trust among healthcare professionals and patients. This integration empowers personalized medicine, supports clinical decision making, and confirms the responsible handling of sensitive patient data. Therefore, this integration promises to revolutionize healthcare by merging the strengths of AI driven insights with the requirement for understandable, trustworthy, and adaptable systems in the IoMT ecosystem. © 2024 Bentham Science Publishers.

关键词： Knowledge management

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TERMS: textual emotion recognition in multidimensional space

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Applied Intelligence 2023年第3期53卷 2673-2693页

作者： Ghafoor, Yusra Jinping, Shi Calderon, Fernando H. Huang, Yen-Hao Chen, Kuan-Ta Chen, Yi-Shin Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica. Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu City Taiwan

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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