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2024 IEEE International Conference on machine learning and Applied Network Technologies, ICMLANT 2024

作者： Gupta, Subir Hoang, Quang-Trung Roy, Bhaswati Chowdhury, Subrata Mukherjee, Sandip Pathak, Aditya Kumar Duc, Diep Can Haldia Institute of Technology West Bengal 721657 India Faculty of Electrical and Electronic Engineering Phenikaa University Hanoi12116 Viet Nam Dept. of FMS Dr.B.C. Roy Engineering College WB Durgapur India Dept. of Computer Science and Machine Learning Sreenivasa Inst. of Technology and Management Studies Andhra Pradesh Chittoor India Dept. of IT Haldia Institute of Technology West Bengal 721657 India Electric Power University Hanoi Viet Nam

ISBN: (纸本)9798350389180

This investigation explores student expectations and perceptions regarding higher education institutions, focusing on aligning professional programs with student career aspirations. The study evaluates how effectively academic institutions understand and meet training needs essential for student success in professional fields. Students were categorized using the Iterative Dichotomiser 3 methodology to forecast their training requirements. Analytical tools such as the confusion matrix and the ROC curve assessed the precision of these predictions. The results show a specificity rate of 74.1%, a sensitivity of 78.6%, and an overall accuracy of 76.7%. The combined sensitivity and specificity score is 3.527, and the empirical area under the ROC curve is 0.76, suggesting the model’s reasonable efficacy. The findings indicate a significant student demand for enhanced instruction in soft skills, underscoring the need for educational institutions to adapt their curricula to better prepare students for professional challenges. © 2024 IEEE.

关键词： Students

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Policy-Guided Lazy Search with Feedback for Task and Motion Planning

Policy-Guided Lazy Search with Feedback for Task and Motion ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mohamed Khodeir Atharv Sonwane Ruthrash Hari Florian Shkurti Robot Vision and Learning Lab University of Toronto Robotics Institute Dept. of Computer Science University of BITS Pilani Dept. of Mathematical and Computational Sciences University of Toronto

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://***/learning-based-tamp.

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The Importance of Robust Features in Mitigating Catastrophic Forgetting

The Importance of Robust Features in Mitigating Catastrophic...

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IEEE Symposium on computers and Communications (ISCC)

作者： Hikmat Khan Nidhal C. Bouaynaya Ghulam Rasool Dept. of Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Dept. of Machine Learning Moffitt Cancer Center Tampa Florida USA

Continual learning (CL) is an approach to address catastrophic forgetting, which refers to forgetting previously learned knowledge by neural networks when trained on new tasks or data distributions. The adversarial robustness has decomposed features into robust and non-robust types and demonstrated that models trained on robust features significantly enhance adversarial robustness. However, no study has been conducted on the efficacy of robust features from the lens of the CL model in mitigating catastrophic forgetting in CL. In this paper, we introduce the CL robust dataset and train four baseline models on both the standard and CL robust datasets. Our results demonstrate that the CL models trained on the CL robust dataset experienced less catastrophic forgetting of the previously learned tasks than when trained on the standard dataset. Our observations highlight the significance of the features provided to the underlying CL models, showing that CL robust features can alleviate catastrophic forgetting.

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The Internal State of an LLM Knows When It's Lying

arXiv

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arXiv 2023年

作者： Azaria, Amos Mitchell, Tom School of Computer Science Ariel University Israel Machine Learning Dept. Carnegie Mellon University PittsburghPA United States

While Large Language Models (LLMs) have shown exceptional performance in various tasks, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. In this paper, we provide evidence that the LLM's internal state can be used to reveal the truthfulness of statements. This includes both statements provided to the LLM, and statements that the LLM itself generates. Our approach is to train a classifier that outputs the probability that a statement is truthful, based on the hidden layer activations of the LLM as it reads or generates the statement. Experiments demonstrate that given a set of test sentences, of which half are true and half false, our trained classifier achieves an average of 71% to 83% accuracy labeling which sentences are true versus false, depending on the LLM base model. Furthermore, we explore the relationship between our classifier's performance and approaches based on the probability assigned to the sentence by the LLM. We show that while LLM-assigned sentence probability is related to sentence truthfulness, this probability is also dependent on sentence length and the frequencies of words in the sentence, resulting in our trained classifier providing a more reliable approach to detecting truthfulness, highlighting its potential to enhance the reliability of LLM-generated content and its practical applicability in real-world scenarios. © 2023, CC BY-NC-ND.

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THE IMPORTANCE OF ROBUST FEATURES IN MITIGATING CATASTROPHIC FORGETTING

arXiv

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arXiv 2023年

作者： Khan, Hikmat Bouaynaya, Nidhal C. Rasool, Ghulam Dept. of Electrical and Computer Engineering Rowan University GlassboroNJ United States Dept. of Machine Learning Moffitt Cancer Center TampaFL United States

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From Lenses to Living Rooms: A Policy Brief on Eye Tracking in XR Before the Impending Boom 6

From Lenses to Living Rooms: A Policy Brief on Eye Tracking ...

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6th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2024

作者： Byrne, Sean Anthony Castner, Nora Bozkir, Efe Niehorster, Diederick C. Kasneci, Enkelejda Imt School for Advanced Studies Lucca MoMiLab Lucca Italy Carl Zeiss Vision International GmbH Tübingen Germany Technical University of Munich Human-Centered Technologies for Learning Munich Germany University of Tübingen Human-Computer Interaction Tübingen Germany Lund University Humanities Lab & Lund University Dept. of Psychology Lund Sweden

ISBN: (纸本)9798350372021

As tech giants such as Apple and Meta invest heavily in Virtual and Augmented Reality (VR/AR) technologies, often collectively termed Extended Reality (XR) devices, a significant societal concern emerges: The use of eye-tracking technology within these devices. Gaze data holds immense value, revealing insights into user attention, health, and cognitive states. This raises substantial concerns over privacy and fairness, with potential risks of targeted ads, unauthorized surveillance, and data re-purposing. As the impact of eye tracking is due to transition from the lab to the broader public, this paper underscores these pivotal issues in a digestible manner to the general audience. To this end, we first outline the eye-tracking data collection process and its potential for user insights. Second, we introduce perspectives from the domain of privacy, emphasizing its significance as a pivotal measure to guard against the improper use of eye-tracking data. Third, we provide a set of guidelines created by researchers actively working within this space. These recommendations are designed to guide policymakers and the general public toward establishing informed, equitable, and privacy-centric standards surrounding these devices. © 2024 IEEE.

关键词： Eye tracking

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Edge Computing Devices Authentication using Quantum Computing

Edge Computing Devices Authentication using Quantum Computin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Vinayak A. Telsang Mahabaleshwar S. Kakkasageri Anil D. Devangavi Dept. of Computer Science and Engineering Biluru Gurubasava Mahaswamiji Institute of Technology Mudhol India Dept. Electronics and Communication Engineering Basaveshwar Engineering College Bagalkote India Dept. of Artificial Intelligence and Machine Learning Basaveshwar Engineering College Bagalkote India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Edge computing allows different technologies such as Internet of Things (IoT), vehicle-to-vehicle communications(V V), Industrial IOT (IIOT) to connect to the cloud computing to facilitate end users. The edge networks uses low bandwidth, and enhances the processing speed. With more number of IoT devices connected in the network the privacy of data stored at the edge server also under threat. Authentication is a major challenge which is be addressed to provide security and privacy to IoT devices connected in the network. The existing works uses cryptographic algorithms to solve the authentication problems, but with increase in growth of quantum computers and quantum computing, breaking the complex cryptographic algorithm is not an issue. Hence one should adopt the quantum approach to provide authentication in edge network. The proposed work uses quantum key and context information of IoT devices to store in the edge server using blockchain, which proves to be more secure. The performance analysis shows that it is better with respect to registration, encryption, decryption and authentication of IoT devices.

关键词： Privacy Quantum computing Costs Authentication Vehicular ad hoc networks Blockchains Registers Servers Industrial Internet of Things Edge computing

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NPC Behavior in Games Using Unity ML-Agents: A Reinforcement learning Approach

NPC Behavior in Games Using Unity ML-Agents: A Reinforcement...

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Automation and Computation (AUTOCOM), International Conference on

作者： Revat Meshram Sachith Krishna Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Shruti Maheshwari Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

machine learning is having a significant impact on video games, from speeding up the development of new games to the development of new methods in gaming. This study discusses the impact of using ML models in the development and improvement of video games. Examples cited in this paper relate to the ways in which NPCs behave. With the aid of Unity's ML-Agents toolkit, developers can create NPCs that respond to different strategies in real time. This makes the games more exciting and complex. The paper also outlines the possibilities of single-player games, such as first-person shooters and a role-playing game, to notice NPC behavior during the gameplay. In these gaming styles, RL frameworks can be applied to NPCs to enable them to learn and adapt to the environment to create an interactive experience for the player. The conclusions put an emphasis on the evolution of video games with the advent of ML tools; as predicted, NPC behavior becomes more intuitive and less expected.

关键词： Industries Procedural generation Automation Role playing games Games Reinforcement learning Real-time systems

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Dark Patterns Detection on E-Commerce Websites

Dark Patterns Detection on E-Commerce Websites

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Automation and Computation (AUTOCOM), International Conference on

作者： Aarya Sawant Amrisha Gamane Shraddha Sonawane Omkaresh Kulkarni Rutuja Rajendra Patil Gagandeep Kaur Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Dark patterns are techniques employed on e-commerce website interfaces for the purpose of influencing users' actions to engage in undesirable behavior such as making unintentional purchases or submitting personal information when they did not want to. This is where this study seeks to address through identification of such patterns using a machine learning method. To cover false and genuine content, a dataset with 10,000 textual samples, obtained from several e-commerce sites, was compiled. Thus, for analysis, the easily interpretable classification model, Logistic Regression, was used. The results showed that the detection accuracy of the model is 92%, where precision and Recall metrics' point to the model's abilities to detect manipulative language. The results point to the ability of machine learning methods in detecting and combating fraud in cyberspace and thus promoting an ethical e-commerce market.

关键词： Measurement Logistic regression Accuracy Computational modeling Mathematical models Fraud Electronic commerce Problem-solving Protection Random forests

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Optimizing Medical Image Classification: Leveraging Advanced Segmentation Models for Enhanced Object Detection in Real-World Scenarios

Optimizing Medical Image Classification: Leveraging Advanced...

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Future Telecommunications and Artificial Intelligence (IC-FTAI), International Conference on

作者： Nore Elrashidy Nourhan Mohamed Elsabawy Alaa Ebraheem Elnakeeb Alaa Hussien Mai Saeed Menisy Abdelkareem Elkhateb Shaker El Sappagh Dept. Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kaferelshikh University Kaferelshikh Egypt Dept. Biological Artificial Intelligence Faculty of Artificial Intelligence Kaferelshikh University Kaferelshikh Egypt Computer Engineering Galala University Suez Egypt

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

In medical imaging, classifiers typically rely on objects of interest, such as nodules, being centered and fully visible within the frame to effectively learn the underlying relationships necessary for accurate classification. However, in real-world scenarios, such conditions are not always met, leading to challenges in classification accuracy. This paper explores the enhancement of classifier performance by incorporating advanced segmentation models, including SAM, SAM2, and Dinov2, all of which are pretrained for automatic semantic segmentation. We compare the results of integrating these modelsto preprocess and segment medical images, thereby improving the classifier's ability to correctly identify and classify objectsin non-ideal conditions. The study aims to demonstrate how leveraging state-of-the-art segmentation techniques can bridge the gap between idealized assumptions and real-world medical data, ultimately leading to more robust and accurate classificationoutcomes.

关键词： Accuracy Semantic segmentation Object detection Telecommunications Object recognition Artificial intelligence Biomedical imaging Image classification

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