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A Comparative Study of Machine learning Classifiers for Electric Load Disaggregation based on an extended NILM dataset 14

A Comparative Study of Machine Learning Classifiers for Elec...

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14th IEEE international conference on Industry Applications (INDUSCON)

作者： Bosco, thais Berrettini Serrao Goncalves, Flavio Alessandro de Souza, Wesley Angelino UNESP Sao Paulo State Univ ICTS Inst Sci & Technol Sorocaba Sorocaba Brazil UTFPR Fed Univ Technol Parana DAELE Dept Elect Engn Cornelio Procopio Brazil

ISBN: (纸本)9781665441186

the appliance evaluation and the power consumption consciousness are becoming essential for improving demand management and power grid enhancement. Load disaggregation becomes a promising engine for this goal, and some researches efforts have been made in the last years. In this sense, achieving the load characterization is essential to the technique's success;moreover, the proper feature extraction becomes essential. In this way, this paper presents a comparative study of machine learning classifiers for electric load disaggregation using an enhanced version of a household appliance dataset proposed by Souza et al. of Brazilian appliances (NILMbr). the load characterization is performed through the Conservative Power theory, a recent power theory that extracts appliance signatures by means of power quantities. then, it is proposed three machine learning models to validate proper load identification, being: classification algorithms - kNearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF). these algorithms were used to assess computational time and performance metrics. Subsequently, the RF algorithm presented the best performance, with an accuracy of 99.5%.

关键词： machine learning load disaggregation power meter artificial intelligence

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A Large Language Model-Based System for Socratic Inquiry: Fostering Deep learning and Memory Consolidation

A Large Language Model-Based System for Socratic Inquiry: Fo...

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international conference on Educational and Information Technology (ICEIT)

作者： Xin Xie Xiaoli Yang Rongyu Cui Higher Vocational College Chengdu Neusoft University Chengdu China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

this paper introduces a novel Large Language Model (LLM)-based system designed to enhance learning effect through Socratic inquiry, thereby fostering deep understanding and longterm knowledge consolidation. Recognizing the challenges of implementing Socratic pedagogy and memory reinforcement principles (as highlighted by the Ebbinghaus forgetting curve) in traditional settings, this research explores a new framework that integrates the power of LLMs with established pedagogical approaches grounded in constructivist learning theory and cognitive load theory. this system, which includes carefully designed AIdriven questioning techniques informed by the Socratic method, dynamic note management, and memory reinforcement strategies guided by the testing effect, was evaluated through a quasiexperimental classroom intervention. the study revealed that students using the LLM-based framework demonstrated significantly better understanding of complex topics, and were also more engaged with the learning process, when compared to students using traditional methods. these results highlight the potential for LLMs to transform educational practices by creating new, more effective and personalized learning pathways that reduce cognitive load and facilitate long-term retention. this research offers practical insights for educators and researchers seeking to leverage AI to promote meaningful learning experiences and to help students internalize knowledge more effectively.

关键词： Deep learning Schedules Reviews Large language models Retrieval augmented generation Memory management Transforms Cognitive load Testing Load modeling

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Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom 14

Low-Stabilizer-Complexity Quantum States Are Not Pseudorando...

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14th Innovations in theoretical Computer Science conference, ITCS 2023

作者： Grewal, Sabee Iyer, Vishnu Kretschmer, William Liang, Daniel The University of Texas AustinTX United States

ISBN: (纸本)9783959772631

We show that quantum states with "low stabilizer complexity" can be efficiently distinguished from Haar-random. Specifically, given an n-qubit pure state |ψ>, we give an efficient algorithm that distinguishes whether |ψ> is (i) Haar-random or (ii) a state with stabilizer fidelity at least 1/k (i.e., has fidelity at least 1/k with some stabilizer state), promised that one of these is the case. With black-box access to |ψ>, our algorithm uses O(k12 log(1/δ)) copies of |ψ> and O(nk12 log(1/δ)) time to succeed with probability at least 1 − δ, and, with access to a state preparation unitary for |ψ> (and its inverse), O(k3 log(1/δ)) queries and O(nk3 log(1/δ)) time suffice. As a corollary, we prove that ω(log(n)) T-gates are necessary for any Clifford+T circuit to prepare computationally pseudorandom quantum states, a first-of-its-kind lower bound. © Sabee Grewal, Vishnu Iyer, William Kretschmer, and Daniel Liang;licensed under Creative Commons License CC-BY 4.0.

关键词： Bell sampling Clifford + T complexity theory Haar random learning theory Pseudorandom quantum states stabilizer extent stabilizer fidelity stabilizer formalism

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Generating Targeted Adversarial Attacks and Assessing their Effectiveness in Fooling Deep Neural Networks 14

Generating Targeted Adversarial Attacks and Assessing their ...

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14th IEEE international conference on Signal Processing and Communications (SPCOM)

作者： Gajjar, Shivangi Hati, Avik Bhilare, Shruti Mandal, Srimanta Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (数字)9781665482509

ISBN: (纸本)9781665482509

Deep neural network (DNN) models have gained popularity for most image classification problems. However, DNNs also have numerous vulnerable areas. these vulnerabilities can be exploited by an adversary to execute a successful adversarial attack, which is an algorithm to generate perturbed inputs that can fool a well-trained DNN. Among various existing adversarial attacks, DeepFool, a white-box untargeted attack is considered as one of the most reliable algorithms to compute adversarial perturbations. However, in some scenarios such as person recognition, adversary might want to carry out a targeted attack such that the input gets misclassified in a specific target class. Moreover, studies show that defense against a targeted attack is tougher than an untargeted one. Hence, generating a targeted adversarial example is desirable from an attacker's perspective. In this paper, we propose 'Targeted DeepFool', which is based on computing a minimal amount of perturbation required to reach the target hyperplane. the proposed algorithm produces minimal amount of distortion for conventional image datasets: MNIST and CIFAR10. Further, Targeted DeepFool shows excellent performance in terms of adversarial success rate.

关键词： Deep learning Perturbation methods Neural networks Signal processing algorithms Rate distortion theory Distortion Reliability

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On Two Simple[st] learning Tasks 14th

On Two Simple[st] Learning Tasks

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14th international conference on Algorithms and Complexity, CIAC 2025

作者： Filtser, Omrit Huynh, Kien Lemetti, Anastasia Mitchell, Joseph Polishchuk, Tatiana Polishchuk, Valentin The Open University of Israel Ra’anana Israel Stony Brook University Stony Brook United States Linköping University Linköping Sweden

ISBN: (纸本)9783031929311

We consider two very basic problems – one in unsupervised and one in supervised learning. In the former, we are given a set of points and have to label half of the points red and half the points blue so as to maximize the red–blue separation, i.e., the length of a shortest bichromatic edge. In the latter, the data (points in the plane) are already labeled red and blue, and we seek a linear classifier (a separator of the two given point sets) that can be described using the smallest integers. We give algorithms for both problems. Our solutions are simple;the main contribution of the paper is highlighting the problems and their algorithmic solutions, which, to our knowledge, have not been presented previously, despite the problems being fundamental to the field. We also consider related problems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational geometry

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An Extendable Sentiment Monitoring Model for SNS Considering Environmental Factors 1

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14th international conference on Social Computing and Social Media (SCSM) Held as Part of the 24th international conference on Human-Computer Interaction (HCII)

作者： Wang, Yenjou Yen, Neil Jin, Qun Waseda Univ Grad Sch Human Sci Tokorozawa Saitama Japan Aizu Univ Sch Comp Sci & Engn Aizu Wakamatsu Japan Waseda Univ Fac Human Sci Tokorozawa Saitama Japan

ISBN: (数字)9783031050619

ISBN: (纸本)9783031050619;9783031050602

Rapid growth of social network service (SNS) has drawn significant attention from the publics. Existing research indicated that emotional state and behavioral tendency of SNS users can be identified and predicted through sentiment analysis. However, it is found that not only the posts can express user's emotions but the overall environmental conditions faced by the user may lead to the generation of different emotions based on the cognitive theory of emotion and observation. therefore, it may lead to bias between the sentiment analysis results and the actual situation if only analyzing the post. this study targets to propose an extendable sentiment monitoring model which considers the actual environment of users in SNS. through this model, the result of sentiment analysis is closer to reality. By analyzing the content of users' continuous posts, the sentiment analysis can take into account the pre- and post-textual relationships. the classification result of external affecting sentiment factors by K-means is used as criteria for weighting method to adjust the results of sentiment analysis based on BERT. Finally, the time series analysis is used to predict sentiment tendency monitor sentiment changes. the experiment results show that the training and validation accuracy are 89.24% and 84.00%, respectively. By our weighting method to revise the BERT results, the F1 score is improved from 0.839 to 0.850.

关键词： Sentiment analysis Emotion model K-means BERT Machine learning Human behavior analysis

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PEACE: Private and Energy-Efficient Algorithm for Cardiac Evaluation on the EDGE using Modified Split learning and Model Quantization

PEACE: Private and Energy-Efficient Algorithm for Cardiac Ev...

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international conference on Information and Communication Systems (ICICS)

作者： Ahmad Ayad Mahdi Barhoush Johnson Loh Tobias Gemmeke Anke Schmeink Chair of Information Theory and Data Analytics Chair of Integrated Digital Systems and Circuit Design RWTH Aachen University Aachen Germany

the classification of Electrocardiograms (ECG) signals is crucial for diagnosing and monitoring cardiovascular diseases. Recently, there has been increasing interest in leveraging efficient machine learning models for ECG classification. Optimizing computational resources in this domain is crucial for real-time analysis and reducing power consumption. In this paper, we introduce an efficient and personal ECG classification system that combines privacy-preserving distributed machine learning with model quantization to decrease computational complexity. the system includes an efficient modified split learning mechanism that saves 71.7% of the communications overhead and 46.5% of the computational complexity at the client compared to the original split learning system. Furthermore, we employed post-training quantization to enhance the efficiency of the client model, resulting in a remarkable 50x reduction in dynamic power consumption. Overall, our proposed system can enable the use of ECG classifiers on portable medical devices much more efficiently while preserving the privacy of patient data.

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Practical Accuracy Evaluation for Deep learning Systems via Latent Representation Discrepancy 23

Practical Accuracy Evaluation for Deep Learning Systems via ...

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14th Asia-Pacific Symposium on Internetware, Internetware 2023

作者： Yin, Yining Feng, Yang Liu, Zixi Zhao, Zhihong State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9798400708947

As deep learning systems have been widely deployed in many safety-critical scenarios, their quality and reliability have raised growing concerns. Assuring the quality and evaluating the accuracy of deep learning models could be challenging because, unlike traditional software, DL systems rely on large amounts of labeled data for training and evaluation. the DL models have variability in their behavioral features on datasets with different distributions. In practical application, the potential distribution shift between training and usage scenarios may have an impact on the performance of the model and bring extra vulnerability to DL systems. Although some neuron coverage testing criteria have been proposed to assist in testing the DL systems, they are still limited by the amount of labeled data. Meanwhile, manual labeling test data collected from real-world application scenarios is very time-consuming and costly. In this paper, we propose a novel testing metric, namely LRD, to evaluate the practical accuracy of deep learning systems without requiring the ground truth of test data. the metric uses optimal transport theory to compare model behavior on real-world test data to that on training and out-of-distribution (OOD) sets, by extracting latent representations from the model during input data processing and constructing representation patterns based on the training dataset. the paper further introduces two algorithms powered by the latent representation for out-of-distribution (OOD) data detection and LRD-guided test selection for model retraining. the experimental results show that the evaluation results of LRD have a significant positive correlation with the actual accuracy of the model, and the proposed algorithms are more effective than related OOD detection and test prioritization techniques. © 2023 ACM.

关键词： Deep neural networks

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Unit Refutations in Horn Constraint Systems 14th

Unit Refutations in Horn Constraint Systems

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14th international conference on Algorithms and Complexity, CIAC 2025

作者： Wojciechowski, Piotr Subramani, K. LDCSEE West Virginia University MorgantownWV United States

ISBN: (纸本)9783031929311

this paper is concerned with algorithmic procedures for checking if a Horn Constraint System (HCS) has unit refutations of linear feasibility. A Horn constraint is a linear constraint of the form a·x≥b, where each ai∈{0,1,-1} and at most one of the ais is positive. Horn constraints generalize Horn clauses and find applications in a number of practical domains such as program verification and econometrics. Our main contribution is showing that the problem of checking if an infeasible HCS has a unit refutation is in P. To this end, we provide a certifying algorithm which runs in O(m·n2·(m+n)) time, where m and n are the number of constraints and number of variables in the input HCS respectively. Our algorithm is based on a combination of new and existing insights regarding Horn constraints. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constraint theory

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Neuro-Fuzzy System with Adaptive Membership-Activation Functions for Pattern Recognition

Neuro-Fuzzy System with Adaptive Membership-Activation Funct...

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international conference on Advanced Computer Information Technologies (ACIT)

作者： Olha Chala Ivan Izonin Iryna Pliss Yevgeniy Bodyanskiy Artificial Intelligence Department Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Artificial Intelligence Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

this paper presents a novel neuro-fuzzy system designed for pattern recognition tasks, particularly classification. Building upon the Takagi-Sugeno-Kang model, our system improves performance by incorporating adaptive membership-activation functions that dynamically adjust during training, enhancing adaptability to varying data complexities. Additionally, it adopts a comprehensive learning approach, integrating lazy learning, self-learning, and controlled learning mechanisms to enhance classification capabilities in both batch and online learning scenarios. Despite its complexity, the system maintains computational simplicity, enabling rapid implementation and high-speed processing. Experimental results demonstrate its effectiveness in real-world pattern recognition tasks, achieving high-quality approximation even with limited training data.

关键词： Training learning systems Takagi-Sugeno-Kang model Adaptive systems Buildings Training data Complexity theory Character recognition Information technology

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