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Explain and Interpret Few-Shot Learning 1

Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

作者： Fedele, Andrea Computer Science Department University of Pisa Italy KDD Laboratory ISTI National Research Council Pisa Italy

Recent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models. © 2023 CEUR-WS. All rights reserved.

关键词： Machine learning

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AI in drug development:advances in response,combination therapy,repositioning,and molecular design

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science China(Information sciences) 2025年

作者： Ren QI Shujia LIU Xingqi HUI Alexey K.SHAYTAN Bin LIU School of Computer Science and Technology Beijing Institute of Technology Department of Biology Lomonosov Moscow State University International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer ScienceHSE University SMBU-MSU-BIT Joint Laboratory on Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Zhongguancun Academy

Artificial intelligence (AI) is revolutionizing the field of drug development, particularly in addressing key challenges such as drug response prediction, drug combination design, drug repositioning, and drug molecule generation. Traditional drug discovery is hindered by long timelines, high costs, and low success rates, necessitating innovative technologies to accelerate the process. AI technologies, such as deep learning, graph neural networks, and generative models, have demonstrated significant potential in enhancing the accuracy of drug response predictions, optimizing drug combination strategies,identifying opportunities for drug repositioning, and generating drug molecules with specific biological activities. These advancements not only accelerate the drug development process but also open up new possibilities for precision *** review discusses the latest applications and developments of AI in drug discovery, highlighting the breakthroughs and challenges AI addresses in drug development. By summarizing the current research progress, this study provides theoretical support and practical guidance for further applications of AI in drug development.

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A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters

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Nano Research 2024年第10期17卷 9181-9192页

作者： Negar Hosseinzadeh Kouchehbaghi Maryam Yousefzadeh Aliakbar Gharehaghaji Safoora Khosravi Danial Khorsandi Reihaneh Haghniaz Ke Cao Mehmet R.Dokmeci Mohammad Rostami Ali Khademhosseini Yangzhi Zhu Terasaki Institute for Biomedical Innovation Los AngelesCA 91367USA Department of Textile Engineering Amirkabir University of Technology(Tehran Polytechnic)Hafez Avenue*** TehranIran Department of Electrical and Computer Engineering University of British ColumbiaVancouverBC V6T1Z4Canada Chemical Sciences Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science University of Southern CaliforniaLos AngelesCA 90007USA

Nanofibrous acoustic energy harvesters(NAEHs)have emerged as promising wearable platforms for efficient noise-to-electricity conversion in distributed power energy systems and wearable sound amplifiers for assistive listening ***,their reallife efficacy is hampered by low power output,particularly in the low-frequency range(<1 kHz).This study introduces a novel approach to enhance the performance of NAEHs by applying machine learning(ML)techniques to guide the synthesis of electrospun polyvinylidene fluoride(PVDF)/polyurethane(PU)nanofibers,optimizing their application in wearable *** use a feed-forward neural network along with solving an optimization problem to find the optimal input values of the electrospinning(applied voltage,nozzle-collector distance,electrospinning time,and drum rotation speed)to generate maximum output performance(acoustic-to-electricity conversion efficiency).We first prepared a dataset to train the network to predict the output power given the input variables with high *** introducing the neural network,we fix the network and then solve an optimization problem using a genetic algorithm to search for the input values that lead to the maximum energy harvesting *** ML-guided wearable PVDF/PU NAEH platform can deliver a maximal acoustoelectric power density output of 829μW/cm^(3) within the surrounding noise *** addition,our system can function stably in a broad frequency(0.1-2 kHz)with a high energy conversion efficiency of 66%.Sound recognition analysis reveals a robust correlation exceeding 0.85 among lexically akin terms with varying sound intensities,contrasting with a diminished correlation below 0.27 for words with disparate semantic ***,this work provides a previously unexplored route to utilize ML in advancing wearable NAEHs with excellent practicability.

关键词： wearable electronics acoustic energy harvester machine learning piezoelectric nanogenerator electrospun nanofiber

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Majorizing Measures, Codes, and Information

Majorizing Measures, Codes, and Information

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Chu, Yifeng Raginsky, Maxim University of Illinois The Department of Electrical and Computer Engineering The Coordinated Science Laboratory UrbanaIL61801 United States

ISBN: (纸本)9781665475549

The majorizing measure theorem of Fernique and Talagrand is a fundamental result in the theory of random processes. It relates the boundedness of random processes indexed by elements of a metric space to complexity measures arising from certain multiscale combinatorial structures, such as packing and covering trees. This paper builds on the ideas first outlined in a little-noticed preprint of Andreas Maurer to present an information-theoretic perspective on the majorizing measure theorem, according to which the boundedness of random processes is phrased in terms of the existence of efficient variable-length codes for the elements of the indexing metric space. © 2023 IEEE.

关键词： Random processes

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Sparse is Enough in Fine-tuning Pre-trained Large Language Models 41

Sparse is Enough in Fine-tuning Pre-trained Large Language M...

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41st International Conference on Machine Learning, ICML 2024

作者： Song, Weixi Li, Zuchao Zhang, Lefei Zhao, Hai Du, Bo National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Luojia Laboratory Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

With the prevalence of pre-training-fine-tuning paradigm, how to efficiently adapt the pre-trained model to the downstream tasks has been an intriguing issue. Parameter-Efficient Fine-Tuning (PEFT) methods have been proposed for low-cost adaptation. Although PEFT has demonstrated effectiveness and been widely applied, the underlying principles are still unclear. In this paper, we adopt the PAC-Bayesian generalization error bound, viewing pre-training as a shift of prior distribution which leads to a tighter bound for generalization error. We validate this shift from the perspectives of oscillations in the loss landscape and the quasi-sparsity in gradient distribution. Based on this, we propose a gradient-based sparse finetuning algorithm, named Sparse Increment Fine-Tuning (SIFT), and validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning. The code is accessible at https://***/song-wx/SIFT. Copyright 2024 by the author(s)

关键词： Benchmarking

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Effects of Including Optimal Solutions into Initial Population on Evolutionary Multiobjective Optimization 23

Effects of Including Optimal Solutions into Initial Populati...

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2023 Genetic and Evolutionary Computation Conference, GECCO 2023

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9798400701191

A long-standing question in the evolutionary multi-objective (EMO) community is how to generate a good initial population for EMO algorithms. Intuitively, as the starting point of optimization, a good initial population can have positive effects on the performance of EMO algorithms. However, in most existing EMO algorithms, one of the commonly-used initialization methods is to randomly generate a set of solutions as an initial population. One possible approach to improve random initialization is to include one or more Pareto optimal (near Pareto optimal) solution(s) in the initial population, which are expected to provide useful information and knowledge on the optimized problem. In this paper, to investigate the effectiveness of this initialization idea, we examine and quantify the effects of including one or more Pareto optimal solution(s) in the initial population on the performance of EMO algorithms. Experimental results demonstrate that it is worthwhile to first obtain and then include some Pareto optimal solutions in the initial population. Through a number of experiments and algorithm behavior analysis, this study provides supports and insights into EMO algorithm design and motivates further research on population initialization for EMO algorithms. © 2023 ACM.

关键词： Evolutionary algorithms

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Enhanced Medical Image Authentication and Precise Tamper Localization Using Discrete Cosine Stockwell Transform

Enhanced Medical Image Authentication and Precise Tamper Loc...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Science and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology Cemse Division Thuwal Saudi Arabia DI2M Laboratory Center for Development of Advanced Technology Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331533311

Advancements in communication have boosted digital image sharing but also heightened security risks due to easy manipulation. Therefore, authentication techniques are crucial for securing medical images. This paper proposes a fully blind fragile watermarking model for color and grayscale medical image authentication and accurate tamper localization. The watermark is initially derived from the host image using the Discrete Wavelet Transform (DWT) and encrypted by a Logistic map-based technique to enhance the model's security. The image is then divided into 4 × 4 blocks, and the Discrete Cosine Stockwell Transform (DCST) is performed on each block. Afterward, QR decomposition is performed on each frequency subband, and the watermark is inserted within the R matrices. Experimental results across various types of medical images indicate that the proposed technique provides high watermarked image quality with a Peak Signal to Noise Ratio (PSNR) exceeding 51dB while maintaining good embedding capacity and high sensitivity to attacks. Furthermore, the scheme achieves a highly accurate tamper localization, detecting tampering rates as low as 0.01% of the image size, which surpasses the accuracy achieved by other models in the literature. © 2024 IEEE.

关键词： Medical imaging

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On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets 1

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12th International Conference on Information Systems and Advanced Technologies, ICISAT 2022

作者： Henouda, Salah Eddine Laallam, Fatima Zohra Kazar, Okba Harous, Saad Houfani, Djihane Department of Computer Science LINATI Laboratory Kasdi Merbah University Ouargla Algeria Department of Computer Science LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9783031253447

ISBN: (纸本)9783031253430

The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Breast cancer prediction Deep learning Dimensionality reduction Machine learning Medical dataset Neural networks

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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Geographic Information System for Air Traffic Optimization Based on Simulated Annealing in Training Environment

Geographic Information System for Air Traffic Optimization B...

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2023 International Conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Amara, Rafik Bougherara, Maamar Gouraya, Brahim Aissa, Mostefa Belhadj Ltir Laboratory Usthb Algiers Algeria Department of Computer Science Ens Kouba Algiers Algeria Limpaf Laboratory Uamob Algiers Algeria

ISBN: (纸本)9798350396720

To ensure the safety and fluidity of air traffic, the air traffic controller applies a set of rules dictated by the ICAO, called separation rules. For this, the controller must first undergo training based in part on practical work in simulated traffic management. From a traffic configuration given by flight plans (FPL) of an exercise, the trainee controller can detect any conflicts that may arise between each pair of aircraft and act accordingly by changing part of the FPL of the one of the planes (or both) so as to avoid a risk of collision. At the end of the exercise, a debriefing is organized to discuss the effectiveness of the trainee's decisions and compare them with the solutions proposed by his instructor. We propose in this work, to reinforce this training by proposing to the trainee a tool which can generate an additional solution (in addition to that of the instructor). For this, we use a GIS for geographic data and a metaheuristic, simulated annealing, for traffic optimization. The objectives to be achieved are: the elimination of conflicts between aircraft, the minimization of the distance traveled by the aircraft and the minimization of the changes made in relation to the initial flight plan. The evaluation of this work goes through several tests carried out on part of the Algerian airspace with the same practical exercises used in training. © 2023 IEEE.

关键词： Simulated annealing

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