Reviewerly/exHarmony huggingface dataset:
An Improved Local Weighted Mean-Based k-Nearest Neighbor Classifier

The article presents a Local Weighted Mean-Based k-Nearest Neighbor (LWMKNN) classifier, which improves the performance of traditional k-nearest neighbor (KNN) and k-nearest neighbor methods based on local means vector. The LWMKNN algorithm incorporates local weighted based on the distance between samples, which enables it to better capture the complex structure of the data. The article provides a detailed analysis of the LWMKNN algorithm, including its underlying principles and mathematical formulations. This provides a clear understanding of how the algorithm works and why it is superior to other KNN methods. The experimental results show that LWMKNN outperforms both KNN and LMKNN on several data sets, demonstrating its effectiveness and versatility. In conclusion, the LWMKNN classifier offers a powerful and flexible tool for machine learning practitioners. By incorporating local weighted, LWMKNN is able to better capture the complex structure of data and outperform traditional KNN methods.
Research on robot path optimization based on artificial intelligence algorithm

With the development of science and technology, intelligent robots are widely used in navigation, exploration and medical fields. At the same time, the development of artificial intelligence algorithm is also gradually mature, and gradually applied in various fields. Specifically, ant colony algorithm is widely used in the field of path planning because it has global search capability and high robustness. However, the ant cluster algorithm has a problem that the convergence speed is slow and locally decreases. In order to solve the lack of colonial algorithms and the problem of path planning problems, we upgraded the ancient colony algorithm, compared the results of different algorithms using simulation software, and compared the path planning parameters, and tested the effectiveness of the colony algorithm.
Towards Cloud-based Infrastructure for Post-Quantum Cryptography Side-channel Attack Analysis

Post-quantum cryptography (PQC) refers to cryptographic algorithms that are thought to be secure against a cryptanalytic attack by a quantum computer. Before PQC algorithms can be widely deployed to replace the current standards such as the RSA algorithm, they need to be rigorously evaluated theoretically and practically. In this work, we present a cloud-based infrastructure being developed for performing side-channel analysis on PQC algorithms for the research community. Multiple types of side-channel attacks, such as timing attacks, power attacks, and electromagnetic attacks can be applied on different types of devices, such as FPGA devices and microcontrollers. An automated tool flow is being developed that can run executables on the target devices, collect traces (e.g., power consumption waveforms and electromagnetic radiation signals), perform leakage assessment (using Test Vector Leakage Assessment), and generate analysis reports. Remote users access the infrastructure through a web portal by uploading the hardware or software implementations of cryptographic algorithms. Side-channel attack and leakage analysis are performed on the given implementation. Finally, the user is informed for downloading the analysis report from the portal.
Comparative Study on Semi-Supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System

Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially for semi-supervised learning which utilizes unsupervised learning as a feature extraction mechanism to aid the supervised part when only a small number of labels are available. This study systematically compares semi-supervised learning methods applied for anomaly detection in hydraulic condition monitoring systems. Firstly, thorough data analysis and feature learning were carried out to understand the open-sourced hydraulic condition monitoring dataset. Then, various methods were implemented and evaluated including traditional standalone semi-supervised learning models (e.g., one-class SVM, Robust Covariance), ensemble models (e.g., Isolation Forest), and deep neural network based models (e.g., autoencoder, Hierarchical Extreme Learning Machine (HELM)). Typically, this study customized and implemented an extreme learning machine based semi-supervised HELM model and verified its superiority over other semi-supervised methods. Extensive experiments show that the customized HELM model obtained state-of-the-art performance with the highest accuracy (99.5%), the lowest false positive rate (0.015), and the best F1-score (0.985) beating other semi-supervised methods.
Drone aerial target detection based on YOLOv8

Target detection in drone aerial photography is an important application scenario in the field of target detection. However, based on the characteristics of drones such as fast movement speed and high flight altitude, the detection target size of drones is small, the image scene changes greatly, and the real-time requirements are higher. This article uses the YOLOv8 model as a target detection method for drone aerial photography, and conducts comparative experiments on other classic target detection algorithms. The experimental results show that YOLOv8 has a better improvement effect compared to other target detection algorithms.
Performance Evaluation of Orange Pi 4 in SHA256 Computation for Blockchain Mining

This article presents a study on the performance of microprocessors, specifically focusing on the case of Orange Pi 4, in the computation of SHA256, a cryptographic hash function used in Blockchain. The study aims to evaluate the performance of microprocessors like Orange Pi 4, to mine blocks and perform cryptographic operations relying on SHA256. The experimental setup includes several test scenarios that vary in the difficulty of the hash function and the size of the data being hashed. Although the study acknowledges its limitations in terms of the limited scenarios analyzed, it provides valuable insights into the performance of Orange Pi 4, in Blockchain mining operations. The performance is evaluated in terms of the time it takes to mine a block,the energy consumption required and the memory usage. The results highlight that Orange Pi 4, can perform SHA256 computations, but their performance is limited compared to dedicated mining hardware. These findings emphasize the need for dedicated mining hardware for high-performance mining tasks. However, the study also highlights the potential for microprocessors like Orange Pi 4, in specific low-power applications that require cryptographic operations with moderate computational requirements.
Influence of Various ML-Based Binary Classifiers on the Performance on Handwritten Digit Recognition

The handwritten dataset from MNIST is a well-known and reliable tool for the study on various classification solutions. The aim of this study is to compare four competing Machine Learning classifiers, including a boosting algorithm, when applied to the MNIST dataset for predicting 0-Images. Accuracy (ACC) of Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), Adaboost and Random Forest models are evaluated through 3-fold and 10-fold Cross Validation. The work has achieved precisely 98.8% of F1 score and 99.9% of AUC for one digit classification without use of deep learning algorithm. When tested on a test dataset (unseen data), it shows accuracy of 99.74% after being tuned. Models are also compared according to run-time and resource utilization (memory and CPU) during training and prediction. Based on this comparison, suitable model can further be fine-tuned for implementation in small and medium-sized enterprises (SMEs).
A Federated Mining Framework for Complete Frequent Itemsets

In this paper, we address the common features of horizontal federated learning in data mining and propose a federated mining framework, which adopts a client-server model that cooperates with multiple data-source clients. The proposed algorithm handles client-side mining and server-side aggregation. For client-side mining, the algorithm uses prelarge itemsets to collect additional information for the server to integrate the clients' local mining results. For server-side aggregation, the algorithm considers the characteristics of large and prelarge itemsets sent from the clients and use a boundary strategy for integration. Experiments show that our method acquires the complete mined results while protecting data.
Improved Mayfly Algorithm Based on Co-Evolution for Feature Selection

Mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved Mayfly algorithm (CEIMA) suitable for feature selection problems. The CEIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CEIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CEIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CEIMA is proven.
A Bottom-Up Capsule Network for Hierarchical Image Classification

Hierarchical image classification is an arduous task in deep learning and computer vision. It requires classifying multiple image classes following a taxonomy or data hierarchy. This paper introduces a bottom-up hierarchical capsule network (BUH-CapsNet) designed to address hierarchical multi-label classification. The hierarchical structure of BUH-CapsNet allows it to build a tree-like structure for classification problems, making use of the data hierarchy. This structure enables the network to learn more complex relationships in the taxonomy by balancing the hierarchical levels and following the fine-to-coarse paradigm, leading to more accurate classification results. Furthermore, the bottom-up architecture of the BUH-CapsNet enforces hierarchical consistency, using the hierarchical structure of the datasets. We trained our BUH-CapsNet considering the hierarchical level weights that keep a balance between the levels. Experiments on six widely available datasets show that BUH-CapsNet achieves better results than existing multi-label classification methods and performs better when handling hierarchical labels.
Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks

Deep learning has become an integral part of various pattern recognition and computer vision systems in recent years due to its outstanding achievements in object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to being fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in physical-world applications. To address this phenomenon, we present, what to our knowledge, is the first ever image-set-based adversarial defense approach. Image set classification has shown exceptional performance for object and face recognition, owing to its intrinsic property of handling appearance variability. We propose a robust deep Bayesian image set classification as a defense framework against a broad range of adversarial attacks. We extensively experiment the performance of the proposed technique with several voting strategies. We further analyse the effects of image size, perturbation magnitude, along with the ratio of perturbed images in each image set. We also evaluate our technique with the recent state-of-the-art defense methods and single-shot recognition task. The empirical results demonstrate superior performance on the CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets. Our code is available at https://github.com/ai-voyage/imageset-adversarial-defence.git.
Adaptive Local Feature Matching for Few-shot Fine-grained Image Recognition

Few-shot fine-grained image recognition aims to recognize fine categories with subtle differences, given only a few labeled examples. Existing methods try to mine the discriminative local regions to do fine-grained image recognition but still suffer from large variations of the same semantic object and noisy background disturbance. To this end, we propose an adaptive local feature matching network to do few-shot fine-grained image recognition, which matches local features between the support and query images adaptively according to their belonged semantics. Specifically, an Adaptive Thresholding Module (ATM) is proposed to automatically depress the irrelevant and noisy background regions for enlarging inter-class differences. Then a Local Feature Matching Module (LFM) is used for learning consistent local features of the same class. We conduct extensive experiments on three benchmark datasets, CUB-200-2011, Stanford Dogs, and Stanford Cars. The results illustrate the effectiveness and superiority of our proposed method.
SupCon-ViT: Supervised contrastive learning for ultra-fine-grained visual categorization

With the increasing availability of datasets exhibiting fine granularity and subtle differences between categories, fine-grained visual categorization tasks have gained significant attention across various domains. However, the focus often lies solely on overall dataset performance metrics such as top-l accuracy, while lacking a comprehensive understanding of the underlying factors. This paper addresses this gap by presenting a detailed analysis of the CUB-200-2011 dataset through extensive experiments. We identify and investigate specific ultra-fine-grained subsets that significantly impact the overall accuracy of the dataset. To enhance the performance of ultra-fine-grained visual classification, we propose SupCon-ViT, an ultra-fine-grained visual categorization network based on supervised contrastive learning. The key component of our approach is a supervised contrastive learning module, which effectively guides the network to learn discriminative local features within samples. This is accomplished by continuously pulling closer the normalized embeddings from the same class and pushing away embeddings from different classes. As a result, our approach achieves discriminative local representations, leading to improved network classification performance. Experimental results demonstrate the effectiveness of our proposed method on four ultra-fine-grained subsets of the CUB dataset. Notably, our approach achieves significant performance improvements without requiring additional expert information during training. This work contributes to the broader understanding of fine-grained visual categorization and offers a practical solution to enhance the accuracy of ultrafine-grained visual classification tasks.The code is available at https://github.comnucinda01ove/SupCon-ViT-pytorch.
Visual Question Answering in the Medical Domain

Medical visual question answering (Med-VQA) is a machine learning task that aims to create a system that can answer natural language questions based on given medical images. Although there has been rapid progress on the general VQA task, less progress has been made on Med-VQA due to the lack of large-scale annotated datasets. In this paper, we present domain-specific pre-training strategies, including a novel contrastive learning pre-training method, to mitigate the problem of small datasets for the Med-VQA task. We find that the model benefits from components that use fewer parameters. We also evaluate and discuss the model’s visual reasoning using evidence verification techniques. Our proposed model obtained an accuracy of 60% on the VQA-Med 2019 test set, giving comparable results to other state-of-the-art Med-VQA models.
Robust quantification of prediction uncertainty by inducing heterogeneity in deep ensembles

Deep ensemble models have garnered attention for their capacity to yield robust predictions along with well-calibrated uncertainty estimates. Empirical evidence suggests that augmenting the number of models within deep ensembles fosters greater diversity in the functional space, enabling the exploration of multiple local optima effectively. This leads to more robust uncertainty estimates in the model. In this paper, we introduce a novel methodology to substantially enhance model diversity by inducing model heterogeneity through controlled perturbation of input images, thereby de-correlating individual models based on their local bias. Drawing inspiration from the bias-variance trade-off, our approach incorporates pre-generated masks during the training process to further refine model performance. To validate the efficacy of our technique in providing more robust estimates under dataset shift, we evaluate model diversity and predictive uncertainty on test samples from both familiar and unfamiliar distributions. Our results demonstrate that our proposed method outperforms recent approaches in detecting out-of-distribution samples, establishing its superiority within the existing literature.
Joint Temporal Pooling for Improving Skeleton-based Action Recognition

In skeleton-based human action recognition, temporal pooling is a critical step for capturing spatiotemporal relationship of joint dynamics. Conventional pooling methods overlook the preservation of motion information and treat each frame equally. However, in an action sequence, only a few segments of frames carry discriminative information related to the action. This paper presents a novel Joint Motion Adaptive Temporal Pooling (JMAP) method for improving skeleton-based action recognition. Two variants of JMAP, frame-wise pooling and joint-wise pooling, are introduced. The efficacy of JMAP has been validated through experiments on the popular NTU RGB+D120 and PKU-MMD datasets.
Techniques for Moment Retrievals and Filtering from Large Volumes of Multimodal Data

This paper focuses on applications of parallel processing and machine learning techniques for the retrieval and filtering of selected “moments” from textual and video-based multimodal data. Given a set of textual and video criteria provided by the user, the system extracts moments (sequences of frames) of a fixed duration from a large video file and stores them. An approach that supports the parallelization of this filtration process is presented. Experiments with Apache Spark and TensorFlow-based prototypes developed to demonstrate the efficacy of the proposed approach were conducted for various combinations of system and workload parameters. Insights into system performance including the reduction in processing time that accrues from applying the proposed parallel processing techniques for data filtering are reported in the paper.
Developing Real-Time Services with High Performance and Cloud Security Enabled Framework via Adjusted TLS v1.3 for On-Demand HIPA Activity Calculations

Scientific computing for particle accelerators, detectors, and experiment automation requires large computer resources. Because of this, running simulations on high-performance computing (HPC) is becoming increasingly popular. Academic research projects have proposed infrastructures that allow high remote performance capacity for running simulations and data analysis. However, these on-demand cloud services also suffer from cloud security leakage and performance degradation issues. The Vis-aS project proposes a framework which has an efficient micro-service workflow with adjusted TLS v1.3 protocol by KC-SPAKE2+ for reducing the communication overhead between the client and the HPC server to provide a high-performance and security-enabled environment. With the proposed framework, the Vis-aS project shows that a web application used for activity calculations for the high-intensity proton accelerator (HIPA) at the Paul Scherrer Institute (PSI) presents scientists a secure application and framework to fit both of requirements from HPC’s high performance demands and cloud security.
ChatGPT-guided Semantics for Zero-shot Learning

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method.
Health Monitor: An IoMT Based Patient Health Monitoring System Using Blockchain and k-means

The healthcare domain is rapidly changing, driven by advances in technology, and new paradigms for healthcare delivery are emerging. Healthcare 5.0 is a new paradigm of digital healthcare system that utilizes artificial intelligence, the Internet of Things, and 5G communication to increase the efficiency, accuracy, and cost-effectiveness of healthcare delivery. Healthcare 5.0 has the potential to improve patient care, reduce healthcare costs, and provide better access to healthcare services.This paper presents an IoT-based patient health monitoring system using blockchain and k-means clustering. The proposed system (denoted Health Monitor) uses sensors to collect patient’s health data and stores it securely on the blockchain. The collected data is then used to generate patient health profiles using k-means clustering. Health Monitor offers a secure, efficient, and cost-effective way to monitor and maintain patient health. It also provides easy access to healthcare professionals to view, analyze, and compare patient health data. The results of the study demonstrate the effectiveness of the proposed system in accurately tracking and managing patient health.