Reviewerly/exHarmony huggingface dataset:
VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution

Since the introduction of deep learning, a wide scope of representation properties, such as decorrelation, whitening, disentanglement, rank, isotropy, and mutual information, have been studied to improve the quality of representation. However, manipulating such properties can be challenging in terms of implementational effectiveness and general applicability. To address these limitations, we propose to regularize von Neumann entropy (VNE) of representation. First, we demonstrate that the mathematical formulation of VNE is superior in effectively manipulating the eigenvalues of the representation autocorrelation matrix. Then, we demonstrate that it is widely applicable in improving state-of-the-art algorithms or popular benchmark algorithms by investigating domain-generalization, meta-learning, self-supervised learning, and generative models. In addition, we formally establish theoretical connections with rank, disentanglement, and isotropy of representation. Finally, we provide discussions on the dimension control of VNE and the relationship with Shannon entropy. Code is available at: https://github.com/jaeill/CVPR23-VNE.
Fabrication of Quadcopter with Face Recognition for Surveillance

In the present scenario, drones are the ruling technology.But their usage has been limited because of their high cost.This work aims to fabricate a low-cost UAV Quadcopter system with face recognition for surveillance.The Quadcopter is controlled by a radio transmitter.The surveillance system used consists of an FPV camera, an FPV transmitter, and a receiver.Face recognition is done using OpenCV in python with the help of the Local Binary Pattern Histogram (LBPH) algorithm.The fabricated Quadcopter is capable of identifying a targeted person from a certain range.
Promoting or Hindering: Stealthy Black-Box Attacks Against DRL-Based Traffic Signal Control

Numerous studies have demonstrated, in-depth, the vulnerability of the deep reinforcement learning (DRL) model’s elements (e.g., reward), which is a factor limiting the widespread deployment of DRL in some crucial domains, including intelligent traffic signal control (ITSC). While partial poisoning attacks with insidious rewards are enabled undetectable by directly employing regularization or cumulative reward restrictions, these constraints are somewhat one-dimensional and fail to consider the time dependence of DRL. Moreover, the adversary should avoid injecting undesirable perturbations when agents’ policies are unstable, namely effectively maximizing the attacking strategy’s benefit. It is thus a challenge to perturb the DRL model stealthily with as few disruption steps or modifications to the original sample as possible while ensuring the attack’s efficiency. In this work, two black-box reward space attack strategies are introduced, where we encourage the adversary to learn a malicious adversarial policy actively. The first is Multi Constraint Stealthy-time Attack which is updated with the penalties earned by attacking crucial moments, and restricted through action confidence and perturbations’ total number, to ensure attack times’ stealthiness. The second technique is Multi Objective Stealthy-modification Attack which is modeled as a multi-objective optimization problem, and the adversary balance attack performance and stealthy modification with weighting factor ω. Extensive simulation results evaluated in SUMO, involving comparison assessment and attack distribution, exhibit a dramatic increase in average travel time, implying that our attacks impose pressure on the traffic flow, namely the efficacy of proposed attack strategies.
An Overview of Quantitative Research Methods

The phrase "research" refers to seeking knowledge. It is a scholarly and systematic search for relevant knowledge on a specified subject. The Oxford Learner’s Dictionaries defines “Research” as “A careful study of a subject, especially to discover new facts or information about it”. Research is a philosophy of systematic study that critically investigates several aspects of professional work, including development of prominent concepts that manage a particular process, and development and analyse novel theories. In accordance with Woody (1927), research includes identifying research problem, creating hypotheses, collecting, analysing, and assessing data and making inference. As a result, research is a distinctive contribution that advances the current knowledge via investigation, observation, comparison, and experimentation. To put it in a nutshell, research is the pursuit of knowledge through a methodical approach to problem-solving.
Beyond Encryption: A Multidimensional approach to Data Security in Cloud Computing and the Impact of AI/ML

Cloud computing, a significant digital entity, enables many enterprises to access flexible and adjustable resources.The growing reliance on cloud technologies calls for stronger data protection measures, marking a pivotal challenge.This paper investigates the importance of data security in the cloud computing landscape, emphasizing its significance in maintaining trust and facilitating operations in this digital era.It explores various technical and administrative hurdles including the necessity for encryption, potential data breaches, and compliance with data protection regulations, presenting possible solutions and leading methodologies.The paper further explores how emergent trends such as artificial intelligence (AI) and machine learning (ML) are transforming strategies to identify and mitigate threats, in addition to ongoing research in this domain.The final consideration is imperative for businesses and cloud service providers to stay vigilant and adaptable in the rapidly evolving cybersecurity landscape, focusing on continuous advancements and progress in cloud computing data security to safeguard the digital future.
THE IMPLEMENTATION OF FLASHCARDS TO TEACH SPOKEN FUNCTIONAL TEXT TO EIGHTH GRADERS

The purpose of this study was to evaluate the speaking abilities of SMPN 4 Yogyakarta students of class VIII by using a descriptive quantitative research. Students from class VIII at SMPN 4 Yogyakarta were the population of this study, and class VIII E students made up the sample. A test was employed as a research tool in this study. The Pretest and Post-Test scores from two tests are compared to determine how Flashcards support students' speaking development. The result shows, (1) learning spoken functional text using flashcard media in class was declared successful with an average score of 81.71 in the post-test because it meets the Passing Grade standards of the school. The average pretest data (M = 74.77) and the average post-test data (M = 81.71); (2) There are significant results in the class posttest score with 0.000 Sig. value (2-tailed) (p &lt; 0.05); (3) Flashcard learning media is appropriate in improving students' speaking performance as it reduces boredom and produces fun learning for students
Multi-threshold image segmentation of 2D OTSU inland ships based on improved genetic algorithm

Waterway transportation is a crucial mode of transportation, but ensuring navigational safety in waterways requires effective guidance of ships by the Water Resources Bureau. However, supervisors may only be interested in the ship portion of a complex image and need to quickly obtain relevant ship information. Therefore, this paper proposes a two-dimensional OTSU inland ships multi-threshold image segmentation algorithm based on the improved genetic algorithm. The improved algorithm enhances search accuracy and efficiency, improving image thresholding accuracy and reducing algorithm time complexity. Experimental verification shows the algorithm has excellent evaluation indexes and can achieve real-time segmentation of complex images. This method can not only address the challenges of complex inland navigation environments and difficult acquisition of target data sets, but also be applied to optimization problems in other fields by combining various metaheuristic algorithms.
Preliminary Results of Applying Transformers to Geoscience and Earth Science Data

Transformers with neural networks (NN) have become a dominant technology in AI/ML to achieve better accuracy in classification and prediction. We experimentally compare transformer-based NN and convolutional NN using various applications such as pollen detection and weather ITCZ prediction. Using machine learning in geoscience, earth sciences, and other natural sciences is not entirely new, but applying transformers to data from these environments creates many new opportunities. We apply Facebook's detection transformer (DETR) neural network, developed in 2020, to pollen and weather data to detect forty-four types of pollen and classify earth snapshots into two categories: having vs. not having the phenomena of double Intertropical Convergence Zones (ITCZs) in them. As we conduct our trials, we observe and document how the model performs on both datasets, detect the biases present in each layer of the network, and mitigate them as we tune the model and improve classification results even further.
Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm

Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent of deep learning, many manual design-based methods have been proposed and have shown promising results in achieving state-of-the-art performance in biomedical image segmentation. However, these methods often require significant expert knowledge and have an enormous number of parameters, necessitating substantial computational resources. Thus, this paper proposes a new approach called GA-UNet, which employs genetic algorithms to automatically design a U-shape convolution neural network with good performance while minimizing the complexity of its architecture-based parameters, thereby addressing the above challenges. The proposed GA-UNet is evaluated on three datasets: lung image segmentation, cell nuclei segmentation in microscope images (DSB 2018), and liver image segmentation. Interestingly, our experimental results demonstrate that the proposed method achieves competitive performance with a smaller architecture and fewer parameters than the original U-Net model. It achieves an accuracy of 98.78% for lung image segmentation, 95.96% for cell nuclei segmentation in microscope images (DSB 2018), and 98.58% for liver image segmentation by using merely 0.24%, 0.48%, and 0.67% of the number of parameters in the original U-Net architecture for the lung image segmentation dataset, the DSB 2018 dataset, and the liver image segmentation dataset, respectively. This reduction in complexity makes our proposed approach, GA-UNet, a more viable option for deployment in resource-limited environments or real-world implementations that demand more efficient and faster inference times.
An Efficient Ship-Detection Algorithm Based on the Improved YOLOv5

Aiming to solve the problems of large-scale changes, the dense occlusion of ship targets, and a low detection accuracy caused by challenges in the localization and identification of small targets, this paper proposes a ship target-detection algorithm based on the improved YOLOv5s model. First, in the neck part, a weighted bidirectional feature pyramid network is used from top to bottom and from bottom to top to solve the problem of a large target scale variation. Second, the CNeB2 module is designed to enhance the correlation of coded spatial space, reduce interference from redundant information, and enhance the model’s ability to distinguish dense targets. Finally, the Separated and Enhancement Attention Module attention mechanism is introduced to enhance the proposed model’s ability to identify and locate small targets. The proposed model is verified by extensive experiments on the sea trial dataset. The experimental results show that compared to the YOLOv5 algorithm, the accuracy, recall rate, and mean average precision of the proposed algorithm are increased by 1.3%, 1.2%, and 2%, respectively; meanwhile, the average precision value of the proposed algorithm for the dense occlusion category is increased by 4.5%. In addition, the average precision value of the proposed algorithm for the small target category is increased by 5% compared to the original YOLOv5 algorithm. Moreover, the detection speed of the proposed algorithm is 66.23 f/s, which can meet the requirements for detection speed and ensure high detection accuracy and, thus, realize high-speed and high-precision ship detection.
Transformer Decoder-Based Enhanced Exploration Method to Alleviate Initial Exploration Problems in Reinforcement Learning

In reinforcement learning, the epsilon (ε)-greedy strategy is commonly employed as an exploration technique This method, however, leads to extensive initial exploration and prolonged learning periods. Existing approaches to mitigate this issue involve constraining the exploration range using expert data or utilizing pretrained models. Nevertheless, these methods do not effectively reduce the initial exploration range, as the exploration by the agent is limited to states adjacent to those included in the expert data. This paper proposes a method to reduce the initial exploration range in reinforcement learning through a pretrained transformer decoder on expert data. The proposed method involves pretraining a transformer decoder with massive expert data to guide the agent’s actions during the early learning stages. After achieving a certain learning threshold, the actions are determined using the epsilon-greedy strategy. An experiment was conducted in the basketball game FreeStyle1 to compare the proposed method with the traditional Deep Q-Network (DQN) using the epsilon-greedy strategy. The results indicated that the proposed method yielded approximately 2.5 times the average reward and a 26% higher win rate, proving its enhanced performance in reducing exploration range and optimizing learning times. This innovative method presents a significant improvement over traditional exploration techniques in reinforcement learning.
Malay Traditional Metal Crafts in the East Coast Region of Malaysia: The Sustainability of Tangible Culture

This paper aims to elaborate on the elements of the Sustainability of Malay Traditional Metal Crafts in the East Coast Region of Malaysia.The traditional Malay metal crafts in Malaysia that have existed for more than 200 years began to deteriorate from time to time.It is a tangible heritage with its uniqueness and almost extinction hereditary since 1948 on the East Coast of Malaysia.Therefore, the main objective of this research was to identify the current development and the elements of developing sustainability in the Malay traditional metal crafts on the East Coast of Malaysia.Furthermore, qualitative observation and interview methods were explicitly conducted at Kelantan and Terengganu.It investigates and analyzes genuine data collection and findings in a specific field.The outcome and contribution of the paper are important for further development in relation parallel with Sustainable Design Goals, which are focused on sustainable innovation, economic growth, full and productive employment, and decent work for all communities.Furthermore, it significantly supports the economic growth of craft and creative industries among the local community and the younger generation to sustain a gloomy future as an indicator of Malaysian identity.This research will benefit the apprentices and young generation, which is inheritance the new approach of metal craft design through innovation and creativity development.
Four stages in the evolution of interactive ESI as part of articles in chemistry journals.

A previous post was triggered by Peter alerting me that interactive electronic supporting information ( &lt;strong&gt; IESI &lt;/strong&gt; ) we had submitted to a journal in 2005[1] appeared to be strangely missing from the article landing page. This set me off recollecting our journey, which had started around 1998, and to explore what the current state of these ancient &lt;strong&gt; IESI &lt;/strong&gt; s were in 2022.
Optimality Guarantees for Particle Belief Approximation of POMDPs

Partially observable Markov decision processes (POMDPs) provide a flexible representation for real-world decision and control problems. However, POMDPs are notoriously difficult to solve, especially when the state and observation spaces are continuous or hybrid, which is often the case for physical systems. While recent online sampling-based POMDP algorithms that plan with observation likelihood weighting have shown practical effectiveness, a general theory characterizing the approximation error of the particle filtering techniques that these algorithms use has not previously been proposed. Our main contribution is bounding the error between any POMDP and its corresponding finite sample particle belief MDP (PB-MDP) approximation. This fundamental bridge between PB-MDPs and POMDPs allows us to adapt any sampling-based MDP algorithm to a POMDP by solving the corresponding particle belief MDP, thereby extending the convergence guarantees of the MDP algorithm to the POMDP. Practically, this is implemented by using the particle filter belief transition model as the generative model for the MDP solver. While this requires access to the observation density model from the POMDP, it only increases the transition sampling complexity of the MDP solver by a factor of O(C), where C is the number of particles. Thus, when combined with sparse sampling MDP algorithms, this approach can yield algorithms for POMDPs that have no direct theoretical dependence on the size of the state and observation spaces. In addition to our theoretical contribution, we perform five numerical experiments on benchmark POMDPs to demonstrate that a simple MDP algorithm adapted using PB-MDP approximation, Sparse-PFT, achieves performance competitive with other leading continuous observation POMDP solvers.
Summary of the 2023 Low-Power Deep Learning Object Detection and Semantic Segmentation Multitask Model Compression Competition for Traffic Scene in Asian Countries

The competition endeavors to accomplish object detection and semantic segmentation traffic objects in Asia, notably in countries such as Taiwan, using low power while achieving a high mean intersection over union (mIOU) and high mean average precision (mAP). This task is challenging as it must be achieved in harsh driving environments. The targeted semantic segmentation objects include several types of lines such as dashed and single white and yellow lines, double dashed and double white and yellow lines, main and alter lanes and the targeted object detection objects include car, pedestrian, motorbike, and bike. To train the model, the participants utilized a total of 35,500 annotated images which were obtained from the iVS-ODSEG-Dataset, a revised version of the Berkeley Deep Drive 100K[1], along with 89,002 annotated training images from iVS-Dataset[2]. Additionally, 130 annotated images have been provided as validation examples from Asian road conditions. The contest evaluation process uses 9,612 testing images, of which 4,900 are used in the qualification stage competition, and the remaining are used in the final stage competition. The competition had 223 registered teams, with the top 15 teams that achieved the highest performance mIOU and mAP entering the final stage competition. Based on the score evaluation and the invited paper review result, the team “Polybahn” produced the overall best model and the best INT8 model development, followed by team “You Only Lowpower Once” and team “ACVLab.”
Label correlation guided borderline oversampling for imbalanced multi-label data learning

Multi-label data classification has received much attention due to its wide range of application domains. Unfortunately, a class imbalance problem often occurs in multi-label datasets, causing challenges for classification algorithms. Oversampling is one of the most important approaches, as it generates minority label instances to balance the class distribution. However, existing oversampling methods ignore existing label correlations, resulting in the generation of inappropriate synthetic minority samples and making multi-label data classification tasks harder. In this work, we propose an oversampling method that considers label correlations and identifies two critical boundary regions for generating synthetic minority samples. Moreover, we propose a weighting strategy to assign weights to these instances based on their distance information. To evaluate the performance of our proposed method, we conducted experiments on sixteen public datasets. The results show that our approach outperforms the state-of-the-art approaches in terms of various assessment metrics, such as Macro F1 and Macro AUC. The code is available at https://github.com/IntelliDAL/Multi-label/tree/main/LCOS.
Dual-Path Information Fusion and Twin Attention-Driven Global Modeling for Solar Irradiance Prediction

Accurate prediction of solar irradiance holds significant value for renewable energy usage and power grid management. However, traditional forecasting methods often overlook the time dependence of solar irradiance sequences and the varying importance of different influencing factors. To address this issue, this study proposes a dual-path information fusion and twin attention-driven solar irradiance forecasting model. The proposed framework comprises three components: a residual attention temporal convolution block (RACB), a dual-path information fusion module (DIFM), and a twin self-attention module (TSAM). These components collectively enhance the performance of multi-step solar irradiance forecasting. First, the RACB is designed to enable the network to adaptively learn important features while suppressing irrelevant ones. Second, the DIFM is implemented to reinforce the model’s robustness against input data variations and integrate multi-scale features. Lastly, the TSAM is introduced to extract long-term temporal dependencies from the sequence and facilitate multi-step prediction. In the solar irradiance forecasting experiments, the proposed model is compared with six benchmark models across four datasets. In the one-step predictions, the average performance metrics RMSE, MAE, and MAPE of the four datasets decreased within the ranges of 0.463–2.390 W/m2, 0.439–2.005 W/m2, and 1.3–9.2%, respectively. Additionally, the average R2 value across the four datasets increased by 0.008 to 0.059. The experimental results indicate that the model proposed in this study exhibits enhanced accuracy and robustness in predictive performance, making it a reliable alternative for solar irradiance forecasting.