Reviewerly/exHarmony huggingface dataset:
Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively.
Shareability: novel perspective on human-media interaction

Interpersonal communication in the twenty-first century is increasingly taking place within digital media. This poses the problem of understanding the factors that may facilitate or hinder communication processes in virtual contexts. Digital media require a human-machine interface, and the analysis of human-machine interfaces traditionally focuses on the dimension of usability. However, interface usability pertains to the interaction of users with digital devices, not to the interaction of users with other users. Here we argue that there is another dimension of human-media interaction that has remained largely unexplored, but plays a key role in interpersonal communication within digital media: shareability. We define shareability as the resultant of a set of interface features that: (i) make sharing of materials with fellow users easy, efficient, and timely (sharing-related usability); (ii) include features that intuitively invite users to share materials (sharing-related affordances); and (iii) provide a sensorimotor environment that includes perceptual information about both presented materials and the behavior of other users that are experiencing these materials through the medium at hand (support to shared availability). Capitalizing on concepts from semiotics, proxemics, and perceptual and cognItive neuroscience, we explore potential criteria to asses shareability in human-machine interfaces. Finally, we show how these notions may be applied in the analysis of three prototypical cases: online gaming, visual communication on social media, and online distance teaching.
Global subclass discriminant analysis

Linear discriminant analysis (LDA) is a powerful supervised dimensionality reduction method for analysing high-dimensional data. However, LDA cannot use locality information in data, which makes LDA degrade dramatically in performance on multimodal data. A number of LDA variants have been proposed to exploit locality information in data, including subclass-based LDAs. We discover a problem with these variants, which is that subclasses are selected on a within-class basis without considering other classes. This causes the loss of important information at class boundaries. In this paper, we present a novel variant of subclass-based LDA, Global Subclass Discriminant Analysis (GSDA). Unlike other subclass-based LDAs, GSDA selects subclasses from global clusters that may cross class boundaries, thus utilising within-class information and between-class information. More specifically, GSDA applies an effective clustering algorithm to the whole data to construct global clusters. It then utilises the local structure refining strategy on these global clusters to construct subclasses. Finally, GSDA learns a representative data subspace by maximising inter-subclass distance and minimising intra-subclass distance simultaneously. GSDA is extensively evaluated on a wide range of public datasets through comparison with the state-of-the-art LDA algorithms. Experimental results demonstrate its superiority in terms of accuracy and run times.
A new algorithm of boundary value problems based on improved wavelet basis and the reproducing kernel theory

Relying upon Legendre polynomials, an improved multiwavelet orthonormal basis is constructed in the reproducing kernel space. We study boundary value problems (BVPs) based on the improved wavelet orthonormal basis and construct the ‐best approximate solution. Convergence and the stability are discussed; this method has higher order convergence. The main advantage of the wavelet based our method is its efficiency and simple applicability for a variety of boundary conditions. Some numerical tests even including singular BVPs reveal the stability and efficiency of the new algorithm.
Proper choice of hyperparameters in reservoir computing of chaotic maps

Abstract Reservoir computing (RC) are powerful to learn and predict dynamical behaviors. However, it has been found that both the reservoir size and the hyperparameters can greatly affect the learning ability of RC on dynamical systems, the mechanism of which still remains unclear. This paper discusses the influence of hyperparameters of RC with different sizes of reservoir on learning typical chaotic maps. An analytic method is purposed to obtain the hyperparameters that can exhibit better learning ability of RC by analyzing high order derivatives of the error loss function. In the case of RC with one or two nodes, the well-performing hyperparameters are analytically obtained for learning the logistic map, which are consistent with numerical results. The analytic method also shows its ability in RC with multiple nodes to learn singer and sine chaotic maps. This work provides deeper insight in learning and predicting behaviors of RC as well as presents guidance for the selection of hyperparameters of RC to learn chaotic systems.
Subspace Adaptation Prior for Few-Shot Learning

Abstract Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent. While these methods have achieved successes in various scenarios, they commonly adapt all parameters of trainable layers when learning new tasks. This neglects potentially more efficient learning strategies for a given task distribution and may be susceptible to overfitting, especially in few-shot learning where tasks must be learned from a limited number of examples. To address these issues, we propose Subspace Adaptation Prior (SAP), a novel gradient-based meta-learning algorithm that jointly learns good initialization parameters (prior knowledge) and layer-wise parameter subspaces in the form of operation subsets that should be adaptable. In this way, SAP can learn which operation subsets to adjust with gradient descent based on the underlying task distribution, simultaneously decreasing the risk of overfitting when learning new tasks. We demonstrate that this ability is helpful as SAP yields superior or competitive performance in few-shot image classification settings (gains between 0.1% and 3.9% in accuracy). Analysis of the learned subspaces demonstrates that low-dimensional operations often yield high activation strengths, indicating that they may be important for achieving good few-shot learning performance. For reproducibility purposes, we publish all our research code publicly.
A new blockchain investment and energy certificate platform

This article proposes a new decentralized asset investment and energy certificate platform: BIC (Blockchain Investment Certificate) which has a Korean patent for its new solution. The platform combines features of decentralized exchanges (DEX), crowdfunding, token economies, and insurance to provide a secure and efficient marketplace for buying and selling energy assets, including renewable energy certificates such as carbon credits. Decarbonization of energy systems has been a recent trend after 2020 where large-scale renewable energy sources (RES) are integrated into the power industry. The new business model and platform in this article offer unique solutions for investors, small and medium-sized enterprises (SMEs), and the overall economy. With its decentralized structure and innovative features, the platform has the potential to accelerate the transition to a low-carbon economy and promote sustainable investment practices. In addition, this platform provides a simple way to offset carbon footprints and support clean energy initiatives, contributing to the fight against climate change. Also, this platform not only supports the renewable energy market, encouraging the generation of renewable electricity on a larger scale but enables businesses to use renewable electricity without investing in costly facilities like solar panels, making renewable energy more accessible. Furthermore, this novel energy certificate platform facilitates the choice of exactly where to buy renewable energy, whether locally or from different regions in different countries, offering flexibility and increasing renewable energy adoption
IAM Identity Access Management—Importance in Maintaining Security Systems within Organizations

Identity and Access Management proposes a web service that assists in controlling the entire work through secured ways. This research study has been started to highlight the importance of IAM by discussing its roles, characteristics, advantages and disadvantages. It is a framework comprising processes, policies, and the latest technologies, allowing the organization to monitor digital identities and control exclusive access to follow information based on user data. The IAM component proposes an approach of centralized user management, account management console, authentication approaches, and so on. In this research work, roles and key components of IAM have been discussed with all types of possible challenges. Furthermore, this research will help readers and future researchers easily identify the importance of IAM in maintaining security systems within organizations.
Evolving a Data Center Into a Microgrid: Industry perspectives and lessons learned

For decades, data center operators have been drawn to the eastern stretches of Oregon and Washington State, USA. In contrast to the coastal climate that most people conjure when thinking of the Pacific Northwest, the land east of the Cascade Mountains is relatively flat, dry in the summer, and snowy in the winter. Affordable prices for land, tax breaks, and cheap hydroelectric power have made the region one of the main hubs of data center development in the United States, with Amazon, Apple, Google, Meta, and Microsoft all investing in multiple campuses.
Reservoir computing using self-sustained oscillations in a locally connected neural network

Understanding how the structural organization of neural networks influences their computational capabilities is of great interest to both machine learning and neuroscience communities. In our previous work, we introduced a novel learning system, called the reservoir of basal dynamics (reBASICS), which features a modular neural architecture (small-sized random neural networks) capable of reducing chaoticity of neural activity and of producing stable self-sustained limit cycle activities. The integration of these limit cycles is achieved by linear summation of their weights, and arbitrary time series are learned by modulating these weights. Despite its excellent learning performance, interpreting a modular structure of isolated small networks as a brain network has posed a significant challenge. Here, we investigate how local connectivity, a well-known characteristic of brain networks, contributes to reducing neural system chaoticity and generates self-sustained limit cycles based on empirical experiments. Moreover, we present the learning performance of the locally connected reBASICS in two tasks: a motor timing task and a learning task of the Lorenz time series. Although its performance was inferior to that of modular reBASICS, locally connected reBASICS could learn a time series of tens of seconds while the time constant of neural units was ten milliseconds. This work indicates that the locality of connectivity in neural networks may contribute to generation of stable self-sustained oscillations to learn arbitrary long-term time series, as well as the economy of wiring cost.
Multi-branch Attention Fusion Network for Aspect Sentiment Classification

For specific aspects (aspect words) of the object described in a given sentence, the research goal of aspect sentiment classification is to predict the sentiment polarity expressed by different aspect words. At present, sentiment classification methods based on attention mechanism and graph neural networks use attention models to capture contextual information related to aspect words, but ignore the global information of the sentence where aspect words are located. The classification accuracy in dealing with complex sentences still needs to be further improved. To solve the above problems, we propose a novel syntax-aware method - Multi-branch Attention Fusion Network (MAFNet) for aspect sentiment classification. Firstly, we use graph attention networks to capture contextual information related to aspect words, while using the mean-pooling layer to capture global information of sentences. Secondly, we propose a novel attention fusion mechanism to integrate contextual information related to aspect words and global information of sentences, which can enrich the semantic relationship between aspect words and opinion words in the sentence. Then, we use orthogonal and differential regularization to constrain the attention weight of contextual information, which can accurately capture the semantic correlation between aspect words and opinion words in the sentence. Finally, we verify the effectiveness of the algorithm on three public datasets. The experimental results prove that our method can correctly establish the semantic relationship between aspect words and opinion words, which can achieve better classification accuracy than traditional methods, attention-based methods, and graph neural network-based methods.
Face Forgery Detection Algorithm Based on Improved MobileViT Network

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization.
Federated Learning on Non-iid Data via Local and Global Distillation

Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of communication traffic and the number of iteration rounds for training such algorithms increases significantly, especially in non-independently and homogeneously distributed scenarios, where they do not achieve satisfactory performance. In this work, we propose FedND: federated learning with noise distillation. The main idea is to use knowledge distillation to optimize the model training process. In the client, we propose a self-distillation method to train the local model. In the server, we generate noisy samples for each client and use them to distill other clients. Finally, the global model is obtained by the aggregation of local models. Experimental results show that the algorithm achieves the best performance and is more communication-efficient than state-of-the-art methods.
Parameter Investigation Study On Task Scheduling in Cloud Computing

The term parameter investigation study is to describe the process of examining different factors that have an effect on the efficiency of work scheduling algorithms in cloud computing. Some examples of such factors are the amount of time allotted for each work, the number of available processors, the amount of resources being used, the amount of communication overhead, and the deadline limitations. For optimal resource utilisation and timely completion of tasks, cloud computing relies heavily on task scheduling. Since metaheuristic optimisation algorithms can quickly and efficiently identify near-optimal solutions in huge and complex problem spaces, they have become a popular method for scheduling tasks on the cloud. In this study, we examine how metaheuristic optimisation algorithms can be used to schedule tasks in the cloud. In addition, a fair research was conducted on a wide range of cloud computing task scheduling parameters.
Channel-Spatial Dynamic Convolution : An Exquisite Omni-dimensional Dynamic Convolution

Most of the dynamic convolution algorithms adopted at this stage use the SE attention mechanism, but the attention mechanism, as a key part of dynamic convolution, has not attracted enough attention, and the relevant research is insufficient. In this paper, an exquisite ODConv which is called Channel-Spatial dynamic convolution is proposed. CSConv introduces the spatial attention module and the channel attention module into the ODConv in parallel, so that the convolution kernel pays more attention to the basic characteristics of the input and effectively improves the accuracy of the model and the efficiency of the convolution kernel. The experimental results show that CSConv achieves good results in the four datasets of ImageNet, COCO, HRRSD and DIOR.
Content based Image Retrieval using Fine-tuned Deep Features with Transfer Learning

This paper introduces a deep learning approach to efficiently retrieve images using a robust deep features extracted from VGG-19 architecture. Our work involves fine-tuning this pre-trained network to adapt it to our dataset by replacing the final layers, we can then train the network so that it learns feature representation for the content based image retrieval task. After applying transfer learning, then, the new network is trained on our dataset after performing augmentation to the set of training images to increase the number of images in order to improve the training accuracy. Augmentation techniques involve using shifting, shearing, and flipping to the original input images. Finally, features are extracted from the ‘fc7’ layer that has 4096 bins for each input image. Euclidean distance is applied to calculate the closest distance between the query image and the features database. Experiments are conducted on a standard dataset called Corel-1k with 1000 images and 10 different categories. Results show that our approach generates high precision accuracy that outperforms traditional image retrieval methods and is in level with deep learning based methods.
Optimization of Multi-Class Non-Linear SVM Image Classifier Using A Sobel Operator Based Feature Map and PCA

Humans are skilled at categorizing things fast. Automation of this skill becomes beneficial for various applications. SVM is a useful machine-learning algorithm for image classification. But sometimes, training large datasets on SVM classifier can be time-consuming and computationally extensive, while not giving good accuracy. In this study, we have used dimensionality reduction using PCA for a multi-class SVM image classification model which uses a custom feature map based on Sobel operator. The kernel used in the SVM model is non-linear type. This makes the procedure highly efficient by using the mentioned feature extraction method and the dimensionality reduction procedure.
Real-Time Object Detection Performance of YOLOv8 Models for Self-Driving Cars in a Mixed Traffic Environment

Self-driving cars have gained significant attention in recent years. Real-time object detection is a critical component of their perception system. One of the main challenges in developing safe and efficient self-driving cars lies in accurately and in real-time detecting objects in diverse and complex traffic environments. This paper presents the performance evaluation of real-time object detection of YOLOv8 models, a state-of-the-art deep learning framework for self-driving cars in mixed traffic environments. The objective is to assess the extent of YOLOv8's object detection capabilities can be used for self-driving car within complex real-world traffic scenarios. The experimental results show that the accuracy of YOLOv8 during normal daylight scenarios ranges from $0.60 \sim 0.80$. In contrast, the accuracy values obtained in night scenarios fall between $0.15 \sim 0.25$. Similarly, the F-Measure of YOLOv8 models under daylight conditions ranges from $0.75 \sim 0.87$. Conversely, the F-Measure in night conditions falls between $0.27 \sim 0.46$. Based on our thorough evaluation results, YOLOv8 has demonstrated its robustness in detecting objects. However, the algorithm requires improvement to effectively handle the challenges of self-driving cars' object detection systems in mixed traffic, such as diverse object classifications, small-scale objects, fast-moving objects, blur, glare, and low-light illumination, particularly in night-time scenarios.
H-MGSR: A Hierarchical Motif-based Graph Attention Neural Network for Service Recommendation

The rapid development of web services has made it increasingly challenging for developers to find desired web services. To address this issue, researchers have developed various powerful models for service recommender systems. Recently, graph neural networks have shown promising performance in various deep learning tasks including service recommendation. This paper proposes a novel graph neural network for web service recommendation using a hierarchical attention mechanism that combines a node-level and a motif-level attention mechanisms. The node-level attention mechanism is responsible for aggregating information by the importance of different neighbors, while the motif-level attention mechanism performs a weighted combination of the node embeddings generated from different motif adjacency matrices. Finally, the generated node embeddings are optimized by the multi-layer perceptron (MLP), which in turn provide recommendations. Experimental results on real-world datasets demonstrate that our proposed model outperforms state-of-the-art approaches. Additionally, we conduct a model analysis to investigate the importance of different motifs. Overall, our proposed method shows promising performance for web service recommendation and highlights the potential of using graph neural networks in this domain.