Reviewerly/exHarmony huggingface dataset:
Hand Written Digit Recognition using Multilayer Deep Convolutional Neural Network

In machine learning, handwritten digit recognition is usually seen as a multi-class classification problems In this approach, the ten possible digits (0-9) are treated as individual classes, and the goal is to train a classifier that can accurately identify them. However, it’s not unusual for a single classifier to have varying levels of success when applied to different datasets, even after being trained using a standard learning algorithm. This indicates that while a given learning algorithm may be effective at training strong classifiers on certain datasets, it may result in weaker classifiers for others. Furthermore, it’s possible for a classifier to exhibit varying levels of performance on multiple test datasets, especially considering that different writers may produce highly diverse image samples of the same numbers. To address this issue, the advancement of ensemble learning methodologies will be critical, as they have the potential to improve overall prediction accuracy and offer more consistent performance across different datasets.
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing

In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s.
E-commerce Product Review Analysis based on Multi-class Support Vector Machine

Recently, an extensive number of consumers can choose their best options from automated retailers and compare products in online stores. Sentiment analysis is frequently employed in applications meant to demonstrate and provide client service as the voice of the user. On E-commerce websites, user reviews provide helpful information about the product. Sentiment analysis of the text reviews helps forecast product sales by assessing user sentiment regarding the product. The Word Embedding Attention approach assigns additional weight to words that have a strong association with a specific class. Sentiment analysis machine learning models provide a thorough depiction of capabilities and far surpass conventional feature-based methods. The obj ective of this research is to improve sentiment analysis performance by creating a weighted ensemble with a Multi-class Support Vector Machine (MSVM) model utilising novel word embedding techniques. Because of the weighted ensemble's higher generalisation skills, MSVM produces superior results. When comparing existing methods for sentiment analysis, the Weighted ensemble with MSVM has produced higher results in terms of accuracy, precision, f-score, and recall. The Weighted ensemble with an MSVM has achieved 99.87 % accuracy, 99.39% precision, 99.03% f-score, and 99.690/0 recall in sentiment analysis when comparing existing methods such as LSTM+FL, APSO-LSTM, ABCDM, SSentiA, and Hybrid CNN+LSTM.
A Two-Stage Edge-Based Intelligent Recognition Method for Substation Pointer Meter Reading

Nowadays, with the digital transformation of the power grid, the method of manually inspecting and recording the status of equipment in power substations is gradually transitioning to intelligent and automated monitoring. Firstly, an improved YOLOv4 algorithm is utilized to extract the dial region of the meters. Subsequently, an enhanced DeepLabv3+ algorithm is used to segment the dial region. Based on the segmented results, the pointer and edges of the dial are effectively extracted, and the angle between the pointer and the edge is calculated. Finally, the meter reading is determined based on this angle. Experimental results demonstrate that the proposed algorithm can accurately recognize meter readings, with the majority of errors falling within a 1.5% margin. Comparative experiments have also validated the effectiveness of each module in the proposed algorithm. Finally, the algorithm is deployed on the Atlas development board at the edge of the substation for the purpose of meter reading recognition.
Deep Learning-Based Anomaly Detection using Hybrid Loss

Recently, deep learning-based (DL) automatic modulation classification (AMC) has been extensively studied in cooperative and non-cooperative communication contexts such as cognitive radio and spectrum surveillance. One of the drawbacks of DL-based AMC is its susceptibility to anomalous or interfering signals. In this paper, we propose a DL-based anomaly detection for AMC, utilizing an autoencoder to process the in-phase and quadrature components of a received signal. In order to detect anomalies, we employ a hybrid loss, a combination of the autoencoder’s reconstruction loss and the Mahalanobis distance of the latent space embedding of the training vector and each input instance. Through computer simulations, we show that the proposed model has superior detection performance with less computational complexity compared to the conventional DL-based model.
Research on Intelligent Customer Service System for Power Industry Based on Semantic Understanding

With the development of the electric power industry and the growth of user demands, it has become an important task to improve the efficiency and quality of the electric power business customer service system. This paper proposes an intelligent customer service system for electric power business based on semantic understanding, which aims to use natural language processing and artificial intelligence technology to understand user intentions and semantics, and provide intelligent service support. The system is trained by deep learning and natural language processing technology to accurately identify users' problems or needs, provide corresponding answers or suggestions according to the preset knowledge base and business rules, and give timely feedback to users. Through the research of this paper, we provide an intelligent and personalized customer service solution for the power business, and look forward to the future development direction and improvement potential of this field.
Design and Development of an Access Point Based on Secure Chip

Digitization has penetrated into every aspect of life, and network security has become one of the main focuses of future security. China has independently developed security chips with intellectual property rights, and the commercial cryptographic algorithm of China (SM algorithm) has been widely promoted and used for many years. At the same time, China also has its own wireless local area network standard called WAPI. This article introduces the basic principles and applications of security chips, and elucidates the construction of a high-information security Access Point (AP) based on domestically developed products.
Image Denoising Based on Median Filter and UNet

Image denoising can improve image quality, improve visual perception, improve the accuracy of subsequent tasks, reduce image compression rate, and protect privacy information. In order to effectively remove the noise of real images, this paper proposes an image denoising method combining adaptive weighted median filtering algorithm, VGG and U-Net network. Firstly, the adaptive weighted median filtering algorithm is used to preprocess the input noise image, and then the VGG-16 network is used to extract the features of the image. On this basis, the U-Net network is constructed for encoding and decoding to obtain the denoised image. Experimental results on the SIDD real image dataset show that compared with other classical methods, the PSNR and SSIM values of the proposed algorithm are greatly improved, with PSNR reaching 35.57 and SSIM reaching 0.957. On the data sets with Gaussian noise standard deviation of 5, 10 and 20, the PSNR reaches 37.614, 35.241 and 35.249, SSIM reaches 0.974, 0.961 and 0.963, and MSE reaches 14.744, 31.985 and 36.120, respectively. The processed image has good visual effect, and the proposed algorithm can retain more image detail information while denoising.
Dual-channel Text Classification Based on the Attention Mechanism

Text classification can realize information organization and induction, information retrieval and search, sentiment analysis, spam filtering, automated text processing, intelligence collection and security. As the text information to be processed becomes increasingly complex, using traditional convolutional neural networks and recurrent neural networks alone is far from satisfactory for addressing the allocation of text information weights. Based on this, this paper improves the text classification model by replacing the convolutional neural network with a bidirectional convolutional neural network as one channel, replacing the recurrent neural network with a bidirectional long short-term memory network as another channel, and injecting attention mechanisms into the two channels respectively. Bi-LSTM channels can more effectively extract contextual information from news texts, while Bi-CNN channels can more effectively extract local feature information from news texts. After both channels undergo individual attention mechanism processing, the features are fused. The fused result is then passed to the softmax function to accomplish the final news text classification task. This paper conducts performance evaluation on two publicly available datasets, namely Today's Headlines and THUCNews. Experimental results demonstrate that the proposed Bi-Modal Text Fusion Network (BiCTFN) outperforms other classification models and exhibits superior classification performance for long text classification tasks.
Deep Learning-Based Automatic Modulation Classification with Prediction Combination in OFDM Systems

Automatic modulation classification (AMC) is one of the important tasks in cognitive radios and spectrum surveillance. This paper proposes a deep learning (DL)-based AMC with prediction combination method for orthogonal frequency division multiplexing (OFDM) systems. We first predict the subcarrier modulation scheme of the transmitted OFDM signal with different DL models and then combine those predictions using the proposed prediction combination method to make the final decision. Through computer simulations, we show that the classification performance can be improved by the proposed prediction combination method in terms of classification accuracy.
MSMEs Readiness for Adopting Artificial Intelligence and Machine Learning

This study explored the factors influencing organizations' artificial intelligence and machine learning technology adoption in the context of MSMEs of developing countries with particular reference to the Indian context. The study shall focus on AI adoption in automotive, industrial manufacturing, consumer products, and aerospace & defence MSMEs. The survey comprised 309 samples. SEM (structural equation modelling) tested the structural model. The factors influencing Artificial intelligence and Machine learning adoption intention for MSME were modelled using an integrated theories approach which included the Technology readiness index (TRI), Technology Acceptance Model (TAM) and Technology-Organization-Environment framework (TOE). The study analyzed the results and stated the implications for theory and practice.
Dual Adaptive Data Augmentation for 3D Object Detection

This paper proposes Dual Adaptive Data Augmentation (DADA) method for 3D object detection. Training deep learning models requires large amounts of data, which is time-consuming and expensive. To address this challenge, data augmentation methods have been proposed to generate augmented objects. However, conventional methods rely on fixed parameters and ignore scene and object characteristics. To address these limitations, we propose DADA, which consists of two modules: Scene-based ADA and Density-based ADA. Scene-based ADA adjusts augmented objects based on the distribution of Ground Truth (GT) objects in each scene, allowing augmentation to focus on sparse scenes with fewer GT objects while keeping overall data volume. Density-based ADA utilizes LiDAR characteristics to apply different sampling methods, generating diverse augmented objects based on object density. Experiment results show considerable improvement in performance on the KITTI and ONCE datasets.
Empowering Instructors with Collective Intelligence: Learning Problem-Solving Paths to Facilitate Feedback Generation

The feedback on the Intelligent Tutoring Systems (ITS) providing the necessary support and guidance to students successfully complete a given task, improving their learning. However, building proper feedback demands time, whereas instructors are often overloaded, and several interactions with experts of the domain knowledge to know the several paths students, which is often infeasible and increases the costs and the complexity to develop ITSs at scale. To address this problem, we proposed a novel approach to build feedback using students’ collective intelligence (CI), where our ITS might learn such paths incrementally building a knowledge graph with more detail than those predicted by instructors, optimizing the identification of problem-solving paths, and facilitating the iterative designing of meaningful and fine-grained feedback for the instructors by presenting domain model’s updated, meaningful visualizations. To evaluate our approach we developed an ITS in a domain of numerical expressions that was used by 99 students. As a result, we observed that our approach helps to create a knowledge graph with a quality equivalent to that built by specialists in less time and considerably reducing the instructor’s overload.
Essays’ Coherence Analysis Via Entity Grid Approach

Coherence analysis is a challenging task, especially when applied to many domains. This paper proposes a strategy that combines Machine Learning and Linguistics to analyze text coherence by understanding entity behavior. It introduces an algorithm that automatically annotates documents based on the Entity Grid Discourse Representation. We defined two datasets, one of academic papers’ abstracts and the other of students’ essays. The annotation technique identifies the influence of grammatical structures on coherence levels and offers a cost-effective solution for coherence analysis. To assess coherence levels Machine Learning methods were used, and the experimental results demonstrate an accuracy of 88% when assessing coherence in abstracts and 74% in essays.
A Coronary Artery Stenosis Detection Method Based on Coarse-To-Fine Network Structure

Current semantic segmentation methods tend to segment objects on lower resolution datasets, but are not as effective for high resolution medical images. The coronary artery stenosis detection task in this experiment, for example, needs not only to ensure the continuity of the vessel as a whole, but also to be able to identify boundary defects at the maximum level of granularity. This paper therefore proposes a Coarse-to-fine componentised model structure to achieve stenosis defect detection while ensuring vessel continuity, successfully improving the Boundary IoU to 84.53% and with only 59% of the number of parameters in ViT.
A Blockchain Consensus Protocol Based on Multidimensional Parameter Credit

With the rapid development of the industrial Internet, it has greatly improved industrial production efficiency. However, challenges such as information asymmetry and the inade-quacy of a single indicator in supporting supply-demand matching have emerged. To address the challenges, we propose a credit-based consensus protocol for the Industrial Internet. It introduces a credit model that combines multidimensional to achieve supply-demand matching and incorporates regulatory preferences for flexible global regulation. Furthermore, a credit-based Proof-of- Work difficulty adjustment method is proposed to enable distributed Industrial Internet collaboration. The effectiveness of the proposed approach is demonstrated through experiments, and future research directions are discussed.
Tour Routes Planning with Matrix-based Differential Evolution

Tourism is an important industry sector that requires tour companies to plan multiple routes for different tour groups, which is called tour multi-route planning. This paper focuses on tour multi-route planning, which can improve the economic benefit and allocation efficiency of tour resources. In this paper, we propose a novel multiple routes planning model that captures the real-world tourism scenario and practical constraints. We use matrix-based differential evolution to optimize the proposed model. Numerical evaluation results show the matrix-based differential evolution can be effective to optimize the proposed model.
Conveying Emotions Through CMC: A Comparative Study of Memoji, Emoji, and Human Face

Emojis and avatars are widely used in online communications, but their emotional conveyance lacks research. This study aims to contribute to the field of emotional expression in computer-mediated communication (CMC) by exploring the effectiveness of emotion recognition, the intensity of perceived emotions, and the perceived preferences for emojis and avatars as emotional expression tools. The following were used as stimuli: 12 photographs from the Yonsei-Face database, 12 Memojis that reflected the photographs, and 6 iOS emojis. The results of this study indicate that emojis outperformed other forms of emotional expression in terms of conveying emotions, intensity, and preference. Indeed, the study findings confirm that emojis remain the dominant form of emotional signals in CMC. In contrast, the study revealed that Memojis were inadequate as an expressive emotional cue. Participants did not perceive Memojis to effectively convey emotions compared with other forms of expression, such as emojis or real human faces. This suggests room for improvement in the design and implementation of Memojis to enhance their effectiveness in accurately conveying intended emotions. Addressing the limitations of Memojis and exploring ways to optimize their emotional expressiveness necessitate further research and development in avatar design.
Media Pembelajaran Pemasangan Kontrasepsi Implan terhadap Pengetahuan dan Keterampilan Mahasiswa Kebidanan

This study aims to analyze the influence of e-books and Augmented Reality learning media for installing contraceptive implants on the knowledge and skills of midwifery students. The method used is experimental, using a one-group pre-test and post-test design, with the development of e-book media products containing text, images, sound, and augmented reality. The research results showed that there was a significant influence on knowledge and skills before and after the use of e-book learning media and Augmented Reality for installing contraceptive implants in midwifery students (p = 0.000). The research conclusion is that e-books and Augmented Reality learning media for installing contraceptive implants are effective in improving the skills of midwifery students.&#x0D; &#x0D; Keywords: Learning Media, E-Book, Augmented Reality, Installation of Implant Contraception
Improving Live Augmented Reality With Neural Configuration Adaptation

Instead of relying on remote clouds, today's augmented reality (AR) applications send videos to nearby edge servers for analysis to optimize user's quality of experience (QoE). Lots of studies have been conducted to help adaptively choose the best video configuration, e.g., resolution and frame per second (fps). However, prior works only consider network bandwidth and ignores the video content itself. In this chapter, the authors design Cuttlefish, a system that generates video configuration decisions using reinforcement learning (RL) based on network condition as well as the video content. Cuttlefish does not rely on any pre-programmed models or specific assumptions on the environments. Instead, it learns to make configuration decisions solely through observations of the resulting performance of historical decisions. Cuttlefish automatically learns the adaptive configuration policy for diverse AR video streams and obtains a gratifying QoE. The experimental results show that Cuttlefish achieves a 18.4%-25.8% higher QoE than the other prior designs.
DeepSlicing

Convolutional neural networks (CNNs) have revolutionized computer vision applications with recent advancements. Extensive research focuses on optimizing CNNs for efficient deployment on resource-limited devices. However, the previous studies had several weaknesses, including limited support for diverse CNN structures, fixed scheduling strategies, overlapped computations, and high synchronization overheads. In this chapter, the authors introduce DeepSlicing, an adaptive inference system that addresses the above challenges. It supports various CNNs and offers flexible fine-grained scheduling, including GoogLeNet and ResNet models. DeepSlicing incorporates a proportional synchronized scheduler (PSS) for balancing computation and synchronization. Implemented using PyTorch, the authors evaluate DeepSlicing on an edge testbed of 8 heterogeneous Raspberry Pis. Results showcase the remarkable reductions in inference latency (up to 5.79 times) and memory footprint (up to 14.72 times), demonstrating the efficacy of this proposed approach.
Gaze Tracking for Hands-Free Human Using Deep Reinforcement Learning Approach

Abstract People with disabilities have new and advanced methods to communicate with the applications for virtual keyboards and other communication tools. In this paper, we utilized a novel deep reinforcement learning-based technique for determining the location of the accessible options for gaze-controlled tree-based menu selection system. A virtual English keyboard has been incorporated into the layout of the new user interface, which also includes improved requests for text modification through the gaze. The two methods used to manage the system are: 1) eye tracking for typing on the virtual keyboard and 2) eye tracking with a device for soft-switch utilizing deep reinforcement learning. Simulation results show that DRL based algorithm outperforms other baseline techniques in terms of total loss and accuracy.