Reviewerly/exHarmony huggingface dataset:
End-to-End Hyperspectral Image Classification Using Hybrid GCNN with SWIN Transformer

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional Machine Learning and Deep Learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets.
Establishing a Model Synthesis Method of a Mobile Platform for Service Collaborative Robot

The development of robotics with the possibility of implementing collaborative robots in practice allows their use in services in new social activities such as health care. The paper examines the possibility of robots finding application in supporting the health system to manage pandemics and natural disasters, for example, as was the case with the Coronavirus infection (COVID-19). Such applications include taking a sample from a patient for screening, hospital disinfection, delivery and logistics of food for the infected patient, collection of physiological materials and waste, etc., activities that improve working conditions and reduce the risk of infection of healthcare workers. This paper aims, based on an analysis of hospital activities, to propose a Synthesis Method for a Collaborative Service Robot Model that is applicable to specific activities. The proposed Example Robot is structurally shaped like a dog and is intended for pediatric hospital settings. It can also be used in other medical institutions such as rehabilitation centers, clinics, dental or ophthalmology centers, etc.
English Teaching Ability Evaluation Algorithm Based on Differential Evolution Algorithm

This article first establishes an evaluation index system for English teaching ability, including teaching attitude, teaching methods, teaching level, and other aspects. Then, differential evolution algorithm is used to optimize the weight of indicators, and the teacher's English teaching ability is quantitatively evaluated based on the optimization results. The experimental results show that the English teaching ability evaluation algorithm based on differential evolution algorithm proposed in this article has good evaluation results and high feasibility, and can provide reference for English teaching ability evaluation. At the same time, this algorithm also has good scalability and can be applied to capability evaluation in other fields.
Leveraging the Decentralised Open IoT Security Protocol ((d)OISP)™: Facilitating Edge-Based Artificial Intelligence in Large-Scale Network Infrastructures

There is an increasing demand for integrating edge-based Artificial Intelligence (AI) into complex large-scale network infrastructures. This need is driven by the requirement for faster data processing, reduced latency, minimized cyberattack surfaces, and the preservation of bandwidth and lowered energy consumption. Yet, one significant hurdle has consistently stood out: ensuring a secure, authenticated seamless point-to point exchange of information in decentralized systems across heterogeneous devices and platforms without the use of central authority. This paper presents a novel approach to this challenge by leveraging the decentralized Open IoT Security Protocol ((d)OISP). We delve into the architectural design, operational dynamics, and merits of (d)OISP, with its decentralized nature, promises to enhance quantum-safe security measures by utilizing quantum safe standardized cryptosystems and offering a unified protocol that caters to diverse devices without necessitating a central point of control. By incorporating (d)OISP into networks, not only is data exchange made more secure, but the efficiency of edge-based AI operations is notably enhanced, especially in large-scale settings.
NNCR: Revising Classifications using Embedding Based K-Nearest-Neighbor Search

The global e-commerce store needs to ensure compliance with various regulations at local, national, and international levels. One business use case is to identify face masks to avoid price gouging during times of high demand. In order to keep billions of items safe and legally compliant, it is important to ensure accurate classifications. Classification revisers aim to enhance classification accuracy by detecting and revising incorrect classifications. In this paper, we introduce this problem, along with appropriate online evaluation metrics for large-scale application scenarios. Our proposed method first learns neural network embedding from item textual features to define similar neighbors, and then simply uses these known classification results from k nearest neighbors to estimate an item’s class assignment. Our experiments demonstrate that it outperforms the state-of-the-art baseline approaches and its robustness to large scales. The proposed method can uniquely revise a significant number of classifications correctly, complementary to a multi-label classification system. The study indicates that our simple yet effective approach empowered by GPU computation is a viable solution of a commercial multi-label-classification revision problem.
Density-Based Prototypical Contrastive Learning on Visual Representations

Self-supervised learning is closing the gap between the fully-labeled supervised learning tasks and the scarce-labeled ones rapidly. The effort goes to various recent achievements of contrastive learning methods. However, researchers learn the weakness of contrastive learning. First, contrastive learning is typically an instance discrimination task in practice, which could be targeted on exploiting low-level image differences regardless the understanding of the semantic information. Second, contrastive learning does not encode the dissimilarity between the augmented views from the same image, which may result in potential under-clustering outcomes. Furthermore, contrastive learning treats two representation samples as a negative pair as long as they are from different image instances, which may lead to an over-clustering result. In this paper, we introduce Density-Based Prototypical Contrastive Learning (DBPCL), a self-supervised visual representation learning method that combines the instance discrimination task with density-based clustering to encode the semantic structures of data. By the proposed ProtoXent loss, the proposed method encourages samples to be closer to their assigned prototypes and push their negative prototypes apart at the same time. With the computed prototypes, DBPCL is able to discover both false-positive and false-negative pairs, and then address the issues of under-clustering and over-clustering. With the aforementioned reasons, the proposed DBPCL is superior to the state-of-the-art methods by improving the quality of learned representations. As an example, the proposed method has its prediction accuracy superior to the best group of methods of the same kind on CIFAR-100. The code of the proposed DBPCL is available at https://github.com/YuehLinChung/DBPCL for reference.
A Study on Watching Support of Werewolf Game Using Log Data Analyzed with Machine Learning

Werewolf game is one of the popular party games. People enjoy not only playing but also watching games. Besides, Werewolf game is studied as an incomplete information game. In this paper, the authors focused on the watching support for Werewolf game. A predictor built with machine learning (logistic regression) calculates the situation of the game by inputting events that occur in the game and provides each player’s execution/attack probability to the audience. Though the predictor was originally built to analyze the games, it is applied to watching support in this paper. In advance, the predictor had to be constructed by analyzing game logs played in the target regulation. In this paper, the authors focused on five-player games as the fundamental setting. The sets of game logs in different situations were inputted in parts by parts. The predictor appropriately estimated each player’s execution/attack probability in most cases. The estimation will help the audience watch Werewolf games. Applying this method to different regulations is one of the future works.
Application of Sierpinski Fractal Slot in Antipodal Vivaldi Antenna

The development of an antipodal Vivaldi antenna implementing the Sierpinski arrowhead curve slot for head imaging is presented in this paper. The proposed antenna has an overall dimension of 65 x 65 x 1.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> using Roger RO4350B as a substrate. The proposed antenna can produce a better reflection coefficient under -30 dB and higher gain achieving 7.35 dBi compared with the antenna without the fractal slot. It is also reported that the application of the fractal slot does not alter the antenna’s performance, which could interfere with the antenna design that already met the head imaging criteria.
An Augmentation-agnostic Semantic Preserving Technique for Data Generation

Data generation is becoming an increasingly important issue in the field of machine learning due to the high cost of data collection and the privacy concerns associated with raw data. Generating new data with high fidelity is extremely challenging because even a minor perturbation in high-dimensional data may alter its semantic meaning. The challenge becomes particularly acute in multivariate time-series data as it often exhibits human-imperceptible temporal patterns and lacks standard representation. To address this issue, we propose a straightforward yet effective technique that helps preserve the fidelity of the original data. Experimental results demonstrate that our technique enhances the performance of time-series forecasting model.
Insights into Automated Diagnosis of Cervical Cancer

The cervix is the last part of the uterus where cervical cancer usually develops. Identification of the cervical cells can be very taxing owing to their complexity of morphological changes in the structural parts of the cell. The problem here is that the detection process is tardy and there is not much awareness about it. By means of this research, we have taken a step to attempt to automate this detection process and understood the various methods used like Logistic regression along with Ll Regularization, CNN, SVM and ANN produced excellent accuracy of 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> .
Design of Semantic Recognition Algorithm Based on Knowledge Graph

An attribute extraction method based on semantic analysis is designed. Firstly, the concept of knowledge graph is proposed and the entities on the platform are described in unorganized text. Then, an expression learning algorithm based on knowledge graph is proposed. And apply it to lower-dimensional vector representations. Then an object-oriented semantic modeling method is proposed. It is proved by examples of program analysis and reasoning. The experimental results show that the designed model has good scalability and accuracy.
Feature Level Ensemble Learning Technique for Cervical Cancer Cell Classification

The early detection of abnormal cells with Pap smear screening is a commonly used strategy for preventing cervical cancer, which is a major public health concern. In this study, we use the SIPaKMeD dataset to present an ensemble learning and multi - model ML learning strategy based on DL and ML for the categorization of cancerous cells in cervical cancer. Here we have use feature-level ensemble learning. ResNet, DenseNet, EfficientNet, and MobileNet were four pre-trained deep learning models that we utilised to extract features from the cervical cell pictures. These features were concatenated to create a feature vector, which was then classified using five base classifiers: Support Vector Machines, K-Nearest Neighbours (KNN), Random Forest (RF), Decision Trees (DT), and Logistic Regression (LR). The combined features were then applied to each classifier individually. The highest accuracy of 95% was achieved by the LR. The accuracy of the individual basic classifiers was 94,.4% (SVM), 87.2% (KNN), 76.5% (RF), and 74.6% (DT), respectively. Our suggested method has the potential to increase the precision of aberrant cell classification in Pap smear-based cervical cancer screening
Controllable Slogan Generation with Diffusion

Slogans form an important part of any marketing campaign. Catchy and effective slogans get remembered by the target audience which leads to a long-term word of mouth and reputation, which is crucial for any product. There have been multiple attempts before to generate slogans from product description using language models. But most of these approaches are limited in variance and control. In this paper, we propose an approach that makes use of diffusion to fine-tune a BERT model to generate a slogan in the context of the input text description along with additional control parameters such as sentiment and number of named entities in a plug-and-play manner which provides scalability. We also evaluate the performance of our model with existing approaches to slogan generation and demonstrate the potential of this approach while also providing better control over slogan generated.
Why is the User Interface a Dark Pattern?: Explainable Auto-Detection and its Analysis

Dark patterns are deceptive user interface designs for online services that make users behave in unintended ways. Dark patterns, such as privacy invasion, financial loss, and emotional distress, can harm users. These issues have been the subject of considerable debate in recent years. In this paper, we study interpretable dark pattern auto-detection, that is, why a particular user interface is detected as having dark patterns. First, we trained a model using transformer-based pre-trained language models, BERT, on a text-based dataset for the automatic detection of dark patterns in e-commerce. Then, we applied post-hoc explanation techniques, including local interpretable model agnostic explanation (LIME) and Shapley additive explanations (SHAP), to the trained model, which revealed which terms influence each prediction as a dark pattern. In addition, we extracted and analyzed terms that affected the dark patterns. Our findings may prevent users from being manipulated by dark patterns, and aid in the construction of more equitable internet services. Our code is available at https://github.com/yamanalab/whydarkpattern.
Cross-Silo Federated Learning Across Divergent Domains with Iterative Parameter Alignment

Learning from the collective knowledge of data dispersed across private sources can provide neural networks with enhanced generalization capabilities. Federated learning, a method for collaboratively training a machine learning model across remote clients, achieves this by combining client models via the orchestration of a central server. However, current approaches face two critical limitations: i) they struggle to converge when client domains are sufficiently different, and ii) current aggregation techniques produce an identical global model for each client. In this work, we address these issues by reformulating the typical federated learning setup: rather than learning a single global model, we learn N models each optimized for a common objective. To achieve this, we apply a weighted distance minimization to model parameters shared in a peer-to-peer topology. The resulting framework, Iterative Parameter Alignment, applies naturally to the cross-silo setting, and has the following properties: (i) a unique solution for each participant, with the option to globally converge each model in the federation, and (ii) an optional early-stopping mechanism to elicit fairness among peers in collaborative learning settings. These characteristics jointly provide a flexible new framework for iteratively learning from peer models trained on disparate datasets. We find that the technique achieves competitive results on a variety of data partitions compared to state-of-the-art approaches. Further, we show that the method is robust to divergent domains (i.e. disjoint classes across peers) where existing approaches struggle.
The Application of Natural Language Processing Technology Based on Deep Learning in Japanese Sentiment Analysis

In the era of artificial intelligence, with the rapid development of human beings, the study of natural language is also receiving increasing attention. This paper aims to explore the use of deep learning technology to detect and predict emotional tendency of artificial neural network models. Firstly, the text expression capability based on feature extraction and classifier construction will be introduced. Secondly, the word vector adaptive method used in the experimental process will be explained in detail, which can achieve semantic computation and emotion mining. Finally, we will also propose a fusion function strategy to mark the content of the article, and build a natural language processing system based on this strategy. At the same time, we will test the performance of the system. The test results show that the adaptability of the system can reach 74% at the lowest and 79% at the highest, which can show that the analysis technology of the system can meet the basic requirements. Through these researches and explorations, we will be able to better understand and demonstrate the importance of natural language processing in modern society. The application of deep learning in Japanese emotion analysis has achieved some research results. By using the deep learning method, we can train large-scale Japanese text, learn the language feature representation, and realize the understanding and analysis of text emotion. Deep learning can improve the accuracy of Japanese emotion analysis by using the technology of word embedding and language model. In addition, deep learning can also be applied to other natural language processing tasks, such as machine translation and question answering systems, and bring better results to these tasks. In the future, we can look forward to the further research and application of deep learning in the field of Japanese emotion analysis.
SWFNet: Efficient Sampling and Weighted Fusion for Enhanced PointPillars in 3D Object Detection

3D object detection based on LiDAR point cloud is one of the key techniques for autonomous driving applications. However, it is not easy to extract effective features from complicated point cloud data and keep a balance between accuracy and speed, because the density of the point cloud data changes over time, usually showing different structures in spatial domain, such as near-dense, far-sparse and empty. In this work, we propose an Efficient Sampling Weighted Fusion encoder (SWFNet) for 3D object detection. First, an efficient point cloud sampling method is employed to select valid points for each pillar, which reduces the computational complexity and improves the detection accuracy to some extent. In addition, a pillar feature learning network, called WFNet herein, is designed to extract key point information. It uses the weighted fusion technique, a finer-grained pillar feature, including three dimensions, i.e., point, feature and pillar, can be obtained. Furthermore, by replacing the encoder part with the SWFNet in the PointPillars framework, the detection performance can be improved significantly, especially for detecting small objects. The superior performance of the proposed SWFNet has been demonstrated using the KITTI dataset, showing that the 3D mean average precision (mAP) has improved by 4.33% and the inference speed has been increased up to 115 frames per second (FPS), respectively.
Exploring Approaches and Techniques for Human Activity Recognition in Video: A Comprehensive Overview

Recognizing human activity in videos is a difficult endeavor with many uses in monitoring, health care, sports analysis, and interaction between humans and machines. This paper delves into both traditional methods and cutting-edge deep learning-based approaches, providing a thorough overview of the advancements in the field. Through this survey, seeks to give researchers and practitioners with a comprehensive understanding of the current state-of-the-art techniques, their strengths, limitations, and future directions. Also discussed the importance and challenges associated with human activity recognition. It then presents a taxonomy of different methodologies used in this domain, including handcrafted feature-based strategies, deep learning-based methods and explores the key components involved in human activity recognition systems. Furthermore, this paper highlights the datasets commonly used for evaluating human activity recognition algorithms and provides an analysis of the performance of va rio us techniques on these datasets. In summary, this comprehensive compilation serves as a valuable resource for researchers and practitioners working in the field of human activity recognition in videos.
Enhancing Disease Diagnosis: A CNN-Based Approach for Automated White Blood Cell Classification

White Blood Cell (WBC) image classification is pivotal for early disease detection and diagnosis. Convolutional Neural Networks (CNNs) have emerged as potent tools for such tasks due to their ability to learn intricate features from raw pixel data. In this study, we present a CNN-based approach for automated WBC classification. Our methodology encompasses image preprocessing to enhance contrast and normalize color, succeeded by CNN training with multiple convolutional and pooling layers, thereby enabling feature acquisition from diverse WBC classes. We evaluate our approach using a publicly accessible WBC image dataset, comparing our results against other contemporary methods. Our proposed method achieves an impressive 96.2% accuracy for six distinct WBC classes, surpassing prior techniques by a considerable margin. This showcases CNNs’ potential in automated WBC classification, underscoring its significance in medical diagnosis and research. In summary, we introduce a CNN-based approach for automated WBC classification that attains state-of-the-art performance on a publicly available dataset. Our methodology encompasses image preprocessing, contrast enhancement, color normalization, and CNN training to capture distinctive features of diverse WBC classes. Our findings underscore CNNs’ promise in this domain and propose its deployment as a valuable tool in medical research and diagnosis. Subsequent efforts will explore advanced techniques like transfer learning to further elevate our method’s performance.