Reviewerly/exHarmony huggingface dataset:
Dual cross-domain session-based recommendation with multi-channel integration

Session-based recommendation aims at predicting the next behavior when the current interaction sequence is given. Recent advances evaluate the effectiveness of dual cross-domain information for the session-based recommendation. However, we discover that accurately modeling the session representations is still a challenging problem due to the complexity of preference interactions in the cross-domain, and various methods are proposed to only model the common features of cross-domain, while ignoring the specific features and enhanced features for the dual cross-domain. Without modeling the complete features, the existing methods suffer from poor recommendation accuracy. Therefore, we propose an end-to-end dual cross-domain with multi-channel interaction model (DCMI), which utilizes dual cross-domain session information and multiple preference interaction encoders, for session-based recommendation. In DCMI, we apply a graph neural network to generate the session global preference and local preference. Then, we design a cross-preference interaction module to capture the common, specific, and enhanced features for cross-domain sessions with local preferences and global preferences. Finally, we combine multiple preferences with a bilinear fusion mechanism to characterize and make recommendations. Experimental results on the Amazon dataset demonstrate the superiority of the DCMI model over the state-of-the-art methods.
Influence of tree-based multi-layer node information on scoring accuracy and speed of speaker verification

In the speaker verification task based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), by constructing the UBM as a tree structure, the kernel Gaussians suitable for different speakers can be quickly selected, which speeds up the modeling of speaker acoustic space by GMM. The tree-based kernel selection algorithm (TBKS) introduces a beam-width, which increases the candidate range of kernels and improves the kernel selection accuracy. In this paper, we improve the TBKS algorithm by introducing a recall rate to adjust the number of nodes recalled in each layer of the tree structure. This adjustment refines the quantity and resolution of Gaussian distributions in various subspaces within the acoustic space, compensating for the loss caused by discarding some significant Gaussians erroneously. Speaker verification experiments are carried out based on the Aishell2 dataset. The results reveal that the modified TBKS algorithm reduces EER by 7.5% relatively and increses computational reduction factor to 42.93, enhancing both recognition accuracy and speed. In addition, the test speech is spliced into different lengths and common environmental noise is added to verify the universality of the improved algorithm.
Diffusion-Denoising Process with Gated U-Net for High-Quality Document Binarization

The binarization of degraded documents represents a crucial preprocessing task for various document analyses, including optical character recognition and historical document analysis. Various convolutional neural network models and generative models have been used for document binarization. However, these models often struggle to deliver generalized performance on noise types the model has not encountered during training and may have difficulty extracting intricate text strokes. We herein propose a novel approach to address these challenges by introducing the use of the latent diffusion model, a well-known high-quality image-generation model, into the realm of document binarization for the first time. By leveraging an iterative diffusion-denoising process within the latent space, our approach excels at producing high-quality, clean, binarized images and demonstrates excellent generalization using both data distribution and time steps during training. Furthermore, we enhance our model’s ability to preserve text strokes by incorporating a gated U-Net into the backbone network. The gated convolution mechanism allows the model to focus on the text region by combining gating values and features, facilitating the extraction of intricate text strokes. To maximize the effectiveness of our proposed model, we use a combination of the latent diffusion model loss and pixel-level loss, which aligns with the model’s structure. The experimental results on the Handwritten Document Image Binarization Contest and Document Image Binarization Contest benchmark datasets showcase the superior performance of our proposed model compared to existing methods.
Question Classification for Intelligent Question Answering: A Comprehensive Survey

In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future.
An Efficient Path Planning Method for the Unmanned Aerial Vehicle in Highway Inspection Scenarios

Unmanned aerial vehicles (UAVs) have received widespread attention due to their flexible deployment characteristics. Automated airports equipped with UAVs are expected to become important equipment for improving quality and reducing costs in many inspection scenarios. This paper focuses on the automated inspection business of UAVs dispatched by automated airports in highway scenarios. On the basis of considering the shape of highway curves, inspection targets, and the energy consumption characteristics of UAVs, planning the flight parameters of UAVs is of great significance for ensuring the effectiveness of the inspection process. This paper first sets the inspection path points for the UAV based on highway curves, and then proposes an efficient heuristic method for the nonlinear non-convex parameter optimization problem, through which the parameters of the UAV’s inspection altitude, hovering altitude, and flight speed are planned. Simulation and analysis show that the proposed method possesses good parameter planning efficiency. By combining several existing trajectory planning methods, e.g., the traversal method, the deep Q-network based method, and the genetic method, it can be concluded that the proposed method in this paper has better overall planning performance including planning efficiency and inspection effectiveness.
Comparative Analysis of TextRank and Latent Semantic Analysis Algorithms for Extractive News Summarization

News summarization is an essential process that condenses news stories or collections of stories while retaining key details and main ideas. It offers readers a concise overview of significant events and essential takeaways from news stories, allowing them to quickly grasp the necessary information without reading the entire article. This paper presents a comparative analysis of two widely used algorithms, TextRank, a graph-based algorithm based on the PageRank algorithm, and Latent Semantic Analysis (LSA), a statistical method that captures latent semantic relationships using singular value decomposition for news summarization. The experiment results obtained using established evaluation metrics such as ROUGE-N and ROUGE-L suggest that the TextRank algorithm performs better than the LSA algorithm for all news categories and consistently demonstrates higher Precision, Recall, and F-Score values. The research aims to contribute to a deeper understanding of the strengths and weaknesses of TextRank and LSA in summarizing news by incorporating five news categories (Business, Politics, Sports, Entertainment, and Technology) and using a significantly large dataset containing 2225 news articles and their summaries. This knowledge will empower researchers and practitioners to make informed decisions when selecting an algorithm for summarization tasks based on their requirements and constraints.
Performance Enhancement of Kernel Extreme Learning Machine Using Whale Optimization Algorithm in Fruit Image Classification

This paper presents an evaluation of the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM), and the Kernel Extreme Learning Machine optimized by the Whale Optimization Algorithm (WOA-KELM) in the context of fruit image classification. Fruits play a crucial role in everyday life, and the application of machine learning for fruit classification can significantly improve efficiency. The ELM, a single hidden layer feedforward neural network, is the foundation for this comparison. The KELM, an evolution of the ELM, incorporates a kernel function to map input data into a high-dimensional feature space, thereby enhancing performance. The development of WOA-KELM involves optimizing the regularization parameter in KELM and the width parameter of the radial basis function through the Whale Optimization Algorithm, further enhancing its capabilities. In classifying twelve types of fruit images, the ELM achieved a classification accuracy of 77%. In contrast, the WOA-KELM, as proposed in this paper, significantly outperforms with a remarkable classification accuracy of 92%.
Technical Analysis of Machine Learning Algorithms in Artificial Intelligence Image Recognition

The role of artificial intelligence in image recognition technology is very important, but there is a problem of inaccurate recognition. OCR recognition technology cannot solve the artificial intelligence problem in image recognition technology, and the recognition accuracy is low. Therefore, this paper proposes a machine learning algorithm for image recognition artificial intelligence analysis. First, computer computing is used to identify images, and indicators are divided according to artificial intelligence requirements to reduce artificial intelligence in the interfering factor. Then, the computer calculates the artificial intelligence on image recognition, forms an artificial intelligence scheme, and performs on the artificial intelligence results Comprehensive analysis. MATLAB simulation shows that under certain evaluation criteria, the accuracy of machine learning algorithms for artificial intelligence image recognition The recognition rate was better than OCR recognition technology.
Offline Signature Forgery Detection using Multi-Layer Perceptron

Numerous things require the participation of all parties, primarily throughout the process of establishing a person's identity, covering everything from medical care to bank account maintenance, aviation services, immigration, and other services. There are several ways to demonstrate one's identity, with employing a signature being the most well-liked one. As a means of identification system, the signature is utilized. By matching the signer's signature, recognition is still done manually. Thus, there is a significant need for a system that can evaluate and detect the signature's properties so that it can be utilized as an alternative to streamline the introduction of people's signatures. This paper discusses signature forgery detection using Multi-layer perceptron. The proposed network has been analyzed using various numbers of hidden layers and also with various classifiers. The proposed method is evaluated and the results show that it significantly outperforms the state-of-the-art methods.
Color and Texture Based Similarity Search to Enable Secure Image Sharing on Social Media Platforms

Image retrieval plays a significant role in various fields, with applications in medicine and healthcare, e-commerce, art and culture, and social media. This study proposes a novel method for image retrieval that utilizes both texture and color information. Color techniques such as Color histogram, moments, layouts, and Color correlogram, whereas texture features such as GLCM, Gabor filters, LBP, and Wavelet Transform, have been tested and compared based on their retrieval time and similarity score. The average of Dot Similarity, Kendall's Tau, Cosine Similarity, Spearman's, and Pearson Correlation scores is used to determine the final similarity score. The tests performed on the Flickr30k dataset show that the suggested strategy can locate the top three feature-matching images. The study's feature fusion highlights how these techniques can improve image retrieval performance. Apart from image retrieval, this paper also uses a technique to embed user IDs with uploaded images to notify users when another user uploads an image that resembles them. The suggested method can increase the precision and effectiveness of image retrieval in various areas, including e-commerce, medicine and healthcare, surveillance and security, education, digital libraries, art and culture, and social media.
Progressive Web Applications, a New Way for Faster Testing of Mobile Application Products

Progressive Web application (PWA) is a rapidly emerging technology that aims to provide an intermediate between native applications and web applications. PWAs provide certain advantages over traditional web applications, making them easier and faster to build compared to native applications. This paper discusses the PWA methodology, its advantages, and applications in detail. It also compares PWA with native and website application developments and presents PWA as an effective method for rapid prototyping. This study uses an app developed for security guards at a university for identification and verification purposes to support the claims and demonstrate the strengths of PWA.
Dual-Arm Dynamic Planning with Considering Arm Reachability Constraint in Task Space

This paper aims to address the issue of planning dual-arm motions in scenarios involving tracking dynamic desired pose and obstacle avoidance. In situations where dual-arm motion requires synchronous behavior, inflexible planning, encountering obstacles, and beyond-the-arm motion capability may lead to task failure. To overcome these issues, we propose a dynamic-system-based method for dual-arm collaborative planning. The method can coordinate the motions of dual arms while keeping synchronous behavior in tracking the moving target and avoiding obstacles. Meanwhile, we incorporate the reachability by modeling the motion boundary to ensure that the arms moving within the reachable space. The experiment successfully verified the above scenarios, showcasing its adaptability in dual-arm planning and its potential applicability in dual-arm applications such as transportation.
Towards an Ontology-Based E-Learning Recommendation System

E-learning recommendation systems enhance learning for students (e-learners) through their provision of tailored recommendations to student queries. Existing e-learning recommendation systems have relied on traditional algorithms, such as collaborative filtering, which are limited when tasked to provide recommendations for new users. This paper contributes to the body of work on personalised recommendation systems by proposing a multi-task ontology based framework capable of supporting multiple personalised recommendations for multiple purposes. These may include personalised learning paths, objects (such as courses or textbooks) and feedback. To generate personalised feedback, the approach uses a rule based system that integrates ontological information to learner profiles in order to provide a recommendation. Additionally, the framework provisions for domain-expert generated feedback to e-learners.
Missing identification of glass insulators based on YOLO v5 algorithm

As one of the three main insulators, glass insulators are widely used in transmission lines. Due to its own characteristics, glass insulators are prone to self explosion during use, which then cause insulator missing and easily lead to power accidents. With the progress of unmanned aerial vehicle inspection and other inspection technologies, reliable identification of missing insulators based on image recognition become very important. This article uses the most advanced YOLO v5 object detection algorithm to identify the missing of glass insulators. The detection rate for insulators is about 97.2%, and the detection rate for missing glass insulators is about 98.6%, with an average mAP@0.5 of 97.9%.
Wavelet transform of vibration signal of CNC machine tool based on python

In this paper,wavelet transform of vibration signal of CNC machine tool is performed based on Python programming language.wavelet transform function in PyWavelets library,the signal is decomposed into sub-signals of different frequencies.The experimental results show that the vibration signal of CNC machine tool has multiple frequency components,and the noise reduction effect is obvious after wavelet transform of the By implementing wavelet analysis method,EMD[1],and EEMD through Python programming,the three feature extraction methods are compared,and it is concluded that wavelet transform can be more convenient for signal processing and analysis,and the analysis method and parameters Therefore,the wavelet analysis method of vibration signal of CNC machine tool based Therefore,the wavelet analysis method of vibration signal of CNC machine tool based on Python has high practicality and scalability,which can provide important support for the optimization design and machining accuracy improvement of machine tools.
UAV image object detection network based on Yolo

This paper proposes an object detection network based on YOLOv7 for UAV(unmanned aerial vehicle) aerial images which have a large field of view, complex backgrounds, small target sizes and uneven position distribution. The proposed network integrates Dyhead (a dynamic target detection head) which is based on attention mechanism into the network, causing the model to focus more on target-rich regions and alleviating noise interference caused by complex backgrounds, thereby improving detection accuracy. Additionally, the network proposes a new loss function which combines CIOU(Complete IOU) and WISE-IOU. The improved loss function balances the IOU and geometric metrics (such as center point distance and aspect ratio) in the position regression loss, increasing the model's tolerance for low-quality data samples and improving its overall generalization capability and detection accuracy. Finally, experiment on the Visdrone UAV image dataset show that the proposed network has a 2.1% and 1.3% increase in mAP@0.5(mean average precision(IOU=0.5)) and mAP@0.5:0.95(mean average precision(IOU=[.5:.95])) evaluation index compared to the base network. Increasement of accuracy are also observed in all ten subdivision categories of the dataset, validating the effectiveness of the proposed network for UAV image object detection tasks.
Research and application based on simulation of ESM model

In this paper, simulation is based on the ESM model, which is traditionally characterized by high nonlinearity and high uncertainty. The complexity of the system poses a great challenge for its overall characterization. Modern simulation technology can perform detailed numerical modeling and simulation calculations to realize the analysis and prediction of the system operation state, providing an effective research tool and decision support for the construction of the ESM model optimization system. In this paper, the simulation application of ESM model is based on matrix equation and dynamical system, and the sensitivity analysis is attempted, which proves that the simulation effect of ESM model is good and has good research value and application significance, in order to provide some reference significance for the research in other fields.
Detection of Disturbances in a Monitoring System on ITS and Usage of Ontologies Approaches: A Critical Review and Challenges in Developing Countries.

The demographic explosion that the world is experiencing today is impacting the demand on road networks. The monitoring of activities on these road networks has evolved thanks to the paradigms provided by intelligent transport system. In developing countries, the lack of sophisticated infrastructures lead to accidents and loss of life. Thus, some work proposes solutions that aim to deploy control points in a way that maximises network coverage while minimising financial expenditures. Despite this, there are still problems of detecting disturbances in this context. The aim of this paper is to do a literature review on the management of disturbances on intelligent transport systems and their use in the context of developing countries. We also review the use of ontologies in disturbance detections. This shows that the perception of disturbances changes from one model to another. No model proposes an approach that guarantees a good detection of disturbances in the context of monitoring road networks in developing countries context. Therefore, taking ontologies into account for this detection could guarantee good results.
Text Summarization: GPT Perspective

The field of artificial intelligence (AI) has witnessed significant advancements in recent years, particularly in the areas of machine learning (ML), deep learning (DL), and transformers. These technologies have revolutionized various industries, including healthcare, finance, and transportation. This study provides an extensive examination of GPT-based models, especially GPT-4, in their ability to effectively condense text. We investigate the architectural elements, training approaches, and multiple strategies used by the model to generate high-quality summaries. Our analysis offers valuable insights into the strengths and possible enhancements for GPT-based models in text summarization tasks.
AI-Powered Conceptual Model for Scrum Framework

Scrum is an incremental, interactive agile framework widely used to develop practical problems; the Scrum team focuses on achieving a common goal each cycle, "Sprint Goal" instead of the traditional sequential technique for delivering products. Although many organizations are adopting Scrum, it is still following the traditional manual approaches. Even though many tools are available in the market to facilitate project management and Scrum master tasks, the support is still limited and lacks the utilization of Artificial Intelligence science. This study focuses on integrating the Scrum framework with Artificial Intelligence to build a predicting model that can assist project management "in particular the Scrum master" to predict the achievement of the Scrum Team per Sprint using Story Points; this prediction is built based on several factors that can affect the burndown charts per Sprint.