Reviewerly/exHarmony huggingface dataset:
An Equilibrium Optimizer-Based Ensemble for Aspect-Level Sentiment Classification

Aspect-level sentiment classification (ALSC) has attracted considerable attention from natural language processing experts and researchers in recent years. ALSC aims to correctly detect the polarity of sentiments of aspects of a product or service that are important to customers. The main focus of research in ALSC is to develop efficient techniques for this task. Various deep learning methods perform well for ALSC but are computationally expensive. To develop a technique that is both accurate and cost-effective, in this work, we propose an ensemble of deep learning methods that have a simple architecture with less computational cost. Further, to find the most relevant methods in the ensemble, we propose a classifier ensemble reduction (CER) technique using the Equilibrium Optimization (EO) algorithm. The experimental results on four benchmark datasets show the effectiveness of our proposed approach. Our proposed EO-based ensemble model outperforms many complex deep learning baselines including its base learners and other ensemble models. We also perform statistical analysis to check for significant improvement in the results.
Semantic Segmentation for Edge Devices—Survey and an End-to-End Readiness on the Target Platform

Semantic segmentation is a method to classify each of the pixels in an image from a given list of predefined classes. This method helps to solve some of the most critical problems in autonomous driving applications. This paper presents the survey centered on semantic segmentation for edge devices, as most of the autonomous driving logic has to be ported on one of the target platforms. In our survey, we preferably explored speedy and embedded system-compliant semantic segmentation approaches. In addition to this, we comprehensively present an end-to-end readiness of our shortlisted segmentation approaches on the target platform as none of the promising network architectures found from the survey is directly portable on the target platform. All modifications done to the networks to make them edge device compatible are comprehensively detailed. The paper also provides a survey on automotive-grade datasets that can be a ready reference for developing semantic segmentation algorithms for autonomous driving applications.
OPTIMALISASI ELEKTRONIK MODUL MATA PELAJARAN SEJARAH KEBUDAYAAN ISLAM DENGAN PLATFORM 3D PAGEFLIP DI MAN 1 SITUBONDO

The increasingly rapid technology makes people have to innovate, demanding that everything must be instant and feel reached through technology, including in the world of education. An interesting presentation in the world of education is demanded to always present interesting things. One of the highlights today is the presentation of teaching for students, one of which is teaching modules for students. Printable modules that are more sophisticated help teachers and students make it easier in all aspects of the lesson, one of which is Islamic cultural history (skiing) lessons. It is known that the purpose of skiing lessons is a complicated lesson from the aspect of memorizing, setting dates and the complexity of historical events, so with the presence of electronic modules using the 3d pageflip platform it makes everything easier in learning will be more fun and the electronic aspects of the module in the platform 3d pageflip considers several components to make it easier for a teacher to make: (1) content feasibility, (2) language that is easy to understand (3) systematic historical events in maple ski (4) graphs that can be understood. This research method uses the SPSS application by giving a validity value against more valid 3D pageflip application.
High-Speed Wavelet Image Processing Using the Winograd Method with Downsampling

Wavelets are actively used to solve a wide range of image processing problems in various fields of science and technology. Modern image processing systems cannot keep up with the rapid growth in digital visual information. Various approaches are used to reduce the computational complexity and increase computational speeds. The Winograd method (WM) is one of the most promising. However, this method is used to obtain sequential values. Its use for wavelet image processing requires expanding the calculation methodology to cases of downsampling. This paper proposes a new approach to reduce the computational complexity of wavelet image processing based on the WM with decimation. Calculations have been carried out and formulas have been derived that implement digital filtering using the WM with downsampling. The derived formulas can be used for 1D filtering with an arbitrary downsampling stride. Hardware modeling of wavelet image filtering on an FPGA showed that the WM reduces the computational time by up to 66%, with increases in the hardware costs and power consumption of 95% and 344%, respectively, compared to the direct method. A promising direction for further research is the implementation of the developed approach on ASIC and the use of modular computing for more efficient parallelization of calculations and an even greater increase in the device speed.
A new efficient adaptive rood pattern search motion estimation algorithm

Motion estimation plays a crucial role in video coding; the Adaptive Rood Pattern Search (ARPS) algorithm is a well known fast motion estimation algorithm. However, ARPS has certain limitations, such as the lack of an accurate starting motion vector, a fixed Zero Motion Prejudgment (ZMP) threshold unsuitable for fast motion video sequences, and the repetitive use of a Unit Rood Pattern (URP) resulting in increased computational complexity. To address these issues, this paper proposes a novel algorithm called Efficient Adaptive Rood Pattern Search (EARPS). EARPS overcomes these limitations by employing the Full Search algorithm to obtain optimal motion vectors for the first column in each frame, adopting a dynamic ZMP threshold that adapts to varying motion speeds in video sequences and utilizing URP only once to reduce computational overhead. The performance of the new proposed EARPS algorithm is evaluated and compared with that of ARPS algorithm using various video sequences with different motion speeds. The number of searching points and Peak Signal-to-Noise Ratio (PSNR) are used to quantify computing complexity and accuracy. The experimental findings show that EARPS surpasses ARPS in terms of computing complexity while retaining a decent degree of PSNR accuracy. The proposed EARPS motion estimation algorithm main contribution is to achieve high speed with reasonable accuracy, regardless of the type of motion speed in the video frames. The EARPS algorithm offers a substantial advancement over ARPS, delivering a more efficient motion estimation method with broader applicability in video processing. It represents a significant contribution to the development of effective motion estimation algorithms.
Data-intelligence Empowering Libraries: Research on Discipline-Specific Precision Services

The discipline-specific precision services provided by university libraries face multiple challenges, such as diverse users and resources, as well as dynamically personalized service demands. The continuously enriched collection resources and dynamically personalized user demands make resource and user profiling crucial for implementing discipline-specific precision services. To address the issues in discipline-specific precision services, we utilize data-intelligence to empower libraries, integrating data-intelligence into resource providers, service providers, and the matching of the two to enhance discipline-specific precision service capabilities. The research includes: investigating and analyzing the current status and demands of discipline-specific precision services, constructing user and literature resource profiles based on multi-source data related to discipline services, and methods for accurately identifying discipline service demands and matching services. The research outcomes show that have important theoretical and practical significance for enhancing discipline construction in universities and the discipline service capabilities of university libraries.
Research and Implementation of Multi Fusion Data Model Construction Technology for Distribution Network Digital Twins

The core of digital twin is the digital reconstruction of physical space, that is the physical space perception and processed data are applied to the virtual space to achieve real-time mapping of physical space through full integration of data. With the rapid development of digital twin technology in the power field, in order to achieve the purpose of real-time data fusion of physical space and virtual space in the digital twin of distribution network, the virtual and real data fusion processing technology for digital twin has become the focus of research in the power field. In this paper, firstly, the characteristics and ontology technology of power digital twin virtual and real data are studied, and an ontology based virtual and real data fusion architecture is proposed. Then, a semantic feature extraction method is proposed for the structured and unstructured virtual and real data of power digital twin, and the extracted semantic features are formatted, described and stored. Then the overall architecture of the distribution network digital twin virtual real data fusion system is designed, the technical route of the system is analyzed and discussed, and the virtual real data fusion processing is completed using the similarity algorithm and fusion rules based on the elements between ontology. Finally, the functional modules of the distribution network digital twin virtual real data fusion system are divided and introduced, and the digital twin virtual real data fusion of the fan hub casting process is verified as an example.
Knowledge Graph Based Fault Diagnosis of Aero Engines Fuzzy Bayesian Diagnosis

Fault knowledge mapping can quickly find one or some fault causes related to the fault phenomenon and lock the scope of investigation through the method of knowledge quiz or sub-map matching. For the mechanics, when the query results in multiple fault causes, each fault cause has the same probability, and it is impossible to formulate targeted maintenance measures. Therefore, this paper introduces the fuzzy Bayesian inference algorithm to take advantage of its causal inference logic in order to realize the optimization of aero-engine fault diagnosis strategy.
Dual Learning for Conversational Emotion Recognition and Emotional Response Generation

Emotion recognition in conversation (ERC) and emotional response generation (ERG) are two important NLP tasks. ERC aims to detect the utterance-level emotion from a dialogue, while ERG focuses on expressing a desired emotion. Essentially, ERC is a classification task, with its input and output domains being the utterance text and emotion labels, respectively. On the other hand, ERG is a generation task with its input and output domains being the opposite. These two tasks are highly related, but surprisingly, they are addressed independently without making use of their duality in prior works. Therefore, in this paper, we propose to solve these two tasks in a dual learning framework. Our contributions are fourfold: (1) We propose a dual learning framework for ERC and ERG. (2) Within the proposed framework, two models can be trained jointly, so that the duality between them can be utilised. (3) Instead of a symmetric framework that deals with two tasks of the same data domain, we propose a dual learning framework that performs on a pair of asymmetric input and output spaces, i.e., the natural language space and the emotion labels. (4) Experiments are conducted on benchmark datasets to demonstrate the effectiveness of our framework.
AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining

With the growing concerns on data security and user privacy, a decentralized mechanism is implemented for federated data mining (FDM), which can bridge data silos and collaborate diverse devices in ubiquitous IoT (Internet of Things) systems and services to extract global and shareable knowledge, i.e., encoded in deep neural networks (DDNs). Moreover, compared with FDM in synchronous mode, asynchronous FDM (AFDM) is more suitable to accommodate devices with diversified computing resources and distinguishable working statuses. However, as AFDM is still in its infancy, how to harness heterogeneous resources and biased knowledge of learning participants within the asynchronous context remains to be addressed. Such that, this paper proposes an adaptive and integrated mechanism, named AiFed, in which, a layer-wise optimization of AFDM is implemented based on the integration of two dedicated strategies, i.e., an adaptive local model uploading strategy (ALMU), and an adaptive global model aggregation strategy (AGMA). As shown by the evaluation results, AiFed can outperform five state-of-the-art methods to reduce communication costs by about 61.76% and 56.88%, improve learning accuracy by about 1.66% and 3.05%, and accelerate learning speed by about 22.16% and 37.81% under IID (independent and identically distributed) and Non-IID settings of four standard datasets, respectively.
Efficient Multiview Representation Learning with Correntropy and Anchor Graph

Graph-based multiview clustering methods have attracted much attention because of their ability to mine nonlinear structural information among instances. Although they perform well in many scenarios, they consume a lot of computational resources when dealing with large-scale multiview scenarios. To address this issue, we present a new insight into the anchor graph mechanism and propose a novel Nonnegative Anchor Graph Reconstruction (NAGR) model. NAGR introduces the sparse similarity graph into the symmetric matrix factorization and gets the nonnegative representation that retains the graph structural information. Thereafter, we develop a novel Efficient Multiview nonnegative Representation learning framework with Correntropy and Anchor graph (EMR-CA), which integrates multiview anchor graph reconstruction and consensus nonnegative representation learning into a unified framework. EMR-CA uses multiview anchor graph reconstruction to learn consensus nonnegative representation, where correntropy rather than F-norm is used as the approximation measurement criterion. Specifically, normalized anchor graphs of different views are decomposed into a consensus nonnegative representation and multiple view-specific representations, where the consensus representation retains the neighbor graph information between multiview instances and representative anchors on different views. Finally, the effectiveness of the proposed EMR-CA framework is verified by theoretical analysis and experimental results on large-scale realistic multiview scenarios.
Coupled Non-negative Matrix Factorization with Deep Part-Based Feature Learning for Low-Resolution Image Recognition

The existing low-resolution (LR) image recognition methods based on coupled relationship learning of high- and low-resolution images usually take similar feature extraction steps for both high- and low-resolution images. However, the high- and low-resolution features thus obtained may differ in terms of information content and information scale. This leads to a robust coupled relationship between the obtained high- and low-resolution images feature is difficult to be learned. Therefore, a coupled non-negative matrix factorization with a deep part-based feature learning method is proposed for the LR image recognition task. This method is divided into two main steps. Firstly, the depth non-negative matrix factorization method by graph convolution is used to obtain a robust depth underlying basis images of HR images for feature representation. Then the coupled relationship between the depth basis images feature of the HR images and the LR basis images feature is learned. Learning More Robust LR Basis images features using depth underlying features of HR images to assist LR images. Improve the expression of LR basis images of features. Experimental results show that the method can provide higher recognition performance. It outperforms existing image recognition methods in image recognition.
Low Resolution Face Image Recognition Based on Consistent Discriminant Correlation Analysis with Weight Correction

Consistent Discriminant Correlation Analysis (CDCA) is an effective algorithm for dual-view learning. However, the importance of information from different samples and features may vary. In CDCA, the sample features from both views are equally weighted during training, which may not fully utilize the information contained in certain sample features. This can negatively impact the decision-making and predictive capabilities of the model. Additionally, CDCA is only applicable to dual-view data and cannot effectively utilize multi-view data. To address these issues, this paper extends CDCA to multi-view scenarios and proposes Consistent Discriminant Correlation Analysis with Weight Correction (CDCA-WC). The feature weighting approach used in this paper involves calculating the average weight of each feature using the ReliefF algorithm. The sample weighting method employed is based on a weighted loss function, where a larger weight indicates a stronger classification ability for the corresponding feature or sample. During the training process, CDCA-WC is used to obtain the feature subspace mappings for the training samples from multiple views. The data’s feature and sample dimensions are then adjusted based on their respective weights, and the adjusted data is used for a second round of training. During testing, the similarity between samples is measured using a KNN classifier. Experimental results on three publicly available face recognition datasets demonstrate that CDCA-WC can effectively improve the accuracy of low-resolution face image recognition.
Delineation of Prospecting Prospect Area Based on Maximum Entropy Model

Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was established by using the exploration data of the deposit and related regions in this area, so as to determine the prospecting prospect area in the study area. In this paper, the Jacknife analysis module of maximum entropy model is used to quantitatively rank the importance of 39 geochemical element variables, and finally obtain the prospecting prospect map of the study area. The research results show that the Dapingzhang mining area has the potential to find hidden ore in the deep and surrounding areas, and the northern and southern ends and western sides of the rock ore control structural belt in the eastern region of the mining area have good prospecting prospects. The research results provide an important basis for the deployment of follow-up exploration work in the study area, and the maximum entropy model has a good application effect in mineral resources exploration.
Time for Securonomics

IPPR Progressive ReviewVolume 30, Issue 3 p. 150-154 Original Article Time for Securonomics Rachel Reeves, Corresponding Author Rachel Reeves [email protected] AUTHOR EMAIL ADDRESS FOR WALS: [email protected] but please also email [email protected]Search for more papers by this author Rachel Reeves, Corresponding Author Rachel Reeves [email protected] AUTHOR EMAIL ADDRESS FOR WALS: [email protected] but please also email [email protected]Search for more papers by this author First published: 15 November 2023 https://doi.org/10.1111/newe.12365Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume30, Issue3Winter 2023Pages 150-154 RelatedInformation
List of academic search engines that use Large Language models for generative answers and some factors to consider when using

List of academic search engines that use Large Language models for generative answers (for the latest version - see this page) This is a non-comprehensive list of academic search engines that use generative AI (almost always Large language models) to generate direct answers on top of list of relevant results, typically using Retrieval Augmented Generation (RAG) Techniques.
Optimized voxel transformer for 3D detection with spatial-semantic feature aggregation

In this paper, we propose a novel 3D object detection model that leverages the advantages of the Voxel Transformer (VoTr) and the Confident IoU-Aware Single-Stage Object Detector (CIA-SSD) to address the challenges of detecting objects in 3D point clouds. Our model adopts the VoTr as its backbone, which enables long-range interactions between voxels via a self-attention mechanism. This overcomes the limitations of conventional voxel-based 3D detectors, which struggle to capture sufficient contextual information due to their restricted receptive fields. Our model also integrates the sparse voxel module and the submanifold voxel module, which efficiently process empty and non-empty voxel positions, effectively handling the natural sparsity and abundance of non-empty voxels. Moreover, inspired by the CIA-SSD design, our model incorporates the Spatial-Semantic Feature Aggregation (SSFA) module, which allows for the adaptive fusion of high-level abstract semantic features and low-level spatial features, ensuring accurate predictions of bounding boxes and classification confidence. Furthermore, based on the IoU-aware confidence rectification module, which refines the alignment between confidence scores and localization accuracy, we devise an Optimized RPN (Region Proposal Network) Detection Head module as a dense head to further predict the IoU loss and improve the accuracy. In this paper, we combine two state-of-the-art techniques to provide a precise and efficient solution for 3D object detection in point clouds. We evaluate our model on the KITTI dataset1 and achieve 76.56 % accuracy in terms of AP3D (%) Hard.
AI-enabled routine H&amp;E image based prognostic marker for early-stage luminal breast cancer

Abstract Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin &amp; Eosin (H&amp;E) image-based prognostic marker for early-stage luminal/Her2-negative BR e A st C anc E r that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival ( p = 0.001, C-index: 0.73) and BC specific survival ( p &lt; 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.