Reviewerly/exHarmony huggingface dataset:
Criticality-based quantum metrology in the presence of decoherence

Because quantum critical systems are very sensitive to the variation of parameters around the quantum phase transition (QPT), quantum criticality has been presented as an efficient resource for metrology. In this paper, we address the issue whether the divergent feature of the inverted variance is realizable in the presence of noise when approaching the QPT. Taking the quantum Rabi model (QRM) as an example, we obtain the analytical result for the inverted variance with single-photon relaxation. We show that the inverted variance may be convergent in time due to the noise. Since the precision of the metrology is very sensitive to the noise, as a remedy, we propose squeezing the initial state to improve the precision under decoherence. In addition, we also investigate the criticality-based metrology under the influence of the two-photon relaxation. Strikingly, although the maximum inverted variance still manifests a power-law dependence on the energy gap, the exponent is positive and depends on the dimensionless coupling strength. This observation implies that the criticality may not enhance but weaken the precision in the presence of two-photon relaxation, due to the non-linearity introduced by the two-photon relaxation.
Semi-supervised learning-based satellite remote sensing object detection method for power transmission towers

It is well known that as the power grid becomes more and more complex, the traditional manual survey of transmission towers is inefficient and cannot meet the requirements of safe and stable operation. Although the development of the satellite remote sensing technology provides a new prospect for the efficient and stable survey of transmission towers, there are still many problems that need to be solved. Due to the harsh climate and limitations of the imaging equipment, some of the transmission tower objects in the remote sensing images are blurred, which makes it extremely difficult to generate datasets and to achieve high precision transmission tower object detection. To further develop the detection precision of transmission towers, the image enhancement algorithm based on the dark channel priori is firstly applied for remote sensing images, which can boost the image interpretability. Then, considering that there are still some transmission towers in the enhanced images that cannot be manually labeled, a pseudo-label-based semi-supervised learning method is employed to maximize the use of the existing data. Based on this high-quality dataset, a satellite remote sensing object detection model for transmission towers is constructed using mobile inverted bottleneck convolution and deformable convolution. Finally, the ablation and comparative experiments are conducted according to the satellite remote sensing image dataset of a certain area in China. The experimental results indicate that both the image enhancement and the semi-supervised learning methods can improve the detection precision, and compared with the existing mainstream model, the proposed method performs better.
Optimized Echo State Network based on PSO and Gradient Descent for Choatic Time Series Prediction

Echo State Network (ESN), as a paradigm of Reservoir Computing (RC), refers to a well-known Recurrent Neural Network (RNN). Its randomly generated reservoir represents the main reason for its ability of rapid learning. Nevertheless, designing a reservoir for a specific role constitutes a difficult task. To resolve the challenge of the reservoir structure design, in this paper, a new combination of two optimization methods, Particle Swarm Optimization (PSO) and Stochastic Gradient Descent (SGD), have been proposed to reach a higher performance. The resulted model was tested using Mackey Glass and NARMA 10 benchmarks. The experimentations proved that the suggested PSO-SGD-ESN model performs well in time series prediction tasks and outperforms the original one.
Improved Shift Graph Convolutional Network for Action Recognition With Skeleton

Shift graph convolutional network (Shift-GCN) achieves remarkable performance for skeleton based action recognition with lower computational complexity than other GCN based methods. However, the current Shift-GCN, with one spatial shift, a static mask and a local temporal convolution, cannot fully explore the spatial-temporal features among skeleton joints of different frames. In order to address these problems, an improved shift graph convolutional network (Ishift-GCN) is proposed in this letter. The Ishift-GCN consists of two parts including a bidirectional spatial shift graph convolution with a dynamic mask, and a multi-scale temporal shift graph convolution. The bidirectional spatial shift graph convolution exploits more spatial information among joints, and the dynamic mask with stronger generalization ability can learn different correlations among features of different joints for different actions. The multi-scale temporal shift graph convolution captures more temporal information by complementing the shifted features with multi-scale convolution. Furthermore, knowledge distillation is used to reduce computational complexity. Compared with Shift-GCN, the proposed Ishift-GCN achieves better results with less computation complexity on two widely used benchmarks, namely the NTU-RGB+D and UAV-Human dataset.
Multi-Modal Fusion Technology Based on Vehicle Information: A Survey

Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' attention in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this article, we introduce the existing application fields of vehicle information and the research progress of related methods, as well as the multi-modal fusion methods based on information. We also introduced the relevant information of the vehicle information dataset in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle information.
Review of Survey Research in Fuzzy Approach for Text Mining

Text mining has been common research field, with the emergence of Web 2.0 and the development of social software, the amount of text generated every day has increased dramatically.The texts contain a lot of valuable information, how to analysis for the information from text is very important.Therefore, many research have explored related methods and various fields of text mining, such as sentiment analysis, text clustering, text summarization, etc., However, unlike other numerical data that can be directly calculated in terms of character performance, the calculation must be performed after vector conversion, and the words may have polysemy, which provides more challenges for natural language processing.Due to the above challenges, various techniques are used for data preprocessing before analysis.In addition to the common statistical and discrete methods for text data, methods based on fuzzy logic provide another option for effective natural language analysis, Therefore, in recent years, more and more studies have added Fuzzy logic to additionally capture the context semantics of individual words to help more accurate natural language processing.This survey research discusses multiple text mining methods, The survey paper discusses multiple text mining methods, subfields and application fields, covering the literature published between 2010 and 2022, It is organized to the subtasks to be performed, the methods and natural language processing techniques used, and the application scenarios.At the end of this study, the research has provided the key point discussion and relevant suggestions for text mining combined with fuzzy logic.
Exploring factors influencing students’ intention to use intelligent personal assistants for learning

ABSTRACTIntelligent Personal Assistants (IPAs) are becoming more prevalent in daily and educational contexts, increasing the possibility of using them as learning partners that can provide more personalized and learner-centric learning opportunities. However, research has primarily focused on educational advantages that IPAs may provide, overlooking investigation on students' acceptance of IPAs. To address this gap, this study empirically explored factors influencing students' intention to use IPAs for learning. A conceptual research model was designed based on the technology acceptance model. Data were collected from 1044 South Korean students, who were experienced in using IPAs. Results were analyzed using structural equation modeling. The findings indicate that the proposed model sufficiently explains students' IPAs acceptance for their learning. In addition, students' computer self-efficacy and self-management of learning ability were prominent factors influencing their intention. Moreover, their trust toward IPA and feeling of social presence on IPAs were found to be significant factors to be considered as well. Based on these findings, future recommendations for fostering students' active use of IPAs for learning are discussed. Additionally, both theoretical and practical implications are highlighted.KEYWORDS: Intelligent personal assistantstechnology acceptance modelself-management of learningcomputer self-efficacysocial presenceperceived trust Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Research Foundation (NRF), Korea, under project BK21 FOUR.Notes on contributorsSeongyune ChoiSeongyune Choi is a PhD candidate in the Department of Computer Science and Engineering at Korea University in Seoul, South Korea. His research interests include computer science education, educational technologies, and artificial intelligence in education.Yeonju JangYeonju Jang is a PhD candidate in the Department of Computer Science and Engineering at Korea University in Seoul, South Korea. Her research interests include machine learning and deep learning applications in the field of education to better understand students and assist their learning.Hyeoncheol KimHyeonchel Kim is a professor in the Department of Computer Science and Engineering at Korea University in Seoul, South Korea. He received his PhD in CISE from the University of Florida in 1998. His research interests include machine learning algorithms and AI education.
An Efficient framework for Metadata Extraction over Scholarly Documents using Ensemble CNN and BiLSTM Technique

The conventional text documents have made it possible to efficiently retrieve large amounts of text data with the development of various search engines. However, these traditional search approaches frequently have lower accuracy in retrieval, particularly when documents have certain characteristics that call for more in-depth semantic extraction. A search engine for algorithms called Algorithm Seer has recently been developed. The normal search engine collects the deep textual metadata and pseudo-codes from research papers. However, such a system is unable to accommodate user searches that attempt to identify algorithm-specific information, such as the datasets on which algorithms operate their effectiveness, runtime complication, etc. A number of improvements to the previously suggested algorithm search engine are given in this study. We provide various ways to identify automatically and extract pseudo-codes and phrases which transmit metadata utilizing various machine learning methods. Around the 89,000 text lines are used for conducting the experiments; we provided new properties to extract algorithmic pseudo-codes. These characteristics include feature groups with a focus on content, font style, and structure. Our suggested pseudo-code extraction method outperforms current strategies by 28% and obtains a 94.23% Classification Accuracy. Additionally, we suggest a technique for extracting phrases linked to algorithms utilizing deep neural networks, which achieves an 82% of accuracy compared to recent Rule–based provides 23.5% and support vector machine provides 21.5%.
Deepfake Video Detection System Using Deep Neural Networks

A deepfake is a type of synthetic media that uses artificial intelligence and machine learning techniques to manipulate or superimpose images, videos, or audio onto existing footage in a way that appears authentic and realistic, often with the intent to deceive or mislead viewers. There are several approaches to using neural networks for deep fake detection. One approach is to use a convolutional neural network (CNN) [1] to analyze the visual artifacts in the image or video. The CNN can detect inconsistencies or anomalies in the image or video that are indicative of manipulation, such as differences in lighting or blurring at the edges of the image. ResN et-50 has been used in deepfake detection by training the network on a large dataset of real and fake videos In this paper, Resnet50 and LSTM [13] are combined to make a hybrid architecture are used in deep fake video detection as a web framework using python. Combining ResNet50 and LSTM can help to leverage the strengths of both architectures and improve the accuracy of deep fake video detection, especially for videos that involve both image-based and sequential data. A comparative analysis of different models were assessed using various datasets such as Celeb-DF, Face Forensic++ datasets.
Scale-Context Perceptive Network for Crowd Counting and Localization in Smart City System

The task of crowd counting and localization is to predict the count and position of people in a crowd, which is a practical and essential sub-task in crowd analysis and smart city systems. However, the inherent problems of scale variation and background disturbance restrain their performance. While recent researches focus on studying counting and localization independently, a few works are capable of executing both tasks simultaneously. To this end, we propose a Scale-Context Perceptive Network (SCPNet) to jointly tackle the crowd counting and localization tasks in a unified framework. Specifically, a scale perceptive (SP) module with a local-global branch schema is designed to capture multiscale information. Meanwhile, a context perceptive (CP) module, by the channel-spatial self-attention mechanism, is derived to suppress the background disturbance. Furthermore, a novel hierarchical scale loss function that combines the Euclidean loss function and structural similarity loss function is designed to prompt the proposed model to fulfill the counting and localization simultaneously. Extensive experiments on challenging crowd datasets prove the superiority of the proposed SCPNet compared with the state-of-the-art competitors in both objective and subjective evaluations.
Automatic image captioning combining natural language processing and deep neural networks

An image contains a lot of information that humans can detect in a very short time. Image captioning aims to detect this information by describing the image content through image and text processing techniques. One of the peculiarities of the proposed approach is the combination of multiple networks to catch as many distinct features as possible from a semantic point of view. In this work, our goal is to prove that a combination strategy of existing methods can efficiently improve the performance in the object detection tasks concerning the performance achieved by each tested individually. This approach involves using different deep neural networks that perform two levels of hierarchical object detection in an image. The results are combined and used by a captioning module that generates image captions through natural language processing techniques. Several experimental results are reported and discussed to show the effectiveness of our framework. The combination strategy has also improved, showing a gain in precision over single models.
Verification of Education Credentials on European Blockchain Services Infrastructure (EBSI): Action Research in a Cross-Border Use Case between Belgium and Italy

The European Blockchain Services Infrastructure (EBSI) is a major policy initiative of the EU and European Blockchain Partnership, to leverage blockchain to create cross-border services for public administrations, businesses, citizens, and their ecosystems, to verify information and increase trust in services. The EBSI aims to provide a secure and interoperable system infrastructure, utilizing new digital technologies such as digital wallets, verifiable credentials, and decentralized identifiers. These technologies are expected to change the way government services are organized between citizens and service providers. This article presents empirical findings from a cross-border pilot that was the first case to utilize this new blockchain infrastructure, and which tested the institutional, technical, and user-specific requirements for wider adoption. The pilot tested and assessed the verification of education credentials through the EBSI blockchain in a cross-border setting between a Belgian and an Italian university. The research was based on action research with the ICT units of the participating universities, wallet solution providers, the Belgian government, and EBSI officials. The findings highlight the following as key challenges for the wider adoption of the EBSI and verification credential use case: (1) onboarding of the EBSI ecosystem governance, (2) issuance of EBSI-compliant digital wallets and data schemes for transcript validation, (3) interoperability issues concerning digital identity systems.
A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition

Human activity recognition (HAR) is crucial to infer the activities of human beings, and to provide support in various aspects such as monitoring, alerting, and security. Distinct activities may possess similar movements that need to be further distinguished using contextual information. In this paper, we extract features for context-aware HAR using a convolutional neural network (CNN). Instead of a traditional CNN, a combined 3D-CNN, 2D-CNN, and 1D-CNN was designed to enhance the effectiveness of the feature extraction. Regarding the classification model, a weighted twin support vector machine (WTSVM) was used, which had advantages in reducing the computational cost in a high-dimensional environment compared to a traditional support vector machine. A performance evaluation showed that the proposed algorithm achieves an average training accuracy of 98.3% using 5-fold cross-validation. Ablation studies analyzed the contributions of the individual components of the 3D-CNN, the 2D-CNN, the 1D-CNN, the weighted samples of the SVM, and the twin strategy of solving two hyperplanes. The corresponding improvements in the average training accuracy of these five components were 6.27%, 4.13%, 2.40%, 2.29%, and 3.26%, respectively.
SRFA-GRL: Predicting group influence in social networks with graph representation learning

Group influence evaluation is the fundamental research in social network analysis, whose main task is evaluating the influence of the group consisting of arbitrary nodes in the social network. Many methods have been proposed to measure group influence, such as centrality-based, Monte Carlo, and path-based methods. Graph Representation Learning (GRL) has profound success in social network node-level, edge-level, and graph-level tasks. GRL integrates nodes, edges, and other information in the network structure to calculate the nodes' embeddings jointly and provide embedding features with richer information than traditional methods. Group influence is related to many factors; existing methods only focus on a single aspect but ignore the various properties. This paper proposes a Subgraph Reconstruction Feature Aggregation Graph Representation Learning based (SRFA-GRL) framework to evaluate the group influence. In the SRFA-GRL framework, a subgraph reconstruction method is proposed to capture the node distribution of the group, and a vector similarity method is proposed to compute the relative distance between the maximal connected subgraph and group embeddings. Vast experiments are conducted on eight real social networks to analyze the effectiveness of the SRFA-GRL model, and the experimental results show that the SRFA-GRL framework outperforms baseline methods.
Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.
Feature fusion via multi-target learning for ancient artwork captioning

Image captioning has made consistent progress due to the development of computer vision and natural language processing techniques. Current research on image captioning commonly tends to the visual caption of natural images. However, these attempts are not applicable to ancient artwork captioning with different appearance attributes and complex cultural metaphors. In this work, we propose a Multi-target Learning Framework, called MLF, for generating captions for ancient artworks with ceramics as the case study. Our MLF contains three novel data-driven modules, including RTE, MTE, and MFD. To be specific, for a given image, the Regular Target Encoder (RTE) is first used to encode its regular targets related to color, textual, profile, and craftsmanship features. Second, a Metaphorical Target Encoder (MTE) is applied to encode its metaphorical targets related to cultural semantic features. Finally, a Multimodal Fused Decoder (MFD) is utilized to fuse the multimodal feature vectors from RTE and MTE separately, then decode them and generate detailed captions containing both regular and metaphorical information with the guidance by a word distribution map. Both quantitative and qualitative evaluation results on our constructed dataset demonstrate the advantages of our work.