Reviewerly/exHarmony huggingface dataset:
VDCL: A supervised text classification method based on virtual adversarial and contrast learning

In recent years, Dual Contrastive Learning (Du-al CL) has achieved better results in the field of text classification by making full use of label and inter-sample information in a supervised environment. But how to improve the generalization performance and robustness of neural language models is still a question that researchers have been exploring. In this paper, we propose a framework for introducing virtual adversarial training into Dual Contrastive Learning, The framework first pre-trains a large neuro-linguistic model and then fine-tunes the model using Dual Contrastive Learning to learn both the features of the input samples and the parameters of the classifier. In summary, this means that virtual adversarial training is used to improve the robustness and generalization performance of the model, and then Dual Contrastive Learning is used to learn better feature representations using supervised signals between training samples. Our experiments on three benchmark text classification datasets demonstrate the improved feature extraction and classification accuracy of VDCL and confirm the capability of VDCL in the area of text classification.
Improving Aspect Sentiment Triplet Extraction with Perturbed Masking and Edge-Enhanced Sentiment Graph Attention Network

Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from texts. Previous studies attempted to use an unsupervised perturbed masking technique to derive syntax trees from pre-trained language models (PLMs) to extract aspect sentiment simply. However, existing methods neglect further exploration of the technique for more complex tasks like ASTE. In this paper, we propose a novel Edge-enhanced Sentiment Graph Attention Network model (ES-GAT), which transforms the impact matrix generated by the technique into a new linguistic feature, and uses a novel effective fusion strategy to incorporate multiple features, which can better capture the implicit connections among sentiment elements. In the graph attention module, we simultaneously consider edge and node attention weight calculations and updates to solve the edge-sensitive end to end ASTE task. Experiments show that our model outperforms the strong baselines. The F1 scores are improved by 2.52% and 1.56% on average on the two versions of the benchmark datasets. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code and datasets are available at https://github.com/SupritYoung/ESGAT
Transfer Learning with Graph Attention Networks for Team Recommendation

In order to complete a common goal, team recommendation problems identify an efficient group of experts who can collectively satisfy a set of required skills. A significant number of studies address this problem through graph-based approaches. Recently, researchers have started to see this problem as a social information retrieval and examine it through neural architectures that recommend the team of experts by learning a relationship between the skills and experts space. However, this learning process faces several challenges including (1) being unable to handle the modification of a network if the training process is over, (2) the time complexity of the learning process being high and proportional to the size of the network. In this paper, we propose a new architecture, LANT, which comprises transfer learning and neural team recommendation, to address these challenges based on graph neural networks and variational inference. Since the transfer learning of team recommendation is an unsupervised task, therefore, to learn node embedding in a self-supervised manner, we use Deep Graph Infomax with Graph Attention Networks as an encoder. We empirically demonstrate how LANT overcomes the challenges in the existing approaches and compare them against the state-of-the-art approaches in terms of effectiveness using the DBLP dataset.
Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust

A fundamental question in natural language processing is - what kind of language structure and semantics is the language model capturing? Graph formats such as knowledge graphs are easy to evaluate as they explicitly express language semantics and structure. This study evaluates the semantics encoded in the self-attention transformers by leveraging explicit knowledge graph structures. We propose novel metrics to measure the reconstruction error when providing graph path sequences from a knowledge graph and trying to reproduce/reconstruct the same from the outputs of the self-attention transformer models. The opacity of language models has an immense bearing on societal issues of trust and explainable decision outcomes. Our findings suggest that language models are models of stochastic control processes for plausible language pattern generation. However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs. This has significant application-level user trust implications as stochastic patterns without a strong sense of meaning cannot be trusted in high-stakes applications.
Uncovering Water Research with Natural Language Processing

In order to address current water challenges, scientific research on water-related issues is crucial. However, traditional techniques for selecting research topics, such as literature reviews and expert opinions, can be time-consuming and may not provide a comprehensive overview of available information. We propose using Natural Language Processing (NLP) techniques in this study to extract, align, and compare water research topics from different corpora. We apply these techniques to the research paper abstracts from the New Mexico Water Resources Research Institute (NMWRRI) and the U.S. Geological Survey (USGS) to assess these institutions’ current research interests and identify potential new research directions. We use a Latent Dirichlet Allocation (LDA) model for topic extraction and a Word2Vec model for topic alignment. This study highlights the benefits of using NLP techniques to analyze trends and identify novel research directions in water studies.
S-Feature Pyramid Network and Attention Module For Small Object Detection

Because of the low resolution and limited information of small objects, and the computing resources are limited in practical applications, small object detection is still challenging. In order to improve the accuracy of small object detection, we propose a new method. It’s included a shallow feature pyramid network with an information extraction block at the shallow features and fused multi-scale semantic information. Further, context information with attention mechanism is adopted to make object detection focus on the significant area. We are one of the top five teams in the Drone-vs-Bird Detection Grand Challenge. The detection ability of our method for small objects is much higher than classical one-stage and two-stage detectors. For limited computer resources, 300×300 inputs are used and the detection speed of 45 fps is reached by the proposed method, which can realize real-time object detection.
Non-Standard Echo State Networks for Video Door State Monitoring

In recent years, Echo State Networks (ESNs), a special type of Recurrent Neural Networks (RNNs), have become increasingly established in the Machine Learning (ML) community due to their relatively simple initialization and training methods. Traditionally, the input and recurrent weights are generated randomly, with only the output weights being trained, typically using linear regression. However, recent publications have proposed alternative ways to initialize the weight matrices, e.g., by using more deterministic methods or data-driven approaches. This is the first work comparing different simple reservoir structures and an ESN with pre-trained input weights for the task of monitoring the state of a door using a surveillance camera in real-time. The results show that deterministic ESN structures perform better than the randomly initialized baseline, achieving a frame error rate of 2.62% vs. 2.93%.
Human Activity Dataset of Top Frequent Elderly Emergencies for Monitoring Applications using Kinect

Gathering a dataset is essential in machine learning to develop new approaches and test their effectiveness. Despite their importance, it is difficult to find available datasets that meet researcher requirements, especially for the healthcare domain. This paper presents a new visual dataset of the top frequent elderly emergencies (heart attack, stroke, and Chronic Obstructive Pulmonary Disease (COPD) exacerbation). It is designed for the evaluation of different computational computer vision monitoring applications and human activity analysis. It can also be used for abnormal activity detection. This dataset covers the most frequent symptoms of each emergency. Each symptom is presented by different scenarios validated by a medical emergency expert. This dataset is recorded using the Kinect camera. Its samples are performed only in-depth and skeleton data to preserve the privacy of participants. Also, it is evaluated using methods based on Kinect skeleton data.
Fusing anomaly detection with false positive mitigation methodology for predictive maintenance under multivariate time series

Anomaly detection aims to identify observations that differ significantly from the majority of the data. Time series, which are data with a temporal component, is often used for anomaly detection. Identifying anomalies is not perfect and may produce many false positives, which labels standard data as anomalous. In this context, false positive mitigation is the task of reducing the number of false positives tagged by the anomaly detector, and thus both problems are closely linked. Moreover, current techniques for false positive mitigation are ad-hoc solutions for specific data sets. In this paper, we propose a novel two-stage methodology for Multivariate Anomaly Detection for Time Series and False Positive Mitigation, namely FADFPM methodology, which creates the fusion of two learning models. The first stage is a multivariate anomaly detection stage. The second stage consists of training a new classifier on the false and true positives from the anomaly detector, which refines the observations labeled as anomalous by the anomaly detector to obtain more accurate and higher-quality results. Experiments using two benchmark data sets, as well as a real-world case study have shown the performance and validity of the proposal.
Efficient fault-tolerant quantum dialogue protocols using a quantum reordering circuit of EPR pairs

Abstract This study proposes two efficient fault-tolerant quantum dialogue (QD) protocols that are robust against the collective-dephasing and collective-rotation noises, respectively. In the proposed protocol, the message carriers are decoherence-free quantum states that are resistant to the corresponding collective noise, provided that all quantum photon pairs of a transmitted unit remain within the same time window. These quantum states and their combinations are used to compose the decoy photon pairs to ensure the security of the transmission. An observation on the Bell measurement has allowed an EPR pair as a message carrier to Require only one of its photons for protection. That is, the measurement of one single photon in an EPR pair will gain no information on its actual Bell state. This property has effectively reduced the number of decoy photons in quantum transmission. Since the photons used in the message carriers are particles of EPR pairs, the proposed two fault-tolerant QD protocols required only half of the decoy photons to ensure the same level of security. In the transmission, one photon of each EPR pair is separated using a reordering mechanism, and a quantum logic circuit is designed and implemented to demonstrate the concept in practice. The reduction of decoy photons has significantly improved the qubit efficiency of the proposed QD protocols compared with other relevant existing works. Furthermore, the proposed schemes also have no information leakage problem.
Controlled transport in chiral quantum walks on graphs

Abstract We investigate novel transport properties of chiral continuous-time quantum walks (CTQWs) on graphs. By employing a gauge transformation, we demonstrate that CTQWs on chiral chains are equivalent to those on non-chiral chains, but with additional momenta from initial wave packets. This explains the novel transport phenomenon numerically studied in (Khalique et al 2021 New J. Phys. 23 083005). Building on this, we delve deeper into the analysis of chiral CTQWs on the Y-junction graph, introducing phases to account for the chirality. The phase plays a key role in controlling both asymmetric transport and directed complete transport among the chains in the Y-junction graph. We systematically analyze these features through a comprehensive examination of the chiral CTQW on a Y-junction graph. Our analysis shows that the CTQW on Y-junction graph can be modeled as a combination of three wave functions, each of which evolves independently on three effective open chains. By constructing a lattice scattering theory, we calculate the phase shift of a wave packet after it interacts with the potential-shifted boundary. Our results demonstrate that the interplay of these phase shifts leads to the observed enhancement and suppression of quantum transport. The explicit condition for directed complete transport or <?CDATA $100\%$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>100</mml:mn> <mml:mi mathvariant="normal">%</mml:mi> </mml:math> efficiency is analytically derived. Our theory has applications in building quantum versions of binary tree search algorithms.
Ore-Grade Estimation from Hyperspectral Data Using Convolutional Neural Networks: A Case Study at the Olympic Dam Iron Oxide Copper-Gold Deposit, Australia

Abstract Acquiring information about the spatial distribution of ore grade in the subsurface is essential for exploring and discovering mineral resources. This information is derived commonly from the geochemical analysis carried out on drill core samples, which allows the quantification of the concentration of ore elements. However, these surveys are generally time-consuming and expensive, usually leading to information at a low spatial resolution due to large sampling intervals. The use of hyperspectral systems in the mining industry to characterize and quantify minerals in drill cores is increasing due to their efficiency and fast turnaround time. Here, we propose the use of convolutional neural networks on hyperspectral data to estimate Cu concentration in drill cores at the Olympic Dam iron oxide copper-gold deposit. The Cu concentration data obtained by drill core geochemical analysis and the mean spectra between the analyzed intervals obtained from hyperspectral data were used to train the machine learning model. The trained model was then used to estimate the Cu concentration of a test drill core, which was not used to train the model. In addition, the trained model was used to extrapolate the Cu concentration, at a centimetric spatial resolution, to the drill core intervals without geochemical analysis. Qualitative and quantitative evaluations of the results demonstrate the capabilities of the proposed method, which provided a root mean squared error of 0.48 for the estimation of Cu percentage along drill cores. The results indicate that the method could be beneficial for determining the spatial distribution of ore grade by supporting the selection of zones of interest where more detailed analyses are appropriate, reducing the number of samples needed to characterize and identify the ore zones, and assisting in the estimation of the volume with commercially viable ore, thereby potentially reducing the geochemical assays needed and decreasing the data acquisition time.
A relaxed condition for avoiding the occur-check

We generalize the notion of "not subject to occur-check" (NSTO), on which most known results on avoiding the occur-check in logic programming are based. NSTO means that unification is performed only on such pairs of atoms for which the occur-check never succeeds in any run of a nondeterministic unification algorithm. Here we show that "any run" can be weakened to "some run". We present some related sufficient conditions under which the occur-check may be safely omitted. We show examples for which the proposed approach provides more general results than the approaches based on well-moded and nicely moded programs (this includes cases to which the latter approaches are inapplicable). We additionally present a sufficient condition based on NSTO, working for arbitrary selection rules.
Radio frequency fingerprint identification for Internet of Things: A survey

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.
Retracted: PageRank Topic Finder based Algorithm for Multimedia Resources in Preschool Education

[This retracts the article DOI: 10.1155/2022/4173243.].
Super Vision Transformer

We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision transformer (SuperViT), is empowered with the versatile ability to solve incoming patches of multiple sizes as well as preserve informative tokens with multiple keeping rates (the ratio of keeping tokens) to achieve good hardware efficiency for inference, given that the available hardware resources often change from time to time. Experimental results on ImageNet demonstrate that our SuperViT can considerably reduce the computational costs of ViT models with even performance increase. For example, we reduce 2 $$\times $$ FLOPs of DeiT-S while increasing the Top-1 accuracy by 0.2% and 0.7% for 1.5 $$\times $$ reduction. Also, our SuperViT significantly outperforms existing studies on efficient vision transformers. For example, when consuming the same amount of FLOPs, our SuperViT surpasses the recent state-of-the-art EViT by 1.1% when using DeiT-S as their backbones. The project of this work is made publicly available at https://github.com/lmbxmu/SuperViT .
Rethinking the Knowledge Transfer Process Through the Use of Metaverse: A Qualitative Study of Organizational Learning Approach for Remote Workplace

Abstract This paper aims to explore the possibilities of implementing the tacit knowledge transfer process and practices in the metaverse and remote workplaces. Tacit knowledge transfer is crucial for organizational knowledge management and maintaining an organization's sustainable development. The research team believes that the use of the metaverse in remote workplaces is expected to revolutionize established organizational learning, knowledge management, and tacit knowledge transfer models. This research attempts to understand employees’ experiences from three perspectives: (1) the acceptance by both senior and junior staff of using the metaverse for training; (2) the experiences of tacit knowledge transfer over the metaverse; and (3) the role of immersion and interactivity during the knowledge transfer process. The significance of this research is the theoretical investigation of the tacit knowledge transfer model in the metaverse. This research is explorative in nature; therefore, this research is not going to generalize or prove the effectiveness of using the metaverse in the tacit knowledge transfer process, but rather explore the factors and deepen our understanding of these fundamental attributes. The findings of this research suggest a modification of the classical model SECI Matrix by adding a new component of “integration” in the transfer process.
Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.
On the crowdsourcing of macroseismic data to characterize geological settings

Macroseismic data are obtained from observing the effects of an earthquake on people, buildings, and the natural environment. The crowdsourced macroseismic data are collected from a large number of people, often through online platforms or mobile applications. These data can be useful for characterizing geological features, depending on several factors, such as the quality of the data, the representativeness of the sample, and the methods used for data collection and analysis. As a case study, we consider the macroseismic data collected in Trieste (NE Italy) through online questionnaires completed by citizens after the 2020 Mw 6.4 earthquake in Petrinja (Croatia). The campaign was promoted through social media and in a short period of time more than 6000 questionnaires were completed by the citizens of Trieste. The analyzed macroseismic data show good agreement with the expected seismic response of the main soil types of the city. A comparison with a similar project we conducted in Trieste in 2012, following the 2012 Emilia May 20 and 29 earthquakes, shows that also in that case, although a much smaller number of questionnaires was collected, the main characteristics identified correspond well with the soil types of Trieste. Thus, our study proves the importance of collecting macroseismic data even in areas of low damage. Moreover, it shows how people's early engagement, computer skills, social networks, and smartphone popularity can influence the results of such data collection and opens new scenarios for a better understanding of earthquake risks and improved awareness and preparedness through citizen participation.