Reviewerly/exHarmony huggingface dataset:
Towards string order melting of spin-1 particle chains in superconducting transmons using optimal control

Utilizing optimal control to simulate a model Hamiltonian is an emerging strategy that leverages the intrinsic physics of a device with digital quantum simulation methods. Here we evaluate optimal control for probing the nonequilibrium properties of symmetry-protected topological (SPT) states simulated with superconducting hardware. Assuming a tunable transmon architecture, we cast the evolution of these SPT states as a series of one- and two-site pulse optimization problems that are solved in the presence of leakage constraints. From the generated pulses, we classically simulate the time-dependent melting of the perturbed SPT string order across a six-site model with an average state infidelity of ${10}^{\ensuremath{-}3}$. The feasibility of these pulses as well as their efficient application indicate that high-fidelity simulations of string order melting are within reach of current quantum computing systems.
Image Super-Resolution Network Based on Feature Fusion Attention

The residual structure may learn the entire input region indiscriminately because the residual connection can still learn well as the network depth grows. To a certain extent, the attention mechanism can focus the network’s attention to the interesting area, enhancing the learning performance of essential areas while decreasing the computational load for the system. As a result, the combination of these two advantages could have substantial research significance, for both improve the efficiency and reduce the computational load. A dense residual connection network that combine feature fusion attention approach in image super resolution process is proposed. The dense residual block is enhanced with pixel and channel attention blocks, and a dual-channel path design incorporating global maximum pooling and global average pooling is utilized. A hybrid loss function is also proposed in order to increase the network’s sensitivity to the maximum error between individual pixels. The PSNR/SSIM/ <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mo>∞</mo> </mrow> </msub> </math> performance metrics increased after applying the hybrid loss function and our attention techniques. The experimental results demonstrated that our novel approach has several advantages over some recent approaches, as well as showing good outcomes on many testing datasets.
Development of an innovative method for evaluating a network of collective defensive interactions in football

Social network analysis (SNA) has been increasingly applied to performance analytics in team sports, seeking to better understand the dynamic properties of competitive interactions. Despite considerable potential to analyze individual (micro) and team (macro) behavioral patterns of play, there are important limitations that can undermine the potential applicability of SNA. One important limitation in existing research is the lack of network analyses of defensive interactions, curtailing understanding of the functionality and adaptability of teams during competitive performance. This study developed an innovative network method for assessing interactions between players in defensive phases of play in football. The networking method was evaluated using a small-sided and conditioned game (SSCG; GK+7v7+GK) of 20 min duration (two halves of 10 min each, interspersed by 5 min intervals of active recovery). The method traced interactions between groups of three players (effective defensive triangulations) as network nodes, weighted according to the number of passes performed by the attacking players. Results showed how this social network analysis method may provide researchers, coaches and performance analysts with relevant information regarding the functional properties of teams in the defensive phase of the game. For instance, coaches and performance analysts can evaluate the geometry of a team’s defense, with players engaged in effective triangular-shaped positioning, that allowed them to provide defensive cover and balance, to protect the goal and recover ball possession.
Improving video quality by predicting inter-frame residuals based on an additive 3D-CNN model

Video compression is essential for uploading videos to online platforms which usually have bandwidth limitations. However, the compression reduces the visual quality. To overcome this problem, the visual quality of the low bitrate compressed videos for various standards, including H.264 and HEVC in decoders, needs to be improved. Accordingly, this paper proposes a novel method for improving video quality based on 3D convolutional neural networks (CNNs). This method is totally compatible with the encoders of video compression standards, i.e., H.264, VVC, and HEVC, and can be implemented easily. In particular, the proposed neural network model receives five frames of the low bitrate compressed video as input and subsequently predicts the compression error of frames using the first and fifth frames. Finally, it reconstructs an improved version of the frame with high quality. The CNN is an Additive (3D) model that can predict the eliminated inter-frame redundancies resulting from compression. Our goal is to increase the peak signal to noise ratio (PSNR) and structural index similarity (SSIM) of the luminance (Y) and chrominance (U, V) frames in the video. Additive 3D-CNN achieves an average of 12.4%, 9.9% and 5% BD-rate increases for LP, LB and RA for the Y component. The results indicate that the new proposed algorithm outperforms the previous methods in terms of PSNR, SSIM, and BD-rate.
Signal demodulator based on in‐phase and quadrature interference‐robust feature

This letter examines the issue of mitigating strong co-channel interference in communication systems is addressed. Unlike conventional model-based methods, a novel data-driven scheme is proposed. A recurrent neural network is trained to directly demodulate the desired signal under strong co-channel interference. Instead of inputting the original received signal, in-phase and quadrature interference-robust features are extracted through preprocess. The recurrent neural network is then trained offline to implement sequence labelling, with the interference-robust feature sequences and known code sequences of the desired signal as inputs and ground-truth labels. Meanwhile, a guard zone is introduced when loading the interference-robust feature sequences to enable better contextual information exploitation by the recurrent neural network demodulator. Online tests validated the low bit error rate of the recurrent neural network demodulator, under strong co-channel interference. Moreover, the proposed scheme outperformed existing model-based and data-driven interference mitigation schemes in terms of the bit error rate, especially in low signal-to-interference ratio region. Inspiringly, the proposed data-driven scheme generalized well to varied unseen test conditions.
SRI-Net: Similarity retrieval-based inference network for light field salient object detection

The cutting-edge RGB saliency models are prone to fail for some complex scenes, while RGB-D saliency models are often affected by inaccurate depth maps. Fortunately, light field images can provide a sufficient spatial layout depiction of 3D scenes. Therefore, this paper focuses on salient object detection of light field images, where a Similarity Retrieval-based Inference Network (SRI-Net) is proposed. Due to various focus points, not all focal slices extracted from light field images are beneficial for salient object detection, thus, the key point of our model lies in that we attempt to select the most valuable focal slice, which can contribute more complementary information for the RGB image. Specifically, firstly, we design a focal slice retrieval module (FSRM) to choose an appropriate focal slice by measuring the foreground similarity between the focal slice and RGB image. Secondly, in order to combine the original RGB image and the selected focal slice, we design a U-shaped saliency inference module (SIM), where the two-stream encoder is used to extract multi-level features, and the decoder is employed to aggregate multi-level deep features. Extensive experiments are conducted on two widely used light field datasets, and the results firmly demonstrate the superiority and effectiveness of the proposed SRI-Net.
Domain-Invariant Feature Progressive Distillation with Adversarial Adaptive Augmentation for Low-Resource Cross-Domain NER

Considering the expensive annotation in Named Entity Recognition (NER), Cross-domain NER enables NER in low-resource target domains with few or without labeled data, by transferring the knowledge of high-resource domains. However, the discrepancy between different domains causes the domain shift problem and hampers the performance of cross-domain NER in low-resource scenarios. In this article, we first propose an adversarial adaptive augmentation, where we integrate the adversarial strategy into a multi-task learner to augment and qualify domain adaptive data. We extract domain-invariant features of the adaptive data to bridge the cross-domain gap and alleviate the label-sparsity problem simultaneously. Therefore, another important component in this article is the progressive domain-invariant feature distillation framework. A multi-grained MMD (Maximum Mean Discrepancy) approach in the framework to extract the multi-level domain invariant features and enable knowledge transfer across domains through the adversarial adaptive data. Advanced Knowledge Distillation (KD) schema processes progressively domain adaptation through the powerful pre-trained language models and multi-level domain invariant features. Extensive comparative experiments over four English and two Chinese benchmarks show the importance of adversarial augmentation and effective adaptation from high-resource domains to low-resource target domains. Comparison with two vanilla and four latest baselines indicates the state-of-the-art performance and superiority confronted with both zero-resource and minimal-resource scenarios.
How to identify an argument type? On the hermeneutics of persuasive discourse

This paper proposes a theory of interpreting argument types as an integral part of a systematic and comprehensive ‘hermeneutics of persuasive discourse’. It first explains how such a hermeneutics can be developed based on pragmatic insights about the use of language for persuasive purposes expressed in the philosophy of argument. Then, after having provided an overview of the main hermeneutical stages involved in interpreting persuasive discourse, the paper focuses on the stage of argument type identification. It formulates a ‘hermeneutics of argument type’ in terms of the Periodic Table of Arguments (PTA), an argument categorization framework systematizing existing accounts of arguments in the broad sense of the term (topoi, loci, argument schemes, fallacies, means of persuasion). For each of the three parameters within this framework, ‘argument form’, ‘argument substance’, and ‘argument lever’, the paper describes how to determine their value by analyzing several examples of natural arguments.
Relevance in processing search queries to bibliographic and fulltext databases in the modern models of scientific research support through open archives

RNPLS&amp;T has been developing the model Single Open Information Archive (SOIA) to integrate all digital fulltext resources created and acquired by the Library. Beside its own content sources, the SOIA portal is to offer user services, among them use of other open repositories’ metadata (primarily that of Russian repositories) as the complementary source in processing of user search queries. Today, the functionality of extended metadata in information retrieval and data exchange has been being studied. This calls for investigating into the problem of relevance in processing search queries for both end users and for exchanging bibliographic and fulltext information between open archives. The purpose of this analysis is to specify the methods and technologies to increase the relevance. The findings of the analysis are cite; the revealed problems are characterized.
From private phones to public screens: Cross-media recontextualization of chat discourse in the case of the Austrian ÖVP corruption affair

The profound establishment of text-based interaction via mobile messenger services has not only changed interpersonal communication, but also reshaped its quotability. Drawing on the notion of recontextualization, this paper investigates the cross-media transfer-and-transformation of text messages from their former confidential contexts into public media environments. The data are drawn from the media discourse on the corruption affair of the Austrian People's Party (ÖVP), which was triggered by the publication of confidential chat messages in the fall of 2021. Comparing the presentation of text messages in the journalistic media with their intertextual antecedents (in investigative files and related documents), the analysis highlights several linguistic and visual transformations. The findings demonstrate how formerly private chat interactions have been represented in media discourse and reveal different multimodal forms of cross-media quotation, ranging from linguistic replay and visual restaging to elaborate forms in which text messaging is audio-visually re-enacted. These modes of cross-media recontextualization differ in the degree to which the mediality of the digital chat discourse is conveyed or imitated. Through this media linguistic lens, the study contributes to the theory of recontextualization and raises the question what concept of quotation and re-enactment is appropriate and linguistically due in the age of a deep digitalization of everyday communication.
An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Summary Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy‐based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean‐Average Precision, F ‐measure, Precision‐recall, and Precision rank are evaluated and analyzed by using TREC‐COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state‐of‐art works. For a total of 60 queries, the proposed model offers an F 1‐Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E 1‐score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.
Augmented and challenging datasets with multi-step reasoning and multi-span questions for Chinese judicial reading comprehension

The existing judicial reading comprehension datasets are relatively simple, and the answers to the questions can be obtained through single-step reasoning. However, the content of legal documents in actual scenarios is complex, making it problematic to infer correct results merely by single-step reasoning. To solve this type of issue, we promote the difficulties of questions included in Chinese Judicial Reading Comprehension (CJRC) dataset and propose two augmented versions, CJRC2.0 and CJRC3.0. These datasets are derived from Chinese judicial judgment documents in different fields and annotated by judicial professionals. Compared to CJRC, there are more types of judgment documents in the two datasets, and the questions become are more challenging to answer. For CJRC2.0, we only preserve complex questions that require to be solved by multi-step reasoning. Besides, we provide additional supporting facts to the answers. For CJRC3.0, we introduce a new question type, the multi-span question, which should be answered by extracting and combining multiple spans in the documents. We implement two powerful baselines to evaluate the difficulty of our proposed datasets. Our proposed datasets fill gaps in the field of explainable legal machine reading comprehension.
Human hand gesture identification framework using SIFT and knowledge‐level technique

Abstract In this study, the impact of varying lighting conditions on recognition and decision‐making was considered. The luminosity approach was presented to increase gesture recognition performance under varied lighting. An efficient framework was proposed for sensor‐based sign language gesture identification, including picture acquisition, preparing data, obtaining features, and recognition. The depth images were collected using multiple Microsoft Kinect devices, and data were acquired by varying resolutions to demonstrate the idea. A case study was designed to attain acceptable accuracy in gesture recognition under variant lighting. Using American Sign Language (ASL), the dataset was created and analyzed under various lighting conditions. In ASL‐based images, significant feature points were selected using the scale‐invariant feature transformation (SIFT). Finally, an artificial neural network (ANN) classified hand gestures using specified characteristics for validation. The suggested method was successful across a variety of illumination conditions and different image sizes. The total effectiveness of NN architecture was shown by the 97.6% recognition accuracy rate of 26 alphabets dataset with just a 2.4% error rate.
Visualization System for Transparency Requirement Analytics

Access to corporate information systems by consumers via the Internet has increased dramatically over the past several decades. In a separate organization, extensive research has been conducted on the free flow of information generated by both external and internal keywords. Research on transparency should aid the audience in making informed decisions. Few have, however, created clear and compelling visual representations of transparency requirements (stakeholders, data, process, policy, and their relationships) utilizing current information visualization and visual analytics methodologies. Maintaining both the quality and visual representation of transparency requirements is a difficult challenge. In this paper, we propose TranspVis, a new visual analytics tool designed for transparency analytics. It consists of multiple views that aid domain experts in efficiently analyzing, updating, and saving application transparency datasets. TranspVis is an interactive tool for displaying TranspLan (i.e., Transparency Language) representations manually generated by experts utilizing the Shield, Infolet, and SitReq forms. In addition to the new circle view, TranspVis generates and synchronizes these latter representations automatically. TranspVis is evaluated using AWS and WhatsApp policy datasets as two case studies. Results show that TranspVis extends the initial TranspLan representation and significantly improves transparency requirement analytics in terms of visual encoding, interactions, and insight extraction.
Research behind a webcam: an exploration of virtual interviewing with children and young people

Background Societal use of digital technology rapidly increased during the COVID-19 pandemic. Face-to-face services converted to online provision where possible. This affected many nurse researchers.Aim To explore conducting research interviews online with children and young people (CYP) about sensitive topics.Discussion This article considers digital inclusion, as well as ethical issues surrounding safety, support and consent, along with choosing tools for collecting data. It also presents a discussion of physical proximity in qualitative interviews with this population and its role in data quality. The authors investigate benefits in the context of researchers’ personal experiences. They acknowledge the disadvantages of conducting interviews online and discuss ways to mitigate these.Conclusion The advantages for researchers include cost-effectiveness, time-efficiency and greater geographical reach of participants. However, CYP’s perspectives are unknown and the specific ethical issues of using this method with CYP need careful consideration.Implications for practice More research is needed to examine virtual interviews from the perspectives of CYP as participants. Virtual acquisition of consent and assent should be investigated to standardise good research practices.