Reviewerly/exHarmony huggingface dataset:
LCD matrix product codes with an application

Matrix product (MP) codes over a finite field [Formula: see text], where [Formula: see text] a prime power, were first introduced by Blackmore and Norton [MP codes over [Formula: see text], Appl. Algebra Engrg. Comm. Comput. 12 (2001) 477–500]. This paper investigates linear complementary dual (LCD) MP codes over finite commutative Frobenius rings. We derive a necessary and sufficient condition for an MP code to have an LCD. We provide some efficient constructions of LCD MP codes. Finally, we have shown an application on cryptography of LCD codes.
Determinant Factors of Learning Tools in Pandemic COVID-19 Era

The COVID-19 pandemic has had a different learning impact than before traditionally. With the current era, everything is required to be with digital technology. So that current learning uses learning tools that make it easier for teachers and students to communicate better in teaching and learning in schools. This research aims to determine which factors influence the perceived usefulness and confirmation of expectation to give satisfaction and continuity intent to the learning tools used by students. Therefore, the research question in this study is which determining factors influence the perceived usefulness and the confirmation of expectations in the use of learning tools? This study uses a quantitative approach by distributing questionnaires and obtaining 485 valid respondents. The results of this study indicate that the factors of compatibility, system quality, and appearance quality positively affect perceived usefulness. In addition, compatibility, convenience, self-efficacy, and information quality positively influence the confirmation of expectation when using learning tools at school. Perceived usefulness and confirmation of expectation positively impact the satisfaction with the use of learning tools and the perseverance intention of students at school.
Factoring discrete-time quantum walks on distance regular graphs into continuous-time quantum walks

We consider a discrete-time quantum walk, called the Grover walk, on a distance regular graph X. Given that X has diameter d and invertible adjacency matrix, we show that the square of the transition matrix of the Grover walk on X is a product of at most d commuting transition matrices of continuous-time quantum walks, each on some distance digraph of the line digraph of X. We also obtain a similar factorization for any graph X in a Bose Mesner algebra.
A Method to Remove Size Bias in Sub-Cortical Structure Segmentation

Segmentation and analysis of sub-cortical structures is of interest in diagnosing some neurological diseases. Segmentation is a challenging task because of brain tissue ambiguity and data scarcity. Deep learning (DL) solutions are widely used for this purpose by considering the problem as a semantic segmentation of the brain. In general, DL approaches exhibit a bias towards larger structures when training is done on the whole brain. We propose a method to address this problem wherein a pre-training step is used to learn tissue characteristics and a rough ROI extraction step aids focusing on local context. We use a Residual U-net for demonstrating the proposed method. Experiments on the IBSR and MICCAI datasets show that our proposed solution leads to an improvement in segmentation performance in general with medium and small size structures benefiting significantly. The performance with the proposed method is also marginally better than a more complex, state of art sub-cortical structure segmentation method. A strength of the proposed method is that it can also be applied as a modification to any existing segmentation solution.
Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure

Abstract When detecting aircraft flared tube defect with the YOLOv4 network structure, tiny object defects will be missed, and resulting in a high missed detection rate and low mAP (mean Average Precision) value. This paper proposes an improved aircraft flared tube defect detection algorithm of YOLOv4 network structure. Firstly, in order to improve the feature extraction capability of the YOLOv4 network for tiny defects, a convolution operation is added to the SPP (Spatial Pyramid Pooling) and PANet (Path Aggregation Network) structure. Secondly, the representation of the feature pyramid is enhanced utilizing the improved PANet. Thirdly, the decoupled head is utilized to improve the model performance. Finally, we construct the aircraft flared tube dataset by labeling the defect samples, and experiment with the improved YOLOv4 network. Experimental results show that the mAP value of defect detection task is increased from 91.26% to 95.31%, average detection time increased from 346.17ms to 278.61ms.
Semi-Supervised Pseudo-Healthy Image Synthesis via Confidence Augmentation

Pseudo-healthy image synthesis, which computationally synthesizes a pathology-free image from a pathological one, has been proved valuable in many downstream medical image analysis tasks, from lesion detection, data augmentation to clinical surgery suggestion. Thanks to the advancement of generative adversarial networks (GANs), recent studies have made steady progress to synthesize realistic-looking pseudohealthy images with the perseverance of the structure identity as well as the healthy-looking appearance. Nevertheless, it is challenging to generate high-quality pseudo-healthy images in the absence of the lesion segmentation mask. In this paper, we aim to alleviate the needs of a large amount of lesion segmentation labeled data when synthesizing pseudo-healthy images. We propose a semi-supervised pseudo-healthy image synthesis framework which leverages unlabeled pathological image data for efficient pseudo-healthy image synthesis based on a novel confidence augmentation trick. Furthermore, we re-design the network architecture which takes advantage of previous studies and allows for more flexible applications. Extensive experiments have demonstrated the effectiveness of the proposed method in generating realistic-looking pseudo-healthy images and improving downstream task performances.
Quantitative System Modeling of Financial Scenario Uncertainty based on Deep Learning in the Context of Algorithm Optimization

Quantitative system modeling of financial scenario uncertainty based on deep learning in the context of algorithm optimization is implemented in the paper. For example, big data technology and artificial intelligence technology can be used to supervise its financial activities by setting risk indicators, collect data in its financial activities in the real time, conduct comprehensive analysis of Internet financial transaction risks, and analyze and determine whether the risk is not. Beyond indicators make the core point of our algorithm, and with this idea, the deep learning in the context of algorithm optimization is designed. The decoder of the core existing point cloud completion network directly uses the fully connected layer to perform feature decoding on the entire latent feature vector to output the completed point cloud, and the system model is used and applied to the financial scenario uncertainty analysis. Through the experimental result, the performance is tested.
Phalcon Framework Realization of Distributed Data-Oriented Collaborative Governance System of Sports Venues in Hubei Colleges

Phalcon framework realization of the distributed data-oriented collaborative governance system of sports venues in Hubei colleges is implemented in the paper. In the study, the virtual stage is considered initially because, to understand the virtual architecture, virtual space must be understood first. Virtual space refers to the virtual space of the non-material existence established in human consciousness through the stimulation of sensory signals. For example, the cyberspace established through the Internet and the virtual reality used for the dynamic demonstration of design schemes belong to virtual space. Based on this, then, distributed data-oriented collaborative governance system is proposed. The traditional data storage tool adopts the warehouse mode to process the data in a single format and process it mechanically. In the context of some large-scale information management, the types and application ways of data are more flexible. And then, the efficient MVC framework is designed and applied to the sports venues in Hubei colleges. In the experiment section, the different data sets are combined and the monitoring performance is validated.
Incremental Learning for a Flexible CAD System Design

Deep neural networks suffer from Catastrophic Forgetting (CF) on old tasks when they are trained to learn new tasks sequentially, since the parameters of the model will change to optimize on the new class. The problem of alleviating CF is of interest to Computer aided diagnostic (CAD) systems community to facilitate class incremental learning (IL): learn new classes as and when new data/annotations are made available and old data is no longer accessible. However, IL has not been explored much in CAD development. We propose a novel approach that ensures that a model remembers the causal factor behind the decisions on the old classes, while incrementally learning new classes. We introduce a common auxiliary task during the course of incremental training, whose hidden representations are shared across all the classification heads. Since the hidden representation is no longer task-specific, it leads to a significant reduction in CF. We demonstrate our approach by incrementally learning 5 different tasks on Chest-Xrays and compare the results with the state-of-the-art regularization methods. Our approach performs consistently well in reducing CF in all the tasks with almost zero CF in most of the cases unlike standard regularisation-based approaches.
CapNeXt: Unifying Capsule And Resnext For Medical Image Segmentation

Capsule Network is a contemporary approach to image analysis that emphasizes part-whole relationships. However, its applications to segmentation tasks are limited due to training difficulties such as initialization and convergence. In this study, we propose a novel Capsule Network, called CapNeXt, that unifies Capsule and ResNeXt architectures for medical image segmentation. CapNeXt advances the existing capsule-based segmentation model by integrating optimization techniques from Convolutional Neural Networks (CNN) to make training much easier than other contemporary Capsule-based segmentation methods. Experimental results on two public datasets show that CapNeXt outperforms the CNNs and other Capsule architectures in 2D and 3D segmentation tasks by 1% of the Dice score. The code will be released on GitHub after being accepted.
A Chinese Grammatical Error Correction Method Based on Iterative Training and Sequence Tagging

Chinese grammatical error correction (GEC) is under continuous development and improvement, and this is a challenging task in the field of natural language processing due to the high complexity and flexibility of Chinese grammar. Nowadays, the iterative sequence tagging approach is widely applied to Chinese GEC tasks because it has a faster inference speed than sequence generation approaches. However, the training phase of the iterative sequence tagging approach uses sentences for only one round, while the inference phase is an iterative process. This makes the model focus only on the current sentence’s current error correction results rather than considering the results after multiple rounds of correction. In order to address this problem of mismatch between the training and inference processes, we propose a Chinese GEC method based on iterative training and sequence tagging (CGEC-IT). First, in the iterative training phase, we dynamically generate the target tags for each round by using the final target sentences and the input sentences of the current round. The final loss is the average of each round’s loss. Next, by adding conditional random fields for sequence labeling, we ensure that the model pays more attention to the overall labeling results. In addition, we use the focal loss to solve the problem of category imbalance caused by the fact that most words in text error correction do not need error correction. Furthermore, the experiments on NLPCC 2018 Task 2 show that our method outperforms prior work by up to 2% on the F0.5 score, which verifies the efficiency of iterative training on the Chinese GEC model.
Data Every Day: Designing and Living with Personal Situated Visualizations

We explore the design and utility of situated manual self-tracking visualizations on dedicated displays that integrate data tracking into existing practices and physical environments. Situating self-tracking tools in relevant locations is a promising approach to enable reflection on and awareness of data without needing to rely on sensorized tracking or personal devices. In both a long-term autobiographical design process and a co-design study with six participants, we rapidly prototyped and deployed 30 situated self-tracking applications over a ten month period. Grounded in the experience of designing and living with these trackers, we contribute findings on logging and data entry, the use of situated displays, and the visual design and customization of trackers. Our results demonstrate the potential of customizable dedicated self-tracking visualizations that are situated in relevant physical spaces, and suggest future research opportunities and new potential applications for situated visualizations.
Exact master equation for an open Jaynes–Cummings system

A master equation for the Jaynes–Cummings (JC) system interacting with a bosonic reservoir through a conserved quantity is derived analytically without any approximation. The derivation is valid for any initial JC state that has no correlation with the initial reservoir at any temperature and yields a Gorini–Kossakowski–Lindblad–Sudarshan-type master equation with time-dependent coefficients. Several physical quantities of the open JC system are examined and the validity or limit of some of the standard approximations used to derive the master equation is discussed on the basis of the exact results obtained here.
Measurement of constant radius swept features in cultural heritage

Abstract The dimensional characterization of archaeological fragment is a very complex operation and could prove to be useful for identifying the presence of standard attributes in the ceramics found from a specific archaeological site, or for making comparisons and analysis of similarities or for studying ancient technologies used for manufacture of objects. The dimensional analysis of the fragments is now carried out manually with traditional measuring devices. Typically, the results obtained are inaccurate and non-repeatable measurements. This paper focuses on the dimensional characterization of a specific geometric class of features: the constant radius swept features (called here CRS features). Several archaeological features, such as rims, bases, decorative motifs, processing marks and grooves are referable from a geometric point of view to the class of CRS features. These are detail features, which may be very interesting for the investigation of some aspects related to the historical-archaeological classification of the find. CRS features are often found on worn, damaged (e.g. chipped) or fragmented objects; they are frequently characterized, from a geometric point of view, by free form surfaces and by a limited cross sectional extension. In some cases, CRS features can be of axially symmetrical geometry: this occurs quite frequently in the case of archaeological pottery. For all these reasons, it is often difficult to apply traditional manual methods for the quantitative dimensional characterization of CRS features. This paper describes an original methodology for the measurement of CRS features acquired by scanning technologies. The algorithmic implementation of this methodology, consisting of a suitable processing of the feature nodes, allows to carry out automatically the dimensional characterization of the feature.
Global-Local Attention Network for Weakly Supervised Cervical Cytology ROI Analysis

Existing supervised Convolutional Neural Network (CNN) approaches for cervical cytology image analysis generally rely on the heavy manual annotation for each cell or cell mass and thus lead to extensive time and effort. In this paper, we propose a global-local network for weakly supervised cervical cytology region of interest (ROI) analysis. It aims to perform the classification for ROIs and further classify the cells only with the ROI labels. Specifically, the proposed method firstly detects the cells within ROI and extracts the CNN features of cells. Then attention-based bidirectional LSTM (Att-BLSTM) is applied to explore the global contextual information within ROI. On the other hand, the Vision Transformer (ViT) is used to exploit the local attentive representations of the cells in ROIs. The cross attention (CA) is applied to incorporate the global contextual features and local patterns and thus generates more discriminative feature representation of ROI. More importantly, the CA score is used as the pseudo label to select top and least attentive cells. Therefore, the in-theclass and out-of-the-class CA branches are trained to achieve the cell classification. Experimental results demonstrate the effectiveness of our method for cervical cytology ROI and cell classification, and the weak supervision of the image-level label has great potential to promote the automatic whole slide cervical image analysis and alleviate the workload of cytologists.
Methods for Evaluating the Fluency of Automatically Simplified Texts with Deaf and Hard-of-Hearing Adults at Various Literacy Levels

Research has revealed benefits and interest among Deaf and Hard-of-Hearing (DHH) adults in reading-assistance tools powered by Automatic Text Simplification (ATS), a technology whose development benefits from evaluations by specific user groups. While prior work has provided guidance for evaluating text complexity among DHH adults, researchers lack guidance for evaluating the fluency of automatically simplified texts, which may contain errors from the simplification process. Thus, we conduct methodological research on the effectiveness of metrics (including reading speed; comprehension questions; and subjective judgements of understandability, readability, grammaticality, and system performance) for evaluating texts controlled to be at different levels of fluency, when measured among DHH participants at different literacy levels. Reading speed and grammaticality judgements effectively distinguished fluency levels among participants across literacy levels. Readability and understandability judgements, however, only worked among participants with higher literacy. Our findings provide methodological guidance for designing ATS evaluations with DHH participants.
Joint Attention for Medical Image Segmentation

Medical image segmentation is crucial for computer aided diagnosis. In recent years, spatial attention mechanisms have leaded to breakthroughs in the task of image segmentation. In this work, we firstly present a unified formula for spatial attention mechanisms. Within this framework, we find that point-wise attention has better localization while self-attention can learn more global features. Motivated by this observation, we then propose a new joint attention module, which jointly leverages the advantages of point-wise attention and self-attention. Moreover, by integrating joint attention with DenseUNet, we conduct image segmentation experiments on two public datasets. The proposed method outperforms recent state-of-the-art models, verifying the superiority of joint attention. Additionally, ablation studies demonstrate that our joint attention obtains more balanced results compared to the previous point-wise attention and self-attention. The design of joint attention provides a novel insight into understanding spatial attention mechanisms.
Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning

With the gradual integration of global economy and finance, the financial market presents many complex financial phenomena. To increase the prediction accuracy of financial data, a new nonlinear volatility risk prediction algorithm is proposed based on the improved deep learning algorithm. First, the financial data are taken as the research object and the closing price is set as the prediction target. Then, the nonlinear volatility risk prediction model of the financial data is established through the wavelet principal component analysis noise reduction module and the long and short-term memory network (LSTM) module, and the nonlinear volatility trend is extracted from multiple financial data series to realize the nonlinear volatility risk prediction of the financial data. During the whole experiment, the time of the research method was less than 1.5 minutes. And for 1200 test samples, the average error of data risk prediction of the proposed method is 0.0217%. The average cost of the research method is 114.25 million yuan, which is significantly lower than other existing algorithms. Experimental results show that the research method can effectively predict the risk of financial data and is more suitable for the risk control early warning of Internet financial platform.
Abstract meaning representation of Turkish

Abstract Abstract meaning representation (AMR) is a graph-based sentence-level meaning representation that has become highly popular in recent years. AMR is a knowledge-based meaning representation heavily relying on frame semantics for linking predicate frames and entity knowledge bases such as DBpedia for linking named entity concepts. Although it is originally designed for English, its adaptation to non-English languages is possible by defining language-specific divergences and representations. This article introduces the first AMR representation framework for Turkish, which poses diverse challenges for AMR due to its typological differences compared to English; agglutinative, free constituent order, morphologically highly rich resulting in fewer word surface forms in sentences. The introduced solutions to these peculiarities are expected to guide the studies for other similar languages and speed up the construction of a cross-lingual universal AMR framework. Besides this main contribution, the article also presents the construction of the first AMR corpus of 700 sentences, the first AMR parser (i.e., a tree-to-graph rule-based AMR parser) used for semi-automatic annotation, and the evaluation of the introduced resources for Turkish.
A Study of Solar Cooking: Exploring Climate-Resilient Food Preparation and Opportunities for HCI

As parts of our planet continue to experience extreme heat waves, it is more urgent than ever for human-food interaction research to examine climate-resilient and sustainable food practices. Our work, conducted in the hottest city in the USA, focuses on solar cooking as a set of creative DIY activities that use extreme heat and mitigate human impact on the environment. We report on a summer-long study whereby 7 enthusiasts built solar ovens from scratch and experimented with solar recipes ranging from slow-cooked pork and chicken to bread, kale chips, brownies, jerky, and fruit rollups. Our findings depict solar cooking as a form of iterative DIY, which, through its challenges and creative workarounds, serves as a point of engagement with both food and extreme heat. We reflect on solar cooking as a climate-resilient food practice and conclude with design considerations for HCI to support solar cooking as a habitual community practice.
Top Ten Intellectual Property Cases and 50 Model Intellectual Property Cases in Chinese Courts in 2020

Journal Article Top Ten Intellectual Property Cases and 50 Model Intellectual Property Cases in Chinese Courts in 2020 Get access GRUR International, Volume 71, Issue 4, April 2022, Pages 357–363, https://doi.org/10.1093/grurint/ikac001 Published: 29 January 2022