Reviewerly/exHarmony huggingface dataset:
Lightweight Scene Text Recognition Based on Transformer

Scene text recognition (STR) has been a hot research field in computer vision, aiming to recognize text in natural scenes using computers. Currently, attention-based encoder-decoder frameworks struggle to precisely align feature regions with the target object when dealing with complex and low-quality images, a phenomenon known as attention drift. Additionally, with the rise of Transformer, the increasing size of parameters results in higher computational costs. In order to solve the above problems, based on the latest research results of Vision Transformer (ViT), we utilize an additional position-enhancement branch to alleviate attention drift and dynamically fused position information with visual information to achieve better recognition accuracy. The experimental results demonstrate that our model achieves a 3% higher average recognition accuracy on the test set compared to the baseline. Meanwhile, our model maintains the advantage of a small number of parameters and fast inference speed, achieving a good balance between accuracy, speed, and computational load.
Second language vocabulary learning in the digital era: Ten reasons it should be on the go!

Word learning is a key component in second language learning and one that L2 learners often find laborious. With the advent of mobile technology in recent years, research on mobile-assisted vocabulary learning has uncovered the immense potential mobile devices (namely smartphones and tablets) have for providing L2 learners with opportunities for effortless learning of L2 lexis. To this end, we pinpoint ten reasons why L2 learners should abandon traditional pen-and-paper-based methods of word learning and shift instead to a digital mobile word learning environment that fully utilizes the virtues of mobile learning. The role of mobile applications (apps) dedicated to word learning is hence stressed as such apps represent the core basis of mobile-assisted vocabulary learning. For EFL learners to entertain a more manageable and less time-consuming word learning experience, we emphasize the role of EFL teachers in calling their learners’ attention to the ten reasons we underscore in this article to help encourage those who are still into conventional word learning and have not yet explored learning words via mobile gadgets to make the digital shift at once.
Multiview abnormal video synopsis in real-time

The proliferation of surveillance cameras has resulted in an exponential growth in video data, which has created several challenges for video data analysis, extraction, and storage. Video synopsis is an efficient methodology for analyzing and storing video data because it creates short videos. However, existing video synopsis methods are inappropriate for abnormal behavior situations (e.g., constructing a video synopsis of only abnormal frames emphasizing gun and knife characteristics to predict violent activity). As most existing video synopsis methods rely on the foreground and multiple objects tracking preprocessing, they perform poorly in real-time scenarios, particularly in crowded situations. We proposed a video synopsis approach suitable for multiview cameras to mitigate this problem; it can also be applied to radically diverse crowdedness. A considerable difference exists between the proposed method and existing approaches because it has several unique properties. We first detected abnormal segments from a given video and then tracked and extracted the frames. We can extract the required meaningful data by applying this method, which increases the synopsis generation performance. Second, we created a summary of all the videos. Finally, a stitching algorithm was used to create the synopsis.
A reconstructed feasible solution-based safe feature elimination rule for expediting multi-task lasso

Multi-task lasso (MTL) is an effective algorithm to handle multi-task problems. By introducing ℓ2,1-norm, it can realize joint feature selection across a group of related tasks. But it is time-consuming when handling high-dimensional problems. Motivated by the row sparsity of the optimal solution, an improved safe feature elimination rule termed IEDPP is proposed to accelerate the training process. It could delete most of the redundant features before we solve the problem. Then the computational efficiency will be improved since only a reduced problem should be solved. Moreover, the properties of the projection operator and the reconstructed feasible solutions ensure the safety of the proposed method. That is to say, it will derive an identical optimal solution to the original problem both in theory and in practice. But our IEDPP could only be used once before solving, there are still some redundant features that are not deleted. Therefore, we further propose an integrated IIEG-ML rule by combining IEDPP with GAP. Then, more and more redundant features could be deleted as the algorithm converges. Moreover, by embedding IIEG-ML into the grid search method, the whole training process will be accelerated. Finally, an improved Nesterov's method is used to solve the reduced problems. Experimental results on different datasets confirm the effectiveness of our method.
Observation of Non-Markovian Evolution of Einstein-Podolsky-Rosen Steering

Einstein-Podolsky-Rosen (EPR) steering is a type of characteristic nonlocal correlation and provides an important resource in quantum information tasks, especially in view of its asymmetric property. Although plenty of works on EPR steering have been reported, the study of non-Markovian evolution of EPR steering, in which the interactions between the quantum system and surrounding environment are taken into consideration, still lacks intuitive experimental evidence. Here, we experimentally observe the non-Markovian evolution of EPR steering including its sudden death and revival processes, during which the degree of memory effect plays a key role in the recovery of steering. Additionally, a strict unsteerable feature is sufficiently verified during the non-Markovian evolution within multisetting measurements. This Letter, revealing the whole evolution of EPR steering under the non-Markovian process, provides incisive insight into the applications of EPR steering in quantum open systems.
Efficient improvement of classification accuracy via selective test-time augmentation

Test-time augmentation (TTA) is typically used in image classification tasks to improve the classification accuracy of convolutional neural networks (CNNs). In the inference phase, TTA aggregates the predictions of a CNN over multiple random transformations of a query image to obtain the final prediction. However, TTA incurs a high computational cost owing to its effectiveness. In this study, we propose a selective-TTA method for reducing computational costs while maintaining the high classification accuracy. Given a query image, we quantify the predictive uncertainty of the CNN on the image to determine whether TTA should be performed. We perform TTA to obtain an enhanced prediction only when the uncertainty is high. Experimental results on benchmark datasets demonstrated that the proposed method significantly reduced average computational costs while attaining comparable or higher classification accuracy than conventional full-TTA. When the proportion of performing TTA was set to 10%, the computational cost was reduced by 90% with no performance degradation on most datasets.
A Novel Bi-LSTM Based Automatic Image Description Generation

Image captioning is the process of creating a textual description of an image.Due to its importance in various fields, it has emerged as the latest and hot research problem.It uses Computer Vision techniques to process an image and Natural Language Processing to generate the caption.Our proposed approach uses the Bi-LSTM (Bi directional Long Short Term Memory) approach to generate the image description.We also propose the Novel Moth Flame Optimization (NMFO).This model uses the correlation-based logarithmic spiral update.The novel proposed model is demonstrated on standard datasets like Flicker 8k, Flicker 30k, and MSCOCO datasets using standard metrics likeBLEU, CIDEr.Performance of various metrics on various datasets shows that our novel Bi-LSTM approach gives better performance when compared to our traditional approaches.
Comparative Analysis of Clustering Methods for Fuzzy Classifiers Simplification

One problem of fuzzy systems for classification tasks is the exponential growth of rule generation, which translates into excessively long processing times. Different proposals in the literature address this problem through exhaustive rule reduction techniques, which achieve competitive results compared to conventional classifiers by reducing their computational complexity as well. This paper proposes a methodology that is comprised of two stages: 1) a clustering technique that helps identify the structure of an initial low-accurate classifier and 2) a differential evolution parameter identification stage that takes that low-accurate classifier and refines it to obtain a high-accurate classifier. In this way, a comparative analysis among several clustering methods is performed, allowing the users to select a reduced rule set and avoid the use of traditional rule search algorithms. The results show that the Gaussian Mixture Model is the most suitable clustering technique to identify the structure of fuzzy classifiers, since it provides the corresponding fuzzy model with highly competitive classification performance compared to other state-of-the-art methods.
Certified Randomness from Quantum Supremacy

We propose an application for near-term quantum devices: namely, generating cryptographically certified random bits, to use (for example) in proof-of-stake cryptocurrencies. Our protocol repurposes the existing “quantum supremacy” experiments, based on random circuit sampling, that Google and USTC have successfully carried out starting in 2019. We show that, whenever the outputs of these experiments pass the now-standard Linear Cross-Entropy Benchmark (LXEB), under plausible hardness assumptions they necessarily contain Ω(n) min-entropy, where n is the number of qubits. To achieve a net gain in randomness, we use a small random seed to produce pseudorandom challenge circuits. In response to the challenge circuits, the quantum computer generates output strings that, after verification, can then be fed into a randomness extractor to produce certified nearly-uniform bits—thereby “bootstrapping” from pseudorandomness to genuine randomness. We prove our protocol sound in two senses: (i) under a hardness assumption called Long List Quantum Supremacy Verification, which we justify in the random oracle model, and (ii) unconditionally in the random oracle model against an eavesdropper who could share arbitrary entanglement with the device. (Note that our protocol’s output is unpredictable even to a computationally unbounded adversary who can see the random oracle.) Currently, the central drawback of our protocol is the exponential cost of verification, which in practice will limit its implementation to at most n∼ 60 qubits, a regime where attacks are expensive but not impossible. Modulo that drawback, our protocol appears to be the only practical application of quantum computing that both requires a QC and is physically realizable today.
A Near-Cubic Lower Bound for 3-Query Locally Decodable Codes from Semirandom CSP Refutation

A code C ∶ {0,1}k → {0,1}n is a q-locally decodable code (q-LDC) if one can recover any chosen bit bi of the message b ∈ {0,1}k with good confidence by randomly querying the encoding x = C(b) on at most q coordinates. Existing constructions of 2-LDCs achieve n = exp(O(k)), and lower bounds show that this is in fact tight. However, when q = 3, far less is known: the best constructions achieve n = exp(ko(1)), while the best known results only show a quadratic lower bound n ≥ Ω(k2/log(k)) on the blocklength.
An Efficient Decoder for a Linear Distance Quantum LDPC Code

Recent developments have shown the existence of quantum low-density parity check (qLDPC) codes with constant rate and linear distance. A natural question concerns the efficient decodability of these codes. In this paper, we present a linear time decoder for the recent quantum Tanner codes construction of asymptotically good qLDPC codes, which can correct all errors of weight up to a constant fraction of the blocklength. Our decoder is an iterative algorithm which searches for corrections within constant-sized regions. At each step, the corrections are found by reducing a locally defined and efficiently computable cost function which serves as a proxy for the weight of the remaining error.
Optimistic MLE: A Generic Model-Based Algorithm for Partially Observable Sequential Decision Making

This paper introduces a simple efficient learning algorithms for general sequential decision making. The algorithm combines Optimism for exploration with Maximum Likelihood Estimation for model estimation, which is thus named OMLE. We prove that OMLE learns the near-optimal policies of an enormously rich class of sequential decision making problems in a polynomial number of samples. This rich class includes not only a majority of known tractable model-based Reinforcement Learning (RL) problems (such as tabular MDPs, factored MDPs, low witness rank problems, tabular weakly-revealing/observable POMDPs and multi-step decodable POMDPs ), but also many new challenging RL problems especially in the partially observable setting that were not previously known to be tractable.
Testing Distributional Assumptions of Learning Algorithms

There are many important high dimensional function classes that have fast agnostic learning algorithms when strong assumptions on the distribution of examples can be made, such as Gaussianity or uniformity over the domain. But how can one be sufficiently confident that the data indeed satisfies the distributional assumption, so that one can trust in the output quality of the agnostic learning algorithm? We propose a model by which to systematically study the design of tester-learner pairs (A,T), such that if the distribution on examples in the data passes the tester T then one can safely trust the output of the agnostic learner A on the data.
Differences in syntactic annotation affect retrieval

Abstract Prepositional phrases (PPs) play an important part in English argument structure constructions, but pose considerable challenges for linguistic investigations of any kind. In addition to the fact that PP-attachment is generally notoriously difficult to model computationally, a particularly striking methodological challenge in investigating verb-dependent PPs across (synchronic and/or diachronic) corpora is that such cross-corpus studies may have to rely on material annotated with different tools. This study evaluates the impact that such differences in corpus annotation may have on retrieval of verb-attached PPs by means of data from Early and Late Modern English corpora. Our intrinsic (recall/precision) and extrinsic parser evaluation shows that annotation does play a role, but that the noise introduced is negligible as far as frequency developments are concerned.
Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions

The transfer of new insights from basic or clinical research into clinical routine is usually a lengthy and time-consuming process. Conversely, there are still many barriers to directly provide and use routine data in the context of basic and clinical research. In particular, no coherent software solution is available that allows a convenient and immediate bidirectional transfer of data between concrete treatment contexts and research settings. Here, we present a generic framework that integrates health data (e.g., clinical, molecular) and computational analytics (e.g., model predictions, statistical evaluations, visualizations) into a clinical software solution which simultaneously supports both patient-specific healthcare decisions and research efforts, while also adhering to the requirements for data protection and data quality. Specifically, our work is based on a recently established generic data management concept, for which we designed and implemented a web-based software framework that integrates data analysis, visualization as well as computer simulation and model prediction with audit trail functionality and a regulation-compliant pseudonymization service. Within the front-end application, we established two tailored views: a clinical (i.e., treatment context) perspective focusing on patient-specific data visualization, analysis and outcome prediction and a research perspective focusing on the exploration of pseudonymized data. We illustrate the application of our generic framework by two use-cases from the field of haematology/oncology. Our implementation demonstrates the feasibility of an integrated generation and backward propagation of data analysis results and model predictions at an individual patient level into clinical decision-making processes while enabling seamless integration into a clinical information system or an electronic health record.
Effects of metaverse-based career mentoring for nursing students: a mixed methods study

There is a lack of studies on metaverse-based career mentoring for college students in both quantitative and qualitative research. This study aimed to examine the effect of metaverse-based career mentoring among nursing students and explore the experiences of mentors and mentees.This study used a mixed methods design using both a survey for collecting quantitative data and focus group interviews for a qualitative one. A total of 8 mentors and 43 mentees participated in the metaverse-based career mentoring program. The program covered eight career fields and was delivered across eight sessions of 60 min each, over six days. Career decision-making self-efficacy among mentees and platform and program satisfaction were measured before and after the program. Afterwards, 7 mentors and 12 mentees participated in the focus group interviews to investigate their experience of participating in the metaverse-based career mentoring program. Quantitative data were analyzed using descriptive statistics, paired t-test, Wilcoxon signed-rank test, and Mann-Whitney U test. The qualitative data underwent thematic analysis.After the metaverse-based career mentoring program, mentees' career decision-making self-efficacy increased significantly compared to the baseline level. From the mentor-mentee focus group interviews, three key themes were derived: (i) communicating frankly and openly, (ii) being satisfied with realistic communication and program functions, and (iii) expecting an even more optimized program.A metaverse-based career mentoring program for nursing students can have a positive effect on their career decision-making self-efficacy. In addition, in terms of education, it is helpful as a non-face-to-face medium and feeling a sense of reality, so it is expected that it will be beneficial in education by applying various contents in the future.
Evaluating the pressure dependence of PZT structures using a virtual reality environment

Pb–Zr–Ti–O (PZT) perovskites span a large solid-solution range and have found widespread use due to their piezoelectric and ferroelectric properties that also span a large range. Crystal structure analysis via Rietveld refinement facilitates materials analysis via the extraction of the structural parameters. These parameters, often obtained as a function of an additional dimension (e.g., pressure), can help to diagnose materials response within a use environment. Often referred to as “ in-situ ” studies, these experiments provide an abundance of data. Viewing structural changes due to applied pressure conditions can give much-needed insight into materials performance. However, challenges exist for viewing/presenting results when the details are inherently three-dimensional (3D) in nature. For PZT perovskites, the use of polyhedra (e.g., Zr/Ti–O 6 octahedra) to view bonding/connectivity is beneficial; however, the visualization of the octahedra behavior with pressure dependence is less easily demonstrated due to the complexity of the added pressure dimension. We present a more intuitive visualization by projecting structural data into virtual reality (VR). We employ previously published structural data for Pb 0.99 (Zr 0.95 Ti 0.05 ) 0.98 Nb 0.02 O 3 as an exemplar for VR visualization of the PZT R3c crystal structure between ambient and 0.62 GPa pressure. This is accomplished via our in-house CAD2VR™ software platform and the new CrystalVR plugin. The use of the VR environment enables a more intuitive viewing experience, while enabling on-the-fly evaluation of crystal data, to form a detailed and comprehensive understanding of in-situ datasets. Discussion of methodology and tools for viewing are given, along with how recording results in video form can enable the viewing experience.
The Role of Cybersecurity in Confronting Intellectual Security Threats

The rapid development of digital human communication platforms and the globalization phenomenon have led to exposure to massive amounts of information every day. Furthermore, social media networks become mature platforms for social participation in information and interests exchanges, news, and highlighting events. This in turn can inevitably lead to various types of intellectual influences among people of different ages. This study aims to identify the role of cybersecurity in confronting intellectual security threats, such as extremism and terrorism. The study sample consists of 500 male and female students at Qassim University.
Context-Based Novel Histogram Bin Stretching Algorithm for Automatic Contrast Enhancement

This article presents CHBS, a novel context-based histogram bin stretching method that enhances the contrast by increasing the range of gray levels and randomness among the gray levels. It comprises image spatial contextual information and discrete cosine transform (DCT). It constitutes the global enhancement with the context-based histogram bin stretching and local details with the DCT. First, it uses the spatial similarities among surrounding pixels to generate random numbers. Unlike the other methods, the similarity map is generated based on the neighboring pixels’ mutual relationship. Intensity values are distributed among the available dynamic range to generate a global contrast-enhanced image. Second, the DCT is further applied to the previous contrast-enhanced image to adjust its local details automatically. Several experiments are conducted on the different levels of contrast degraded images. Both subjective and objective assessment outcomes validate that the projected approach is better or comparable with several state-of-the-art approaches in terms of brightness preservation, richer details, and natural appearance.