Reviewerly/exHarmony huggingface dataset:
Research on information construction of university management system app based on cognitive model

Based on the results of interviews conducted with university students in a certain university in Yunnan Province, this study identifies the problems of excessive information navigation, complex interface layout, and difficulty in locating the school system within the university management system APP. From the perspective of information architecture, this study designs a cognitive model that is suitable for the university management system APP of a certain university in Yunnan Province. By conducting tracking surveys and interviews with students from different majors, the study utilizes the affinity diagram method to construct a cognitive model for student users, which is further categorized by involving 10 participants. The data obtained after categorization is analyzed using hierarchical cluster analysis, and the information construction of the university management system APP is restructured accordingly. Through experiments, the characteristics and existing problems of the informationization construction of the university management system APP are determined, and the navigation label information of the management system is improved. The cognitive model of the university management system APP is reconstructed, resulting in the development of navigation names and classification methods for information construction that align with the cognitive models of student users. The research findings provide a basis and reference for other university management system APPs.
Research on distributed coding computation based on matrix multiplication

Matrix multiplication is used in machine learning to train larger, more accurate models with massive data more effectively. The distributed machine learning paradigm necessitates the distributed computing of many matrix multiplications once it is adopted. The execution time of distributed machine learning algorithms increases on dropout nodes, which are computational nodes in distributed clusters that randomly slow down computation due to a variety of factors (e.g., node failure, system failure, communication bottlenecks, etc.), becoming a significant bottleneck in distributed computing systems. It has been discovered that coded computation is less expensive than replica methods and can more effectively reduce the effects of dropped nodes. In this study, we present theoretical insights on how encoded solutions might produce significant gains over unencoded solutions.
HQ‐I2IT: Redesign the optimization scheme to improve image quality in CycleGAN‐based image translation systems

Abstract The image‐to‐image translation (I2IT) task aims to transform images from the source domain into the specified target domain. State‐of‐the‐art CycleGAN‐based translation algorithms typically use cycle consistency loss and latent regression loss to constrain translation. In this work, it is demonstrated that the model parameters constrained by the cycle consistency loss and the latent regression loss are equivalent to optimizing the medians of the data distribution and the generative distribution. In addition, there is a style bias in the translation. This bias interacts between the generator and the style encoder and visually exhibits translation errors, e.g. the style of the generated image is not equal to the style of the reference image. To address these issues, a new I2IT model termed high‐quality‐I2IT (HQ‐I2IT) is proposed. The optimization scheme is redesigned to prevent the model from optimizing the median of the data distribution. In addition, by separating the optimization of the generator and the latent code estimator, the redesigned model avoids error interactions and gradually corrects errors during training, thereby avoiding learning the median of the generated distribution. The experimental results demonstrate that the visual quality of the images produced by HQ‐I2IT is significantly improved without changing the generator structure, especially when guided by the reference images. Specifically, the Fréchet inception distance on the AFHQ and CelebA‐HQ datasets are reduced from 19.8 to 10.2 and from 23.8 to 17.0, respectively.
Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm

Denoising is a vital aspect of image preprocessing, often explored to eliminate noise in an image to restore its proper characteristic formation and clarity. Unfortunately, noise often degrades the quality of valuable images, making them meaningless for practical applications. Several methods have been deployed to address this problem, but the quality of the recovered images still requires enhancement for efficient applications in practice. In this paper, a wavelet-based universal thresholding technique that possesses the capacity to optimally denoise highly degraded noisy images with both uniform and non-uniform variations in illumination and contrast is proposed. The proposed method, herein referred to as the modified wavelet-based universal thresholding (MWUT), compared to three state-of-the-art denoising techniques, was employed to denoise five noisy images. In order to appraise the qualities of the images obtained, seven performance indicators comprising the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Structural Content (SC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Signal-to-Reconstruction-Error Ratio (SRER), Blind Spatial Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) were employed. The first five indicators – RMSE, MAE, SC, PSNR, SSIM, and SRER- are reference indicators, while the remaining two – NIQE and BRISQUE- are referenceless. For the superior performance of the proposed wavelet threshold algorithm, the SC, PSNR, SSIM, and SRER must be higher, while lower values of NIQE, BRISQUE, RMSE, and MAE are preferred. A higher and better value of PSNR, SSIM, and SRER in the final results shows the superior performance of our proposed MWUT denoising technique over the preliminaries. Lower NIQE, BRISQUE, RMSE, and MAE values also indicate higher and better image quality results using the proposed modified wavelet-based universal thresholding technique over the existing schemes. The modified wavelet-based universal thresholding technique would find practical applications in digital image processing and enhancement.
Organization of the anti-corruption education system through the potential of educational institutions

The study analyzes the main regulatory and theoretical provisions on the potential of educational institutions in the organization of the anti-corruption education system. This paper examines one of the aspects of the anti-corruption education program associated with the use of such a mechanism for combating corruption as anti-corruption education.&#x0D; The article discusses the positive experience of the leading university of the Murmansk region – the Federal state autonomous institution of Higher education «Murmansk Arctic University» in combating corruption. The author substantiates proposals for improving anti-corruption work in the Murmansk region, which can be used in practice with the help of educational institutions in the region.
An Automated Method for Generating Prefabs of AR Map Point Symbols Based on Object Detection Model

Augmented reality (AR) technology enables paper maps to dynamically express three-dimensional geographic information, realizing the fusion of virtual and real information. However, in the current mainstream AR development software, the virtual information usually consists of prefabricated components (prefabs), and the content creation for AR maps heavily relies on manual prefabrication. It leads to repetitive and error-prone prefabrication work, which restricts the design of the dynamic, interactive functions of AR maps. To solve this problem, this paper explored the possibility of automatically generating AR map prefabs using object detection models to establish a data conversion interface from paper maps to AR maps. First, we compared and analyzed various object detection models and selected YOLOv8x to recognize map point symbols. Then, we proposed a method to automatically generate AR map prefabs based on the predicted bounding boxes of the object detection model, which could generate prefabs with corresponding categories and positional information. Finally, we developed an AR map prototype system based on Android mobile devices. We designed an interaction method for information queries in the system to verify the effectiveness of the method proposed in this paper. The validation results indicate that our method can be practically applied to the AR map prefabrication process and can quickly generate AR map prefabs with high information accuracy. It alleviated the repetitive workload established through the manual prefabrication method and had specific feasibility and practicality. Moreover, it could provide solid data support for developing dynamic interactive functions of AR maps.
“Tell Me Why”: using natural language justifications in a recipe recommender system to support healthier food choices

Abstract Users of online recipe websites tend to prefer unhealthy foods. Their popularity undermines the healthiness of traditional food recommender systems, as many users lack nutritional knowledge to make informed food decisions. Moreover, the presented information is often unrelated to nutrition or difficult to understand. To alleviate this, we present a methodology to generate natural language justifications that emphasize the nutritional content, health risks, or benefits of recommended recipes. Our framework takes a user and two recipes as input and produces an automatically generated natural language justification as output, based on the user’s characteristics and the recipes’ features, following a knowledge-based recommendation approach. We evaluated our methodology in two crowdsourcing studies. In Study 1 ( $$N=502$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>502</mml:mn> </mml:mrow> </mml:math> ), we compared user food choices for two personalized recommendation approaches, based on either a (1) single-style justification or (2) comparative justification was shown, using a no justification baseline. The recommendations were either popularity-based or health-aware, the latter based on the health and nutritional needs of the user. We found that comparative justification styles were effective in supporting choices for our health-aware recommendations, confirming the impact of our methodology on food choices. In Study 2 ( $$N=504$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>=</mml:mo> <mml:mn>504</mml:mn> </mml:mrow> </mml:math> ), we used the same methodology to compare the effectiveness of eight different comparative justification strategies. We presented pairs of recipes twice to users: once without and once with a pairwise justification. Results indicated that justifications led to significantly healthier choices for first course meals, while strategies that compared food features and emphasized health risks, benefits, and a user’s lifestyle were most effective, catering to health-related choice motivations.
Universal Quantum Computation in Globally Driven Rydberg Atom Arrays

We develop a model for quantum computation with Rydberg atom arrays, which only relies on global driving, without the need of local addressing of the qubits: any circuit is executed by a sequence of global, resonant laser pulses on a static atomic arrangement. We present two constructions: for the first, the circuit is imprinted in the trap positions of the atoms and executed by the pulses; for the second, the atom arrangement is circuit-independent, and the algorithm is entirely encoded in the global driving sequence. Our results show in particular that a quadratic overhead in atom number is sufficient to eliminate the need for local control to realize a universal quantum processor. We give explicit protocols for all steps of an arbitrary quantum computation, and discuss strategies for error suppression specific to our model. Our scheme is based on dual-species processors with atoms subjected to Rydberg blockade constraints, but it might be transposed to other setups as well.
Relating Context and Self Awareness in the Internet of Things

Context- and self- awareness are two terms that have been living with us for many years. In principle, both state a similar meaning even though the literature points out a very different path. One is inspired by location-related mechanisms in mobile environments, whereas the other is inspired by biology. In the area of the Internet of Things, the term context-awareness has seen a higher adoption in the field of Cloud Computing, while the term self-awareness is more widely used in the area of Wireless Sensor Networks. This paper evaluates the entire IoT Cloud-to-Thing Continuum in an attempt to reconcile both terms. We contextualize and discuss the literature around context and self-awareness, and we propose a conceptual architecture that handles both concepts, with the aim of having a better understanding of how to develop a software environment that integrates both concepts. To show the real-life applicability of our proposed architecture, it is introduced in a realistic setting such as wildfire monitoring, including a conceptual overview of how the proposed architecture could be implemented in this domain. Additionally, our evaluation of a river flooding scenario concluded that the proposed architecture significantly reduced flood detection delay by over 47% compared to the naive method and over 20% compared to standalone self-awareness and context-awareness methods.
EXABSUM: a new text summarization approach for generating extractive and abstractive summaries

Abstract Due to the exponential growth of online information, the ability to efficiently extract the most informative content and target specific information without extensive reading is becoming increasingly valuable to readers. In this paper, we present 'EXABSUM,' a novel approach to Automatic Text Summarization (ATS), capable of generating the two primary types of summaries: extractive and abstractive. We propose two distinct approaches: (1) an extractive technique (EXABSUM Extractive ), which integrates statistical and semantic scoring methods to select and extract relevant, non-repetitive sentences from a text unit, and (2) an abstractive technique (EXABSUM Abstractive ), which employs a word graph approach (including compression and fusion stages) and re-ranking based on keyphrases to generate abstractive summaries using the source document as an input. In the evaluation conducted on multi-domain benchmarks, EXABSUM outperformed extractive summarization methods and demonstrated competitiveness against abstractive baselines.
Deform2NeRF: Non-Rigid Deformation and 2D–3D Feature Fusion with Cross-Attention for Dynamic Human Reconstruction

Reconstructing dynamic human body models from multi-view videos poses a substantial challenge in the field of 3D computer vision. Currently, the Animatable NeRF method addresses this challenge by mapping observed points from the viewing space to a canonical space. However, this mapping introduces positional shifts in predicted points, resulting in artifacts, particularly in intricate areas. In this paper, we propose an innovative approach called Deform2NeRF that incorporates non-rigid deformation correction and image feature fusion modules into the Animatable NeRF framework to enhance the reconstruction of animatable human models. Firstly, we introduce a non-rigid deformation field network to address the issue of point position shift effectively. This network adeptly corrects positional discrepancies caused by non-rigid deformations. Secondly, we introduce a 2D–3D feature fusion learning module with cross-attention and integrate it with the NeRF network to mitigate artifacts in specific detailed regions. Our experimental results demonstrate that our method significantly improves the PSNR index by approximately 5% compared to representative methods in the field. This remarkable advancement underscores the profound importance of our approach in the domains of new view synthesis and digital human reconstruction.
Beating the standard quantum limit for binary phase-shift-keying discrimination with a realistic hybrid feed-forward receiver

We propose a hybrid feed-forward receiver (HFFRE) for the discrimination of binary phase-shift-keyed coherent states based on the appropriate combination of a displacement feed-forward receiver (DFFRE) and a homodynelike setup employing a low-intensity local oscillator and photon-number-resolving detectors. We investigate the performance of the proposed scheme, also addressing realistic scenarios in the presence of nonunit quantum detection efficiency, dark counts, and visibility reduction. The present HFFRE outperforms the DFFRE in all conditions, beating the standard quantum limit in particular regimes.
MOBILE-ASSISTED LANGUAGE LEARNING IN KOREAN LANGUAGE CLASSES: INDONESIAN UNDERGRADUATE STUDENTS’ EXPERIENCES AND PERCEPTIONS

The ubiquitous presence of mobile devices has benefited the implementation of foreign language learning through Mobile-Assisted Language Learning (MALL). Previous research highlights the portability aspect of mobile devices such as smartphones that can lead to spontaneous, flexible, and personalized learning activities. However, challenges remain; particularly the connectivity issues. The present study seeks to continue the discussion by investigating 79 Indonesian undergraduate students’ experiences and perceptions of MALL in their Korean language classes. The data were obtained by distributing a questionnaire to the students. The validity and reliability coefficients were also measured. Interview sessions were conducted afterward with eleven students to dig for further explanations. The results demonstrate that most students used smartphones for sending messages to peers, checking notifications, accessing materials, submitting their work, and sending messages to lecturers. Regarding the applications used, they frequently utilized smartphones for accessing Google Classroom, Zoom, and WhatsApp during their Korean classes. The results also revealed that while they perceived MALL as useful and flexible for learning Korean toward increased language skills, motivation, autonomy, and critical thinking, technical problems, and connectivity issues remain to be the main challenges. This study discusses some concerns in implementing MALL and its implications for future research.
Research on the Enhancement Method of Specific Emitter Open Set Recognition

Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor performance when the unknown class is very similar to the known class. Considering the subtle individual differences under the same type in specific emitter identification (SEI) applications, it is difficult to distinguish between known classes and unknown classes in open set scenarios. This paper proposes a pseudo signal generation and recognition neural network (PSGRNN) to address relevant problems in this situation. PSGRNN applies complex-value convolution operations to accommodate IQ signal inputs. Its key idea is to utilize samples of known classes to generate pseudo samples of unknown classes. Then, the samples of known classes and the generated pseudo samples of unknown classes are jointly input into the neural network to construct a new classification task for training. Moreover, the center loss is improved by adding inter-class penalties to maximize the inter-class difference. This helps to learn useful information for separating known and unknown classes, resulting in clearer decision boundaries between the known and the unknown. Extensive experiments on various benchmark signal datasets indicate that the proposed method achieves more accurate and robust open set classification results, with an average accuracy improvement of 4.62%.
Partitioning qubits in hypergraph product codes to implement logical gates

The promise of high-rate low-density parity check (LDPC) codes to substantially reduce the overhead of fault-tolerant quantum computation depends on constructing efficient, fault-tolerant implementations of logical gates on such codes. Transversal gates are the simplest type of fault-tolerant gate, but the potential of transversal gates on LDPC codes has hitherto been largely neglected. We investigate the transversal gates that can be implemented in hypergraph product codes, a class of LDPC codes. Our analysis is aided by the construction of a symplectic canonical basis for the logical operators of hypergraph product codes, a result that may be of independent interest. We show that in these codes transversal gates can implement Hadamard (up to logical SWAP gates) and control-Z on all logical qubits. Moreover, we show that sequences of transversal operations, interleaved with error correction, allow implementation of entangling gates between arbitrary pairs of logical qubits in the same code block. We thereby demonstrate that transversal gates can be used as the basis for universal quantum computing on LDPC codes, when supplemented with state injection.
The Rogue Scholar API now automatically indexes blog posts

The dedicated API for the Rogue Scholar science blog archive launched two weeks ago. The initial release supported fetching metadata and content from Rogue Scholar. Today this API was updated with important new functionality: parsing of science blog posts and storing the metadata and content in the Rogue Scholar.
A compressed sensing-based progressive secret image sharing scheme and security analysis

Cryptographic secret image sharing (SIS) allows a secret image to be shared among participants, and any authorized set of participants can reconstruct the secret. In this paper, we first propose a conceptually simple way to construct a progressive SIS scheme based on semi-tensor product compressed sensing (STP-CS). In the sharing phase, the dealer employs STP-CS to compress the secret image and extracts several different rows of the compressed image in turn. Then the sub-shadows for participants are formed. In addition, he needs to employ a perfect secret sharing to share the measurement matrix (or an initial state of the linear feedback shift register) among all participants. Finally, the sub-shadow and the share of the measurement matrix together constitute the shadow of each participant. In the combination phase, any legitimate access structure can reconstruct the secret image gradually. The perfect secret sharing ensures that the combination phase can work if and only if the access structure is authorized. Simultaneously, the use of STP-CS ensures that the proposed scheme has the properties of gradual reconstruction, noise-resilience, and smaller shadow size. Also, we have proven that the proposed scheme achieves the extended Wyner-sense perfect secrecy. Besides, we provide an extend version, referred to as homomorphic secret image sharing (HSIS), for improving the efficiency of the combination algorithm. HSIS can outsource the combination phase to the cloud servers securely.