Reviewerly/exHarmony huggingface dataset:
An alternative, versatile, high‐tolerance design of a modified <scp>Richter–Slevogt</scp> camera, using standard glasses

Abstract Prime‐focus catadioptric astrographs have been used for a long time in various astronomical applications. The prime advantage offered by them is the capability to produce remarkably wide fields of view, and hence generate a huge amount of data in relatively less observation time. An emerging application of such wide‐field astrographs is in the form of telescope arrays. While this has been implemented mostly, using commercial refractive lenses, low‐cost catadioptric objectives can be used as an alternative for wide‐field or high‐contrast array applications. Commercial catadioptric systems are generally available as modifications of Schmidt and Maksutov systems, that too, mostly in the Cassegrain configuration. Here, we present a low‐cost alternative prime focus camera design of Richter – Slevogt configuration, which is in turn an extension of the Houghton correctors. The Richter – Slevogt design has the potential for a very high performance due to several degrees of freedom. The presented one is a 150 mm aperture, system, providing 3.5° (circular) diffraction‐limited FOV (strehl ), using only standard glasses, N‐BK7 and F2. We present the performance analysis, tolerance sensitivity, and statistical (Monte‐Carlo) analysis for this design. Potential applications of the system, other than as an array are also briefly discussed.
Empowering complex-valued data classification with the variational quantum classifier

The evolution of quantum computers has encouraged research into how to handle tasks with significant computation demands in the past few years. Due to the unique advantages of quantum parallelism and entanglement, various types of quantum machine learning (QML) methods, especially variational quantum classifiers (VQCs), have attracted the attention of many researchers and have been developed and evaluated in numerous scenarios. Nevertheless, most of the research on VQCs is still in its early stages. For instance, as a consequence of the mathematical constraints imposed by the properties of quantum states, the majority of research has not fully taken into account the impact of data formats on the performance of VQCs. In this paper, considering a significant number of data in the real world exist in the form of complex numbers, i.e., phasor data in power systems and the result of Fourier transform on image processing, we develop two categories of data encoding methods, including coupling data encoding and splitting data encoding. This paper features the coupling data encoding method to encode complex-valued data in a way of amplitude encoding. By leveraging the property of quantum states living in a complex Hilbert space, the complex-valued data is embedded into the amplitude of quantum states to comprehensively characterize complex-valued information. Optimizers will be utilized to iteratively tune a parameterized ansatz, with the aim of minimizing the value of loss functions defined with respect to the specific classification task. In addition, distinct factors in VQCs have been explored in detail to investigate the performance of VQCs, including data encoding methods, loss functions, and optimizers. The experimental result shows that the proposed data encoding method outperforms other typical encoding methods on a given classification task. Moreover, different loss functions are tested, and the capability of finding the minimum value is evaluated for gradient-free and gradient-based optimizers, which provides valuable insights and guidelines for practical implementations.
A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation

Abstract With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present, recommendation methods which based on heterogeneous information network (HIN) have attracted wide attention. Recently, HIN-based recommendation methods need to be modeled from two aspects: node structural association and semantic association. To this end, we propose a graph contrastive learning model based on structural and semantic view for HIN recommendation (GCL-SS). GCL-SS utilizes U-I interactive view to obtain node structural embeddings, and utilizes U-I semantic view to obtain node semantic embeddings. Based on these two kinds of embeddings, we establish a self-supervised contrastive learning mechanism to effectively integrate structural information and semantic information of user (item) nodes in HIN, and finally learn a more discriminative user (item) embedding. In addition, in order to strengthen the semantic association between nodes, we innovatively utilize time sequence encoder (TSE), such as LSTM, to encode semantic homogeneous network decomposed by HIN in U-I semantic view. At last, based on the user and item embeddings, we adopt bilinear decoder to model the potential association between user and item, so as to realize rating prediction of user to item. The experimental results on three real datasets confirm that our GCL-SS model performs better than state-of-the-art recommendation methods in rating prediction task. In addition, the results of four ablation experiments indicate that our GCL-SS model can effectively improve the performance of rating prediction in recommendation.
A multi-center distributed learning approach for Parkinson's disease classification using the traveling model paradigm

Distributed learning is a promising alternative to central learning for machine learning (ML) model training, overcoming data-sharing problems in healthcare. Previous studies exploring federated learning (FL) or the traveling model (TM) setup for medical image-based disease classification often relied on large databases with a limited number of centers or simulated artificial centers, raising doubts about real-world applicability. This study develops and evaluates a convolution neural network (CNN) for Parkinson's disease classification using data acquired by 83 diverse real centers around the world, mostly contributing small training samples. Our approach specifically makes use of the TM setup, which has proven effective in scenarios with limited data availability but has never been used for image-based disease classification. Our findings reveal that TM is effective for training CNN models, even in complex real-world scenarios with variable data distributions. After sufficient training cycles, the TM-trained CNN matches or slightly surpasses the performance of the centrally trained counterpart (AUROC of 83% vs. 80%). Our study highlights, for the first time, the effectiveness of TM in 3D medical image classification, especially in scenarios with limited training samples and heterogeneous distributed data. These insights are relevant for situations where ML models are supposed to be trained using data from small or remote medical centers, and rare diseases with sparse cases. The simplicity of this approach enables a broad application to many deep learning tasks, enhancing its clinical utility across various contexts and medical facilities.
Breast mass regions classification from mammograms using convolutional neural networks and transfer learning.

This study introduces a novel approach aimed at enhancing the quality of digital mammography images through pre-processing techniques, to improve breast cancer detection accuracy. The primary objective is to enhance image resolution, thus leading to more precise breast tissue segmentation and subsequent classification utilizing convolutional neural networks (CNNs). Three recognized public mammography databases: CBIS-DDSM, Mini-MIAS, and Inbreast were used as pre-processing data. Our statistical findings revealed that the EDSR method (PSNR = 39.05 dB/ SSIM = 0.90) consistently outperformed the visual quality of images when compared to SR-RDN (PSNR = 32.68 dB/SSIM = 0.82). Similarly, UNet demonstrated superior performance over SegNet, boasting an average Intersection over Union (IoU) of 0.862, an average Dice coefficient of 0.991, and an accuracy rate of 0.947 in Region of Interest (RoI) segmentation results. In conclusion, the ResNet model contributed to enhanced accuracy compared to conventional machine learning algorithms. However, it did not surpass state-of-the-art deep CNN-based classifiers, achieving an accuracy rate of 75%.
“Dirclustering”: a semantic clustering approach to optimize website structure discovery during penetration testing

Dirbusting is a technique used to brute force directories and file names on web servers while monitoring HTTP responses in order to enumerate server contents. Such a technique uses lists of common words to discover the hidden structure of the target website. Dirbusting typically relies on response codes as discovery conditions to find new pages. It is widely used in web application penetration testing, an activity that allows companies to detect website vulnerabilities. Dirbusting techniques are both time and resource-consuming, and innovative approaches have never been explored in this field. We hence propose "Dirclustering", an advanced technique to optimize the dirbusting process by leveraging semantic clustering. Specifically, we use semantic clustering techniques to organize wordlist items in different groups according to their semantic meaning. The created clusters are used in an ad-hoc implemented next-word intelligent strategy. This paper demonstrates that clustering techniques outperform the commonly used brute-force methods. Performance is evaluated by testing eight different web applications. Results show a performance increase that is up to 50% for each of the conducted experiments.
Model of collaborative information retrieval behavior using information systems among academic researchers of medical sciences

Aim The purpose of this study was to analyze the collaborative information retrieval (CIR) behavior among faculty members of research centers based in the University of Medical Sciences to present a structural model. Methodology The type of this experimental study was based on a pretest-posttest with the control group. Data were collected using a questionnaire, and the participants were selected by stratified random sampling. Out of a total of 73 participants, there were 42 people in the intervention group and 31 participants in the control group. Findings The variables of awareness, information sharing, division of labor, and team communication had a significant positive relationship with the team collaboration (p &lt; 0.05), collaboration had effect on the evaluation of retrieved information with a significant positive relationship (p &lt; 0.05). Conclusion Based on the findings, CIR is the set of activities that can include factors such as the ability to share the obtained knowledge, analyze the retrieved information, communicate, and collaborative awareness. The structural model confirmed our findings that developing the interactive and synergies capabilities of research team members to increase collaboration through a network-based structure reduces the loss of stored collective knowledge and also accelerates the process of access to information resources, and required content. Originality This research has carried on as the one of the first studies on collaborative information retrieval behavior with an experimental method in Iran and many others countries. A considerable point in the present study was assessing the nature of CIR behavior from the perspective of human behaviors, which was mentioned as a very important approach in information science. Accordingly, a human behavioral approach to information retrieval was considered and strengthened, regardless of technical aspects. Another strength of this study was the model extracted from our findings which have emphasized specifically CIR as specific and dynamic activities.
Data privacy model using blockchain reinforcement federated learning approach for scalable internet of medical things

Abstract Internet of Medical Things (IoMT) has typical advancements in the healthcare sector with rapid potential proof for decentralised communication systems that have been applied for collecting and monitoring COVID‐19 patient data. Machine Learning algorithms typically use the risk score of each patient based on risk factors, which could help healthcare providers decide about post‐COVID‐19 care and follow‐up where the data privacy is another severe concern. The authors investigate the applicability of a distributed reinforcement learning approach in a Federated Learning (FL) multi‐disciplinary reinforcement system and explores the potential benefits of incorporating Blockchain Technology (BT) in the distributed system. Intermediate dependency features and transactions are avoided by applying Blockchain‐enabled reinforcement FL for the post‐COVID‐19 patient data of IoMT applications. The proposed approach helps to improvise clinical monitoring and ensure secure communication and data privacy in a decentralised manner. The main objective is to improve the efficiency and scalability of the reinforcement FL process in a distributed environment while ensuring data privacy and security through BT for IoMT applications. Results show that proposed approach achieve comparatively high reliability and outperforms the existing approaches.
The semiotics of emojis in advertising: An integrated quantitative and qualitative examination of emotional versus functional ad dynamics

Abstract In today's marketplace, consumers encounter a multitude of advertisements incorporating emojis. This research undertook two complementary studies to assess how individuals appraise emotional versus functional advertisements containing emojis, examining outcomes like processing fluency, claim believability, attitude toward the ad, and purchase intention. Study 1 employed a factorial design, resulting in four experimental conditions: emotional ads without emojis, functional ads without emojis, emotional ads with emojis, and functional ads with emojis. Study 2 delved deeper, offering a qualitative exploration of consumer perceptions and sentiments concerning emojis in advertising contexts. Grounded in the Emotion as social information theory, results indicated that within the sports drink advertising domain, emotional advertising without emojis led to enhanced ad and brand attitudes, purchase intentions, claim believability, and information processing when contrasted with conditions incorporating emojis.
MA‐CAT: Misclassification‐Aware Contrastive Adversarial Training

Vulnerability to adversarial examples poses a significant challenge to the secure application of deep neural networks. Adversarial training and its variants have shown great potential in addressing this problem. However, such approaches, which directly optimize the decision boundary, often result in overly complex adversarial decision boundaries that are detrimental to generalization. To deal with this issue, a novel plug‐and‐play method known as Misclassification‐Aware Contrastive Adversarial Training (MA‐CAT) from the perspective of data distribution optimization is proposed. MA‐CAT leverages supervised decoupled contrastive learning to cluster nature examples within the same class in the logit space, indirectly increasing the margins of examples. Moreover, by taking into account the varying difficulty levels of adversarial training for different examples, MA‐CAT adaptively customizes the strength of adversarial training for each example using an instance‐wise misclassification‐aware adaptive temperature coefficient. Extensive experiments on the CIFAR‐10, CIFAR‐100, and SVHN datasets demonstrate that MA‐CAT can be easily integrated into existing models and significantly improves robustness with minimal computational cost.
An energy-efficient image filtering interpolation algorithm using domain-specific dynamic reconfigurable array processor

With the development of micro-LED displays, the bicubic interpolation algorithm has become a critical technology. It is widely utilized in micro-LED displays to enhance the scaled details of video frames. However, the produced image exhibits the blurring effect and boundary artifacts after scaling. To address the above problems, we propose an image filtering interpolation algorithm. The smooth and Laplace filters are pre-filters to enhance image edges and reduce noise. The four corner-aligned bicubic interpolation algorithm has the high-quality effect of scaling. Secondly, to enhance the processing speed and energy-efficient of the image filtering interpolation algorithm, a domain-specific dynamic reconfigurable array processor is also proposed, which contains a 8 × 8 processing element array with two types of heterogeneous processing elements. The processor supports the energy-efficient acceleration of the image filtering interpolation algorithm. To reduce hardware costs, we integrate the attributes of the smooth filter and the Laplace filter into a simplified composite filter. Besides, we utilize the efficient alternating line caching technology to eliminate the power consumption of repeatedly reading data through reorchestrates data cache order and keep reused data. The experiment results show that the peak signal-to-noise ratio of the proposed image filtering interpolation algorithm is 0.32 dB better than the state-of-the-art methods. In the implementation of the HLMC 55 nm process, the energy-efficient of the proposed processor is 23,661 MPixels/J. Moreover, the processing rate achieves 583.5 MPixels/s for three DRAP, enough to 3840 × 2160@60fps. Using alternating line caching technology, the reconfigurable array processor reduces 37.5% of line buffer resources.
Revisiting technology-assisted language learning affordances for language components: a decade survey

Given the important technology-assisted language learning (TALL)-empowered affordances, a comprehensive understanding of how these TALL tools facilitate learning across different language components, e.g., phonology, morphology, syntax, semantics, and pragmatics, could not only present the panoramic scenery of the subject matter but also inform pedagogy for applied linguistics researchers and practitioners. Despite the increasing attention being paid to the affordances of a particular TALL platform, there is still a lack of more holistic research practice to the affordances for the five language components. To bridge the gap, this study aims to systematically and quantitively revisit the TALL-empowered affordances with respect to these language components. To this end, a total of valid 201 articles published from 2010 to 2023 were selected and coded based on the proposed inclusion/exclusion criteria. Results revealed that different TALL platforms (viz. computer, mobile, tablet, virtual reality, and robot) help improve students' language learning performance and efficacy among the five language components. The implications of the study, in perfect tune with the distance language learning experiences in the post COVID-19 pandemic era, justify the need to take full advantage of the TALL affordances and accelerate the move toward a more technology-friendly field of study.
Bidirectional Obstacle Avoidance Enhancement‐Deep Deterministic Policy Gradient: A Novel Algorithm for Mobile‐Robot Path Planning in Unknown Dynamic Environments

Real‐time path planning in unknown dynamic environments is a significant challenge for mobile robots. Many researchers have attempted to solve this problem by introducing deep reinforcement learning, which trains agents through interaction with their environments. A method called BOAE‐DDPG, which combines the novel bidirectional obstacle avoidance enhancement (BOAE) mechanism with the deep deterministic policy gradient (DDPG) algorithm, is proposed to enhance the learning ability of obstacle avoidance. Inspired by the analysis of the reaction advantage in dynamic psychology, the BOAE mechanism focuses on obstacle‐avoidance reactions from the state and action. The cross‐attention mechanism is incorporated to enhance the attention to valuable obstacle‐avoidance information. Meanwhile, the obstacle‐avoidance behavioral advantage is separately estimated using the modified dueling network. Based on the learning goals of the mobile robot, new assistive reward factors are incorporated into the reward function to promote learning and convergence. The proposed method is validated through several experiments conducted using the simulation platform Gazebo. The results show that the proposed method is suitable for path planning tasks in unknown environments and has an excellent obstacle‐avoidance learning capability.
Alternative method to reveal encoded images via Gaussian distribution functions

In this work, we present an image processing approach that proved to be effective to reveal an image that was codified by different substitution boxes. The image was hidden by applying a different substitution box on each line producing an image with uniform distribution. Once the original image was codified through this procedure, the codified picture presented a shapeless uniform grey tone due to encoding. In order to reveal the image several assumptions were made, the most important of which was to consider that the gray tones of the original image could be reproduced by a normal o Gaussian distribution or by a superposition of several Gaussian functions. This assumption was implemented at the level of each line of the image by replacing the encoded pixels with those generated by a Gaussian distribution. Among the main findings of this work, the following are worth highlighted: (i) According to our research, for the first time Gaussian distribution functions are used to reveal encoded images, without the need to decode them; (ii) a general technique was implemented to reveal an encoded image, which is applicable to encoded images in which a different encoding rule was applied to each row of the original image; (iii) the values generated by the Gaussian functions were used to replace the pixel values of the encoded image, but their original positions were maintained; (iv) Image enhancement, including contrast, gray tone balance and feature definition, was achieved either by varying the number of Gaussian functions, or dividing the image in zones, or by adjusting pixels 0 and 255, among other resources used. Finally, it is also worth noting that this is a general procedure applicable to decode any image encoded using substitution boxes, as long as the original positions of the encoded pixels are preserved. This method has considerable simplicity and the best results were obtained when applied to high-resolution images.