Reviewerly/exHarmony huggingface dataset:
A YOLO-based AI system for classifying calcifications on spot magnification mammograms

Abstract Objectives Use of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies. Methods In this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis. Results We included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868–0.908), with a sensitivity of 88.4% (95% CI 86.9–8.99%), specificity of 80.8% (95% CI 77.6–84%), and an accuracy of 84.6% (95% CI 81.8–87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided. Conclusion The AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.
Improving Student’s Cognitive Performance during the Pandemic through a Machine Learning-Based Virtual Museum

This study presents the investigation of the innovative virtual application developed for cultural education through museum collections. It’s designed as an innovative virtual museum model for learning media containing various contents. Furthermore, it integrates machine learning to predict student interests which are expected to be effective in improving cognitive performance. The conceptual model design is continued with the development of an innovative virtual museum application tested on students, and the effectiveness was evaluated by taking a sample of N1 = N2= 32 participants in experimental and control group. Meanwhile, the pre-test and post-test were conducted to measure the user’s cognitive performance before and after using the innovative virtual museum application. The experimental group results show that the proposed application strongly affects students’ cognitive performance improvement in cultural education. The effect size value is 1.396, where the average score of post-test = 57.946 and the standard deviation = 21.421. Therefore, the proposed application strongly affects museum learning, while the t-test results show a significant difference between the experimental and control groups.
Leakage-Resilient Certificateless Signcryption Scheme Under a Continual Leakage Model

Signature can be used to verify the integrity of both a message and the identity of a signer, whereas encryption can be used to ensure the confidentiality of a message.In the past, cryptography researchers have studied and proposed numerous certificateless signcryption (CLSC) schemes to combine the benefits of both signature and encryption.However, these schemes may not be robust enough to withstand side-channel attacks.Through such attacks, an attacker can constantly retrieve a portion of a private key of the system, and could eventually recover the entire private key.Leakage-resilient certificateless signcryption (LR-CLSC) can ensure its security when the attacker launches such attacks.As far as we know, the existing LR-CLSC schemes can only guarantee the security under a bounded leakage model, where the portion of the private key that an attacker can obtain through side-channel attacks is limited.In this paper, we propose the first LR-CLSC scheme under a continual leakage model.Also, we demonstrate the proposed scheme is secure for the existential unforgeability and the ciphertexts indistinguishability against attackers with sidechannel attacking abilities.
FPGA Implementation of Parameter-Switching Scheme to Stabilize Chaos in Fractional Spherical Systems and Usage in Secure Image Transmission

The main objective of this work was to implement the parameter-switching chaos control scheme for fractional-order spherical systems and develop a chaos-based image encryption and transmission system. The novelty in the developed secure communication system is the application of the parameter-switching scheme in the decryption of RGB and grayscale images, which undergo one round of encryption using the chaotic states of the fractional system and a diffusion process. The secure communication system has a synchronized master and slave topology, resulting in transmitter and receiver systems for encrypting and decrypting images, respectively. This work was demonstrated numerically and also implemented on two FPGAs, namely Artix-7 AC701 and Cyclone V. The results show that the parameter-switching scheme controls chaos in the fractional-order spherical systems effectively. Furthermore, the performance analysis of the image encryption and transmission system shows that there is no similarity between the original and encrypted images, while the decryption of the encrypted images is without a loss of quality. The best result in terms of the encryption was obtained from the chaotic state x of the fractional-order system, with correlation coefficients of 0.0511 and 0.0392 for the RGB and grayscale images, respectively. Finally, the utilization of the FPGA logical resources shows that the implementation on Artix-7 AC701 is more logic-efficient than on Cyclone V.
Global guidance-based integration network for salient object detection in low-light images

Most of current salient object detection (SOD) methods focus on well-lit scenes, and their performance drops when generalized into low-light scenes due to limitations such as blurred boundaries and low contrast. To solve this problem, we propose a global guidance-based integration network (G2INet) customized for low-light SOD. First, we propose a Global Information Flow (GIF) to extract comprehensive global information, for guiding the fusion of multi-level features. To facilitate information integration, we design a Multi-level features Cross Integration (MCI) module, which progressively fuses low-level details, high-level semantics, and global information by interweaving. Furthermore, a U-shaped Attention Refinement (UAR) module is proposed to further refine edges and details for accurate saliency predictions. In terms of five metrics, extensive experimental results demonstrate that our method outperforms the existing twelve state-of-the-art models.
Secure Model-Contrastive Federated Learning With Improved Compressive Sensing

Federated Learning (FL) has been widely used in various fields such as financial risk control, e-government and smart healthcare. To protect data privacy, many privacy-preserving FL approaches have been designed and implemented in various scenarios. However, existing works incur high communication burdens on clients, and affect the training model accuracy due to non-Independently and Identically Distributed (non-IID) data samples separately owned by clients. To solve these issues, in this paper we propose a secure Model-Contrastive Federated Learning with improved Compressive Sensing (MCFL-CS) scheme, motivated by contrastive learning. We combine model-contrastive loss and cross-entropy loss to design the local network architecture of our scheme, which can alleviate the impact of data heterogeneity on model accuracy. Then we utilize improved compressive sensing and local differential privacy to reduce communication costs and prevent clients’ privacy leakage. The formal security analysis shows that our scheme satisfies (ε,δ)-differential privacy. And extensive experiments using five benchmark datasets demonstrate that our scheme improves the model accuracy by 3.45% on average of all datasets under the non-IID setting and reduces the communication costs by more than 95%, when compared with FedAvg.
Comparison of LSTM and IndoBERT Method in Identifying Hoax on Twitter

In recent years, social media users have been increasing significantly, in January 2022 social media users in Indonesia reached 191 million people which has an increase of 12.35% from the previous year as many as 170 million people, With this massive increase every year, more and more people tend to seek and consume information through social media. Despite the many advantages provided by social media, However, the quality of information on social media is lower than in traditional news media there is a lot of hoax information spreading. With many disadvantages felt by hoax information, it has led to many research to detect hoax information on social media, especially information that is widely spread on Twitter. There are several previous researches that use various models using machine learning and also using deep learning to detect hoax. deep learning is very well used to perform several text classification tasks, especially in detecting hoax. The aim of this paper is to compare the LSTM and IndoBERT methods in detecting hoax using datasets taken from Twitter. In this study, two experiments work are conducted, LSTM and IndoBERT methods. The experimental results is average value obtained from experiments using 10-fold cross-validation. The IndoBERT model shows good performance with an average accuracy value of 92.07%, and the LSTM model provides an average accuracy value of 87.54%. The IndoBERT model can show good performance in hoax detection tasks and is shown to outperform the LSTM model which can provide the best average accuracy results in this study.
The impact of strategic intelligence and asset management on enhancing competitive advantage: The mediating role of cybersecurity

Companies utilize competitive advantage as a tool to assist them gain more value for their products at a cheaper cost without sacrificing quality to provide greater features and services. Companies and services must use cybersecurity tools, training, and risk management strategies, as well as regularly upgrade systems as technology changes and evolves, to secure organizations, employees, and individuals. As a result, with the mediating function of cybersecurity, this study clarified the influence of strategic intelligence and asset management on boosting competitive advantage. A questionnaire was designed, and 300 questionnaires were collected out of 350 distributed to respondents working in Jordanian telecom companies. The study found a positive impact of both strategic intelligence and asset management on enhancing competitive advantage through the presence of the mediating role of cybersecurity.
The moderating effect of strategic momentum on the relationship between big data analytics capabilities and lean supply chain practices

The present study aimed to explore the moderating role of strategic momentum in the relationship between big data analytics capabilities and lean supply chain practices in eight textile companies in Jordan. A quantitative research methodology incorporating a cross-sectional design was adopted to gather questionnaire-based responses to investigate the hypotheses put forth. The sample for the study consisted of 116 respondents, who were selected from a diverse group of senior executives from various fields, including IT, logistics, marketing, production, and strategic planning. These individuals possessed both knowledge and skills in data and business analytics disciplines. The data were analyzed utilizing SPSS version 28 and the PROCESS v3.5 macro developed by Andrew F. Hayes. The results revealed that the strategic momentum positively moderates the relationship between big data analytics capabilities and lean supply chain practices. These findings indicate that high levels of strategic momentum allow an organization to increase resources and focus on investing in and developing its big data capabilities, thereby supporting the implementation of lean supply chain practices.
Sustainable strategy for online physical education teaching using ResNet34 and big data

Since the global COVID-19 outbreak in the spring of 2020, online instruction has replaced traditional classroom instruction as the main method of educating students. Teaching physical education online can be challenging, as it may be difficult to teach students certain movements, accurate student mobility, and appropriate exercise assignments. This paper proposed an online teaching support system with sustainable development features that utilize several large data sets. The system is based on the deep learning image recognition algorithm ResNet34, which can analyze and correct student actions in real-time for gymnastics, dance, basketball, and other sports. By combining the attention mechanism module with the original ResNet34, the detection precision of the system can be enhanced. The sustainability of the system is evident from the fact that the data set can be expanded in response to the emergence of new sports categories and can be kept current in real-time. According to experiments, the target identification accuracy of the proposed system, which combines ResNet34 and the attention mechanism, is higher than that of several other methods currently in use. The proposed techniques outperform the original ResNet34 in terms of accuracy, precision, and recall by 4.1%, 2.8%, and 3.6%, respectively. The suggested approach significantly improves student action correction in virtual sports instruction.
EmbedR-Net: Using CNN to Embed Mark with Recovery through Deep Convolutional GAN for Secure eHealth Systems

The goal of security is to protect digital assets, devices and services from being disrupted, exploited or stolen by unauthorised users. It is also about having reliable media information available at the right time. However, the media distortion will pose many potential risks in eHealth systems. In this paper, a new data hiding method called EmbedR-Net based on Convolutional Neural Network (CNN) and Deep Convolutional Generative Adversarial Network (DCGAN) is proposed, which can prevent the copyright violation of the medical images. First, a CNN based embedder network is designed for imperceptibly hiding medical images as marks in the carrier image. Second, we compute the diagonal value of the marked image for hidden mark recovery. Last, the DCGAN network is designed to robustly recover the hidden mark using the diagonal value of the marked image. Compared to existing methods, experiments on five different datasets have shown that the proposed EmbedR-Net obtains superior performance.
Development of e-Comic Science Interactive Learning with Scratch (eCILS) Based on Problem Based Learning to Train Critical Thinking Skills for Junior High School Students

This study aims to develop e-comic science interactive learning with scratch (eCILS), to analyze eCILS’ characteristics, test eCILS’ validity, and determine user responses. The research method is development research. The research procedure used is 4D (defined, designed, developed, and disseminated). The research was only up to development due to the pandemic. The result of this research is eCILS. ECILS is an e-comic learning media that have characteristics: (a) equipped with a bibliography, (b) in accordance with problem-based learning syntax, (c) could train critical thinking skills, (d) stories that are displayed in accordance with everyday life, (e) equipped with instructions to use, and (f) in accordance with the characteristics of interactive media learning. ECILS media is very valid to use, this is based on the assessment of material validators who get an average score of 95.63% and media validators 94.38%. Response students to the media are very good indicated by the results of the questionnaire responses of students who get an average of 82.15%. Based on this, it can be concluded that eCILS media can be used as a medium to train critical thinking skills.
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses

For vehicular metaverses, one of the ultimate user-centric goals is to optimize the immersive experience and Quality of Service (QoS) for users on board. Semantic Communication (SemCom) has been introduced as a revolutionary paradigm that significantly eases communication resource pressure for vehicular metaverse applications to achieve this goal. SemCom enables high-quality and ultra-efficient vehicular communication, even with explosively increasing data traffic among vehicles. In this article, we propose a hierarchical SemCom-enabled vehicular metaverses framework consisting of the global metaverse, local metaverses, SemCom module, and resource pool. The global and local metaverses are brand-new concepts from the metaverse's distribution standpoint. Considering the QoS of users, this article explores the potential security vulnerabilities of the proposed framework. To that purpose, this study highlights a specific security risk to the framework's SemCom module and offers a viable defense solution, so encouraging community researchers to focus more on vehicular metaverse security. Finally, we provide an overview of the open issues of secure SemCom in the vehicular metaverses, notably pointing out potential future research directions.
Publishers and their publication practices: the case of Bulgarian indexed journals

The objective of this paper is to reveal details of publishers and their publication practices.The research scope includes the all 81 Bulgarian journals which have an impact factor / ranking in the database of Scopus and Web of Science.Data for the academic staff of National Centre for Information and Documentation were used.The results of research revealed that the publication practices of Bulgarian indexed journals are comparable to the international good practices.The national publication practices are consequence of the external environmentdue to new legislation for academic career of researchers and the implementation of international publication norms by local journals.The present research is the first for Bulgaria and because of its pioneering nature, there is no basis for data comparison and tracking of dynamics.
Flexible HTTP-based Video Adaptive Streaming for good QoE during sudden bandwidth drops

We have observed a boom in video streaming over the Internet, especially during the Covid-19 pandemic, that could exceed the network resource availability. In addition to upgrading the network infrastructure, finding a way to smartly adapt the streaming system to the network and users’ conditions to satisfy clients’ perceptions is exceptionally critical, too. This paper proposes a new QoE-aware adaptive streaming scheme over HTTP - ABRA - to make flexible adaptations based on the network and the client’s current status. Besides, we propose a technique that can keep the buffer at an average high for more than 10s. We were limiting the phenomena of rebuffering due to unexpected and unpredictable bandwidth changes. The algorithm keeps the quality of subsequent versions’ quality constant even when the average bitrate decreases, increasing the QoE. Experimental results show that our method can improve QoE from 7.86% to 20.41% compared to state-of-the-art methods. ABRA can provide better performance in terms of QoE score in all buffer conditions compared to the existing solutions while maintaining a minimum secured buffer level for the worst case.
Exploration and Application Research on New Exhibition Models in the Digital Era

The utilization of digital technology has emerged as a fundamental aspect in the advancement of exhibitions. Through the incorporation of virtual reality, augmented reality, interactive devices, and multimedia displays, technology is harnessed to generate captivating and engaging experiences for visitors across various dimensions. The proficient integration of digital technologies within exhibitions has the potential to transcend the conventional limitations of temporal and spatial constraints associated with traditional physical exhibition models. This, in turn, leads to significant reductions in exhibition costs while concurrently offering novel exhibition and consumer experiences to patrons. The establishment of virtual reality consumer scenarios may serve as an extension of venue operations, thereby fostering the emergence of diverse business models for venues and revitalizing the scenes and value inherent in traditional exhibitions.
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects

Significant attention has been attracted to deep learning-based depth estimates. Dynamic objects become the most hard problems in inter-frame-supervised depth estimates due to the uncertainty in adjacent frames. Thus, integrating optical flow information with depth estimation is a feasible solution, as the optical flow is an essential motion representation. In this work, we construct a joint inter-frame-supervised depth and optical flow estimation framework, which predicts depths in various motions by minimizing pixel wrap errors in bilateral photometric re-projections and optical vectors. For motion segmentation, we adaptively segment the preliminary estimated optical flow map with large areas of connectivity. In self-supervised depth estimation, different motion regions are predicted independently and then composite into a complete depth. Further, the pose and depth estimations re-synthesize the optical flow maps, serving to compute reconstruction errors with the preliminary predictions. Our proposed joint depth and optical flow estimation outperforms existing depth estimators on the KITTI Depth dataset, both with and without Cityscapes pretraining. Additionally, our optical flow results demonstrate competitive performance on the KITTI Flow 2015 dataset.
Experimental quantum natural gradient optimization in photonics

Variational quantum algorithms (VQAs) combining the advantages of parameterized quantum circuits and classical optimizers, promise practical quantum applications in the noisy intermediate-scale quantum era. The performance of VQAs heavily depends on the optimization method. Compared with gradient-free and ordinary gradient descent methods, the quantum natural gradient (QNG), which mirrors the geometric structure of the parameter space, can achieve faster convergence and avoid local minima more easily, thereby reducing the cost of circuit executions. We utilized a fully programmable photonic chip to experimentally estimate the QNG in photonics for the first time, to the best of our knowledge. We obtained the dissociation curve of the He-H + cation and achieved chemical accuracy, verifying the outperformance of QNG optimization on a photonic device. Our work opens up a vista of utilizing QNG in photonics to implement practical near-term quantum applications.