Reviewerly/exHarmony huggingface dataset:
Classical Surrogates for Quantum Learning Models

The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine learning, where variational quantum learning models based on parametrized quantum circuits are discussed. In this work, we introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model and reproduces its input-output relations. As inference can be performed classically, the existence of a classical surrogate greatly enhances the applicability of a quantum learning strategy. However, the classical surrogate also challenges possible advantages of quantum schemes. As it is possible to directly optimize the Ansatz of the classical surrogate, they create a natural benchmark the quantum model has to outperform. We show that large classes of well-analyzed reuploading models have a classical surrogate. We conducted numerical experiments and found that these quantum models show no advantage in performance or trainability in the problems we analyze. This leaves only generalization capability as a possible point of quantum advantage and emphasizes the dire need for a better understanding of inductive biases of quantum learning models.
A Literature Survey on Word Sense Disambiguation for the Hindi Language

Word sense disambiguation (WSD) is a process used to determine the most appropriate meaning of a word in a given contextual framework, particularly when the word is ambiguous. While WSD has been extensively studied for English, it remains a challenging problem for resource-scarce languages such as Hindi. Therefore, it is crucial to address ambiguity in Hindi to effectively and efficiently utilize it on the web for various applications such as machine translation, information retrieval, etc. The rich linguistic structure of Hindi, characterized by complex morphological variations and syntactic nuances, presents unique challenges in accurately determining the intended sense of a word within a given context. This review paper presents an overview of different approaches employed to resolve the ambiguity of Hindi words, including supervised, unsupervised, and knowledge-based methods. Additionally, the paper discusses applications, identifies open problems, presents conclusions, and suggests future research directions.
A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica

Antarctic mapping with satellite images is an important basic task for polar environmental monitoring. Since the first Chinese marine satellite was launched in 2002, China has formed three series of more than 10 marine satellites in orbit. As global operational monitoring satellites of ocean color series, HY-1C and HY-1D have good coverage characteristics and imaging performance in polar regions, and they provide an effective tool for Antarctic monitoring and mapping. In this paper, Antarctic images acquired by the HY-1 satellite Coastal Zone Imager (CZI) sensor were used to study color matching in the mosaic process. According to the CZI characteristics for Antarctic imaging, experiments were carried out on the illuminance nonuniformity of a single image and color registration of multiple images. A gray-level segmentation color-matching method is proposed to solve the problem of image overstretching in the Antarctic image color-matching process. The results and statistical analysis show that the proposed method can effectively eliminate the color deviation between HY-1 Antarctic images, and the mosaic results have a good effect.
Mining multi-center heterogeneous medical data with distributed synthetic learning

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%.
Image Steganalysis Network Based on Dual-Attention Mechanism

Most of the existing image steganography methods are content adaptive steganography, which prefer to modify the pixel values of texture information-rich regions, making them more difficult to be detected. Many steganography detectors have been developed using a CNN network with selected channels. However, the utilization and processing of the prior information inside the network may not be completely adequate. To address such problem, we propose a strategy based on a spatial and channel multiple attention mechanism for improving the accuracy of the network. To further exploit the texture information of the image itself, spatial attention is used throughout the network, which is achieved by fusing the Sobel operator features with the embedded probability map in a suitable way. On the other hand, channel attention uses the relationship between two-dimensional DCT and global average pooling to weight the features. Experimental results show that our strategy has competitive performance in both spatial and DCT domains on the ALASKA #2 dataset.
Anomaly VAE-Transformer: A Deep Learning Approach for Anomaly Detection in Decentralized Finance

DeFi, a decentralized financial service based on blockchain, not only provides innovative financial services, but also poses various risks, such as the Terra Luna crash. Therefore, anomaly detection in DeFi is necessary to ensure the safety and reliability of the DeFi ecosystem. However, this is very difficult because of the complex protocol, interaction among smart contracts, and high market volatility. In this study, we propose a novel method to effectively detect anomalies in DeFi. To the best of our knowledge, this is the first study that utilizes deep learning to detect anomalies in DeFi. We propose a deep learning model, anomaly VAE-Transformer, which combines the variational autoencoder to extract local information in the short term, and the transformer, to identify dependencies between data in the long term. Based on a deep understanding of DeFi protocols, the proposed model collects and analyzes various on-chain data of Olympus DAO, a representative DeFi protocol, for extracting features suitable for anomaly detection. Then, we demonstrate the superiority of the proposed model by analyzing four anomaly cases detected successfully by the proposed model in Olympus DAO. A malicious attack attempt and structural changes in DeFi protocols can be identified quickly using the proposed method; this is expected to help protect the assets of DeFi users and improve the safety, reliability, and transparency of the DeFi market. The dataset and codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fialle/Anomaly-VAE-Transformer</uri>
3D-ShuffleViT: An Efficient Video Action Recognition Network with Deep Integration of Self-Attention and Convolution

Compared with traditional methods, the action recognition model based on 3D convolutional deep neural network captures spatio-temporal features more accurately, resulting in higher accuracy. However, the large number of parameters and computational requirements of 3D models make it difficult to deploy on mobile devices with limited computing power. In order to achieve an efficient video action recognition model, we have analyzed and compared classic lightweight network principles and proposed the 3D-ShuffleViT network. By deeply integrating the self-attention mechanism with convolution, we have introduced an efficient ACISA module that further enhances the performance of our proposed model. This has resulted in exceptional performance in both context-sensitive and context-independent action recognition, while reducing deployment costs. It is worth noting that our 3D-ShuffleViT network, with a computational cost of only 6% of that of SlowFast-ResNet101, achieved 98% of the latter’s Top1 accuracy on the EgoGesture dataset. Furthermore, on the same CPU (Intel i5-8300H), its speed was 2.5 times that of the latter. In addition, when we deployed our model on edge devices, our proposed network achieved the best balance between accuracy and speed among lightweight networks of the same order.
Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.
Integrated conversion and photodetection of virtual photons in an ultrastrongly coupled superconducting quantum circuit

Abstract The ground state of an artificial atom ultrastrongly coupled to quantized modes is entangled and contains an arbitrary number of virtual photons. The problem of their detection has been raised since the very birth of the field, but despite the theoretical efforts still awaits experimental demonstration. Recently, experimental problems have been addressed in detail showing that they can be overcome by combining an unconventional design of the artificial atom with advanced coherent control. In this work, we study a simple scheme of control-integrated continuous measurement, which makes remarkably favorable the tradeoff between measurement efficiency and backaction showing that the unambiguous detection of virtual photons can be achieved within state-of-the-art quantum technologies.
Learning Privacy-Preserving Embeddings for Image Data to Be Published

Deep learning shows superiority in learning feature representations that offer promising performance in various application domains. Recent advances have shown that privacy attributes of users and patients (e.g., identity, gender, and race) can be accurately inferred from image data. To avoid the risk of privacy leaking, data owners can resort to releasing the embeddings rather than the original images. In this article, we aim at learning to generate privacy-preserving embeddings from image data. The obtained embeddings are required to maintain the data utility (e.g., keeping the performance of the main task, such as disease prediction) and to simultaneously prevent the private attributes of data instances from being accurately inferred. We also want the hard embeddings to be successfully used to reconstruct the original images. We propose a hybrid method based on multi-task learning to reach the goal. The key idea is twofold. One is to learn the feature encoder that can benefit the main task and fool the sensitive task at the same time via iterative training and feature disentanglement. The other is to incorporate the learning of adversarial examples to mislead the sensitive attribute classification’s performance. Experiments conducted on Multi-Attribute Facial Landmark (MAFL) and NIH Chest X-ray datasets exhibit the effectiveness of our hybrid method. A set of advanced studies also shows the usefulness of each model component, the difficulty in data reconstruction, and the performance impact of task correlation.
Advanced Image Steganography Using a U-Net-Based Architecture with Multi-Scale Fusion and Perceptual Loss

Image-to-image steganography refers to the practice of hiding secret image within a cover image, serving as a crucial technique for secure communication and data protection. Existing image-to-image generative adversarial network-based steganographic methods for image hiding demonstrate a high embedding capacity. However, there is still significant room for improvement in terms of the quality of the stego images and the extracted secret images. In this study, we propose an architecture for inconspicuously hiding an image within the Y channel of another image, leveraging a U-Net network and a multi-scale fusion ExtractionBlock. The network is jointly trained using a loss function combining Perceptual Path Length (PPL) and Mean Square Error (MSE). The proposed network is trained and tested on two datasets, Labeled Faces in the Wild and Pascal visual object classes. Experimental results demonstrate that the model not only achieves high invisibility and significant hiding capacity (8 bits per pixel) without altering the color information of the cover image but also exhibits strong generalization ability. Additionally, we introduce the Modified Multi-Image Similarity Metric (MMISM), which integrates the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) values of images, to comprehensively evaluate the network’s hiding and extraction capabilities.
Human factors in cybersecurity: Designing an effective cybersecurity education program for healthcare staff

Leaders who promote cybersecurity education focused on the human factors of cyberattack build a resilient workforce that complements technical protections, reducing organizational risk. Cybersecurity is a priority for information technology teams, relying primarily on technology to protect systems. As technical protections mature, the vulnerability shifts to human factors. Education must focus on the risk presented by humans rather than machines. A human factors-centred education program trains human reaction to threats considering the unique healthcare environment. Leaders may look to industries, like aviation, experiencing similar technical advancement, for education practices based on human factors. This article outlines a cybersecurity education program developed for healthcare, applying strategies adopted from commercial aviation. Four core pillars of training are defined: (1) dynamic education delivery options, (2) social engineering focused simulations, (3) high-risk positions and role-based training, and (4) stakeholder and leadership engagement. The first phase of implementation has been analyzed and offers some lessons for health leaders.
A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
Artificial intelligence in the educational process and scientific research of higher education applicants: responsible boundaries of AI content

The article focuses on the issue of intensification of comprehensive research on modern information technologies, in particular the basics of artificial intelligence, in the education system and scientific research of higher school graduates with the aim of forming a favorable, effective, high-quality and ethical process of obtaining knowledge in the context of the formation of an information society. The scientific observation presents a detailed retrospective analysis of the conceptual-categorical apparatus “artificial intelligence” from the standpoint of generally recognized scientific theories and leading ideas of domestic and foreign scientists for its further theoretical and methodological consolidation. The relevance of scientific research is due to the high current dependence of higher education students on science-intensive technologies, which help to solve the tasks of self-determination, self-control and self-realization, as well as contribute to the formation of a holistic, variably thinking, creative personality with a new type and level of reasoning, with the latest values, competencies and views on professional level of the future specialist. Taking into account modern visions, through the prism of the informatization component of the development of education and science among students, it is determined that thanks to the technologies of artificial intelligence, which simulates the processes of human intelligence for conclusions, computer systems can offer applicants personalized, adaptive or interval training, automatic evaluation of educational achievements, support and feedback during the educational process. It has been found that virtual reality as a newly organized social space leads to planned, consistent and systematized changes in the educational process and research work of applicants of higher education institutions, improvement of existing and formation of hitherto unseen organizational forms, types and methods of education, as well as changes interaction between the teacher and the student and the educational environment as a whole. In addition, it is determined that artificial intelligence as a modern pedagogical mechanism of modern times has an important conceptual and methodological significance in the process of fruitful union of education and science.
Light-Matter Interaction at the Transition between Cavity and Waveguide QED

Experiments based on cavity quantum electrodynamics (QED) are widely used to study the interaction of a light field with a discrete frequency spectrum and emitters. More recently, the field of waveguide QED has attracted interest due to the strong interaction between propagating photons and emitters that can be obtained in nanophotonic waveguides, where a continuum of frequency modes is allowed. Both cavity and waveguide QED share the common goal of harnessing and deepening the understanding of light-matter coupling. However, they often rely on very different experimental setups and theoretical descriptions. Here, we experimentally investigate the transition from cavity to waveguide QED with an ensemble of cold atoms that is coupled to a fiber-ring resonator, which contains a nanofiber section. By varying the length of the resonator from a few meters to several tens of meters, we tailor the spectral density of modes of the resonator while remaining in the strong coupling regime. When increasing the resonator length, we observe a continuous transition from the paradigmatic Rabi oscillations of cavity QED to non-Markovian dynamics reminiscent of waveguide QED.
An efficient Optimization State-based Coyote Optimization Algorithm and its applications

Coyote Optimization Algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the Optimization State-based Coyote Optimization Algorithm (OSCOA). In the OSCOA algorithm, a Population Optimization State Estimation Mechanism is employed for estimating the current population optimization state. Then, the estimation result is used to guide the algorithm in setting the number of packs appropriately as well as selecting appropriate evolutionary strategies to refine search directions, thereby avoiding blind exploration. Additionally, the estimation result assists each pack in selecting suitable parents to generate pups, further improving the global search efficiency of the algorithm. To validate the effectiveness of the proposed algorithm, the OSCOA algorithm is subjected to comprehensive testing and analysis along with seven efficient optimizers on 71 benchmark functions derived from the CEC2014, CEC2017, and CEC2022 benchmark suites. The results of these extensive experiments indicate the competitive performance of OSCOA. Furthermore, to further assess the capability of the OSCOA algorithm in addressing real-world problems, two practical applications is considered: wireless sensor network deployment and image segmentation. The outcomes of these applications further confirm the efficacy and stability of the OSCOA algorithm in tackling real-world scenarios.