Reviewerly/exHarmony huggingface dataset:
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the following two essential criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module (GAM); 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module (GCM) conditioning on the inconsistent consensus. To further improve the accuracy, we design a series of simple yet effective components as follows: i) a recurrent auxiliary classification module (RACM) promoting model learning at the semantic level; ii) a confidence enhancement module (CEM) assisting the model in improving the quality of the final predictions; and iii) a group-based symmetric triplet (GST) loss guiding the model to learn more discriminative features. Extensive experiments on three challenging benchmarks, i.e., CoCA, CoSOD3k, and CoSal2015, demonstrate that our GCoNet+ outperforms the existing 12 cutting-edge models. Code has been released at https://github.com/ZhengPeng7/GCoNet_plus.
Unbalanced beam splitters enabling enhanced phase sensitivity of a Mach-Zehnder interferometer using coherent and squeezed vacuum states

An optical interferometer seeded by coherent and squeezed vacuum states seems to be the most promising platform for gravitational wave detection. Prior studies regarding this estimation protocol focus on the scenario in which the transmittances of two beam splitters are $\frac{1}{2}$. In this paper, with respect to the same inputs, we analyze the phase sensitivity of an unbalanced Mach-Zehnder interferometer (MZI) followed by balanced homodyne measurement. We give the optimal transmittance and demonstrate the advantage of our scheme over a balanced MZI scheme. Additionally, when the average photon number of the coherent state is dominant, the phase sensitivity of our scheme can nearly saturate the single-parameter quantum Cram\'er-Rao bound. Our results may contribute to the development of practical quantum sensing.
Convolutional Neural Networks Analysis Reveals Three Possible Sources of Bronze Age Writings between Greece and India

This paper analyzes the relationships among eight ancient scripts from between Greece and India. We used convolutional neural networks combined with support vector machines to give a numerical rating of the similarity between pairs of signs (one sign from each of two different scripts). Two scripts that had a one-to-one matching of their signs were determined to be related. The result of the analysis is the finding of the following three groups, which are listed in chronological order: (1) Sumerian pictograms, the Indus Valley script, and the proto-Elamite script; (2) Cretan hieroglyphs and Linear B; and (3) the Phoenician, Greek, and Brahmi alphabets. Based on their geographic locations and times of appearance, Group (1) may originate from Mesopotamia in the early Bronze Age, Group (2) may originate from Europe in the middle Bronze Age, and Group (3) may originate from the Sinai Peninsula in the late Bronze Age.
Cost-Optimization-Based Quantum Key Distribution over Quantum Key Pool Optical Networks

The Measurement-Device-Independent-Quantum Key Distribution (MDI-QKD) has the advantage of extending the secure transmission distances. The MDI-QKD combined with the Hybrid-Trusted and Untrusted Relay (HTUR) is used to deploy large-scale QKD networks, which effectively saves deployment cost. We propose an improved scheme for the QKD network architecture and cost analysis, which simplifies the number of QKD transmitters and incorporates the quantum key pool (QKP) in the QKD network. We developed a novel Hybrid-QKD-Network-Cost (HQNC) heuristic algorithm to solve the cost optimization problem. Simulations verified that the scheme in this paper could save the cost by over 50 percent and 90 percent, respectively.
Nut Geometry Inspection Using Improved Hough Line and Circle Methods

Nuts are the cornerstone of human industrial construction, especially A-grade nuts that can only be used in power plants, precision instruments, aircraft, and rockets. However, the traditional nuts inspection method is to manually operate the measuring instrument for conducting an inspection, so the quality of the A-grade nut cannot be guaranteed. In this work, a machine vision-based inspection system was proposed, which performs a real-time geometric inspection of the nuts before and after tapping on the production line. In order to automatically screen out A-Grade nuts on the production line, there are 7 inspections within this proposed nut inspection system. The measurements of parallel, opposite side length, straightness, radius, roundness, concentricity, and eccentricity were proposed. To shorten the overall detection time regarding nut production, the program needed to be accurate and uncomplicated. By modifying the Hough line and Hough circle, the algorithm became faster and more suitable for nut detection. The optimized Hough line and Hough circle can be used for all measures in the testing process.
Historical time machines: Experimentally investigating potentials and impacts of immersion in historical VR on history education and morality.

Virtual reality (VR) is known for its immersive characteristics that enable users the experience of presence in virtual environments.However, the experience of presence in emotionally charged historical VR is controversially discussed as it might decrease the critical, cognitive reflection of the historical content and lead to strong emotional reactions.Therefore, we examined the effects of the VR content's emotionality on presence and emotional response.Following the transportation-imagery model and the model of intuitive morality and exemplars, we investigated the effects of presence and emotional response on the recipients' enjoyment, historical interest, beliefs, awareness, and moral reasoning.Moreover, we tested whether additional information after watching historical VR can support the critical reflection of the historical content.Taking different modalities into account, we conducted two experiments following a 2 (emotionality of the content) × 2 (reception of additional information)-between-subjects design in which participants were exposed to historical VR via a media player (Experiment 1, N = 493) or VR glasses (Experiment 2, N = 99).In line with the assumptions, the recipients' experience of presence and emotional response supported historical interest, enjoyment, and affected moral reasoning.Moreover, findings indicated that additional material promotes the critical reflection of historical VR.
Low-field magnetic resonance image enhancement via stochastic image quality transfer

Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.
Healthcare Cybersecurity Threat Context and Mitigation Opportunities

With the rapid increase in cybersecurity incidents around the globe, no business or industry is hidden from cybercriminals' crosshairs. Cyber-attacks are evolving and becoming more complex each day, causing much damage to different financial and reputational sectors. The health sector (including healthcare technology) is increasingly digitized and continues to offer life-critical services. It continues to improve diagnosis, treatment, and patient care with state-of-the-art technologies. However, cybercriminals continue to exploit vulnerabilities and exfiltrate confidential patient data and other confidential information. The health care sector offers cybercriminals a rich source of confidential data and is very easy to exploit as the defense and security controls are weak. Because of the growing number of threats associated with the health sector, it is essential to understand how health care practitioners - along with concerned critical Information Technology (IT) teams - can take steps to improve the cybersecurity posture of this industry and help reduce the attack surface. Hence, this paper discusses a critical approach to analyze the current cybersecurity challenges faced by the health care sector and relevant mitigation and other scholarly articles and research papers.
HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides

Cervical cancer is a critical imperilment to a female’s health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.
Deep Learning Network-Based Evaluation method of Online teaching quality of International Chinese Education

The development of vocational education in the information age requires us to think about the path and strategy of active change. Course teaching quality evaluation should also shift from passive evaluation of online teaching development to active construction of a mixed teaching quality evaluation system. In the information age, the development of teaching resources is dizzying. From paper to digital, from single to diverse, from offline to online, from scarcity to mass—various changes impact the traditional teaching model. Aiming at the online teaching quality evaluation of international Chinese education on the Internet, this paper proposes a method based on deep learning. Firstly, this paper proposes an index system construction and evaluation index weighting for online teaching of international Chinese education, and collects online data as a corpus at the same time. Then construct the CNN_BiLSTM_Att model, which is composed of the CNN module, the BiLSTM module and the Att module. Finally, compare with other model experiments. The experimental results show that CNN_BiLSTM_Att has achieved the best results in the evaluation index results, with P and F1 reaching 97.89% and 97.85%. Compared with other models, the overall effect is improved by 2%~5%. From this, the superiority of the model in the online teaching quality evaluation standard task of this paper can be obtained.
Sustainable Education and Digitalization through the Prism of the COVID-19 Pandemic

Our paper discusses how sustainable education has evolved at higher education institutions (HEIs) during the COVID-19 pandemic by embracing digitalization and novel technologies. In addition, it describes the innovative methods for integrating technology into professional education during and after the COVID-19 pandemic. We evaluate the global practices and fundamental changes in the sphere of higher education, particularly regarding sustainable education and digitalization. The paper aims at describing the challenges faced by higher education institutions in adopting sustainable education and digitalization, as well as the opportunities for growth and innovation that have arisen due to the pandemic. Our methods include the empirical model, which assesses the acceptance and usage of novel digital technologies for promoting sustainable education by university professors and academic researchers in four major Russian cities (Moscow, Krasnodar, Kazan, and Saint Petersburg) during the COVID-19 pandemic. The results of the model reveal the significance of implementing sustainability in the classroom, which could bring positive outcomes to both students and teachers. Our study provides a valuable resource for educators seeking techniques, models, and practical advice to overcome the obstacles of teaching in a digitally connected learning environment in Russia and beyond. Specifically, we propose that the primary focus of digitalizing professional education should be on teachers and instructors who are enthusiastic about leveraging technology for learning and who are familiar with novel digital technologies. It appears that digital technologies are capable of transforming teaching practices for sustainable education and economic development.
Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012–2021)

This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author’s choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.
Fingerhints: Understanding Users’ Perceptions of and Preferences for On-Finger Kinesthetic Notifications

We present “fingerhints,” on-finger kinesthetic feedback represented by hyper-extension movements of the index finger, bypassing user agency, for notifications delivery. To this end, we designed a custom-made finger-augmentation device, which leverages mechanical force to deliver fingerhints as programmable hyper-extensions of the index finger. We evaluate fingerhints with 21 participants, and report good usability, low technology creepiness, and moderate to high social acceptability. In a second study with 11 new participants, we evaluate the wearable comfort of our fingerhints device against four commercial finger- and hand-augmentation devices. Finally, we present insights from the experience of one participant, who wore our device for eight hours during their daily life. We discuss the user experience of fingerhints in relation to our participants’ personality traits, finger dexterity levels, and general attitudes toward notifications, and present implications for interactive systems leveraging on-finger kinesthetic feedback for on-body computing.
Synthesis of and compilation with time-optimal multi-qubit gates

We develop a method to synthesize a class of entangling multi-qubit gates for a quantum computing platform with fixed Ising-type interaction with all-to-all connectivity. The only requirement on the flexibility of the interaction is that it can be switched on and off for individual qubits. Our method yields a time-optimal implementation of the multi-qubit gates. We numerically demonstrate that the total multi-qubit gate time scales approximately linear in the number of qubits. Using this gate synthesis as a subroutine, we provide compilation strategies for important use cases: (i) we show that any Clifford circuit on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> qubits can be implemented using at most <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>2</mml:mn><mml:mi>n</mml:mi></mml:math> multi-qubit gates without requiring ancilla qubits, (ii) we decompose the quantum Fourier transform in a similar fashion, (iii) we compile a simulation of molecular dynamics, and (iv) we propose a method for the compilation of diagonal unitaries with time-optimal multi-qubit gates, as a step towards general unitaries. As motivation, we provide a detailed discussion on a microwave controlled ion trap architecture with magnetic gradient induced coupling (MAGIC) for the generation of the Ising-type interactions.
Education 4.0: The Concept, Skills, and Research

Introduction. With Industry 4.0 and Work 4.0 entering the world, modern education is undergoing transformations in terms of educational practices, skillsets and competencies, teaching and learning methodologies (including flipped classroom, blended learning, self-regulated learning, project-based learning, inquiry-based learning, student-centred pedagogy), digital tools used at all educational levels, as well as barriers and challenges. This string of changes is covered by the new buzzword “Education 4.0”. It is not so far finally defined. There are various explanations of the concept. Most align with the Fourth Industrial Revolution and Industry 4.0. The JLE editorial aims to overview the emerging research field of Education 4.0 aligned with Industry 4.0, outlining the potential lines of research for JLE authors.&#x0D; Industry 4.0. The transformation of production at large is beginning on the basis of a set of innovative technologies and completely brand-new processes. Their combination constitutes the underpinning of Industry 4.0.&#x0D; Skillsets in Education 4.0. There are numerous views of the skills needed for graduates ready for Industry 4.0. One of the most replicated lists embraces the 10 skills offered by the World Economic Forum in 2016 with later updates. Most researchers outline numerous technical, communication, digital, and cognitive skills as a skillset of Industry 4.0.&#x0D; Teaching and Learning in Education 4.0. The traditional pedagogy or face-to-face learning, still dominant worldwide, is going to combine with innovative approaches, including, e-learning, and blended learning as a mixture of e-learning ang face-to-face learning. In addition, all student-centered technologies add to the future pedagogical landscape: self-regulated learning, project-based learning, flipped classroom, etc.&#x0D; Research on Education 4.0. The authors searched the Scopus for the documents related to “Education 4.0” to find that the total of 483 results unevenly distributed from 2010 to 2023, with a high of 137 in 2022. The analysis of the publications on Education 4.0 proves that the research field is developing fast, though publications authored by researchers from the developing countries prevail in the search results. At the same time, most of the selected publications came out in the Scopus-indexed low-quartile or discontinued journals. A disproportionately low number of articles published by the authors from the OECD countries depletes the quality of the research field.&#x0D; Conclusion. The editorial overview of the concept of Education 4.0 may serve as a topical guidance for researchers at large and potential JLE authors focused on educational research. Further studies in the field may cover skillsets and competencies for Industry 4.0; teaching and learning approaches in Education 4.0; new educational frameworks and environments.
A Fundamental Overview of SOTA-Ensemble Learning Methods for Deep Learning: A Systematic Literature Review

The rapid growth in popularity of Deep Learning (DL) continues to bring more use cases and opportunities, with methods rapidly evolving and new fields developing from the convergence of different algorithms. For this systematic literature review, we considered the most relevant peer-reviewed journals and conference papers on the state of the art of various Ensemble Learning (EL) methods for application in DL, which are also expected to give rise to new ones in combination. The EL methods relevant to this work are described in detail and the respective popular combination strategies as well as the individual tuning and averaging procedures are presented. A comprehensive overview of the various limitations of EL is then provided, culminating in the final formulation of research gaps for future scholarly work on the results, which is the goal of this thesis. This work fills the research gap for upcoming work in EL for by proving in detail and making accessible the fundamental properties of the chosen methods, which will further deepen the understanding of the complex topic in the future and, following the maxim of ensemble learning, should enable better results through an ensemble of knowledge in the future.