Reviewerly/exHarmony huggingface dataset:
Underwater Image Restoration Using White Balance and Retinex Algorithm

When light travels through water, attenuation and scattering of light have a negative impact on underwater vision. Additionally, this negative impact affects the underwater image quality and creates a fuzz problem. An innovative retinex-based enhancing method is suggested in this study to improve a single underwater image. The proposed method consists of three phases: white balancing approach, decomposition of an image into illumination and reflectance using the retinex-algorithm, and enhancing the illumination and reflectance. The enhanced illumination and reflectance multiply to obtain an improved y-component of YCbCr color space. Finally, concatenate all components of YCbCr color space and convert it into RGB color space to get an enhanced image. The author evaluates metric parameters to validate the proposed method.
The Robotic-Human Service Trilemma: the challenges for well-being within the human service triad

Purpose The human service triad (i.e. the relationship between the customer, frontline employee (FLE) and managerial employee) experiences a range of well-being challenges when faced with the introduction of service robots. Despite growth in service robot scholarship, understanding of the well-being challenges affecting the human service triad remains fragmented. Hence, the purpose of this paper is to synthesise the literature and offer a research agenda aligned with the proposed Robotic-Human Service Trilemma. By taking a job performance approach (which considers the actions, behaviours and outcomes linked to organisational goals), the Robotic-Human Service Trilemma conceptualises three well-being challenges (intrusion, sideline and interchange). These challenges are realised via the realistic capabilities and constraints of service robot implementation. Design/methodology/approach This research relies on a systematic review of all disciplines concerning service robots. In total, 82 articles were analysed using thematic coding and led to the development of the Robotic-Human Service Trilemma and research agenda. Findings The analyses reveal the Robotic-Human Service Trilemma consists of three challenges: intrusion, sideline and indifference. The findings demonstrate that FLEs are required to counterbalance the constraints of service robots, leading to an uneven well-being burden within the human service triad. This paper suggests a research agenda for investigation of the challenges that underpin the Robotic-Human Service Trilemma. Originality/value Through the conceptualisation of the Robotic-Human Service Trilemma, this study is the first to explore how states of well-being equilibrium exist within the human service triad and how these states are challenged by service robots. The authors present a balanced centricity perspective to well-being that contrasts previous trade-off approaches and that enhances the body of service robot literature with a well-being lens.
A Survey on Organization/Operation and Support Plan of 2022 Revised National Curriculum for Invention and Intellectual Property in High School

이 연구는 2022 개정 교육과정에 신설·개정된 고등학교 발명·지식재산 교육과정의 도입 및 정착화를 위하여 편성·운영과 지원방안에 대하여 전문가 대상으로 설문조사를 실시하고 분석하였다. 연구 결론은 다음과 같다. 첫째, 발명·지식재산 교육과정의 편성·운영 방식들에 대한 적절성은 평균 4점대로 높았으며, 일반고와 직업계고는 다른 경향성과 특징들이 나타났다. 둘째, 발명·지식재산 전 교과목에 대한 중요도는 평균 4.0점 이상으로 높았다. 지식 재산 일반 교과와 전공 일반 과목 난이도는 보통 수준이며, 전공 실무 과목은 어렵다고 인식하여 지원체제가 더욱 필요하다. 셋째, 지원방안은 우선적으로 발명특허 교원 양성 및 역량 강화 연수와 과목 지원사업 확대, 시·도 교육청 수준 교육과정 편성·운영 지침이 필요하고, 다음으로는 교육 자료와 장비, 지식재산교육 조례, 인식 제고, 직업 및 진로 발굴 등이 필요하다고 요구하였다. 결론적으로 2022 개정 고등학교 발명·지식재산 교육과정은 미래 융복합 인재 양성을 위하여 중요하며, 학교 목적과 교육 목적 등에 따라 효율적으로 편성·운영하도록 지원할 필요가 있다. 이를 위해 교육과정의 지원체제를 정비하고 확충해야 한다.This study is aimed at introducing and settling the newly established and revised high school invention and intellectual property curriculum in 2022, by conducting a survey of experts and analysis on organization/operation and support plans. The conclusions of this study are as follows;First, adequacy of invention and intellectual property curriculum by organization and operation method was as high as 4 points on average, and general high schools and vocational high schools showed different characteristics. Second, the importance of all subjects in invention and intellectual property was high, with an average score of 4.0 or more. The difficulty level of the general subjects of intellectual property and the general subjects of the major is normal, and the practical subjects of the major are recognized as difficult, so a support system is needed more. Third, the support plan it was required that the important task is training for invention patent teachers and capacity build-up program, and curriculum organization/operation guidelines at the level of Metropolitan and provincial offices of education, and expansion of projects to support subjects. Additionally, there is a need for educational materials and equipment, intellectual property education ordinances, awareness-raising activities, discovery of jobs and career paths. In conclusion, the 2022 revised national curriculum for invention and intellectual property is important for fostering future convergence talents, therefore, support is needed to efficiently organize and operate according to the purpose of school and education. It is necessary to aligning and expanding support system of the curriculum.
Development of Automated People Counting System using Object Detection and Tracking

The emergence of automation in the current economic trend promotes the usage of computer vision systems in various applications. Counting people in a specified area or on the street can bring many benefits in terms of security and marketing. The people counting system is one of the applications that utilize the computer vision system to count people with higher reliability and accuracy. Thus, this project is to develop an offline automated people counting system based on captured video file input using MATLAB software and a notification system to update and send notifications about the number of occupants in a target area using ThingSpeak. For project development, simulation and development of coding for object detection that involves deep learning approach, object tracking and counting, and development of notification system have been done. Three videos were taken to be used for three trials to evaluate the functionality and performance of the developed system. Based on the results and analysis, the system can perform people detection, people tracking and people counting on the recorded input videos with high accuracy of 94.45%, visualize the data on the ThingSpeak platform and send notifications through Twitter.
Adversarial Heterogeneous Graph Neural Network for Robust Recommendation

Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types. Moreover, graph structures are susceptible to noise and even unnoticed malicious perturbations, as perturbations from connected nodes can create cumulative effects on a target node in the graph. To enhance the robustness and generalization of GNN-based recommendations, we propose a new optimization model named Adversarial Heterogeneous Graph Neural Network for RECommendation (AHGNNRec). First, AHGNNRec learns user and item embeddings by exploring the distinct contributions of various types of interactions between users and items using a hierarchical heterogeneous graph neural network (HGNN). Second, to produce more robust embeddings for recommendations, we employ the adversarial training (AT) method to optimize the HGNN layers. AT is a min-max optimization training process where the generated adversarial fake nodes from normal nodes with intentional perturbations try to maximally deteriorate the recommendation performance. Following this, we learn about these adversarial user or item nodes by minimizing the impact of an additional regularization term for the recommendation. The experimental outcomes on two real-world benchmark datasets demonstrate the effectiveness of AHGNNRec.
Parallel photonic acceleration processor for matrix–matrix multiplication

We propose and experimentally demonstrate a highly parallel photonic acceleration processor based on a wavelength division multiplexing (WDM) system and a non-coherent Mach–Zehnder interferometer (MZI) array for matrix–matrix multiplication. The dimensional expansion is achieved by WDM devices, which play a crucial role in realizing matrix–matrix multiplication together with the broadband characteristics of an MZI. We implemented a 2 × 2 arbitrary nonnegative valued matrix using a reconfigurable 8 × 8 MZI array structure. Through experimentation, we verified that this structure could achieve 90.5% inference accuracy in a classification task for the Modified National Institute of Standards and Technology (MNIST) handwritten dataset. This provides a new effective solution for large-scale integrated optical computing systems based on convolution acceleration processors.
English Thematic Integrated Textbook for Young Learners Based on Local Content with a Multiliterative Approach to Pronunciation and Vocabulary Mastery

This study aims to find the proper teaching method through Integrated English Thematic Textbooks for Young Students Based on Local Content with a Multiliterative Approach to Pronunciation and Vocabulary Mastery. The study used a quasi-experimental design using the Experimental and the control class. The research trials were conducted on students from various educational study programs at SD xyz for the 2020/2021 school year consisting of 27 class students. The results showed that the Eyl's thematic integrated textbook based on local original materials with multi-literacy approaches in vocabulary have positive effects on pronunciation and the cognate of the learners in experimental class.
Passenger Counting in Mass Public Transport Systems using Computer Vision and Deep Learning

Estimating the number of people in vehicles and stations in public transport systems is crucial to improve service quality. The TransMilenio system in Bogota has serious drawbacks due to the lack of information in congestion situations. In this work we present a computer vision method that estimates the number of people in TransMilenio stations using deep learning techniques. We offer free use of the TransMilenio-Javeriana database with nearly 900,000 head labels on buses and stations. From these images a deep learning architecture tuned for crowd counting was trained to generate density maps around the heads in the scene. Several head count methods were evaluated on the density maps. After testing the method with 10,800 images, the results show a mean absolute error of 1 head per frame, equivalent to 11% relative error. The accuracy of this method is much better than its manual counterpart. This method is also scalable and low cost, which indicates that it has great potential to provide information for the planning and operation of public transport systems.
Super-resolution of 2D ultrasound images and videos

Abstract This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of $$1.7\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1.7</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on obstetric 2X raw images, $$6.1\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.1</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on cardiac 2X raw images, and $$4.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>4.4</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of $$9.0\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>9.0</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on obstetric 4X raw images, $$5.2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on cardiac 4X raw images, and $$6.2\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.2</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> on abdominal 4X raw images. The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network’s prediction on local devices. Graphical abstract
Edu-Larp as Revision of Subject-Matter Knowledge

The paper presents theoretical foundations of the author's approach to the design of edu-larps. It is deliberately steering away from cross-disciplinary teaching, artistic education or soft skills training in order to advocate larps tailored to single school subjects, focused on integration and consolidation of curricular knowledge. Putting larp in the context of applied drama, or Drama in Education, the text argues that well-designed edu-larp is likely to be accepted by the average teacher, not just by dedicated and trained drama educators. The concept of print-and-play “larp for dummies” downplays the importance of preparation, acting and immersion, while emphasising the game-like structure of goals and conflicts: gameplay set to work alongside role-play. Therefore, the ideas and tools borrowed from video games and board games design can be useful in the creation of edu-role plays, breeding a form which might be called 'gamified drama'. The recommended function for edu-larps is final revision of a large textbook unit, an idea discussed on the example of high-school history classes. The suggested model for classrom larp is a negotiation game between conflicted but cooperating factions, divided into three stages: a) preparation of all factions separately; b) informal meeting of all factions shortly before the official talks; c) official negotiation session which is supposed to bring forward all major (textbook-based!) issues for public debate. The paper ends with a section on troubleshooting, i.e. an overview of risk factors frequently pointed in the discussion of game-based learning, with ideas of how to eliminate or minimise the risks through careful design. Referenced sources come from the fields of edu-larp, edu-drama, game-based learning, and general educational theory.
Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost

Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions.
Efficient matrix key homomorphic encryption of medical images

A sure way of providing privacy to sensitive images is to encrypt them, especially when they are stored in a public cloud server. Homomorphic encryption enables arithmetic operations over encrypted data without access to the secret key. This facility can be well harnessed for secure outsourced image processing by exploiting the available computational capabilities of modern cloud servers. This paper presents a new homomorphic image encryption scheme that uses integer matrix keys. The homomorphic operations are carried out in the finite field Zp to avail the advantages of integer arithmetic and to limit the cipher text sizes to reasonable levels. Our method does not use any error vector as in learning with errors (LWE) to improve the security.
Museum exhibits brought to life

The nature of museum exhibitions mean they are full of history, but technology can bring the lessons of the past to life - in some cases, almost literally. In the late 20th century, museums underwent a transformation, shedding their image as places with roomfuls of static exhibits in glass cabinets featuring information-heavy labels but little context. Today most have some interactivity elements, whether it is a touchscreen display or virtual reality, as used by the Tate Modern in London to depict Modigliani&apos;s Paris studio from 1919 in the artist&apos;s retrospective exhibition. This article discusses topics such as AR (augmented reality) experiences/glasses, animated 3D holograms, 3D audio and 3D modelling in the museum context.
Improvement of the Performance of Machine Learning Algorithms in Predicting Breast Cancer

Introduction: Breast cancer is one of the most common cancers among women compared to all other ones. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for tumor classification prediction.Materials &amp; Methods: Several supervised machine learning algorithms were utilized to diagnosis and prediction of cancer tumor such as Logistic Regression Decision Tree, Random Forest and KNN. The algorithms are applied to a dataset taken from the UCI repository including 699 samples. The dataset includes Breast cancer features. To enhance the algorithms’ performance, these features are analyzed, the feature importance score and cross validation are considered. In this paper ML algorithms improved coupled by limited and selective features to produce high classification accuracy in tumor classification.Results: As a result of evaluation, Logistic Regression algorithm with accuracy value equal to 99.14%, AUC ROC equal to 99.6%, Extra Tree algorithm with accuracy value equal to 99.14% and AUC ROC equal to 99.1% have better performance than other algorithms. therefore, these techniques can be useful for diagnosis and prediction of cancer tumor and prescribe it correctly.Conclusions: The technique of machine learning can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate breast cancer and indeed, the diagnosis and prediction of breast cancer is compared to determine the most appropriate classifier.
Quantum annealing for neural network optimization problems: A new approach via tensor network simulations

Quantum Annealing (QA) is one of the most promising frameworks for quantum optimization. Here, we focus on the problem of minimizing complex classical cost functions associated with prototypical discrete neural networks, specifically the paradigmatic Hopfield model and binary perceptron. We show that the adiabatic time evolution of QA can be efficiently represented as a suitable Tensor Network. This representation allows for simple classical simulations, well-beyond small sizes amenable to exact diagonalization techniques. We show that the optimized state, expressed as a Matrix Product State (MPS), can be recast into a Quantum Circuit, whose depth scales only linearly with the system size and quadratically with the MPS bond dimension. This may represent a valuable starting point allowing for further circuit optimization on near-term quantum devices.
The Random Feature Method for Time-Dependent Problems

We present a framework for solving time-dependent partial differential equations (PDEs) in the spirit of the random feature method.The numerical solution is constructed using a space-time partition of unity and random feature functions.Two different ways of constructing the random feature functions are investigated: feature functions that treat the spatial and temporal variables (STC) on the same footing, or functions that are the product of two random feature functions depending on spatial and temporal variables separately (SoV).Boundary and initial conditions are enforced by penalty terms.We also study two ways of solving the resulting least-squares problem: the problem is solved as a whole or solved using the block time-marching strategy.The former is termed the space-time random feature method (ST-RFM).Numerical results for a series of problems show that the proposed method, i.e.ST-RFM with STC and ST-RFM with SoV, have spectral accuracy in both space and time.In addition, ST-RFM only requires collocation points, not a mesh.This is important for solving problems with complex geometry.We demonstrate this by using ST-RFM to solve a two-dimensional wave equation over a complex domain.The two strategies differ significantly in terms of the behavior in time.In the case when block time-marching is used, we prove a lower error bound that shows an exponentially growing factor with respect to the number of blocks in time.For ST-RFM, we prove an upper bound with a sublinearly growing factor with respect to the number of subdomains in time.These estimates are also confirmed by numerical results.
The Role of Accounting Information Systems in the Industrial Revolution 4.0 Era

The purpose of this research study is to provide a solution so that the role of accountants continues to exist in the era of the industrial revolution 4.0 by starting to learn computer programming so that they can adapt to existing changes. This study report used a descriptive qualitative method with data collection from articles on google or internet browsing. Because in recent years there have been various technological breakthroughs that produce new value in human life through the form of artificial intelligence. Business circles call this the core of the 4.0 industrial revolution. This condition has an impact on the role of humans, especially the accounting profession which will be replaced by the role of robots.