Reviewerly/exHarmony huggingface dataset:
The influence of resonator outcoupling and losses on the photon statistics of the LASER

Abstract An extension to the quantum-mechanical laser master equation for the density operator is derived to incorporate the beam-splitter effect caused by typical dielectric laser output couplers. This effect gives rise to a significant change in the photon statistical distribution of the part of the laser light reflected back into the resonator and, therefore, may have an influence on the total laser output photon statistics. Different cases without and with additional intra-cavity losses were discussed and their influence on the expected laser photon statistics was deduced. As a result, it was found that the well-known Poisson distribution of laser light is in most cases the result of additional losses or absorption, which act uncorrelatedly on single photons. In a laser with negligible additional losses where outcoupling is dominated by the beam-splitter effect, the photon statistics reveal to be mainly non-Poisson. A Poisson distribution would only occur for very low outcoupling rates, i.e., high finesse cavities. It is found that in the limit of strong outcoupling even the distribution of thermal light can result.
A Novel Approach to Detect Copy Move Forgery Using Deep Learning

With the development of readily available image editing tools, manipulating an image has become a universal issue. To check the authenticity, it is necessary to identify how various images might be forged and the way they might be detected using various forgery detection approaches. The importance of detecting copy-move forgery is that it identifies the integrity of an image, which helps in fraud detection at various places such as courtrooms, news reports. This article presents an appropriate technique to detect Copy-Move forgery in which to some extent an image is copied and pasted onto an equivalent image to hide some object or to make duplication. The input image is segmented using the real-time superpixel segmentation algorithm DBSCAN (Density based spatial clustering of application with noise). Due to the high accuracy rate of the VGGNet 16 architecture, it is utilized for feature extraction of segmented images, which will also enhance the efficiency of the overall technique while matching the extracted patches using adaptive patch matching algorithm. The experimental results reveal that the proposed deep learning-based architecture is more accurate in identifying the tempered area even when the images are noisy and can save computational time as compared to existing architectures. For future research, the technique can be enhanced to work on other forgery detection techniques such as image splicing and multi-cloned images.
Design of a Multimodal Oculometric Sensor Contact Lens

Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field.
BENEFITS OF FLASHCARD MEDIA IN SPEAKING SKILLS OF STUDENTS WITH SPECIFIC LEARNING DIFFICULTIES

Speaking skills are one of the language skills that must be mastered by students, including elementary school (SD) students. This skill must always be honed, especially by elementary school students who have specific learning difficulties, so that students can play an active role in communicating. Therefore, it is necessary to have good stimulation to improve the speaking skills of elementary school students. Flashcard media can be used as a medium to improve students' speaking skills. The method used in this research is a literature review or literature study. The results of this study indicate that the flashcard medium is practical and effective when used in learning. By utilizing Flash Card media, learning is more conducive and fun because Flashcard media can attract students' attention. Students' mastery of language develops and students' speaking skills, especially those of students with specific learning disabilities, increase. Students are easier to add and pronounce vocabulary and memorize vocabulary, which helps students recognize letters and make it easier for students to be able to tell stories according to what is in the picture, so that students' imaginations develop, helping students to learn to think critically, students' enthusiasm for learning increases, and students become more courageous.
PENGARUH MEDIA PEMBELAJARAN E-LEARNING TERHADAP HASIL BELAJAR MAHASISWA PRODI PGSD UNIVERSITAS QUALITY BERASTAGI

With this era of increasingly developing technology, learning programs are directed to be able to make better use of technology. One of the current uses of technology is e-Learning using the web to access it. There is no denying that many students now have smart phones, so it is easier to access them from wherever they are and at any time. Utilization of e-learning that is commonly developed today is using LMS (Learning Management System). Quality Berastagi University is one of the universities that have utilized e-Learning as a learning medium this semester. Learning activities using e-learning media on campus utilize the E-Learing application. In the learning process, students are given assignments by the lecturer and send the results of their reports via E-Learning. In addition, students are also given additional material in order to understand more broadly the material that may not be conveyed directly when face to face in class. E-learning is an alternative media to provide test exam questions and improvised media that do not always use print media. Based on the above phenomenon, it can be said that in the learning process, a method, strategy, media, or learning model is needed that can train students' abilities in improving learning outcomes, involving student activities optimally, and making mathematics learning more meaningful and fun. Lecturers are required to choose learning media that can stimulate the enthusiasm of each student to be actively involved in their learning experience. This study aims to determine student learning activities increase with E-Learning learning media in the PGSD Study Program, Quality Berastagi University.
Landscape Creation of Children’s Outdoor Activity Space in Urban Residential Areas Based on Child Psychology Analysis

The investigation of the unidentified world and the restriction field has intensified as the application concept of Artificial Intelligence (AI) continues to expand, due to which lifestyle of people and manufacturing modes have also changed dramatically. Based on these revolving trends, this work examines AI and studies children’s outdoor spaces from the perspective of their behavioral psychology. Furthermore, based on the analysis of text information, this study first defines the relevant concepts, then studies the characteristics and composition of children’s psychology and behavior, their habits, and activity space, respectively. In addition, this study interprets the excellent case design of children’s outdoor activity space at home and abroad from the perspective of behavioral psychology to provide a theoretical basis and design a reference for the design of children’s outdoor activity space. Besides, a survey of children’s residential areas analyzes the current situation and problems of children’s outdoor activity spaces and explores their renovation. Finally, using the author’s participation in the design of children’s outdoor activity spaces in residential neighborhoods as an example, it relates theory to practice. It studies and discusses the planning and design of children’s outdoor activity spaces by constructing an outdoor activity space under children’s behavior and psychology.
A Multi-channel Chinese Text Correction Method Based on Grammatical Error Diagnosis

Text error correction is a key research direction in natural language processing (NLP). Using a single model to diagnose and correct grammatical errors is the current mainstream approach. Although these methods have high performance in diagnosing grammatical errors. They also have the problem of low error correction performance. In order to improve the error correction performance, this paper proposes a multi-channel Chinese text correction method based on grammatical error diagnosis (GED-MCCTC). First, in order to accurately identify the types of grammatical errors and error position information, the Bi-LSTM-CRF model is utilized to give the labels of grammatical error types and positions. Then, four corresponding correction channels are designed according to the five different types of grammatical errors defined to complete the error correction task. Experimental results on shared datasets show that the proposed method improves the average F1 score by 4.83% compared with the optimal baseline model. The multi-channel correction model can greatly improve the text correction performance, on the SIGHAN15 and Corpus500 test datasets, the average F1 score has reached 76.07% and 72.62%.
Evaluation of the effectiveness of the use of digital educational technologies in the educational process of a university

The paper presents a comparative analysis of the effectiveness of the use of various learning technologies: digital, traditional, and blended learning in the educational process of a university. The authors show the results of research related to the analysis, development, and refinement of parameters, capabilities and implementation of digital technologies in the educational process. The analysis is carried out based on a set of surveys of students and teachers. A comparison of the impressions of the participants of the educational process and an analysis of its results during the transition from the classic model of the educational process to a blended (classic model with the use of digital educational technologies) anddistance learning is carried out. The results obtained were correlated with objective indicators — compliance with the academic schedule, average academic performance, and the level of residual knowledge. Significant advantages and disadvantages of the introduction of distance learning in the educational process were identified, on the part of both trainees and teachers. In conclusion, the authors summarize that the use of digital technologies makes it possible to create a learning environment around the student that forms professional digital innovative competencies.
Hybrid Code Lifting on Space-Hard Block Ciphers

There is a high demand for whitebox cryptography from the practical use of encryption in untrusted environments. It has been actively discussed for two decades since Chow et al. presented the whitebox implementation of DES and AES. The goal is to resist the key extraction from the encryption program and mitigate the code lifting of the program. At CCS2015, Bogdanov and Isobe proposed space-hard block ciphers as a dedicated design of whitebox block ciphers. It ensures that the key extraction is as difficult as the key recovery in the standard blackbox model. Moreover, to mitigate code lifting, they introduce space hardness, a kind of leakage-resilient security with the incompressibility of a huge program. For space-hard ciphers, code lifting (a partial leakage of the entire program) is useless to copy the functionality.In this paper, we consider a new attack model of space-hard block ciphers called hybrid code lifting. Space-hard block ciphers are intended to ensure security under a size-bounded leakage. However, they do not consider attackers (in the standard blackbox model) receiving the leakage by code lifting. If such attackers can recover the encryption program of a space-hard block cipher, such a cipher does not always satisfy the intention. We analyze Yoroi proposed in TCHES 2021. We introduce the canonical representation of Yoroi. Using the representation enables the recovery of the programs of Yoroi-16 and Yoroi-32 with 233 and 265.6 complexities, respectively, in spite of slight leakage. The canonical representation causes another attack against Yoroi. It breaks an authors’ security claim about the “longevity”. We additionally analyzed SPNbox proposed in Asiacrypt 2016. As a result, considering security on the hybrid code lifting, the original number of rounds is insufficient to achieve 128-bit security under quarter-size leakage.
Healthcare related event prediction from textual data with machine learning: A Systematic Literature Review

In the field of healthcare, as well as many others, textual descriptions of events are logged. With the use of Natural Language Processing (NLP), these texts are used to train event prediction machine learning algorithms. In this review the aim was to assess the state-of-the-art within current literature concerning prediction of events on textual records. Thus, this study follows a standard Systematic Literature Review (SLR) process. Primary articles are selected from PubMed, IEEE and WebOfScience with a search query, and then exclusion and quality assessment criteria are used to select the articles that are relevant to this study. Published performance metrics for the prediction algorithms used in the studies were then extracted from the included articles and used to assess the different methods. The general-purpose neural network algorithms: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) and Conditional Random Fields (CRF) demonstrate the highest F1-scores amongst all the methods in this review, of 98.5%, 98% and 90.13% respectively. The algorithms that were designed specifically for NLP such as word2vec and BERT also performed well with F1-scores of 88.93% and 91.50%. This review does not give a comparison between methods but gives an indication about which machine learning methods perform well according to the authors of the selected studies. Not enough performance results are published under comparable circumstances to give conclusive results about which methods perform the best. More research needs to be done in comparing algorithms on the same dataset to proof the performance of the methods.
Step on it: asymmetric gain functions improve starting and stopping in virtual reality walking

Abstract Transfer functions with a high translational gain can increase the range of walking in virtual reality. These functions determine how much virtual movements are amplified compared to the corresponding physical movements. However, it is unclear how the design of these functions influences the user’s gait and experience when walking with high gain values. In a mixed-methods study with 20 users, we find that their best transfer functions are nonlinear and asymmetrical for starting and stopping. We use an optimization approach to determine individually optimized functions that are significantly better than a common approach of using a constant gain. Based on interviews, we also discuss what qualities of walking matter to users and how these vary across different functions. Our work shows that it is possible to create high-gain walking techniques that offer dramatically increased range of motion and speed but still feel like normal walking.
TRANSFER LEARNING WITH PIX2PIX GAN FOR GENERATING REALISTIC PHOTOGRAPHS FROM VIEWED SKETCH ARTS

Our approach for producing realistic human faces from artist sketches is based on conditional GAN image translation, which we present in this article. Here, face features are centered in front-facing positions in a typical illumination, with an unbiased look and no obstacles. The image-to-image translation technique is used using traits with the conditional generated adversarial network (cGAN) that has been formed to understand the correspondence among a source image input and a target image by employing a training dataset matched set of images. Using the pix2pix convolution neural network to produce images potentially helps overcome challenges such as short datasets and class imbalance. We develop excellent loss functions by constraining pixels, edges, and high-level properties of the generated realistic images in the system design. The CUHK dataset is employed in this research, and image augmentation is accomplished using an effective sampling strategy since the dataset is restricted. A significant variation in the network's performance is observed, caused by fewer amounts of data available. The results of our experiments demonstrate that our approach can produce face sketches/photos that are both aesthetically pleasing and retain the subject's individuality. The deployment of transfer learning for the sketch to image is carried out to get an understanding of the usefulness of this method when the target and source are different. With the usage transfer learning method, the system obtained 10%-15% improved efficiency compared to previous works.
A deep CT to MRI unpaired translation that preserve ischemic stroke lesions

Stroke is the second-leading cause of death world around. The immediate attention is key to patient prognosis. Ischemic stroke diagnosis typically involves neuroimaging studies (MRI and CT scans) and clinical protocols to characterize lesions and support decisions about treatment to be administered to the patient. Nowadays, multiparametric MRI images are the standard tool to visualize core and penumbra of ischemic stroke, supporting diagnosis and lesion prognosis. Specially, DWI modality (Diffusion Weighted Imaging) allows to quantify the cellular density of the tissue, and therefore allowing to quantify the lesion aggressiveness, and the recognition of micro-circulation properties. Nevertheless, MRI availability at hospitals is not widespread, and acquisition require special conditions requiring considerable time. Contrary, CT scans commonly have major availability but brain structures are poorly delineated, and even worse, ischemic lesions are only visible at advanced stages of the disease. This work introduces a deep generative strategy that allows ischemic stroke lesion translation over synthetic DWI-MRI images. This encoder-decoder architecture, include U-net modules, hierarchically organized, with inter-level connections that preserve brain structures, while codifying an embedding representation. Then a cyclic loss was here implemented to receive CT inputs and decode DWI-MRI images. To avoid mode collapse, this learning is inversely propagated, i.e., from synthetic DWI-MRI images to original CT-scans. Finally, an embedding projection is recovered to show a proper lesion-slice discrimination, regarding control studies. Clinical relevance- To recover synthetic DWI-MRI that preserved ischemic lesion using CT scans as an input and following an unpaired image translation setup.
Autonomous Reading of Gauges in Unstructured Environments

This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations.
PENERAPAN GERAKAN LITERASI POJOK BACA UNTUK MENUMBUHKAN MINAT BACA SISWA KELAS I SDI PERMATA NUSANTARA PADA KEGIATAN KAMPUS MERDEKA

BSTRACTThe purpose of this study was to find out with the literacy movement reading corners can be used as an alternative and supporter to increase students' reading interest. The reading corner literacy movement encourages students to always have a thirst for knowledge, makes them happy to read and finally students have broader insights and get new information for class II at Permata Nusantara Islamic Elementary School. This research was conducted at Permata Nusantara Islamic Elementary School involving 40 students. The research method used for data collection Data collection in this study used observation sheets, interviews, and documentation. The data analysis technique uses data reduction, data presentation, and drawing conclusions.
Advanced fault-tolerant visual multi-secret sharing scheme

Aim: In visual cryptography, a secret image is encrypted into two meaningless random images called shares. These two shares can be stacked to recover the secret image without any calculations. However, because of the alignment problem in the decryption phase, risk of poor quality of the restored image exists. Encrypting multiple secrets on two images simultaneously can improve execution efficiency. Methods: Let 7 × 7 pixels be a unit; this paper designs a codebook for any unit in the secret images by using a random grid. Besides, this paper shows a general shifting approach that can embed N (≥ 2) secret images simultaneously with adjustable distortion. Results: This paper provides a visual multi-secret sharing scheme without pixel expansion; the proposed scheme can encrypt more than two secret images into two shares simultaneously. During decoding, aligning the shares precisely is not necessary. Conclusion: Theoretical analysis and simulation results indicate the effectiveness and practicality of the proposed scheme.
Efficient Echocardiographic Image Segmentation

In this paper, we propose an improved region-based active contour method based on the development of a novel signed pressure force (SPF) function. To obtain the required boundary, the method is applied to the echocardiographic images. Ultrasound image segmentation is particularly challenging due to speckle noise, low contrast, and intensity inhomogeneity. Because of these factors, segmenting echocardiographic images is a difficult task. All of these issues are addressed by the proposed model, which detects the true boundary without any noise. The proposed model is more robust, effective, and accurate when applied to images with weak edges and inhomogeneous intensity.
Revolutionizing Medical Education with Metaverse

Metaverse in Healthcare/Medical education has the potential to be an interactive, immersive, and recreational experience tailored to every individual. It can provide new opportunities for healthcare providers to interact with patients in ways that are more intimate. It could for example as walking through a three-dimensional model of the human body with patients, discussing diagnoses and treatments. This would allow providers to simulate the effect of a proposed treatment on the patient’s body before it is applied, creating a more personal and informative experience compared to what is currently possible with two-dimensional images on a screen. This paper focuses on the methods that could be leveraged for revolutionizing the medical education and training using metaverse.