Reviewerly/exHarmony huggingface dataset:
Observation of the performance enhancement of a non-degenerate JPA versus degenerate JPA in a QTMS radar simulation

Abstract Small differences in the types of Josephson parametric amplifiers (JPAs) can cause significant changes in quantum two-mode squeezed (QTMS) radars. In this work, taking into account these differences, we examine two types of JPA in terms of pumping in the quantum radar, including degenerate JPA (DJPA) and non-degenerate JPA (NDJPA) under the same conditions, leading to results very significant for the design of a QTMS radar. This study shows that QTMS radar performance is better than that of the DJPA scenario when using the NDJPA scenario. In addition, we calculated the signal-to-noise (SNR) ratio and receiver operating characteristic (ROC) for two scenarios, both of which show the superiority of the NDJPA scenario. The SNR of the QTMS radar in the NDJPA case is about 1.7 dB higher than in the DJPA case. In addition, the channel number of detection probability in the NDJPA scenario is lower than that in the DJPA scenario, which indicates better ROC performance. Finally, using an NDJPA in a quantum radar can achieve a performance improvement over a quantum radar using a DJPA.
Moving Beyond POCs and Pilots to Mainstream: Discovery and Lessons from Blockchain in Healthcare

Blockchain technologies has been in the forefront of innovation and is maturing at a fast pace with fintech leading the way. Though the innovation in the permissionless space is high, the enterprise adoption has still not caught up. The reasons include a variety of reasons including inadequate understanding of the technology, full awareness even in enterprises, unclear regulations, and lack of exploration and evangelism to really understand the reimagination of shared enterprise business processes. Most enterprise blockchain efforts are run from a small exploratory group in large companies and has NOT moved beyond pilots or limited production tryouts in to mainstream adoption at scale. This is because there is not much knowledge about what is possible for reimagining current business processes which are mostly unilateral. This is more obvious in healthcare. A horizontal transformation enabler like the blockchain technology can effect collaborative business processes in a networked world. This paper explores the root causes for the challengesand suggests a methodology for discovery of the key inflection points for potential collaborative business processes and the re-engineering that would be needed. Blockchain in enterprise are not an isolated piece of technology, and has to interact with the enterprise assets, and there has to be mechanisms for aggregating the data that is meaningful to the counter parties for shared collaboration. Equally important is solid and a stair-stepping change management plan for the blockchain efforts to meet operations mainstream and gradually move the operations to the end state. Possibilities and results of the collaborative processes have to be shown in meticulous economic rigor for any possibilities of blockchain efforts getting the enterprise portfolio dollars against many enterprise systems that are already overloading budgets. The paper will cite the journey in a reference implementation with results, findings at the technical, functional, operational and change management levels for healthcare administration executives and portfolio managers.
Comprehensive Browser Extension for Analysing YouTube User Engagement, Controversy, User Requirements, and Trending Keywords

In the dynamic realm of YouTube, addressing the diverse needs of its extensive audience while empowering content creators is of paramount importance. This research paper introduces a robust solution encapsulated in a Chrome extension, meticulously designed to enhance user engagement and fortify YouTube content creators, specifically in the sphere of information technology-related videos. Our approach leverages state-of-the-art Natural Language Processing (NLP) methodologies and Machine Learning (ML) techniques. The extension provides a comprehensive suite of four critical functionalities: analysis of user engagement, extraction of user requirements, identification of controversial topics, and provision of invaluable keyword and title recommendations. To achieve these goals, we employed a range of techniques and algorithms, including Random Forest, VADER (Valence Aware Dictionary and Sentiment Reasoner) Lexicon, Multinomial Naive Bayes (MNB), Latent Dirichlet Allocation (LDA), Bag of Words (BOW), and the GPT-3 model. The system boasts an impressive overall accuracy of 89.72%, with individual components achieving accuracies of 83.040/0, 92.83 %, 94 %, and 89%, respectively. Beyond its direct application to information technology-themed YouTube content, the methodologies presented can be adapted for other platforms and a variety of categories, such as entertainment, gaming, cooking, travel, and tourism, among others. This research not only redefines the possibilities in YouTube analytics but also offers tangible tools and insights to help creators optimize engagement and foster deeper online dialogues.
Enhancing Irradiance Prediction Using Short-Term Data: A Machine Learning Approach to Resilient Energy Management

Accurate irradiance prediction is necessary for the effective integration of photovoltaic (PV) systems into smart grids. However, rapidly changing weather patterns pose a challenge, rendering long-term historical data less reflective of current weather trends. This study, therefore, investigates the use of short-term data (Oct’22 - Apr’23) for hourly irradiance prediction at Kengeri, Bengaluru. Multiple Linear Regression (MLR) is employed as the base case. Subsequently, Random Forest (RF), Estreme Gradient Boosting (XGBoost), and a combination of Isolation Forest (IF) with RF and XGBoost are applied to enhance prediction accuracy. The results indicate cloud cover and humidity as key weather parameters influencing irradiance. The prediction improved upon the addition of hour polynomials and irradiance lag values to the weather parameters. For the MIR model, the R2, R.MSE, and M.AE value were 0.86, 111.19 W/sqm, and 83.62 W/sqm, respectively. In contrast, the most suitable model (IF+RF) model achieved an R 2 of 0.90, an RMSE of 94.51 W/sqm, and an MAE of 61.24 W/sqm, marking significant improvement against the baseline. It achieved a 24% reduction in overprediction and 31% in underprediction, with a daily average irradiance prediction error of −337.15 W/sqm. A consistent trend of underpredicting is observed across all models, struggling to accurately account for unpredictable intra-day cloud cover fluctuations due to limited variations in the weather conditions. Future work would extend the observation period and explore more complex models. Improvements in irradiance predictions are vital to optimize the operation of PV systems, supporting smart grid integration and a more resilient, sustainable energy infrastructure.
Severity Level Labeling for Software Issue Data Using Machine Learning Techniques

Reporting issues is a crucial activity in software development. However, some reported issues receive less attention from the project development team, leaving important issues unaddressed. Things get worse when the issue reports are incorrectly labeled. In order to avoid such a scenario, it is crucial to prioritize issues based on their degree of criticality. The criticality of an issue can be determined by assessing its severity. In this research, we use two datasets. The first is an unlabeled dataset collected from the biggest known code repository platform, GitHub, through its public API. The second dataset is taken from an existing study, and it is a labeled one. Both datasets then undergo a sequence of text preprocessing steps. The output of text preprocessing is fed to a vectorization process using TF-IDF. The vectorization result of the labeled data is used to calculate term weights and determine the vocabulary in the word dictionary. On the other hand, the vectorized GitHub data issues undergo clustering using K-Means and are manually labeled. The labeled data is utilized to build a classification model employing four different algorithms: Multinomial Naïve Bayes, Linear SVM, Non-Linear SVM, and KNN. The clustering results yield a silhouette coefficient evaluation of 0.535. Among the classification models, the Non-Linear SVM model demonstrates the best performance with an accuracy value of 0.979.
Reference surface self-calibration stitching algorithm based on Legendre polynomials for testing synchrotron radiation flat mirrors

X-ray mirrors play a critical role in transmitting and focusing beams in advanced light source devices like synchrotron radiation (SR) and free electron laser (FEL) light sources. The Root-Mean-Square (RMS) value of surface shape accuracy required for these mirrors is typically sub-micro radian/sub-nanometer, and their aperture can be hundreds of millimeters or even meters. To achieve this accuracy, deterministic figuring is required and surface figure error is the premise. Interferometric stitching test is commonly utilized. However, interferometric test obtains the surface figure error relative to the reference surface. To obtain the absolute surface figure error of X-ray mirrors, it is crucial to obtain the surface figure error of the reference surface before or during stitching measurement and separate it from the stitching result. To achieve this, a reference surface self-calibration interferometric stitching algorithm based on Legendre polynomial fitting is reported. The algorithm fits the reference surface by orthogonal Legendre polynomials in the rectangular domain, and the polynomial coefficients are expressed as interlocking terms in the stitching algorithm. The fitting coefficients are optimized and fitted together with the free compensation terms, i. e. the translation and tilt terms. The paper established the theoretical model of the algorithm and conducted simulations on an X-ray mirror with a length of 300 mm. A two dimensional stitching interferometry device was built. An X-ray mirror with a length of 300 mm was tested utilizing the proposed method. The short-term repeatability was preliminarily analyzed.
An investigation of high dynamic range imaging of highly reflective surfaces based on deep learning 3D fringe projection

Three-dimensional reconstruction of highly reflective surfaces has always been an important and difficult problem in structured light technology. In this paper, a novel method by combining optimal exposure with the adjustment of the intensity of projection fringes by deep learning is proposed a single frame method is used to extract the overexposed region in the optimal exposure time series for composite projection. By using the statistical histogram of the number of overexposed pixels of the measured object under different projection conditions, and combining with the proportion of pixels in the image that are not exposed, the optimal exposure time series is calculated and the overexposed area is automatically extracted. The single-frame composite high dynamic image is obtained by adjusting the projection intensity through absolute coordinates, which improves the single-frame high dynamic range technique for highly reflective surfaces. Several simulations and experiments were conducted. The experimental results show that the proposed method can effectively improve the 3D reconstruction accuracy of the highly reflective surface, and the number of effective pixels in the point cloud can reach more than 98%.
Memristör Tabanlı Kaotik Sistemler ve DNA Kodlama Kullanılarak Görüntü Şifreleme Uygulaması

The rapid advancement of internet technologies has accentuated the need for robust data security mechanisms, particularly in the realm of image transmission. Addressing this, our study introduces a cutting-edge encryption system that blends arithmetic operations with DNA-inspired biological processes and the complexity of chaotic systems, presenting a significant evolution in encryption methodologies. The system employs a synergetic fusion of DNA-based encryption and XOR operations, bolstered by a memristor-based chaotic system, to heighten the security barriers of image encryption. This innovative approach not only provides a secure means to transmit images over the internet but also lays new groundwork in the field of cryptographic research. Rigorous security analyses, including correlation, histogram, and differential attack assessments, are performed, with the findings validating the robustness and efficacy of the encryption process. The outcomes of this research extend the discourse on data security, offering a fortified method that could revolutionize the protection of information in an increasingly digitalized world.
Embodiment in Virtual Reality and Augmented Reality Games: An Investigation on User Interface Haptic Controllers

Undoubtedly, virtual reality technologies stand out as one of the most rapidly expanding domains in the present era. Apart from features like clarity of vision, viewing angle and time distortion that are improving daily, user interfaces are another crucial part of this technology that is not talked about much but must be. This study investigates and summarizes the literature on wearable technology and haptic controllers, which serve as the player’s interface between virtual and augmented reality. The controllers and devices we investigate, try to achieve a holistic approach in the experience thus it appears to be a rich soil for future studies. Although many technological advancements and entirely new ones are anticipated, a thorough analysis of the phenomena of interaction, participation, and integration, as well as embodiment and presence, should shed light on the question of its scope, depth, assessment, and limitations.
Digital Financial Strategy: New Direction or the Usual Old?

This paper explores the intricate relationship between technology and law, specifically delving into the legal regulation of technology. The author addresses the challenges posed by cybercrime in the context of IoT devices and blockchain technology. The research focuses on digital financial strategy, particularly its connection to blockchain technology. Employing a descriptive and comparative methodology, the study then introduces the technology underlying cryptocurrencies and subsequently examines the issues about the Digital Financial Package of the European Union. The findings highlight that the digital finance strategy represents one of the most significant and comprehensive legislative packages in recent years.
Analysis On the Reform Of Sports Calisthenics Curriculum In Higher Vocational Colleges

Along with our country education reform and innovation, many disciplines and education teaching methods, great changes have taken place in the higher vocational colleges and universities sports aerobics curriculum is deeply influenced by the content of attention, is also a higher vocational college students main choice of sports, but due to the continuous improvement of living standards, the students so many higher vocational college students is more and more do not take the physical training, Even some students because of poor overall physical quality, so as to affect other aspects of learning, but there are still some problems in the process of developing sports aerobics courses in higher vocational colleges. In this paper, the author analyzes the problems existing in the teaching process of higher vocational college physical calisthenics, and expounds the relevant countermeasures to improve the curriculum of higher vocational college physical calisthenics. After "knowing", to realize "the generation flip" of the learning content by online classroom exercises, “understanding” and “applying”, to realize "the generation flip" of the learning content, can be achieved by the online classroom discussion function.
Teacher Readiness in Implementing Children's Literature to Enhance English Language Learning in Young Learners Classroom

This study employs a qualitative approach as a research design. This qualitative study aims to investigates teacher readiness for incorporating children's literature into English language learning with young learners. The research employs in-depth interviews as the primary data collection method. This study involves two young learner English teacher as the participants who were selected purposively, one from an international kindergarten and one from a state primary school. The findings of this study reveal that teachers are generally aware of the benefits of using children's literature in the classroom and acknowledge the importance of preparation. Teachers emphasize the need to be well-prepared before entering young learners' classrooms, ensuring that everything is ready for effective implementation. They emphasize that children's literature not only benefits young learners but also aids teachers in delivering content more effectively. Teachers report that children's literature creates a fun and active learning environment that fosters enthusiasm among students.
VOICETAINER SEBAGAI PELUANG KERJA BAGI GENERASI MILENIAL DALAM BIDANG KOMUNIKASI DAN PUBLIC SPEAKING

The ability to communicate effectively and good public speaking skills, today is indispensable for various needs, including being used and exploited as job opportunities in the field of communication and public speaking. Against this background of the need for public speaking skills, especially for the millennial generation, a public speaking workshop was held that focused on introducing and practicing voice coaches. The implementation of the public speaking workshop has two main objectives, namely: (1) Get to know the Voicetainers; (2) Practicing Voicetainer. Methodologically, this public speaking workshop activity is carried out using service learning method. At the operational level, the service learning method in this activity is carried out by teaching participants about public speaking by combining academic approaches (voicetainer concepts and theory) and training practical skills (practicing voicetainer) of activity participants in the fields of communication and public speaking which can be used as job opportunities. Specifically, this activity has been carried out well and has achieved maximum results on the following two points: (1) Workshop participants can recognize and know about a voice coach, which is a public speaking skill that can support work in the field of communication; (2) Workshop participants can practically and applicatively practice public speaking skills which can be used as job opportunities in the field of communication.
Asosiasi Surat Kabar Digital terhadap Minat Membaca Koran Masyarakat di Kota Makassar

The purpose of this study was to determine the effect of digital newspapers on community newspaper reading interest in Tamalanrea Indah Village, Tamalanrea District, Makassar City. The research method used is quantitative method by distributing questionnaires and analyzed using SPSS. The results of this study indicate that Digital Newspapers have a significant influence on Reading Interest in the community in Tamalanrea Indah Village, Tamalanrea District, Makassar City, with tcount = 4.711&gt; ttable = 1.984 and significance 0.000 &lt;0.05. In the reliability test of variable X stated with Cronbach alpha value is 0.734 and variable Y is 0.702. And based on the coefficient of determination which shows that there is an association of Digital Newspapers and people's newspaper reading interest. It predicted approximately 0.185 or 18.5% of reading interest.
Automated Chemical Equation Balancing Using the Apriori Algorithm

Chemical equations must be balanced to maintain mass conservation. Traditional chemists employed manual processes with meticulous investigation and trial-and-error iterations. Automating and enhancing this difficult process is becoming more popular as machine learning (ML) progresses. We provide a novel Apriori algorithm-based chemical equation balancing method in this paper. Our solution uses the Apriori algorithm to find common itemsets of balanced reactions and translates unbalanced equations into machine-readable language. After that, it reconstructs balanced equations, automating a tedious task.
Bulut Hesaplama İçin Evrimsel Algoritma Kullanarak İş Akışı Planlaması

Cloud computing provides powerful, highly scalable, flexible resources for real world applications. It also reduces the cost and operation expenses. Workflow scheduling is important for getting higher performance, reducing cost and using resources more efficiently in cloud computing. Workflow scheduling in cloud systems assigns tasks to resources available in the system and aims to utilize cloud resources by decreasing makespan of the workflow. In this study, an evolutionary algorithm is proposed to solve workflow scheduling problem. The main objective of this work is to minimize the makespan of the schedule. To achieve this goal, problem specific crossover operator and mutation operators are proposed in the evolutionary algorithm. The crossover operator will combine the problem-specific information stored in both parents to create a new individual. The mutation operators will explore neighbor solutions using some intelligent search mechanisms. This unique design of the operators increases the diversity of the search space and the quality of the solutions. As a result, the workflow schedules obtained from the evolutionary algorithm decreases the makespan of the workflow in the cloud system. The performance of the proposed study is measured using well-known scientific workflows and is compared with the algorithms from the literature. The proposed study outperforms all related algorithms in 67% of the test cases and obtains the same results in the remaining test cases.
Reversible Data Hiding in Encrypted Images Using Four-Color Approach

Reversible data hiding in encrypted images (RDHEI) has become a hot topic, and a lot of methods are proposed to optimize this technology. However, these methods do not make full use of image spatial correlation, which may cause their outcomes to be flawed. In this paper, we propose an advanced scheme, the main contributions of which include two points. First, we introduce the four-color theorem approach to the image processing field for the first time. Second, we extend the four-color theorem to facilitate RDHEI. Experiments show that the embedding rate of the proposed scheme can outperform other RDHEI algorithms, and the PSNR value of the recovered image can be infinite.
Local Controller Design for T-S Discrete Fuzzy Systems

This paper deals with the problem of local control for T-S discrete fuzzy systems. First, an fault estimation (FE) observer is constructed. Then, by using the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{\infty}$</tex> analysis method, the synthesis conditions of the observer are derived. Then, by using the estimation of the fault, which is provided by the observer, the local controller scheme is designed to constrain the state of the system into a local area. The merit of the presented method is that it has the ability of reducing the influence of premise part of the controller such that the performance of the local stabilization can be guaranteed. In the end, an example is used to demonstrate the efficacy of the presented approach.