Reviewerly/exHarmony huggingface dataset:
Blurring the lines: how AI is redefining artistic ownership and copyright

Abstract The rapid advancement of AI technology has brought about significant changes across various industries, including the realm of art and creative endeavors. Presently, AI demonstrates the capability to produce high-quality works that closely resemble those crafted by humans. However, amidst these advancements, ownership rights remain ambiguous, given that existing copyright laws do not explicitly confer copyright protection to works not originating from human creators. The primary objective of this research is to present a spectrum of opinions and perspectives on a pivotal question: should the ownership of AI-generated works belong to individuals or AI entities? Simultaneously, the study aims to scrutinize the suitability of the existing copyright laws in addressing the complexities of AI-generated works. Additionally, this research paper endeavors to pinpoint the necessary adjustments and enhancements required within the current legal framework to harmonize with the contemporary landscape. To attain these objectives, two surveys were conducted. The first, a quantitative survey, involved a sample size of 100 participants, while the second, a qualitative survey, featured professionals within the creative field and legal experts. Each group comprised five individuals. The research findings gleaned from these surveys strongly suggest that AI-generated works exhibit quality on par with human creations and, therefore, merit classification as human works. Furthermore, there is a compelling argument that these works should be afforded copyright protection to provide clarity regarding their utilization by other individuals. Consequently, the prevailing copyright laws should undergo revision and amendment to reflect the demands of the modern era, establishing precise regulations tailored for AI-generated works.
Higher key rate in asymmetric quantum-classical integrated measurement-device-independent quantum-key-distribution systems

Integrating measurement-device-independent quantum key distribution (MDI QKD) with classical networks guarantees security and economy. Unfortunately, the performance of MDI QKD, suppressed though it is by network asymmetry, is further degraded by the addition of integration noise. As a consequence, symmetric systems are always believed to be better for a higher key rate. However, we reveal a counterintuitive fact that asymmetric MDI QKD has better performance in integrated systems. Modeling the mechanism of the asymmetric effect in integrated MDI systems and analyzing the noise-suppression effect of asymmetric structures, we observe an unanticipated increase of the key rate up to 260%, giving a conclusion diametrically opposite that for the original system. The phenomenon found and the proposed analytical model provide a method for noise suppression and performance improvement, permitting high adaptability for realistic asymmetric networks.
Morphological diversity of cancer cells predicts prognosis across tumor types

Abstract Background Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin-eosin–stained histopathology images. Methods We analyzed publicly available digitized whole-slide hematoxylin-eosin images for 2000 patients. Four tumor types were included: lung, head and neck, colon, and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on hematoxylin-eosin images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intranuclear variability and internuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine-learning model to predict patient prognosis. Results A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range = 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine-learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range = 1.62-3.23, P &amp;lt; .035) in validation cohorts and further improved prognostication when combined with clinical risk factors. Conclusions Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.
E-Agri: A Game-Changer for Indian Agriculture

The paper examines the potential of e-agriculture in India, a rapidly developing nation with a large agricultural sector. It looks at the current state of e-agriculture in India and the opportunities and challenges faced by stakeholders in the sector. The study reviews the current initiatives taken by the government of India to promote e-agriculture and how these initiatives are impacting the agricultural sector. It also examines the effects of technology, such as the use of mobile phones and the internet, on the agricultural sector. The paper concludes by discussing the potential of e-agriculture in India and the need for further research in the area.
Innovation In English Language Learning Model Based Digital Literacy To Construct Young Learners’ Character

Learning English for children is important, apart from making it easier for children to enter a higher level of education, it also accustoms children to face the demands of the times that use English as an international language, children will be able to communicate well. The aim of this study was to innovate English language learning model based digital literacy to construct young learners’ character. The method used quantitative research with the one group pretest-posttest with the total sample 43 students. Based on the results of study show that learning using software in the form of digital literacy-based English learning media to develop the character education of children. The English learning model applied is not only oriented towards pure science which prioritizes students' cognitive abilities, but is also oriented towards psychomotor and affective abilities, namely the construct the character of the children. It can be concluded that using applications like Canva, Sparkol Video Scribe, and Wordwall, students find studying English to be enjoyable, and this is done in an effort to build the characters of the students
Accuracy improvement of multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization

Multi-camera laser scanning measurement is emerging as a pivotal element in three-dimensional (3D) optical measurements. It reduces occlusion and enables the gathering of more 3D data. However, it also increases the difficulty of system algorithms in obtaining high measurement accuracy. To improve the measurement accuracy, there is an urgent need to address global calibration and error correction issues caused by the employment of multi-view systems. An accuracy improvement method for multi-view 3D laser scanning measurements based on point cloud error correction and global calibration optimization is then proposed. First, a planar asymmetric circular grid target is designed to calibrate the cameras, laser planes, and initial global transformation matrices of the multi-view 3D laser scanning probe simultaneously. The influence of the position of the laser plane on the measurement error is analyzed and what we believe to be novel mathematical error influencing factors are then modelled for point accuracy. Furthermore, a believed to be novel error model based on the backpropagation (BP) neural network is established for the regression analysis of the mathematical error influencing factors and measurement deviations for each point based on the standard sphere plate measurement. The final measurement is optimized by the correction of point cloud for each camera of the multi-view system and the global calibration optimization based on the error model. The proposed method is reliable and easy to implement, since it only requires a standard sphere plate and a planar target. Several experiments show that the method can effectively improve the measurement accuracy of multi-view 3D laser scanning probe through point cloud error correction and calibration optimization.
Optimising Customer Service Delivery and Response Time through AI-Enhanced Chatbots in Facilities Management-A Mixed-Methods Research

The present study aimed to assess the effect of AI-enhanced chatbots that optimize customer service delivery and response times on facility management. It utilised the Technology Acceptance Model (TAM) and Social Response Theory (SRT) for this purpose. The research adopted a mixed-methods methodology aimed to explore the multiple perspectives of 10 facility managers and facility service providers affiliated with facilities management departments in the UAE, Qatar and Saudi Arabia regarding the benefits and challenges of AI-enhanced chatbots. This research used correlation analysis and regression to examine the relationships between variables. Correlation analysis, using SPSS 24.0, showed strong positive correlations between five AI-enhanced chatbot factors: Perceived Usefulness (PU), Perceived Ease of Use (PEoU), Behavioural Intention to Use (BIoU), Responsiveness (RP), and User Satisfaction (US) (Pearson Correlation Coefficient&gt;0.7). Regression analysis indicated a significant impact of all these variables on facilities management (p&lt;0.05). The study found that AI-enhanced chatbots in facilities management improve communication, responsiveness, and operational efficiency. They automate workflows, handle manual tasks, predict failures, and respond to customer queries. However, challenges include technical issues, limited human-human interaction, system quality and security, and user adoption. Chatbots deliver productivity gains and are used for automated reporting, identifying hazards, conducting briefings, managing meetings, providing training, supporting teamwork, ensuring well-being, and enhancing customer service.
Impact of Innovative Education on the Political and Social Life of the Society

Objective: The article presents data on the origin, essence, and philosophy of the concept of STEAM education, analyzes the idea of STEAM education, and its difference from the traditional educational system. Approaches to using the STEAM- system at Western Caspian University are being explored. Various approaches to the interpretation of the term "STEAM-education" are analyzed to identify a modern vision of the definition of the concept. Theoretical framework: The modernization of the educational system will prevent the aggravation of social problems and allow society to become a more reliable and less unpredictable part of a nowadays global world that should ideally be composed of integrated, inclusive, and cooperating communities. To implement such a new model, it is necessary to establish a well-selected and efficient team. Such a team will be able to develop adequate projects and address existing problems. Experience tells us that a project-based strategy is the most effective way toward an integrated approach to teaching. It will allow us to achieve our main objective, which consists of the training of students that will immediately be able to contribute to a positive and progressive transformation of society. Results: The system of education has more than just a serious impact on the political structure of society, its dynamism, and perspectives of development. In this regard, we can observe STEM and STEAM methodology mostly applied in schools, replaces the traditional approach when different, isolated, and unrelated subjects are taught. This phenomenon already gives considerable results. We think that this approach is not only applicable to schools but should also be completely implemented at every level of higher education. Originality/value: Nations and countries where education is still based on the principle “I transmit what I know”, face serious challenges, with little if not any ability to tackle them and lose in global competition. On the other side, nations and countries pay much more serious attention to the essence of education and acknowledge it as the main motor for the development of their society, make efforts to enhance the system, and set the principle “I teach how to get knowledge” as its dominant element. Those societies become the leaders of progress. These dynamics ensure their scientific, social, technological, and, as a result, economic leadership.
Utilizing Blockchain Technology in the Realm of Sports Medicine

Blockchain, or distributed ledger technology (DLT), originally devised for cryptocurrencies, notably Bitcoin, has garnered widespread interest for its capacity to transform data administration, enhance transparency, and fortify security across diverse sectors. Its progressive assimilation into Sports Medicine has surfaced as a compelling realm of exploration. This book chapter delves into extant research and advancements regarding the integration of distributed ledger technology in Sports Medicine, elucidating potential advantages and obstacles. By scrutinizing the multifaceted applications of distributed ledger technology, this review underscores its promise in revolutionizing aspects of data management within the domain while acknowledging the inherent challenges that necessitate further consideration.
Power Distance and Strategic Decision Implementation: Exploring the Moderative Influence of Organizational Context

This study examined the strategic decision implementation process. It focused on the factors affecting its implementation. Aiming at exploring the moderative role of the organizational context. As well as the power distance supporting the implementation of the strategic decision. To reach the goal of the study, and address the gap. Two hypotheses have been put. The first focused on the role of power distance in implementing strategic decisions. The second focused on the moderative role of the organizational context and its impact on the relationship between them. Where these two hypotheses were examined in one of the educational institutions (Al-Kufa University). The study sample consisted of (75) individuals. The study data were processed using SmartPLS. The results revealed that there is an effect of the power distance in the implementation of the strategic decision. Supported by the moderative effect of the organizational context. Some of the recommendations of the study, pay attention to the motives that the administration urges in the process of implementing the strategic decision. Enabling middle management to deal with strategic decisions.
Rogue Scholar updates: GUIDs and fields of science

The Rogue Scholar science blog archive launched two new features today: GUIDs (globally unique identifiers) and support for OECD Fields of Science and Technology. Globally unique identifiers (GUIDs) GUIDs are used to globally identify a blog post and are part of the RSS, Atom, and JSON Feed specifications.
Types and Semantics for Extensible Data Types

Developing and maintaining software commonly requires (1) adding new data type constructors to existing applications, but also (2) adding new functions that work on existing data. Most programming languages have native support for defining data types and functions in a way that supports either (1) or (2), but not both. This lack of native support makes it difficult to use and extend libraries. A theoretically well-studied solution is to define data types and functions using initial algebra semantics. While it is possible to encode this solution in existing programming languages, such encodings add syntactic and interpretive overhead, and commonly fail to take advantage of the map and fold fusion laws of initial algebras which compilers could exploit to generate more efficient code. A solution to these is to provide native support for initial algebra semantics. In this paper, we develop such a solution and present a type discipline and core calculus for a language with native support for initial algebra semantics.
Role-playing Method for Language Development in Elementary School

This study aims to explore and analyze the use of the role-playing method as a means of children's language development in primary schools. The study examined the impact of the role-playing method in improving speaking skills in children aged 6-8 years. In the study a qualitative research methodology. the research design was devised by selecting relevant participants, such as elementary school students and teachers involved in learning role-playing at State Elementary School 3 Pardasuka. The results showed that the role-playing method significantly contributed to children's language development in elementary schools. Active participation and social interaction in role-playing activities help improve children's speaking skills by being more confident and expressive. In addition, children develop better listening skills by acting as active listeners. The content analysis results showed improved vocabulary and understanding of language concepts through the role-play experience. It is hoped that the results of this study can contribute to developing more effective and enjoyable learning methods in supporting children's language development in primary schools
GAME‘S APPLICATION: Learning Method Using Maze Game Media to Increase the Learning Outcomes of Early Childhood Students

Student learning outcomes can measure the achievement of learning objectives, so increased learning outcomes can also determine the success of a lesson. Efforts to improve student learning outcomes can be made by implementing Classroom Action Research (CAR). Providing action in the form of a game-based learning approach using maze game media in classes where the learning process tends to be monotonous has the potential to be a solution to improving student learning outcomes. This action was carried out on class II students who had problems with low student learning outcomes. At the pre-cycle stage, the average grade II grade was only 51.6, and the classical completeness was only 40%. The action in applying a game-based learning approach using maze game media given to class II succeeded in increasing student learning outcomes in each cycle. In the first cycle, the average score was 68.3 and the classical learning mastery was 65.2%; in the second cycle, the average value was 78.3 and the classical learning mastery was 82.6%.
Adaptive Accelerated Gradient Algorithm for Training Fully Complex-Valued Dendritic Neuron Model

This paper presents an adaptive complex-valued Nesterov accelerated gradient (ACNAG) algorithm for the training of fully complex-valued dendritic neural model (FCVDNM). Firstly, based on the complex-valued Nesterov accelerated gradient (CNAG) algorithm, an adaptive stepsize update method is introduced by using local curvature information. Secondly, the obtained adaptive stepsize is further constrained by scaling the Malitsky-Mishchenko criterion. Experimental results demonstrate the superior convergence and efficiency of the proposed algorithm compared to CNAG for the training of FCVDNM.
High-Precision Matching Algorithm for Multi-Image Segmentation of Micro Animation Videos in Mobile Network Environment

Abstract In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image feature matching. This article designs a high-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environments. Fully denoise micro animation video images using 2D High Density Discrete Wavelet Transform (HD-DWT), and apply fixed block count segmentation to process micro animation video images; Using Harris algorithm to complete image corner detection and obtain corner features of sub images; In the K-means clustering algorithm, SIFT feature vectors are divided into clusters and paired with the nearest neighbor cluster in another sub image to form a sub image matching pair, completing block based sub image matching; Combine all sub image matching results to obtain video image matching results, and use the Improved Random Sampling Consistency (RANCAS) algorithm to remove incorrect matching during the matching process, improving matching accuracy. The experimental results show that the designed algorithm can effectively reduce image noise, improve image quality, and generate a large number of matching pairs in mobile network environments. After the application of the designed algorithm, the production effect of micro animated videos in mobile networks can be significantly improved.
Using Interaction Style Dimensions to Characterize Spoken Dialog Corpora

The construction of spoken dialog systems today relies heavily on appropriate corpora, but corpus selection is more an art than a science. As interaction style properties govern many aspects of dialog, they have the potential to be useful for relating and comparing corpora. This paper overviews a recently-developed model of interaction styles and shows how it can be used to identify relevant corpus differences, estimate corpus similarity, and flag likely outlier dialogs.
Controllable User Dialogue Act Augmentation for Dialogue State Tracking

Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1.
Evaluating N-best Calibration of Natural Language Understanding for Dialogue Systems

A Natural Language Understanding (NLU) component can be used in a dialogue system to perform intent classification, returning an N-best list of hypotheses with corresponding confidence estimates. We perform an in-depth evaluation of 5 NLUs, focusing on confidence estimation. We measure and visualize calibration for the 10 best hypotheses on model level and rank level, and also measure classification performance. The results indicate a trade-off between calibration and performance. In particular, Rasa (with Sklearn classifier) had the best calibration but the lowest performance scores, while Watson Assistant had the best performance but a poor calibration.