Reviewerly/exHarmony huggingface dataset:
Learning When to Communicate Among Actors with the Centralized Critic for the Multi-agent System

Centralized training and decentralized execution have become a basic setting for multi-agent reinforcement learning. As the number of agents increases, the performance of the actors that only use their own local observations with centralized critics is prone to bottlenecks in complex scenarios. Recent research has shown that agents learn when to communicate to share information efficiently, that agents communicate with each other in a right time during the execution phase to complete the cooperation task. Therefore, in this paper, we proposed a model that learn when to communicate under the centralized critic supporting, so that the agent is able to adaptive control communication under the centralized critic learned by global environmental information. Experiments in a cooperation scenario demonstrate the advantages of model. With our proposed cooperation model, agents are able to block communication at an appropriate time under the centralized critic setting and cooperation with each other at the task.
Dynamical Features of Isolated Two- and Three-Level Atoms Interacting with a Cavity Field

Some dynamical features of two- and three-level atoms interacting locally with an ideal cavity field are investigated. These dynamical features are introduced by employing the statistical atomic inversion, entropy squeezing, and tomographic entropy. Our results show that the initial setting states play an essential role in the temporal evolution of the three quantities. The sensitivity of the two-level atomic state is less than that depicted by the three-level atom. The initial state has a small impact on the two types of entropies for the two-level atom. However, it has an appreciable effect in the case of the three-level atom for different regulations of the initial atomic state.
Remembering Urban Village: Using CloudXR Technology as an Enhanced Alternative to Better Disseminate Heritage

Urban villages are strictly related to urban growth.It reflects the era characteristics and memory in urban growth, which has significant value in heritage and sustainable cities (SDG 11).Due to the continuous development of urbanization and the shortage of urban land, many urban villages will be replaced by more valuable functions.Therefore, better preserving the digitalization of urban villages and making more people understand the value of urban villages is particularly important.However, the existing technology still has shortcomings in disseminating digital heritage.For urban villages, usually a large-scale and complex environment, the hardware requirements will be very high for high-precision visualization.Most existing solutions use large hardware devices, such as the virtual sand table.Unlike hand-held devices, such devices are expensive and not portable, limiting better dissemination of such heritage.Due to the hardware limitation of hand-held devices, neither the display resolution nor the interaction effect is satisfying.Therefore, this paper proposes a new workflow by NVIDIA CloudXR streaming technology to achieve high-precision visualization and a rich interactive experience on handheld devices.Such heritage can be promoted and cities can be more sustainable.
Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results.
Research Data Management needs assessment of Clemson University

Research data management (RDM) encompasses a wide range of activities that include documenting and managing data during a research project, as well as sharing and preserving data after the research project is completed.&amp;nbsp; Academic libraries can offer a variety of services that support researchers during the research life cycle which vary among institutions. &amp;nbsp; The faculty, staff, and graduate students of Clemson University were surveyed by Clemson Libraries about their research data management (RDM) needs in the spring of 2021.&amp;nbsp; The survey was based on previous surveys from 2012 and 2016 to allow for comparison, but language was updated and additional questions added because the field of RDM has evolved.&amp;nbsp; Survey findings indicated researchers are overall more likely to back up and share their data, but the process of cleaning and preparing the data for sharing was an obstacle.&amp;nbsp; Few researchers reported including metadata when sharing, or consulting the library for help writing a Data Management Plan (DMP).&amp;nbsp; Researchers want RDM resources, and offering and effectively marketing those resources will enable libraries to both support researchers and encourage best practices. Understanding researcher needs and offering time-saving services and convenient training options makes following RDM best practices easier for researchers.&amp;nbsp; Communicating how RDM benefits researchers and integrating RDS into the research life cycle through partnerships is crucial for ensuring effective data management.
Displaying the Map Server Data using Arc-GIS server JavaScript API

Applications for the web, desktop, and mobile are developed using the Arc-GIS server. Applications and services for managing, visualising, and analysing geographic data are offered by Arc-GIS for servers. The work makes use of the Popup-layout API reference and also makes use of two of its features as necessary. Each programming interface has features that help clients connect to geospatial information more easily. Keen web maps are a remarkable tool for visualising complex data. They help to break apart seemingly unrelated data, find hidden examples, and mine massive databases. Information can be created on the work surface, uploaded to the cloud, and shared online using the Arc- GIS server. The objectives of the work deals with exhibiting of geo-spatial attribute data using the facility of Java script application programming interfaces (API’s) from Arc-GIS server. For a given Layer or Graphic, a PopupTemplate prepares and defines the content of a Popup. When a feature in the view is selected, the user can use the PopupTemplate to access values from feature attributes and values returned from Arcade expressions.
Teaching and Learning in Post-Covid-19 Era: An Evaluation of Digital Transformation Experience

This study aimed to ascertain how digital technologies have impacted teaching and learning capabilities in private universities post COVID-19 experience. Utilizing a descriptive research design, the response of a sample size of 229 lecturers from private universities in Abuja was obtained through a structured questionnaire whose reliability was tested with Cronbach’s Alpha method. Data obtained was analyzed with descriptive statistics, while the hypotheses postulated were tested with the multiple regression models through the application of SPSS version 20. Based on the analyses, the finding is represented in the following formula: Y = F(X1, X2, X3, U). Where Y represents learning capabilities measured through content assimilation and skill acquisition; F represents a function or dependent; X1 represents Zoom teaching and learning method; X2 represents Goggle meet/classroom teaching and learning method; X3 represents Social Media teaching and learning method; while U technical factors and acceptable error. Based on the findings, the study recommends that private universities should: start utilizing a combination of virtual and physical methods of teaching and learning; motivate lecturers to develop the culture of online teaching and learning through Zoom meetings and Goggle Classroom; as a matter of priority, provide the needed technical needs such as steady electricity, internet connectivity, data and laptops or computers for lecturers; and encourage and execute constant training for lecturers on the use of digital tools for teaching and learning.
Fuzzy Active Contour Model With Markov Random Field for Change Detection

The traditional active contour models are sensitive to the speckle noise in the synthetic aperture radar (SAR) images. In this paper, the Markov random field (MRF) theory is incorporated into the fuzzy active contour model to detect the changes of multitemporal SAR images. In the proposed method, neighboring information is considered to modify the pointwise prior probability for exploiting the mutual and spatial information. In addition, we incorporate MRF into the fuzzy active contour model and get the resulting MRF-based energy function. Finally, we drive the associated first variation of the energy function to compute the fuzzy membership. Due to the introduction of MRF, the proposed MRF-based fuzzy active contour model is robust to the speckle noise in the SAR images and can achieve accurate change detection results. Experiments on four SAR image datasets demonstrate that the proposed MRF-based fuzzy active contour model is able to accurately segment the difference image and has better performance in comparison with other change detection techniques.
Fashion Image Classification Using Deep Convolution Neural Network

We present an analysis of optimal Convolution Neural Network (CNN) for fashion data classification by altering the layers of CNN in this paper. The suggested system employs three deep convolution layers, max pooling layers, and two fully connected layers, as well as dropout layers. While modified layers enhance the test accuracy of Adam optimizer when compared to start-of-art-models. The objective of this work is to address the multi class classification problem and to evaluate the performance of CNN’s Adam and RMSProp optimizer. The experiment was carried out using the Fashion-MNIST benchmark dataset. The suggested method has a test accuracy of 92.68%, compared to 91.86% in CNN using the softmax function and 92.22% in CNN utilizing batch normalization.
Optimization and Quality Evaluation of Online Teaching Courses Based on Machine Learning

Due to the epidemic, online courses have become an important form of school courses, so it is very necessary to optimize online teaching courses. This paper analyzes the survey data by using factor analysis and ANP model and finally determines 18 evaluation indicators by scoring the evaluation indicators by the respondents. The analysis of these 18 indicators shows that the average score of goal setting is the highest, and the average score of interface design is the highest. Lowest: the low-scoring portion of the course interaction is the most important aspect of developing a suggested strategy, and setting the environment, stimulating interest, interface design, and performance evaluation are also important factors in improving the quality of online courses. It can be seen that teacher-student interaction, media presentation, and interest stimulation are three more important factors, and these three factors are relatively less important for performance evaluation, course duration, and language level. It can be seen that learners pay relatively low attention to performance evaluation and pay the highest attention to intelligent learning. These indicators with high attention are improved to optimize online teaching courses.
The Cryptogameur: A New Approach to Entrepreneurial Design

Cryptogames, blockchain-based games with play-to-earn mechanics, are paving the way forward for the Metaverse. As digital technologies, cryptogames are attracting a new breed of entrepreneurs, whom we refer to as cryptogameurs, whose entrepreneurial agency primarily manifests in the digital-asset based economies of the virtual worlds. Our pioneering study establishes the relevance of cryptogaming in the entrepreneurship literature, particularly in the digital entrepreneurship space. Through a conceptual framework, integrating the seemingly disparate theories of affordance-actualization and external enablers, with a novel characterization of the cryptogames as layered digital technologies, we illustrate the centrality of non-fungible tokens in entrepreneurial design. We identify six unique economic and social affordances of cryptogames which support cryptogameurs socio-economic goals. Proposing that cryptogameurial agency is manifested in the actualization of the cryptogame affordances, our study also offers theoretical insights into the enabling mechanisms of the socio-economic context. Through this study, we make significant contributions to emerging research on entrepreneurship theory and practice in the Metaverse.
Personalized recommendation model of electronic commerce in new media era based on semantic emotion analysis

Electronic commerce (E-commerce) through digital platforms relies on diverse user features to provide a better user experience. In particular, the user experience and connection between digital platforms are exploited through semantic emotions. This provides a personalized recommendation for different user categories across the E-commerce platforms. This manuscript introduces a Syntactic Data Inquiring Scheme (SDIS) to strengthen the semantic analysis. This scheme first identifies the emotional data based on user comments and repetition on the E-commerce platform. The identifiable and non-identifiable emotion data is classified using positive and repeated comments using the deep learning paradigm. This classification attunes the recommendation system for providing best-affordable user services through product selection, ease of access, promotions, etc. The proposed scheme strengthens the user relationship with the E-commerce platforms by improving the prioritization of user requirements. The user's interest and recommendation factors are classified and trained for further promotions/recommendations in the learning process. The recommendation data classified from the learning process is used to train and improve the user-platform relationship. The proposed scheme's performance is analyzed through appropriate experimental considerations. From the experimental analysis, as the session frequency increases, the proposed SDIS maximizes recommendation by 15.1%, the data analysis ratio by 9.41%, and reduces the modification rate by 17%.
Bio-medical Image Encryption Using the Modified Chaotic Image Encryption Method

Protection of personal data from unwanted access while assuring speed and precision is essential in today's information and speed driven world. Recent research on picture encryption methods have increasingly been based on chaotic systems, although in one-dimensional chaotic cryptosystems there are apparent disadvantages of a tiny key space and low security. The chaotic approach has been used to suggest a novel picture encryption algorithm to avoid the aforesaid limitation. A chaotic dynamic mechanism is employed in the synchronization of two chaotic systems and controllers. The picture is cramped by adding the chaotic sequence to the disturbance term associated with the plaintext. Then the dynamic index's cipher text feedback device is adopted at the diffusion stage. This means that the cipher text cipher index utilized to provide feedback is locative indexed. The proposed stenography with the chaotic map technology, as regards the time required to mark a certain collection of images and with greater space, smaller iteration times and high security analysis such as key space analysis, statistical analysis and sensitivity analysis, is simpler and more effective. Image encryption is accompanied with a confused logic to create a secret key. In addition, experimental findings like key space analysis, analysis of key sensitivity, differential analysis, histogram, entropy information and correlation coefficients reveal a safe and reliable image encryption technique with significant potential for application.
Reconstruction of New Media Art Ecology Based on Intelligent Technology Environment

With the rapid development of modern multimedia technology and Internet technology, the traditional media in people’s daily life, including newspapers, magazines, radio, and television, are far less timely and extensive than the new media that use the Internet to spread, such as today’s headlines, microblog, TikTok, and NetEase News. In the process of its own development, Internet new media has gradually derived new media art, which is different from traditional art forms, with the mixture of sound, light, and electricity as the form of expression and the Internet as the media. The wide spread of new media art depends very much on the development of the Internet. In the early stage, the audience of new media is not wide, such as Tianya Forum and Sina blog. It is only for information exchange and communication with people who use the computer Internet. At the same time, the content shared is limited to words and pictures. When the computer Internet began to popularize and the mobile network terminal began to develop, new media art immediately sprouted. Starting from instant messaging, Tencent QQ integrates the strengths of all kinds of new media and promotes the further development of new media art. Standing at the juncture of 2022, after more than ten years of continuous innovation and development, the expression terminal has come from the computer to the mobile phone we use every day. The media has quickly entered the 5 g network from 2G and 3G networks, and the communication path and time have been quite flexible. In addition, AI (artificial intelligence) and other intelligent technologies have begun to enter people’s daily life. The combination of intelligent technology and new media art has brought new media art into another period of transformation and sublimation. This paper will discuss that in the current environment where intelligent technology has begun to be popularized in people’s daily life, new media art achieves the result of ecological reconstruction by innovating and deepening its own characteristics in combination with intelligent technology.
“Comandante’’: Braving the Waves With Near Real-Time Virtual Production Workflows

The naval feature film “Comandante” is to be shot on a water stage within a waterproof but very low resolution LED volume. We developed our Near Real-Time (NRT) workflow to immediately improve the quality of a shot, seamlessly bridging In-Camera VFX (ICVFX) and post-production, allowing VFX to start on-set as soon as the director says ‘cut’. The NRT workflow brings together key advancements in lens calibration, machine learning and real-time rendering to deliver higher quality composites of what was just shot to the filmmakers, in a matter of minutes.
A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation

In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.
Efficient construction of witnesses of the stellar rank of nonclassical states of light

The stellar hierarchy of quantum states of light classifies the states according to the Fock-state resources that are required for their generation together with unitary Gaussian operations. States with stellar rank n can be also equivalently referred to as genuinely n -photon quantum non-Gaussian states. Here we present an efficient method for construction of general witnesses of the stellar rank. The number of parameters that need to be optimized in order to determine the witness does not depend on the stellar rank and it scales quadratically with the number of modes. We illustrate the procedure by constructing stellar rank witnesses based on pairs of Fock state probabilities and also based on pairs of fidelities with superpositions of coherent states.
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation

Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples. However, this feature sharing mechanism inevitably causes semantic aliasing between novel classes when they have similar compositions of semantic concepts. In this paper, we reformulate few-shot segmentation as a semantic reconstruction problem, and convert base class features into a series of basis vectors which span a class-level semantic space for novel class reconstruction. By introducing contrastive loss, we maximize the orthogonality of basis vectors while minimizing semantic aliasing between classes. Within the reconstructed representation space, we further suppress interference from other classes by projecting query features to the support vector for precise semantic activation. Our proposed approach, referred to as anti-aliasing semantic reconstruction (ASR), provides a systematic yet interpretable solution for few-shot learning problems. Extensive experiments on PASCAL VOC and MS COCO datasets show that ASR achieves strong results compared with the prior works.