Reviewerly/exHarmony huggingface dataset:
Design Domain-Adaptive Generative Performance Optimization Using Designable Data Augmentation

Composing an experiment or a credible simulation model to search for a new optimized design may be costly in terms of both money and time as the design has not been realized yet. To alleviate this problem, we propose using Designable generative adversarial network (DGAN) to quickly suggest a new effective design. DGAN is derived from the Generative adversarial network (GAN) with the addition of an inverted generative network structure. DGAN not only augments input data but also yields the corresponding design variable data for the augmented data. Assuming a product of an analogous domain was designed in advance, its performance data is utilized with DGAN in order to adapt to a new design domain and to bring out a design that attains similar performance. The effectiveness of the proposed methodology is investigated with problems designed to incorporate equations and simulation models.
Research on the Regeneration Status and Development Mode of the Landscape of the Third Tier Construction Industry Sites in China under the Background of Urban Renewal

Industrial sites are brownfields left in the process of urban development, which were originally considered as the burden of the city. However, in today’s lack of land resources, industrial sites have become valuable assets of the city. Further excavation and effective use of them is one of the important ways to alleviate the tension of urban land use. The strategy of ecological reconstruction and sustainable design in the landscape of industrial sites is mainly reflected in the minimal intervention in the natural ecology of the site, the use of renewable materials and resources, the retention of native plants, human history and site memory. It makes the ecology of urban industrial site landscape more balanced, enhances the uniqueness and experience of urban industrial site landscape, meets the spiritual and physiological needs of people in the city, and realizes the sharing of industrial site landscape to the public’s natural ecology, land, industrial spirit, history and humanity.
Multi-object Paper Money Recognition Technology Based on AlexNet Model

The use of physical currency is currently the most widely used method of transaction. The pattern recognition technology of paper currency has a wide range of applications, such as self-checkout machines, ATMs, vending machines, and devices for the blind. In modern society, there are more and more convenience stores. If they can have the RMB recognition system, they will save energy from cashiering. That is the reason for choosing this research Direction. However, it fails to identify images rotated over a certain angle or with excessive noise at first, and it cannot be 100&#x0025; exact that the accuracy of our work now. But it will be perfected. To realize the function, this paper proposed a neural network model, which can accurately identify RMB. The best performance of the image process can be achieved by utilizing the Automatic white balance, the Automatic brightness, and the Automatic de-noising. Then it is time to cut and extract the image. At last, the image is put into the neural network model for recognition. The defect of this study is that it ignores the influence of possible fluctuation of image quality on the recognition results in the real situation. The related work established a Radial Basis Function Network based on notes interesting features and correlations between images. The image processing technology is used to improve image quality. Through the above methods, the work can identify all kinds of RMB. Moreover, it can already ensure that the success rate of RMB identification can reach more than 90&#x0025;.
Some Aspects of Developing a Universal Server Discord-bot

The purpose of the article is to research, analyze and consider general problems and prospects for the development of a universal server Discord-bot with the ability, in particular, to search for music on YouTube, channel administration and viewing the weather. The research methodology consists in methods of semantic analysis of this subject area’s basic concepts (server bots). The article considers approaches to the development and operation of a universal server discord-bot. The scientific novelty is the analysis of the modern messenger systems functioning and universal server bots that can be used to develop the user’s own bot, such as music or game content, a description of some aspects of such development The conclusion of the research conducted in the article is that the paper examines the existing views on modern messenger systems that can be used to develop their own universal server bots. Taking into account the results of the analysis, the authors decided to develop a software product – a universal server bot based on Discord with the ability to search for music from YouTube, channel administration, and view the weather.
Towards Automated Generation of Data Models for the ECHONET Lite Protocol

Providing data models is essential for any protocol or standard to achieve cooperation with other ecosystems. This paper proposes a solution to save human efforts from tedious, repetitive, and easy-to-make mistake tasks when creating data models from the standardized ECHONET device objects. The proposed solution was able to generate data models such as device description for the ECHONET Lite web API, thing description schemes for the ECHONET Lite-Web of things integration, and the SAREF extended ontology for ECHONET Lite. Other data models and open API documents for each data model could be easily generated by simply adding more conversion rules. Generated data models have matched the requirements of human-generated data models. This solution is utilizing in a workflow to create the data model for the ECHONET Lite web API.
A Survey of Unsupervised Learning in Medical Image Registration

Medical image registration has important value in actual clinical applications. From the traditional time-consuming iterative similarity optimization method to the short time-consuming supervised deep learning to today's unsupervised learning, the continuous optimization of the registration strategy makes it more feasible in clinical applications. This survey mainly focuses on unsupervised learning methods and introduces the latest solutions for different registration relationships. The registration for inter-modality is a more challenging topic. The application of unsupervised learning in registration for inter-modality is the focus of this article. In addition, this survey also proposes ideas for future research methods to show directions of the future research.
Evaluating Data Characterization Measures for Clustering Problems in Meta-learning

An accurate data characterization is essential for a reliable selection of clustering algorithms via meta-learning. This work evaluates a set of measures for characterizing clustering problems using beta regression and two well-known machine learning regression techniques as meta-models. We have observed a subset of meta-features which demonstrates greater resourcefulness to characterize the clustering datasets. In addition, secondary findings made it possible to verify the direction and magnitude of the influence and the importance of such measures in predicting the performance of the algorithms under analysis.
Improving Machine Translation of Arabic Dialects Through Multi-task Learning

Neural Machine Translation (NMT) systems have been shown to perform impressively on many language pairs compared to Statistical Machine Translation (SMT). However, these systems are data-intensive, which is problematic for the majority of language pairs, and especially for low-resource languages. In this work, we address this issue in the case of certain Arabic dialects, those variants of Modern Standard Arabic (MSA) that are spelling non-standard, morphologically rich, and yet resource-poor variants. Here, we have experimented with several multitasking learning strategies to take advantage of the relationships between these dialects. Despite the simplicity of this idea, empirical results show that several multitasking learning strategies are capable of achieving remarkable performance compared to statistical machine translation. For instance, we obtained the BLUE scores for the Algerian $$\rightarrow $$ Modern-Standard-Arabic and the Moroccan $$\rightarrow $$ Palestinian of 35.06 and 27.55, respectively, while the scores obtained with a statistical method are 15.1 and 18.91 respectively. We show that on 42 machine translation experiments, and despite the use of a small corpus, multitasking learning achieves better performance than statistical machine translation in 88% of cases.
Quantum Chaos and Universal Trotterisation Performance Behaviours in Digital Quantum Simulation

Maximising the power of digital quantum simulators requires careful optimisation of experimental resources. We conclusively demonstrate a direct connection between quantum chaos and the breakdown of Trotterisation, exhibiting surprisingly universal behaviour across diverse simulator systems [arXiv:2110.11113 (2021)].
THE LIBRARY AS A COMPONENT OF THE EDUCATIONAL AND CULTURAL POTENTIAL OF THE TERRITORY

The analysis of publications on the sociology of education revealed the contradictions in the educational space under the influence of which the role of the library is changing: 1. Equality of rights to education and social differentiation. The contradiction between the declared equality of rights to education and socio-territorial barriers to obtaining education is reduced by libraries that provide free access to socially significant information and knowledge. 2. "Demographic pit" and the decline in education. A decrease in the number of enrollee at the beginning of the ХХI century led to lower requirements for enrollees and to a decrease in the quality of higher education. Modern GEF establish strict requirements for information support for educational programs of universities. The task of libraries in these conditions is to evolve along with the development of education. Libraries are included in the knowledge management process, developing services aimed at servicing new research practices. 3. Disproportions in the structure of education. The structure of educational programs does not fully satisfy the needs of the economy in specialists of the coincident profile. This creates the need for additional education, self-education, lifelong education. Libraries can promote lifelong education and self-education in various areas: – traditional informational support of training programs; – development of disciplinary discourses for teachers and students; – deepening personalization in service; – analysis of open resources for remote users and the creation of tools for their differentiation and use; – participation in the information culture formation; – Promoting the humanization of education; – project activities in popular areas; – informing about educational programs of the territory. 4. The formation of the cultural "capital" of the individual and the "products" of education. In addition to knowledge, abilities and skills in the course of education, the cultural capital of the individual and a special creative environment are formed. The library, associated with the knowledge of a new text, discourse, becomes an instrument of "cultural creation": it facilitates the search and creation of new meanings, forms and supports the creative environment. The formation of the intellectual potential of the territory is based on the interaction and development of scientific, educational and cultural spaces. The library, integrated with all its elements into this complex, dynamic sphere, is changing after its transformation. Library activities are enriched in new directions and expanding functionally, new tasks and intellectual practices appear. The library also retains its immanent functions, which no one else can perform in a changed environment: creating a content resource in the sociocultural and educational discourse of the territory and managing these collections, ensuring equal access to these resources for all users, their organization, presentation and preservation.
Benign and Malignant Breast Cancer Features Based on Region Characteristics

Breast cancer is one of the most common malignant diseases among women. Mammography is at present one of the available method for early detection of abnormalities, which is related to breast cancer; there are different lesions that are breast cancer characteristic such as masses, which can be detected through this technique. In this work, we explore the application of density slicing and region growing techniques to the problem of finding the boundary of different breast tissue regions in mammograms and extracting these regions. The goal of the segmentation algorithm is to see if density slices images and region growing algorithm could separate different densities for the different breast patterns and calculating there features. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes and for extracting the cancer regions. Our proposed methodology for the segmentation of mammograms has been tested on Mini-MIAS database images. The final result is the segmentation of Region of I interest (ROI) with edge map and different features extraction for analysis where the extracted regions are saved as images and the texture features were calculated using Gray Level Co-Occurrence Matrix (GLCM) along 0^°such as contrast, correlation, homogeneity, sum variance, entropy, energy, mean values and others to differentiate between the Benign and Malignant images. It was found that the features such as contrast, correlation, smoothness, and difference variance are the optimal features to differentiate between benign and malignant regions where the other features are approximately the same. Also region growing method is very fast for extracting cancer regions. Our results are important to design artificial intelligence system to diagnostic breast cancer into benign and malignant cancer.
Medical Chatbot (Medibot)

In recent years, medical services are getting more open to a more extensive gathering of individuals with the help of innovation. The ideas of computerized reasoning, machine learning and neural organizations have given generous help with the field of medical services. In the present quick moving world, individuals will in general disregard their wellbeing which may result in a basic issue. Such an issue can be kept away from by utilizing the symptom-driven chatbot. Our project focuses on giving the clients accurate and precise prediction of the disease based on their symptom,The disease envisaging chatbot is created utilizing a Decision tree. For the forecast of sicknesses, we have utilized two arrangement calculations specifically, Decision tree and KNN (K-Nearest Neighbours). The presentation of these strategies are analyzed and dependent on their accuracy, the best model is chosen. According to our project, the accuracy of the Decision Tree and KNN are 95% and 92.5% respectively. Our medical chatbot also provides precautions and measures for the envisaged disease. Our medical chatbot also has a text-to-speech feature.,for providing a human element to the conversation, rather than it being an automated conversation, which may feel mundane to the user.
Three-Dimensional Imaging and Reconstructions of Objects Under Rainy Conditions Using the Generation and Propagation of Coherent Structured Signal

This investigation is aiming at the development of a method for in-situ 3D imaging and reconstructions of objects in the rain. The proposed method is based on the use of monochromatic sinusoidal fringe pattern generated by the designed optical system, the polarization technique, and the Fourier-transform-based algorithm of reconstruction. Based on the theoretical analyses and experimental results, it shows that the generated laser-beam-based signal keeps being coherent until reaching the observed object. The coherence of the projected sinusoidal signal is the key feature of the proposed method and ensures the accuracy of measurement and reconstruction of objects under rainy conditions. Moreover, the effects resulted from the spectral absorption and multiple scattering on the propagation of the projected sinusoidal fringe pattern in the rain can be removed using the polarization technique. The developed method is capable of obtaining accurate 3D reconstructions of the objects under rainy conditions together with background illumination, the multiply-scattered light, the vibration from environmental influences including the wind, and the inhomogeneous medium of rain. The application of this method does not suffer the limitation of focusing, precise synchronization, and the speed of measurement that might be a problem for the time-of-flight technique and digital-light-processing-based profilometry.
Performance Prediction of Listed Companies in Smart Healthcare Industry: Based on Machine Learning Algorithms

With the development of wireless network, communication technology, cloud platform, and Internet of Things (IOT), new technologies are gradually applied to the smart healthcare industry. The COVID-19 outbreak has brought more attention to the development of the emerging industry of smart healthcare. However, the development of this industry is restricted by factors such as long construction cycle, large investment in the early stage, and lagging return, and the listed companies also face the problem of financing difficulties. In this study, machine learning algorithm is used to predict performance, which can not only deal with a large amount of data and characteristic variables but also analyse different types of variables and predict their classification, increasing the stability and accuracy of the model and helping to solve the problem of poor performance prediction in the past. After analysing the sample data from 53 listed companies in smart healthcare industry, we argued that the conclusion of this study can not only provide reference for listed companies in smart healthcare industry to formulate their own strategies but also provide shareholders with strategies to avoid risks and help the development of this emerging industry.
A Method to Recognize Facial Gesture using Infrared Distance Sensor Array on Ear Accessories

Many hands-free input methods using ear accessories have been proposed. Although most of previous studies use canal type earphones, we focus on the following two points. 1) A method for using the hands-free input function with ear accessories that are not canal type. 2) A method for using the same hands-free input function across multiple ear accessories. Then, using an infrared distance sensor attached to the ear accessory, we propose a method for recognizing the gesture of the user’s facial expression. Based on the change in distance between the infrared distance sensor attached to the ear accessory and the skin, the proposed method detects skin movement around the ear, which differs for each facial expression gesture. We created a prototype system for the root of the ear, earlobe, and tragus ear accessories. The evaluation result for nine gestures and five subjects showed that F-value was 0.94 or more for one device alone, and the F-value was 0.97 or more for the pattern combining multiple devices.
Ethical and Legal Principles for the Practitioner

Platforms like Yelp, TripAdvisor, and Facebook empower consumers to communicate and review products and services and the businesses offering them. These communications can reveal an organization's strengths and weaknesses, but they can also be weaponized to destroy a business's reputation. Social media crises are significant because they include three parties: the organization, the consumer (or user), and the platform. Practitioners and communication teams must be aware of the potential threats to organizational reputations and how the threats can lead to crises and litigation. Platforms respond through content moderation and by algorithmically sorting reviews that appear fraudulent or contrived, and organizations occasionally file defamation claims related to offending reviews. This litigation can be effective regardless of its outcome, but it should not be taken lightly, in a crisis or not, because many such claims have failed on First Amendment and other grounds. This chapter explores crisis situations on social media through ethical and legal perspectives; how consumers use platforms to review products and services in good and bad faith; how platforms moderate the reviews; how businesses respond through litigation; and the practical and legal effectiveness of that litigation, in view of the corresponding First Amendment and procedural challenges.
Server Cloud Scheduling

Consider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the Server Cloud Scheduling problem, in which the jobs have to be processed either on a single local machine or on one of many cloud machines. Both the source and the sink have to be scheduled on the local machine. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server, the other in the cloud. The server can process jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS with respect for the makespan objective for a fairly general case and strong hardness for the case with unit processing times and delays.