Reviewerly/exHarmony huggingface dataset:
Application of Price Competition Model Based on Computational Neural Network in Risk Prediction of Transnational Investment

Aiming at the scenario where edge devices rely on cloud servers for collaborative computing, this paper proposes an efficient edge-cloud collaborative reasoning method. In order to meet the application’s specific requirements for delay or accuracy, an optimal division point selection algorithm is proposed. A kind of multichannel supply chain price game model is constructed, and nonlinear dynamics theory is introduced into the research of the multichannel supply chain market. According to the actual competition situation, the different business strategies of retailers are considered in the modeling, which makes the model closer to the actual competition situation. Taking the retailer’s profit as an indicator, the influence of the chaos phenomenon on the market performance is analyzed. Compared with the previous studies, this thesis uses nonlinear theory to better reveal the operating laws of the economic system. This paper selects company A in the financial industry to acquire company B in Sweden. It is concluded that company B is currently facing financial difficulties, but its brand and technical advantages are far superior to company A. The indirect financial risk index of company B, that is, the investment environment, is analyzed, and the final investment environment score of the country where company B is located is 90 points, which is an excellent grade by scoring the investment environment of the target enterprise. Combining the investment environment score and the alarm situation prediction score, it is concluded that the postmerger financial risk warning level of company A is in serious alarm.
Extension and Analysis of the ARG algorithm to 2D

Two-dimensional (2D) adaptive filtering is a technique that has been used for denoising in applications such as biomedical image processing in recent years. In this paper, we design the extension of the 1D-ARG adaptive filtering schemes to form new 2D-ARG adaptive filters. To compare the performance of the proposed algorithm in noise reduction in digital images, three images of different sizes from the Matlab library, Moon, Pout, and Cameraman, are taken as reference. This reduction is compared with two other gradient algorithms least mean squares (LMS) and normalized least mean squares (NLMS) for 2D adaptive filter design. Based on the simulation results and the established metrics, we demonstrate that the proposed method achieves a noise reduction eventually superior to the other 2D gradient algorithms.
Coordinated Planning of Power Grids in Information Parks considering the Time-Space Transfer Characteristics of Data Center Loads

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper.
Global weighted average pooling network with multilevel feature fusion for weakly supervised brain tumor segmentation

Medical image segmentation plays a vital role in computer-aided diagnosis and intelligent medical treatment. It can preprocess medical images to help doctors better diagnose diseases. Class activation map (CAM) is an important technology in weakly supervised segmentation, which can achieve image segmentation without pixel-level label training. This technology can well meet the needs of medical image segmentation. However, CAM obtaining is still unperfect due to global average pooling (GAP). GAP will cause important and non-important regions to be given equal attention during the training process. So, CAM cannot demarcate the boundary of the target regions well. In order to solve this problem, a global weighted average pooling network fusing the grayscale information of medical images is proposed. The proposed network can solve the problem that GAP has the same concern for important regions and non-important regions of the feature map, because the different weights can be learned for different positions of the feature map before the GAP in the proposed model. At the same time, because of the grayscale difference between the tumor area and the non-tumor area in the brain tumor image, the low-level grayscale information of the medical image is fused with the high-level semantic information extracted by the network to learn the weights. This operation gives full play to the advantages of feature maps of different levels. The experiment results on the popular medical image dataset BraTS2019 show that the proposed method can well improve the performance of CAM and help CAM fit the boundaries of objects. Meanwhile, in the DSC evaluation, the proposed method achieves a score of 64.1%, which is a 4.6% improvement over a recent research method.
Psychotherapeutic Counseling in Promoting Personal Development

The article presents a theoretical and methodological analysis of psychotherapeutic counseling support of the process of personal development of the client. Psychotherapeutic support of the process of personal development as a complex integrative and dynamic process is considered. An attempt to substantiate psychotherapeutic counseling is made. Psychotherapeutic counselling is considered for the first time as culturological training providing personal growth of both the client and the counsellor. The relationship between psychological counseling and psychotherapy is considered. The requirements for the personality of the consultant are defined as the main criterion of the effectiveness of psychotherapeutic counseling. The correspondence of psychotherapeutic technology of personal development to the model of a holistic and self-sufficient person is reflected. The significance of the issue of the theory of personality change on the optimization of personal functions of self-development in the context of the integration of psychotherapeutic and counseling practice is emphasized. It is offered to carry out the psychotherapeutic process of development of an integral personality in neuropsychocorrective support, in combination with modern psychotherapeutic methods. Contemporary methods of psychotherapy and psychological counseling for the personal development of the client are reviewed.
Development of Wetland-Based Teaching Materials to Improve English Vocabulary, Reading Literacy Skills, Early Childhood Numeracy and Elementary School

This research was motivated by low English vocabulary skills, reading literacy and numeracy based on data and findings in the field. In addition, it is also caused by English being removed from subjects in elementary schools and the absence of facilities to stimulate students to improve reading and numeracy literacy in kindergarten children and early grade elementary school students. Based on these problems, researchers create or develop teaching materials in improving English vocabulary skills, reading literacy and numeracy of kindergarten and elementary school students in the early grades of elementary school. Teaching materials are a product needed in learning to improve teacher efficiency and improve student performance. Teaching materials found in the field are still unable to improve students' English vocabulary skills, reading literacy and numeracy. In addition, the teaching materials used by students are not based on the environment around students, especially wetlands. This research uses a type of research and development (Research and Development) using the Plomp model intended to produce a product. The research stage can be detailed as follows: (1) earlystage research, (2) prototype stage, wetland-based teaching materials, (3) development results are tested based on validity, practicality, and effectiveness. This research produced teaching materials based on Tanah Basah to improve English vocabulary, reading literacy and numeracy skills of low-grade children. The teaching materials developed are in accordance with the development of Plom. The results of the research on this teaching material product were assessed by 4 validators of linguists, mathematicians, English linguists, and design experts and the results showed that the teaching materials criteria were valid to be applied in schools. In the practicality test assessment, obtain practical categories, both kindergarten and low-grade teaching materials. And the effectiveness test assessment obtained an effective category on kindergarten and low-grade teaching materials.
Multilayer fuzzy cognitive maps for the management of the professional sports product based on the “par conditio” principle

Multilayer Fuzzy Cognitive Maps (MFCM) represents an approach for analyzing situations, which allows the simulation, inference, theorem proving of tendencies, and verification of theories, among other things. This paper reviews the promise of this method in a novelty case study of management of professional sports clubs. Professional sports clubs have the possibility of making their management profitable through the proper management of their commercial product portfolio. This possibility is conditioned by the adequate configuration of each component of the commercial product portfolio, namely: Brand licensing, sponsorship, television and media rights, sporting and training rights, and hospitality. Each component of the commercial portfolio must maximize its attractiveness and desirability for each target customer segment. For these purposes, tools that contribute to manage the sports products will be vital to guarantee their commercial success. This article proposes a model based on MFCMs for supporting the management of products in professional sports – an intelligent decision-making system –, conceptually based on the “par conditio” principle. The model considers different aspects related to the “par conditio” principle, such as the socioeconomic environment where the sports activity is carried out, the legal bases of the professional competence, and other sporting aspects (stadium, fans, etc.), which are organized in layers, to analyze the behavior of the different components of the commercial portfolio. It is tested for the two most common sports in Latin America, football and baseball, considering countries of this region where these sports have good economic performance, both to analyze and understand the current situation of the sports products, and infer more appropriate configurations. Several specific case studies are carried out with encouraging results. The paper successfully shows the potentialities of MFCM for diverse purposes, particularly those named above.
Implementation of a hybrid adaptive system for SLA violation prediction in cloud computing

The cloud has to commit with service level agreements (SLA) which ensures a specific level of performance and sets a penalty if SLA is violated by the provider. These days, managing and applying penalties have become an essential and critical issue for cloud computing. In this research, adaptive neuro-fuzzy inference system (ANFIS) is used to develop proactive fault prediction model which has been designed by utilising the power of machine learning and tested on the datasets to highlight the accurate models for faults prediction in the cloud environment. The suggested algorithm has achieved a percentage accuracy of 99.3% in detecting violations. The performance of the proposed model has been compared with Bayesian regularisation and scaled conjugate gradient methods which reflect the facts as obtained from the results that effectiveness of this scheme in terms of predicting system's violation is more effective.
Multivariate data integration using R: Methods and applications with the mixOmics package. Kim‐AnhLê Cao and Zoe MarieWelham, New York: CRC Press, 2021. https://doi.org/10.1201/9781003026860

BiometricsVolume 78, Issue 3 p. 1272-1273 BOOK REVIEWS Multivariate data integration using R: Methods and applications with the mixOmics package. Kim-Anh Lê Cao and Zoe Marie Welham, New York: CRC Press, 2021. https://doi.org/10.1201/9781003026860 Yuehua Cui, Corresponding Author Yuehua Cui cuiy@msu.edu orcid.org/0000-0001-8099-1753 Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, Michigan, USA Correspondence Yuehua Cui, Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, Michigan, USA. Email: cuiy@msu.eduSearch for more papers by this author Yuehua Cui, Corresponding Author Yuehua Cui cuiy@msu.edu orcid.org/0000-0001-8099-1753 Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, Michigan, USA Correspondence Yuehua Cui, Department of Statistics and Probability, Michigan State University, 619 Red Cedar Rd, East Lansing, Michigan, USA. Email: cuiy@msu.eduSearch for more papers by this author First published: 01 October 2022 https://doi.org/10.1111/biom.13730Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume78, Issue3September 2022Pages 1272-1273 RelatedInformation
Piezoelectric control of spin quantum memories in a cryogenic programmable photonic circuit platform

We demonstrate piezoelectric strain control of diamond tin vacancy defects in cryogenic photonic integrated circuits fabricated in a 200 mm CMOS platform, enabling frequency tuning and control bandwidth above 20 GHz and 2 GHz, respectively.
Simultaneous two-way classical communication and measurement-device-independent quantum key distribution on oceanic quantum channels

Abstract Simultaneous two-way classical and quantum (STCQ) communication combines both continuous classical coherent optical communication and continuous-variable quantum key distribution (CV-QKD), which eliminates all detection-related imperfections by being measurement-device-independent (MDI). In this paper, we propose a protocol relying on STCQ communication on the oceanic quantum channel, in which the superposition-modulation-based coherent states depend on the information bits of both the secret key and the classical communication ciphertext. We analyse the encoding combination in classical communication and consider the probability distribution transmittance under seawater turbulence with various interference factors. Our numerical simulations of various practical scenarios demonstrate that the proposed protocol can simultaneously enable two-way classical communication and CV-MDI QKD with just a slight performance degradation transmission distance compared to the original CV-MDI QKD scheme. Moreover, the asymmetric situation outperforms the symmetric case in terms of transmission distance and optical modulation variance. We further take into consideration the impact of finite-size effects to illustrate the applicability of the proposed scheme in practical scenarios. The results show the feasibility of the underwater STCQ scheme, which contributes toward developing a global quantum communication network in free space.
Bilingual strategies on news media production in the post-digital age

Latinx media has been part of the American news media landscape for more than two centuries. As media reflects demography, the changes in the audience directly influence their production and consumption practices. Since the foundation of the first Spanish-language print newspaper, El Mississippí, in 1808, to the launching of Conecta Arizona, a Spanish-language daily stream of news to subscribers on WhatsApp, Facebook, and other social media platforms in May 2020, not only have more than two hundred years passed, but also there have been a series of transformations in the digital and social media world. However, both examples show how news media ventures seek to meet the information needs of the communities they serve. Latinx media become translators in the diasporic transnational mediatic environment moving back and forth through bilingual practices when reporting and disseminating relevant information for its diverse audiences. By addressing preliminary findings of a larger research project that seeks to map digital-native Latinx media in the United States (Retis & Cueva Chacón, 2021), in this chapter we examine bilingual strategies in news media production in the post-digital era (Cramer, 2015). We present a survey of the diverse practices of bilingualism implemented by Latinx media since the advent of the Internet, and, more specifically, during the last decade. We evaluate and extend the framework proposed by Lewis (2008) that categorized bilingual journalism under three forms: production, content, and tailored bilingualism, while paying attention to new opportunities for bilingualism afforded by new media (i.e., social media, text messaging apps).
Cerberus

Hardware enclaves rely on a disjoint memory model, which maps each physical address to an enclave to achieve strong memory isolation. However, this severely limits the performance and programmability of enclave programs. While some prior work proposes enclave memory sharing, it does not provide a formal model or verification of their designs. This paper presents Cerberus, a formal approach to secure and efficient enclave memory sharing. To reduce the burden of formal verification, we compare different sharing models and choose a simple yet powerful sharing model. Based on the sharing model, Cerberus extends an enclave platform such that enclave memory can be made immutable and shareable across multiple enclaves via additional operations. We use incremental verification starting with an existing formal model called the Trusted Abstract Platform (TAP). Using our extended TAP model, we formally verify that Cerberus does not break or weaken the security guarantees of the enclaves despite allowing memory sharing. More specifically, we prove the Secure Remote Execution (SRE) property on our formal model. Finally, the paper shows the feasibility of Cerberus by implementing it in an existing enclave platform, RISC-V Keystone.
Personalized Classification of Non-Spam Emails Using Machine Learning Techniques

With the advent of computer networks and communications, emails have become one of the most widely accepted communication means, which is faster, more reliable, cheaper, and accessible from anywhere. Due to the increased use of email communications, day-to-day computer users; particularly corporate users, find it cumbersome to filter the most important and urgent emails out of the large number of emails they receive on a given business day. Enterprise email systems are able to automatically identify spam emails but still, there are many non-urgent and unimportant emails among such non-spam emails which cannot be filtered by conventional spam filter programs. Though it may be feasible to set up some static rules and categorize some of the e-mails, the practicality and sustainability of such rules are questionable due to the magnitude of such rules, and the validity period as such rules may become redundant after some time. Thus, it is desired to have an email filtering system for non-spam emails to filter unimportant emails, based on the user’s past behaviour. Despite the availability of research on identifying spam e-mails in the area of further classifying the non-spam e-mails, is lacking. The purpose of this research is to provide a machine learning-based solution to classify non-spam e-mails considering the importance of such e-mails. As part of the research, several machine learning models have been developed and trained using non-spam e-mails, based on the personal mailbox of the first author of this research. The results showed a significant accuracy, particularly with a decision tree, random forests and deep neural network algorithms. This paper presents the modelling details and the results obtained accordingly.
IOT-BDA Architecture for Smart Cities

The concept of smart cities transformed as global implementation is hampered by a host of technical, economic, and government road barriers. In addition, the integration of Internet of Things (IoT) and big data technology has opened new horizons in terms of smart home technology and smart city development that is yet to be explored. IoT is a technology that allows devices to connect to the Internet, and applications are designed to meet the needs of the community. Communities are very interested in using this and other information technology in cities to build smart cities in recent years. It is important to consider big data analytics (BDA) when using IoT in a small ecosystem because there is less data to be managed than a big city where there is data from more smart devices and people BDA aims to make data informed decisions on the basis of raw facts provided. Aside from the fact that there are many articles in the literature that talk about the BDA as a decision-maker in smart cities, general research on BDA and smart cities focuses on the technical or intellectual properties of a particular smart city.
Emerging Practices of Design and Economic as Studio-Ceramic Entrepreneurial Concepts

This research attempt to re-designing craft entrepreneurial practices from the conventional approach in the design process and design activity, integrating new technology such as the Internet of thing (IoT) into online visual communication between user-designer. Product design often emphasizes aspects of design elements that result in a good product and a balance between design and economics (Designnomic).Purchasing the product will impact the products to be marketed and give new exposure to the user-designer in Designomic ecosystems. Keywords: Design; economic; studio-ceramic eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3782