Reviewerly/exHarmony huggingface dataset:
New MDS self-dual codes from GRS codes and extended GRS codes

Maximum-distance-separable (MDS) codes and self-dual codes are important families of classical codes in coding theory. It is of interest to investigate MDS self-dual codes. The existence of MDS self-dual codes over finite field Fq has been completely solved for q is even. In this paper, for finite field with odd characteristic, we obtain some new results on the existence of MDS self-dual codes by generalized Reed-Solomon (GRS) codes and extended GRS codes.
[Management Research of Secondary Warehouse of Medical Consumables in Operating Room by Using TRM Method under Support of Smart Healthcare].

To explore the application effect of the team resource management (TRM) mode in the management of the secondary warehouse of medical consumables in the operating room based on the support of smart healthcar.Using the TRM management method， a new mode of intelligent management of medical consumables in the operating room was implemented to form a complete closedloop management process of medical consumables through the unique identification (UDI) and radio frequency identification (RFID) scanning technology of smart medical.The average purchase amount of high-value consumables for each operation in the operating room of the hospital decreased by 6.2%, the proportion of low-value consumables used decreased by 32%, and the efficiency of supplier distribution increased by 11.7% in 2021. The cumulative reduction in medical costs is more than 40 million CNY.Based on the new management model of the secondary warehouse of medical consumables in the operating room by using the TRM method under the support of smart healthcare, the team cooperation has been strengthened, and the management level of medical consumables in the operating room has been effectively improved.
Developing an Augmented Reality Application as Instruction Media to Help in Learning the Solar System

This study aimed to develop an application to help in learning the solar system using Augmented Reality (AR) technology. In an era where technology is very developed, learning about the solar system can be made more interactive by providing 3D illustrations to the students. One of the technologies that can be applied to support the development of educational applications to help in learning about the solar system is AR technology. It can create 3D illustrations. The study is the Research and Development (R&amp;D). The research produced an AR-based solar system introduction application. This application can be used as a learning media for students. The developed AR application was tested using alpha and beta testing. The alpha testing was the marker accuracy testing and black-box testing, while the beta testing was done by distributing questionnaires to 30 respondents and then doing validity and reliability test. This study produced an AR application to help in learning the solar system. The black-box testing showed that the AR application generally was functioning well. The marker accuracy testing showed that the AR camera succeeded in scanning markers up to 25% of the marker area. The data obtained from distributing questionnaires were processed to know the validity in terms of attractiveness and effectiveness, and the results showed the data was valid. Moreover, the reliability testing was carried out with Cronbach's alpha, and the result was 0.771 for the attractiveness aspect and 0.742 for the effectiveness aspect. These values mean that most beta testers agree that the AR application was attractive and effective.
Garbage classification based on a cascade neural network

Most existing methods of garbage classification utilize transfer learning to acquire acceptable performance. They focus on some smaller categories. For example, the number of the dataset is small or the number of categories is few. However, they are hardly implemented on small devices, such as a smart phone or a Raspberry Pi, because of the huge number of parameters. Moreover, those approaches have insufficient generalization capability. Based on the aforementioned reasons, a promising cascade approach is proposed. It has better performance than transfer learning in classifying garbage in a large scale. In addition, it requires less parameters and training time. So it is more suitable to a potential application, such as deployment on a small device. Several commonly used backbones of convolutional neural networks are investigated in this study. Two different tasks, that is, the target domain being the same as the source domain and the former being different from the latter, are conducted besides. Results indicate with ResNet101 as the backbone, our algorithm outperforms other existing approaches. The innovation is that, as far as we know, this study is the first work combining a pre-trained convolutional neural network as a feature extractor with extreme learning machine to classify garbage. Furthermore, the training time and the number of trainable parameters is significantly shorter and less, respectively.
PENet: Pre-Enhanced Network for Object Detection and Instance Segmentation

Multi-level feature fusion has always been regarded as a powerful structure that can improve the performance of models for instance segmentation. In this work, we propose a novel proposal-based Pre-Enhanced Network (PENet) performing a bottom-up feature fusion between backbone and neck of the network, achieving a more adequate information propagation. Our main contribution is the new Pre-Enhanced Module (PEM) that utilizes feature maps generated by stages of backbone for multi-level fusion, which performs feature extraction and fusion on adjacent levels from bottom to top. Our PEM will equip feature maps with more sufficient information from the bottom, obtaining a more effective performance for object detection and instance segmentation. Evaluation and comparative experiments of models are conducted on Cityscapes dataset. Our PENet outperforms its baseline model obtaining absolute mAP gains of 1.3% for object detection and 0.9% for instance segmentation, and also achieves improvements compared with other corresponding models based on the same baseline. We hope our work will provide a new view of model structure modification for object detection and instance segmentation. Code will be made published.
Adaptive edge-based bilinear interpolation for smart healthcare

The brain tumor is a mass of abnormal cells in the brain. The use of medical imaging techniques is essential in diagnosing brain tumors. The most commonly used methods for identifying the tumor region are magnetic resonance imaging (MRI) and computed tomography (CT). The segmentation of brain tumors is critical for tumor diagnosis. This study employs an adaptive edge-based bilinear image interpolation approach to improve the quality of HR images. An edge detector detects the edge pixels whose intensity levels decrease during the interpolation process. The detected edge pixels are treated separately to enhance the edge information. Furthermore, sharpening filters eliminate any image noise, and interpolation is performed using the bilinear interpolation method to expand the size of the image by maintaining quality, and an edge-enhanced output image is supplied as a result. The output images are assessed qualitatively by measuring the Peak Signal to Noise Ratio (PSNR) values and comparing each with other benchmarking methods. This approach achieves an average PSNR of 34.797dB, outperforming conventional interpolation methods.
A new type of audio steganography with increased privacy using different ratios of LSB embedding

The technique known as steganography was used to hide theory and application. The secret message or plain text can be concealed in one way or another. Steganography techniques hide the identity of the message, while cryptography methods deem the message incomprehensible to outsiders by special text changes. We require a wrapper or container as a host file in order to conceal a secret message. Use several wrappers, host files, audio, video, text, and media to hide the secret message. In order to expand the availability of audio steganography, one LSB (Least Significant Bit) coding scheme is presented in this study. We are working on an improved secret text capability high-bit rate LSB audio watermarking method that minimizes host audio embedding distortion. By incorporating watermark bits into higher layers for LSB using accepted and advised techniques, robustness against unneeded noise is increased and is constrained by perceptual transparency.
A comparative analysis of cloud based services platform

Data processing at high speeds and storage solutions on a massive scale are two things that are becoming increasingly important as a result of the proliferation of new start-up companies and the vast volume of data that users consume daily. The cloud platforms offer a solution to these issues in the shape of virtualization, which entails the creation of several distinct virtual machines within a single actual system. Because of this, the efficiency of the processor increases, and it now has far less downtime. The four major cloud platforms are discussed, and each has its own set of benefits, which helps make them successful in their unique conducts. Although Amazon EC2 is one of the most used and earliest that offers assistance for the utmost numeral of already configured system software termed as operating systems, which falls short in terms of both its scope and the support that it makes available. In a similar vein, the Google Cloud Platform can support the greatest number of databases and has an excellent repository of in-built libraries. The Microsoft Azure has the greatest number of users out of four, however, it is badly lacking in the database supports it offers. IBM Watson offers user friendly services but has scalability issues. As a result, a conclusion has been reached, that the selecting a particular cloud platform, there is extreme dependence on the needs of the users. These requirements may vary from client to client which are the end-users of the applications. Before deciding on a particular platform, it is best to conduct a thorough analysis of the needs.
A survey of image super-resolution algorithms based on convolutional neural networks for smart city health care applications

Image super-resolution (SR) is a technique for improving an image by increasing its spatial resolution. A super-resolution approach can improve an image&apos;s pixel intensity to the next optimized level. The SR schemes have a challenging task in several applications like remote sensing, medical imaging, and biological detection. The SR technique is divided into single-image SR (SISR) and multi-image SR (MISR) based on the number of input images (MISR). This work reviews SISR with diverse image datasets like Urban 100, Set5, Set4, and DIV2k. Convolution neural networks (CNN) are considered one of the boosting solutions to implement the superresolution and contribute to remarkable progress. This survey deals with the review of diverse convolution neural networkbased SR techniques used in various applications. The purpose of this proposed survey is to investigate several convolution neural network-based SR approaches that are employed in various applications. This study analyses the characteristics of various convolution neural network-based image super-resolution algorithms in terms of peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM). According to the survey, the 3D dilated convolutional encoder-decoder network is the best qualitative &amp; quantitative analysis method for brain MRI super-resolution.
Designing Critical and Secondary Information in Augmented Reality Headsets for Situational Awareness

Augmented Reality (AR) headsets are being used in different contexts (e.g., the oil industry, healthcare, military); however, there is a lack of research and design recommendations on how information should be presented in the AR headset displays, especially for aiding users' situational awareness.We present two studies: one examining if existing findings on the perceptibility of three types of visual stimulus (color, text, shapes) can be applied to AR headsets for critical information, and one analyzing three different presentation styles (Display, Environment, Mixed Environment) for textual secondary information in AR headsets.Our study on secondary information is an extension of prior work.For critical information, we found that existing visual perception findings can be applied to AR headsets; there is a hierarchy of salient visual features.Understanding that we can utilize prior work on visual features helps in designing salient critical information for AR headset displays.For secondary information, we found that having the text in the Display and Environment presentation styles assisted in participants' perception and comprehension when compared to the Mixed Environment presentation style.Based on our results, we provide design recommendations on how to present critical and secondary information in AR headset displays to aid in users' situational awareness, which is essential in safety crucial domains such as the military.
An image encryption algorithm based on heat flow cryptosystems

&lt;abstract&gt;&lt;p&gt;Image encryption has been an important research topic in information security. Different from traditional encryption methods, heat flow cryptosystem is a new encryption method. This paper proposes an image encryption algorithm based on heat flow cryptosystem. First, a class of heat flow cryptosystem based on nonlinear pseudo-parabolic equations are given in this paper. Second, a numerical method with high precision namely barycentric Lagrange interpolation collocation method is proposed to solve the nonlinear pseudo-parabolic equation. Third, an image encryption algorithm based on the heat flow cryptosystem is designed, the detailed process of encryption and decryption algorithm is given, the flow diagram of algorithm is showed. Finally, the proposed encryption algorithm is applied to various image with gray and RGB format and compared with the current popular chaotic encryption algorithm. Many indicators such as histograms, information entropy and correlation are used to objectively evaluate the image encryption algorithm. The experimental results show that the proposed image encryption algorithm is better in most indicators and the algorithm is sensitive to the change of key and plaintext.&lt;/p&gt;&lt;/abstract&gt;
User Authentication Using Body Vibration Characteristics

Traditional mobile phone authentication systems are based on knowledge or biological information. This paper shows an authentication system based on human vibration characteristics and implement it on smartphone. This kind of inspiration comes from signal transmitted based on solid conduction and is applied to the link between vibration motor and accelerometer. Therefore, we designed a system to extract user biometric features by active vibration. However, there is a great challenge for smartphone systems with low sampling rate and requiring real-time response. Therefore, we would realize the system through multiple signal processing stages rather than choose a high-performance neural network. Our system solves the problem of low sampling rate of mobile phone sensors through supersampling reconstruction method. Besides, we select appropriate statistical features and MFCC-based features through PCA algorithm, and finally complete the training through Gradient Boosting Tree. In order to avoid the threshold division problem of the multi-level classifier, we train each sample in two classifications at the time of registration, and store the parameters in the user profile. When the system performs user authentication, the user data is divided into five sections for testing, so as to increase the robustness of the system. Our approach could achieve short-time identity authentication, with an average accuracy rate of 85.3%.
Compact lensless convolution processor for an optoelectronic convolutional neural network

Abstract To our knowledge, optical 4 f systems have been widely used as a convolutional layer to perform convolutional computation in free-space optical neural networks (ONNs), which makes ONNs too bulky to be easily applied to miniaturized smart systems. Hence, we propose a compact lensless optoelectronic convolutional neural network (LOE-CNN) architecture in which a single optimized diffractive phase mask acts as an analog convolution processor to perform convolutional operation without a Fourier lens or lenslet array. We demonstrate that this LOE-CNN can be functionally comparable to existing electronic counterparts in classification performance, achieving a classification accuracy of 98.07% and 95% over the Modified National Institute of Standards and Technology dataset in simulation and experiment, respectively, which not only opens new application prospects for free-space ONNs based on a compact single-chip convolution processor, but also facilitates the development of ONN-based smart devices.
The metaverse and consumer psychology: An introduction to Research Dialogue

Journal of Consumer PsychologyEarly View RESEARCH DIALOGUE The metaverse and consumer psychology: An introduction to Research Dialogue L. J. Shrum, Corresponding Author L. J. Shrum [email protected] orcid.org/0000-0001-7112-6418 HEC Paris, Jouy-en-Josas, France Correspondence L. J. Shrum, HEC Paris, 1 rue de la Libération, 78351, Jouy-en-Josas, France. Email: [email protected]Search for more papers by this author L. J. Shrum, Corresponding Author L. J. Shrum [email protected] orcid.org/0000-0001-7112-6418 HEC Paris, Jouy-en-Josas, France Correspondence L. J. Shrum, HEC Paris, 1 rue de la Libération, 78351, Jouy-en-Josas, France. Email: [email protected]Search for more papers by this author First published: 26 April 2023 https://doi.org/10.1002/jcpy.1355 Accepted by Lauren Block, Editor Target article: Rhonda Hadi, Shiri Melumad, and Eric Park. The metaverse: A new digital frontier for consumer behavior https://doi.org/10.1002/jcpy.1356 Commentary 1: Russell Belk. The digital frontier as a liminal space https://doi.org/10.1002/jcpy.1357 Commentary 2: Dr. Stefano Puntoni. Already here: Metaverse in touch and sound https://doi.org/10.1002/jcpy.1358 Read 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 No abstract is available for this article. REFERENCES Bayer, J. B., Triệu, P., & Ellison, N. B. (2020). Social media elements, ecologies, and effects. Annual Review of Psychology, 71, 471– 497. Belk, R. (2023). The digital frontier as liminal space. Journal of Consumer Psychology, 34(1). Hadi, R., Melumad, S., & Park, E. (2023). The metaverse: A new digital frontier for consumer behavior. Journal of Consumer Psychology, 34(1). Puntoni, S. (2023). Already here: Metaverse in touch and sound. Journal of Consumer Psychology, 34(1). Valkenburg, P. M., Peter, J., & Walther, J. B. (2016). Media effects: Theory and research. Annual Review of Psychology, 67, 315– 338. Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
Defining iron-bearing rock sources in compositional data statistical analysis for provenance studies: A case study from South Africa

In iron-bearing rock provenance researches the definition of what is a source vary from an author to another. Issues might occur when the sources are associated with complex weathering profiles. How may the definition of the source influence the results of provenance studies when different types of iron-bearing rocks are encountered within the same weathering profile? In order to deal with this research question, we used a geochemical dataset previously obtained from the study of the ochre geological collection collected around Diepkloof rock shelter, South Africa. We tested the efficiency of Linear Discriminant Analyses (LDA, supervised analysis) and Principal Component Analysis (PCA, non-supervised analysis) in source discrimination and in the attribution of archaeological pieces to the right source. Different assumptions were made: sources were defined by separating or grouping the different lithologies (shale and ferricrete) encountered at the same location. The results show that grouping the different lithologies allows a good attribution of the archaeological pieces to the source it comes from when elements positively correlated with iron are used in the analysis. Separating the different lithologies do not allow a rate of success in archaeological pieces attribution as high as when the lithologies are grouped together. This result might be explained by a highest number of samples that define a source when the different lithologies are grouped together. Further research should be considered in this direction in order to define if this is a general trend.
Leveraging Existing Literature on the Web and Deep Neural Models to Build a Knowledge Graph Focused on Water Quality and Health Risks

A knowledge graph focusing on water quality in relation to health risks posed by water activities (such as diving or swimming) is not currently available. To address this limitation, we first use existing resources to construct a knowledge graph relevant to water quality and health risks using KNowledge Acquisition and Representation Methodology (KNARM). Subsequently, we explore knowledge graph completion approaches for maintaining and updating the graph. Specifically, we manually identify a set of domain-specific UMLS concepts and use them to extract a graph of approximately 75,000 semantic triples from the Semantic MEDLINE database (which contains head-relation-tail triples extracted from PubMed). Using the resulting knowledge graph, we experiment with the KG-BERT approach for graph completion by employing pre-trained BERT/RoBERTa models and also models fine-tuned on a collection of water quality and health risks abstracts retrieved from the Web of Science. Experimental results show that KG-BERT with BERT/RoBERTa models fine-tuned on a domain-specific corpus improves the performance of KG-BERT with pre-trained models. Furthermore, KG-BERT gives better results than several translational distance or semantic matching baseline models.
Results of the Implementation in 2020–2022 Independent Assessment of the Quality of the Conditions for the Implementation of Educational Activities by Organizations Engaged in Educational Activities on Educational Programs of Higher Education and Additional Professional Programs

The article summarizes the results of the 2020–2022 independent assessment of the quality of the conditions for the provision of services in relation to educational organizations that carry out educational activities on educational programs of higher education and additional professional programs. The dynamics of the number of participants in the independent assessment procedure and their distribution among the main administrators of federal budget funds are presented. The results of the analysis of the final quality assessment for all organizations, as well as in the context of individual criteria and indicators are given. The authors noted the rather high quality of the conditions for the implementation of educational activities provided by the participating organizations of the procedure and a clear improvement in the quality of these conditions in the period from 2020 to 2022, and also made a conclusion about the effectiveness of the IQC procedure, about its stimulating, and not just controlling effect.