Reviewerly/exHarmony huggingface dataset:
Responsibly Strategizing with the Metaverse: Business Implications and DEI Opportunities and Challenges

This viewpoint article aims to lay out the foundations for IS research on the strategic implications as well as DEI opportunities and challenges of the metaverse, a sociotechnical system providing users with immersive, synchronous 3D experience of (virtual) reality through interoperable platforms and sensor devices. We first examine the metaverseâ€™s technologies and its underlying business models, which hold immense strategic implications for organizations and industries, and might require rethinking the future of work. As per most emerging technologies able to generate, collect and process user data, the metaverse comes at a price; leveraging the metaverse strategically will require ethical and DEI considerations. After examining all these aspects of the metaverse, we reflect on ways IS scholarship engaged in strategizing can advance theoretical and practical knowledge by generating novel research questions and outlying a research agenda. Our contribution is twofold: first, we analyze the metaverse using a sociotechnical lens, thereby setting the ground for future studies of the phenomenon. Second, we identify specific strategic implications and DEI opportunities and challenges associated with the metaverse, and discuss how scholarship versed in IS strategizing can contribute to addressing them, theoretically and empirically.
Detect Arbitrary-Shaped Text via Adaptive Thresholding and Localization Quality Estimation

The two-stage scene text detection algorithms based on Mask R-CNN have achieved good performances on multiple challenging benchmarks. However, their effectiveness is degraded due to artificially setting constant thresholds and low localization quality of candidate boxes. In this paper, we present a novel scene text detection method based on Mask R-CNN and the proposed method, named LOAD, proposes adaptive threshold module and localization quality estimation module to address the above two problems. We propose two kinds of adaptive thresholds which are used for the filtering of candidate boxes and the binarization of pixels respectively. We introduce the self-attention mechanism to obtain the global information for generating the adaptive thresholds. Besides, we introduce the localization quality estimation into our model to obtain more accurate candidate boxes for subsequent segmentation. Comparative experiments are conducted on five benchmarks(ICDAR 2015, ICDAR 2017, MSRA-TD500, Total-Text and CTW1500), and the results demonstrate that the proposed method achieves the state-of-the-art performance with an F-measure of 91.0%, 78.7%, 87.4%, 90.6% and 86.0%. We also provide adequate ablation experiments to demonstrate the effectiveness of the proposed components.
GenHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials

In this paper, we propose a rotation invariant neural network based on Gaussian derivatives. The proposed network covers the main steps of the Harris corner detector in a generalized manner. More precisely, the Harris corner response function is a combination of the elementary symmetric polynomials of the integrated dyadic (outer) product of the gradient with itself. In the same way, we define matrices to be the self dyadic product of vectors composed with higher order partial derivatives and combine the elementary symmetric polynomials. A specific global pooling layer is used to mimic the local pooling used by Harris in his method. The proposed network is evaluated through three experiments. It first shows a quasi perfect invariance to rotations on Fashion-MNIST, it obtains competitive results compared to other rotation invariant networks on MNIST-Rot, and it obtains better performances classifying galaxies (EFIGI Dataset) than networks using up to a thousand times more trainable parameters.
On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy

The incorporation of prior knowledge into a medical segmentation task allows to compensate for the issue of weak boundary definition and to be more in line with anatomical reality even though the data do not explicitly show these characteristics. This motivation underlies the proposed contribution which aims to provide a unified variational framework involving topological requirements in the training of convolutional neural networks through the design of a suitable penalty in the loss function. More precisely, these topological constraints are implicitly enforced by viewing the segmentation assignment as a registration task between the considered image and its associated ground truth under incompressibility condition, making them homeomorphic. The application falls within the scope of organ-at-risk segmentation in CT (Computed Tomography) images, in the context of radiotherapy planning.
Explicit Diffusion of Gaussian Mixture Model Based Image Priors

In this work we tackle the problem of estimating the density $$ f_X $$ of a random variable $$ X $$ by successive smoothing, such that the smoothed random variable $$ Y $$ fulfills $$ (\partial _t - \varDelta _1)f_Y(\,\cdot \,, t) = 0 $$ , $$ f_Y(\,\cdot \,, 0) = f_X $$ . With a focus on image processing, we propose a product/fields-of-experts model with Gaussian mixture experts that admits an analytic expression for $$f_Y (\,\cdot \,, t)$$ under an orthogonality constraint on the filters. This construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show preliminary results on image denoising where our model leads to competitive results while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our model can be used for reliable noise estimation, allowing blind denoising of images corrupted by heteroscedastic noise.
An Improved Super-Resolution Model for Bubble Feature Extraction Process

The 3D reconstruction of bubbles plays a significant part in the theoretical study of two-phase flow. In the current works, the bubble flow trajectory images are captured mainly by dual orthogonal high-speed cameras and computed the bubble edge points by digital image process, however, the effect of pre-processing image will be affected by the image quality to a certain extent, which will affect the accuracy of the subsequent bubble 3D space model parameter estimation. Therefore, focused on the degradation of image quality due to the bubble movement and camera aberration, we made targeted improvements to the more advanced one of the existing real-world super-resolution models, and the deep learn-based method is combined with the traditional image processing method, which not only enhances the image quality, but also obtains more excellent bubble feature extraction results. Specifically, we first modified the network structure of the model to assist the model in learning more efficiently in the training process. Secondly, we added the loss function of the gradient map to make the model better recover the edge contour information of the image. Extensive comparison results show that the improved super-resolution model can restore more precise and more real contour information of the image, and further improve the image perception quality. More realistic contour information brings more accurate bubble feature extraction results, which has been proved in our experiments.
FaaSDeliver: Cost-Efficient and QoS-Aware Function Delivery in Computing Continuum

Serverless Function-as-a-Service (FaaS) is a rapidly growing computing paradigm in the cloud era. To provide rapid service response and save network bandwidth, traditional cloud-based FaaS platforms have been extended to the edge. However, launching functions in a heterogeneous computing continuum (HCC) that includes the cloud, fog, and the edge brings new challenges: determining <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">where functions should be delivered and how many resources should be allocated.</i> To optimize the cost of running functions in the HCC, we propose an adaptive and efficient function delivery engine, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> , which automatically unearths a cost-efficient function delivery policy (FDP) for each function, including the FaaS platform selection and resource allocation. Real system implementation and evaluations in a practical HCC demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> can unearth the most cost-efficient FDPs from among 180,200 FDPs after a few trials. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FaaSDeliver</i> reduces the average cost of function execution from 38% to 78% compared to some state-of-the-art approaches.
IoT-Based Indoor Air Quality Using Esp32

The Abundance of Information on the Air Quality Index (AQI) uses a lot of energy in commercial buildings. As a result, energy efficiency and consumer convenience have decreased, mainly as a result of weak supervision. We recommend using a live AQI control system that complies with User Settings energy management guidelines to automate AQI operations in real time. The devices in our system are housed in a lab and built on an IoT (Internet of Things) architecture. Thermal parameters are also received automatically from the sensor with the right sensor, and user input data is collected. to process information in real-time in a distributed cloud setting. To do this, we use the Blynk Application Programming Interface (API). by using an automation system method to monitor sensor data in real-time allowing adjustments to dynamic temperature settings and regular user input. It is based on user feedback, power management guidelines, and sensor readings. The efficacy of the AQI is measured mathematically. The purpose of this study was to determine the level of cleanliness of the air from a room. Our Tests reveal that we have managed to save a total of 0.9 kWh while continuing to maintain the thermal comfort of our users.
Optimizing SEO (Search Engine Optimization) Implementation on a Website Content Management System

In recent years, there has been a fast growth in the year-to-year acceleration of technology and information. With the convenience of creating a website, it is one of the key causes in the appearance of new websites every day. Another item that is growing because of the advancement of existing communication technology is the increase in the number of internet users throughout the world. Supported by the advancement of website and internet technology, as well as ground-breaking innovations, the website has become the most frequented and sought-after on the internet. White Hat SEO is a search engine optimization method that adheres to search engine rules and conditions. It is believed that by implementing the White Hat SEO strategy to the Website Management System, visitor traffic would improve, and promotional media can enhance the number of website visits. A good SERP is essential to at least be in the top place in the search engines in order to acquire the desired amount of visits. Search engines are used by about 95% of users in online activities such as browsing and surfing. In its implementation, the White Hat SEO method in the Website Management System employs SEO techniques based on on-site optimization methods that include four critical components: keyword optimization, website page directory structure optimization, website content optimization, and link optimization on website pages (backlinks).
Artificial Intelligence Based on Recommendation System for Startup Matchmaking Platform

Artificial intelligence platform-based software is currently required and recommended for education and have innovation used to develop, implement, and evaluate the process of an activity. An artificial intelligence-based Startup Matchmaking platform with a recommendation feature in the system can make it easier to bring together startups and partners as needed through a matchmaking process. In general, Startups that are being built must look for partners one by one to work, which will take a long time. To solve this problem, we need to replace the manual method to get answers to all kinds of theory questions and common questions related to startups. The main goal of this research is to create an intelligent platform that can help the industry find information through matchmaking startups. To develop this study, we use the Study Literature Review (SLR) method based on the needs where startups that are growing fast. In this research, the system is implemented entirely with integration with Communicate. Novelty in this study is the use of Artificial Intelligence in Matchmaking startups.
AI-Enabled Blockchain for Supply Chain in Agriculture

The supply chain is a critical process involving farmers, buyers, suppliers, and many more entities. The entire system of farming and agriculture is based on it. The conventional method is very tedious and causes issues at multiple levels. AI and Blockchain are the two most used technologies nowadays. We are proposing an AI-enabled Blockchain ecosystem for the supply chain in Agriculture. A case study is given to explain the possibilities of the proposed model. The entire process of supply chain management has been transformed by artificial intelligence. AI plays a vital role in the warehousing process in collecting and analyzing information and inventory processes, enabling companies to increase efficiency and boost revenue. Precisely, AI predicts demand, modifying orders and rerouting products in transit. Major companies such as Google and Amazon have invested considerably in AI. Blockchain technology has been used in several areas, such as insurance, natural state, and securing personal information. Agriculture is one such field where Blockchain is being used extensively for the development and reformation of its various aspect. Unlike conventional, centralized and monopolistic agricultural management systems have been evolved. Blockchain provides a decentralized data structure to store and retrieve shared data with multiple un-trusted parties. The technology offers several advantages, including eliminating the dependenon the centralized supply chain management database, which is also referred to as single point failure, and the high cost due to verifying and monitoring transactions by third-party users. Figure.4 explains the blockchain process.
Applying UML and Machine Learning to Enhance System Analysis and Design

System analysis and design (SAD) is a crucial process in the development of software systems.The impact of modeling techniques and software engineering practices on SAD has been the focus of research for many years.Two such techniques that have had a significant impact on SAD are Unified Modeling Language (UML) and machine learning.UML has been used to model the structure and behavior of software systems, while machine learning has been used to automatically learn patterns in data and make predictions.The purpose of this paper is to review the literature on the impact of UML and machine learning on SAD.We summarize the findings from several studies and highlight the key insights related to the benefits and limitations of these techniques for SAD.Our review shows that both UML and machine learning have had a positive impact on SAD, with UML improving communication and documentation, and machine learning improving the accuracy of predictions.However, there are also challenges associated with their use, such as the need for expertise and the difficulty of interpreting machine learning models.Our findings suggest that a combination of UML and machine learning can enhance SAD by leveraging the strengths of each technique.
FEATURES OF IN-COMPANY TRAINING OF COMPANY EMPLOYEES IN THE CONDITIONS OF TRANSFORMATION

The country's transition to a new socially oriented economic model based on an innovative type of development requires the training of competent personnel capable of working in an increasingly competitive environment with high efficiency and efficiency. The desire of the Republic of Uzbekistan to take a worthy place in the world economy system significantly enhances the importance of professional and qualification development of the workforce. In this regard, investments in improving the educational level, qualifications of employees, their skills and abilities are becoming one of the priority areas of development not only of the state, but also of organizations of all forms of ownership.The study examines the directions of the possibility of increasing the efficiency of an industrial enterprise through the implementation of an in-company training program for its employees. It is proved that the increase in the efficiency of the enterprise is achieved by increasing the level of motivation and output per employee.
The Professional <i>Go</i> Annotation Dataset

The field of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Go</i> game research is hampered by the absence of records and analytical tools. In recent years, the increasing number of professional competitions and the advent of AlphaZero-based algorithms have provided an excellent opportunity for analyzing human games on a large scale. In this article, we present the ProfessionAl <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Go</i> annotation datasEt (PAGE), containing 98 525 games played by 2007 professional players and spans over 70 years. PAGE incorporates both game-level metadata and in-game statistics across two dimensions. The game-level metadata pertinent to games, players, and tournaments is annotated by consolidating data from numerous reliable sources. The comprehensive in-game statistics are generated from the KataGo engine. Beyond the preliminary analysis of the dataset, we propose sample tasks that highlight PAGE's potential applications in various research areas. To the best of our knowledge, PAGE is the first dataset with extensive annotation in the game of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Go</i> . This article is an extended version of (Gao, 2022), where we perform a more detailed description, analysis, and applications.
Edge and Skeleton Guidance Network for Salient Object Detection in Optical Remote Sensing Images

The salient object detection of optical remote sensing images (ORSI-SOD) is an important research direction in ORSI processing, which has achieved promising results in the last few years. Many recent works heavily rely on feature learning of regions for the improvement of the detection accuracy, while neglecting the concrete role of edge and skeleton information in the calculation. In this work, we propose a two-stage edge and skeleton guidance network (ESGNet) for ORSI-SOD in a coarse-to-fine way, and further demonstrate that the fused features of edge and skeleton are essential for ORSI-SOD. In the first stage, we construct the spatial graph attention (SGA) module for saliency features to generate an initial saliency map, and apply the spatial self-optimization (SSO) to enhance edge and skeleton features. The multi-level interactive fusion (MIF) module is used for the adequate integration of edge and skeleton features into saliency features. In the second stage, with the aim to accomplish better prediction of salient object localization and shape, the feature enhancement integration operation is introduced to recover object details from the learned edge and skeleton features. Extensive experiments on three public ORSI-SOD datasets demonstrate that our ESGNet achieves competitive performance with the state-of-the-art methods and also confirms the importance of edge and skeleton information for ORSI-SOD. Meanwhile, generalizability experiments on natural image datasets show that our method is competent for many types of SOD tasks. The code and results of our method are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/aoao0206/ESGNet</uri> .
Self-Supervised Nodes-Hyperedges Embedding for Heterogeneous Information Network Learning

The exploration of self-supervised information mining of heterogeneous datasets has gained significant traction in recent years. Heterogeneous graph neural networks (HGNNs) have emerged as a highly promising method for handling heterogeneous information networks (HINs) due to their superior performance. These networks leverage aggregation functions to convert pairwise relations-based features from raw heterogeneous graphs into embedding vectors. However, real-world HINs contain valuable higher-order relations that are often overlooked but can provide complementary information. To address this issue, we propose a novel method called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> elf-supervised <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> odes- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> yperedges <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> mbedding (SNHE), which leverages hypergraph structures to incorporate higher-order information into the embedding process of HINs. Our method decomposes the raw graph structure into snapshots based on various meta-paths, which are then transformed into hypergraphs to aggregate high-order information within the data and generate embedding representations. Given the complexity of HINs, we develop a dual self-supervised structure that maximizes mutual information in the enhanced graph data space, guides the overall model update, and reduces redundancy and noise. We evaluate our proposed method on various real-world datasets for node classification and clustering tasks, and compare it against state-of-the-art methods. The experimental results demonstrate the efficacy of our method. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/limengran98/SNHE</uri> .
A Feedback-Driven DNN Inference Acceleration System for Edge-Assisted Video Analytics

With the proposal of edge computing, lots of intelligence applications have made significant progress. For enormous video analysis, how to further accelerate the process is still a major challenge. To overcome the challenge, researchers propose various video frame filtering systems to reduce the data transmission. In this work, we propose a Feedback-Driven DNN Inference Acceleration system (FDDIA). FDDIA is committed to further reducing the latency of DNN inference and the transmission according to the feedback information. Specifically, on the device side, FDDIA first uses the detection results of prior frames as feedback to determine the key candidate regions and uses the inter-frame difference to determine the new object regions. Those regions containing large objects are down-sampled to further reduce the frame. On the edge side, FDDIA first integrates all candidate regions into a smaller new image. Then it remaps the detection result for the new image back to the original frame and returns the result to the device. We evaluate FDDIA on different video benchmarks for three object detection tasks. The results show FDDIA improves the average end-to-end latency by 44% and average bandwidth usage by 41% than the existing advanced method while maintaining a high accuracy.