Reviewerly/exHarmony huggingface dataset:
An Automated Framework For Dynamic Web Information Retrieval Using Deep Learning

The success of e-commerce through innovative web information retrieval technologies is crucial in today&#x0027;s world where customer feedback and information are highly sought after. Basic knowledge-gathering systems struggle to respond to dynamic changes on the webpage. This research uses the You Only Look Once (YOLO) algorithm and the Tesseract Long Short-Term Memory to develop a smart and flexible information extraction framework with convolutional and Long Short-Term Memory systems to facilitate automated dynamic Internet discovery. The Yolo Algorithm and Tesseract Long Short-Term Memory retrieve product descriptions found as images from Internet links. Experimental results include image recognition and 98&#x0025; character recovery accuracy. Also, given the input database of 52 objects or images, an average of 75&#x0025; accuracy is achieved.
Blockchain-Based Smart Wheat Supply Chain Model in Indian Context

Food supply chain transparency and traceability is very important to address the issue regarding quality and safety. In traditional tracing system, with increasing the complexity of supply chain making product recalls difficult to manage and putting human lives at risk. To eliminate such types of risks, blockchain technology gives more efficient and reliable system for food tracing. Recently, there is an exponential rise in adoption of blockchain technology and most disparate IOT (Internet of things) devices in agriculture and food supply chain. It is an evolving technology that comforts the food supply chains by providing transparent data records and manage the food movement in the chain using distributed (P2P) network. That is more secured and there's no need for third party verifications. Our focus in this research will be on the Indian wheat supply chain and issues related to food losses caused by a lack of transparency and traceability. In order to improve the transparency of the wheat supply chain, we created an end-to-end smart wheat supply chain solution that combines blockchain technology, NFC tags, IoTs, and smart contracts. The solution is supported by entity relationship diagrams, information and money flow sequence diagrams, and a blockchain network diagram. We also used a security algorithm and the “NFC-Tag writer by NXP” program to validate and assess our system. This work could serve as a springboard for more in-depth research in this area. Depending on the existing situation in the industry, this research can also advise corporate procedures to deploy blockchain-based applications in the supply chain and logistics industry.
Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning

Classification has been a major task for building intelligent systems because it enables decision-making under uncertainty. Classifier design aims at building models from training data for representing feature-label distributions-either explicitly or implicitly. In many scientific or clinical settings, training data are typically limited, which impedes the design and evaluation of accurate classifiers. Atlhough transfer learning can improve the learning in target domains by incorporating data from relevant source domains, it has received little attention for performance assessment, notably in error estimation. Here, we investigate knowledge transferability in the context of classification error estimation within a Bayesian paradigm. We introduce a class of Bayesian minimum mean-square error estimators for optimal Bayesian transfer learning, which enables rigorous evaluation of classification error under uncertainty in small-sample settings. Using Monte Carlo importance sampling, we illustrate the outstanding performance of the proposed estimator for a broad family of classifiers that span diverse learning capabilities.
D-ART for collaboration in evaluating design alternatives

Evaluating design ideas is an integral part of designing built environments. It involves multiple stakeholders with diverse backgrounds reviewing design solutions by studying their form and performance data. Although there are computational systems for supporting evaluation tasks, they are either highly specialised for designers or configured for a particular workflow with limited functions. We developed a Design Analytics method aiming at a collaborative and data-driven evaluation of alternatives in the design-evaluate-feedback cycle. Adopting this approach, we introduce D-ART as a prototype system composed of customisable Web interfaces for presenting design alternatives, enabling stakeholders to participate in data-informed discourse on alternatives and providing feedback to the design team. Its system design considers requirements gathered through literature review, critical analysis of the existing systems and collaboration with our industry partners. Finally, we assessed D-ART’s design through an expert review evaluation, which generally reported positive results on the system’s goals.
Fingertip interactive tracking registration method for AR assembly system

Aiming at the problems of single input mode and lack of naturalness in the assembly process of existing AR systems, a tracking registration method of mobile AR assembly system is proposed based on multi-quantity and multi-degree of freedom natural fingertip interaction. Firstly, the real-time and stable tracking of hand area in complex environment is realized based on the hand region tracking; secondly, the fingertip detection and recognition based on K-COS and parallel vector is used to improve the precision and stability of fingertip recognition; thirdly, the special movement track of fingertip is recognized based on improved DTW algorithm, which has strong compatibility and feature gradient transformation for complex fingertip trajectory recognition; finally, through the real-time transformation of projection relationship between fingertip and virtual object, the interaction between fingertip and virtual object is made more natural and realistic. The experimental results show that in the complex environment of background, illumination, scale and rotation, the precision of fingertip detection and recognition is about 93%, and the precision of fingertip motion template matching is about 91%. The translation error of the registration method based on visual feature recognition is reduced by about 100pix compared with fingertip tracking registration method, and the efficiency of mobile AR-guided assembly method is improved by about 24.77% compared with the traditional manual assisted assembly method. These data verifies the strong interaction and practicability of the fingertips based on the user's multi-quantity and multi-degree of freedom features in the process of mobile AR guided assembly.
Intelligent Classroom Teaching Assessment System Based on Deep Learning Model Face Recognition Technology

AI + education technology has had a wide and far-reaching impact on the education industry, which has been applied to classroom teaching in order to achieve real-time monitoring of the classroom and complete closed-loop feedback on teaching. In this paper, AI face recognition based on deep learning is improved and optimized through key points such as image data acquisition, localization, calibration, comparison, and deepening data models, so that the detection can be completed accurately and quickly even under the condition of light and external object interference, maximizing the accuracy and efficiency of face recognition technology under the influence of the objective environment. The traditional manual operation in classroom teaching is automated and intelligent throughout, reducing the workload of teachers and related personnel while further ensuring the scientific organization and management of classroom teaching. While making classroom teaching and learning life safer, more convenient, and more efficient, it also provides a favourable reference for the wide application and development of facial recognition technology.
Apache submarine

As machine learning is applied more widely, it is necessary to have a machine-learning platform for both infrastructure administrators and users including expert data scientists and citizen data scientists [24] to improve their productivity. However, existing machine-learning platforms are ill-equipped to address the "Machine Learning tech debts" [36] such as glue code, reproducibility, and portability. Furthermore, existing platforms only take expert data scientists into consideration, and thus they are inflexible for infrastructure administrators and non-user-friendly for citizen data scientists. We propose Submarine, a unified machine-learning platform, and takes all infrastructure administrators, expert data scientists, and citizen data scientists into consideration. Submarine has been widely used in many technology companies, including Ke.com and LinkedIn. We present two use cases in Section 5.
Economical VR/AR method for Interior Design Programme

Interior design is one of Sharjah University's key programmes in the College of Fine Arts and Design (CFAD). Students' presentations are aided by visualisation technologies like sketches, CAD, 3D, 2D artwork, marker rendering, and animation. These tools and techniques are considered traditional methods to assist students' presentation. With the evolution of technology, we feel that a new tool should be introduced. This research looks at how technology, specifically Augmented Reality (AR) and Virtual Reality (VR), can improve present visualisation techniques in this field. The potential technology in this sector was listed and described in-depth for CFAD's future implementation. This idea is based on the industry's most recent advances and technology. The findings suggest that most students were amazed by VR/AR technology in interior design and wanted to learn more about it if included in the curriculum. We outline the economical and practical approach to integrate interior design 3D scene into VR/AR application using smartphones and headset devices.
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Optical Parametric Oscillators (OPOs) have wide applications in quantum optics for generating squeezed states and developing advanced technologies. When the phase or/and the amplitude of the pumping field for an OPO have fluctuations due to fault signals, time-varying uncertainties will be introduced in the dynamic parameters of the system. In this paper, we investigate how to design a fault-tolerant $H^\infty$ controller for an OPO with a disturbance input and time-varying uncertainties, which can achieve the required $H^\infty$ performance of the quantum system. We apply robust $H^\infty$ control theory to a quantum system, and design a passive controller and an active controller based on the solutions to two Riccati equations. The passive controller has a simple structure and is easy to be implemented by using only passive optical components, while the active quantum controller may achieve improved performance. The control performance of the proposed two controllers and one controller that was designed without consideration of system uncertainties is compared by numerical simulations in a specific OPO, and the results show that the designed controllers work effectively for fluctuations in both the phase and amplitude of the pumping field.
Deep Neural Network-Based Sports Marketing Video Detection Research

With the rapid development of short video, the mode of sports marketing has diversified, and the difficulty of accurately detecting marketing videos has increased. Identifying certain key images in the video is the focus of detection, and then, analysis can effectively detect sports marketing videos. The research of video key image detection based on deep neural network is proposed to solve the problem of unclear and unrecognizable boundaries of key images for multiscene recognition. First, the key image detection model of the feedback network is proposed, and ablation experiments are conducted on a simple test set of DAVSOD. The experimental results show that the proposed model achieves better performance in both quantitative evaluation and visual effects and can accurately capture the overall shape of significant objects. The hybrid loss function is also introduced to identify the boundaries of key images, and the experimental results show that the proposed model outperforms or is comparable to the current state-of-the-art video significant object detection models in terms of quantitative evaluation and visual effects.
Multimodal image registration for power equipment using clifford algebraic geometric invariance

Imaging inspection and video monitoring are important means for power equipment condition monitoring and diagnosis. However, single model image cannot supply sufficient information for power equipment condition monitoring, such as infrared images and visible images. The image registration can bridge this gap. This paper develops an image registration method fused by infrared and visible images, which constructs the principal direction of corner points using neighborhood contour point geometric invariance under Clifford algebraic space. Simultaneously, we also design a law for fitting optimal spatial information in the main direction of corner points, where the Clifford rotation operator is utilized to realize the spatial transformation of the image to be registered. The experiment reveals that the average registration error is less than 3 pixels by this developed method, reaching sub-pixel registration, subsequently obtaining satisfactory fusion effect of power equipment. This algorithm enriches the multimodal image registration scheme and meets the requirements of monitoring power equipment.
Demonstrating and negotiating the adoption of web design technologies: Cascading Style Sheets and the CSS Zen Garden

Cascading Style Sheets (CSS) express the visual design of a website through code and remain an understudied area of web history. Although CSS was proposed as a method of adding a design layer to HTML documents early on in the development of the web, they only crossed from a marginal position to mainstream usage after a long period of proselytising by web designers working towards “web standards”. The CSS Zen Garden grassroots initiative aimed at negotiating, mainstreaming and archiving possible methods of CSS web design, while dealing with varying levels of browser support for the technology. Using the source code of the CSS Zen Garden and the accompanying book, this paper demonstrates that while the visual designs were complex and sophisticated, the CSS lived within an ecosystem of related platforms, i.e., web browsers, screen sizes and design software, which constrained its use and required enormous sensitivity to the possibilities browser ecosystems could reliably provide. As the CSS Zen Garden was maintained for over ten years, it also acts as a unique site to trace the continuing development of web design, and the imaginaries expressed in the Zen Garden can also be related to ethical dimensions that influence the process of web design. Compared to Flash-based web design, work implemented using CSS required a greater willingness to negotiate source code configurations between browser platforms. Following the history of the individuals responsible for creating and contributing to the CSS Zen Garden shows the continuing influence of layer-based metaphors of design separated from content within web source code.
A Renovated CNN-Based Model Enhances KGC Task Performance

Knowledge graph (KG) contains a large number of real-world knowledge and has become an invaluable aid to assist the application of artificial intelligence. Knowledge graph completion (KGC) is the task to complete the missing triple in KG database. Our goal in this study is to enhance the performance of KGC tasks based on CNN model. To do this, we first investigated the effect of adding multiple filters of different shapes into the pioneer model. The obscure improvement leads us to seek other approaches. Our second proposed model, termed DP-ConvKB, which is a deep convolution-neural-network-based model, outperforms state-of-the-art models on several metrics. Our study provides supporting evidence that, by cooperating deep pyramid network structure into models, it can significantly improve the KGC performances.
The death of GeoCities: seeking destruction and platform eulogies in Web archives

GeoCities was once one of the most popular platforms on the web. This free web-hosting site was a place where people from a range of geographic and socio-cultural locations could build their own websites and communities online. In 1999, Yahoo! acquired the platform in a historic USD$3.7B transaction, but the following decade saw the platform decline into a state of near total non-use. In 2009, it was taken offline. This paper demonstrates how the GeoCities web archives can be used to find digital traces of destroyed web pages and “platform eulogies” from the user’s perspective that provide insights into the tensions that ultimately led to GeoCities death.
A Study on Chinese-English Machine Translation Based on Transfer Learning and Neural Networks

The existing Chinese-English machine translation has problems such as inaccurate word translation and difficult translation of long sentences. To this end, this paper proposes a new machine translation model based on bidirectional Chinese-English translation incorporating translation knowledge and transfer learning, and the components of this model include a recurrent neural network-based translation quality assessment model and a self-focused network-based model. The experimental results demonstrate that our method works better on the dataset of machine translation quality assessment task for Chinese-English translation with more information, and the Pearson correlation coefficient of its quality assessment feature vector (such as word prediction vector representation) is higher.
Accurate 3D Reconstruction of White Matter Hyperintensities Based on Attention-Unet

White matter hyperintensities (WMH), also known as white matter osteoporosis, have been clinically proven to be associated with cognitive decline, the risk of cerebral infarction, and dementia. The existing computer automatic measurement technology for the segmentation of patients' WMH does not have a good visualization and quantitative analysis. In this work, the author proposed a new WMH quantitative analysis and 3D reconstruction method for 3D reconstruction of high signal in white matter. At first, the author using ResUnet achieves the high signal segmentation of white matter and adds the attention mechanism into ResUnet to achieve more accurate segmentation. Afterwards, this paper used surface rendering to reconstruct the accurate segmentation results in 3D. Data experiments are conducted on the dataset collected from Shandong Province Third Hospital. After training, the Attention-Unet proposed in this paper is superior to other segmentation models in the segmentation of high signal in white matter and Dice coefficient and MPA reached 92.52% and 92.43%, respectively, thus achieving accurate 3D reconstruction and providing a new idea for quantitative analysis and 3D reconstruction of WMH.
Towards Improving the Performance of Question Answering System using Knowledge Graph - A Survey

A question answering system attempts to give a precise response to questions posed in natural language. Question answering systems must capture some context information in order to accurately answer non-trivial queries. Question answering is a challenging research issue because of the contextual interconnected information. Knowledge graphs are a form of data representation that is both structured and interconnected. The process of retrieval of exact answer from a knowledge graph for a given question is named as knowledge graph question answering (KGQA). Different ways for executing KGQA exist depending on the type of query, searching patterns over the knowledge graph, ranking of candidate answers, and so on. This paper surveys various approaches for KGQA, the major challenges, the current techniques for evaluations and the research gaps in KGQA.
stigLD: Stigmergic Coordination of Linked Data Agents

While current Semantic Web technologies are well-suited for data publication and integration, the design and deployment of dynamic, autonomous and long-lived multi-agent systems (MAS) on the Web is still in its infancy. Following the vision of hypermedia MAS and Linked Systems, we propose to use a value-passing fragment of Milner’s Calculus to formally specify the generic hypermedia-driven behaviour of Linked Data agents and the Web as their embedding environment. We are specifically interested in agent coordination mechanisms based on stigmergic principles. When considering transient marker-based stigmergy, we identify the necessity of generating server-side effects during the handling of safe and idempotent agent-initiated resource requests. This design choice is oftentimes contested with an imprecise interpretation of HTTP semantics, or with rejecting environments as first-class abstractions in MAS. Based on our observations, we present a domain model and a SPARQL function library facilitating the design and implementation of stigmergic coordination between Linked Data agents on the Web. We demonstrate the efficacy our modeling approach in a Make-to-Order fulfilment scenario involving transient stigmergy and negative feedback.
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise

Humans process visual information with varying resolution (foveated visual system) and explore images by orienting through eye movements the high-resolution fovea to points of interest. The Bayesian ideal searcher (IS) that employs complete knowledge of task-relevant information optimizes eye movement strategy and achieves the optimal search performance. The IS can be employed as an important tool to evaluate the optimality of human eye movements, and potentially provide guidance to improve human observer visual search strategies. Najemnik and Geisler (2005) derived an IS for backgrounds of spatial 1/f noise. The corresponding template responses follow Gaussian distributions and the optimal search strategy can be analytically determined. However, the computation of the IS can be intractable when considering more realistic and complex backgrounds such as medical images. Modern reinforcement learning methods, successfully applied to obtain optimal policy for a variety of tasks, do not require complete knowledge of the background generating functions and can be potentially applied to anatomical backgrounds. An important first step is to validate the optimality of the reinforcement learning method. In this study, we investigate the ability of a reinforcement learning method that employs Q-network to approximate the IS. We demonstrate that the search strategy corresponding to the Q-network is consistent with the IS search strategy. The findings show the potential of the reinforcement learning with Q-network approach to estimate optimal eye movement planning with real anatomical backgrounds.