Reviewerly/exHarmony huggingface dataset:
Diversify Search Results Through Graph Attentive Document Interaction

The goal of search result diversification is to retrieve diverse documents to meet as many different information needs as possible. Graph neural networks provide a feasible way to capture the sophisticated relationship between candidate documents, while existing graph-based diversification methods require an extra model to construct the graph, which will bring about the problem of error accumulation. In this paper, we propose a novel model to address this problem. Specifically, we maintain a document interaction graph for the candidate documents of each query to model the diverse information interactions between them. To extract latent diversity features, we adopt graph attention networks (GATs) to update the representation of each document by aggregating its neighbors with learnable weights, which enables our model not dependent on knowing the graph structure in advance. Finally, we simultaneously compute the ranking score of each candidate document with the extracted latent diversity features and the traditional relevance features, and the ranking can be acquired by sorting the scores. Experimental results on TREC Web Track benchmark datasets show that the proposed model outperforms existing state-of-the-art models.
Distributionally Robust Policy Learning via Adversarial Environment Generation

Our goal is to train control policies that generalize well to unseen environments. Inspired by the Distributionally Robust Optimization (DRO) framework, we propose DRAGEN — Distributionally Robust policy learning via Adversarial Generation of ENvironments — for iteratively improving robustness of policies to realistic distribution shifts by generating adversarial environments. The key idea is to learn a generative model for environments whose latent variables capture cost-predictive and realistic variations in environments. We perform DRO with respect to a Wasserstein ball around the empirical distribution of environments by generating realistic adversarial environments via gradient ascent on the latent space. We demonstrate strong Out-of-Distribution (OoD) generalization in simulation for (i) swinging up a pendulum with onboard vision and (ii) grasping realistic 3D objects. Grasping experiments on hardware demonstrate better sim2real performance compared to domain randomization.
Augmenting Novelty Search with a Surrogate Model to Engineer Meta-diversity in Ensembles of Classifiers

Using Neuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using gradient descent to train evolved architectures during the search can be computationally prohibitive. Here we propose a method to overcome this limitation by using a surrogate model which estimates the behavioural distance between two neural network architectures required to calculate the sparseness term in Novelty Search. We demonstrate a speedup of 10 times over previous work and significantly improve on previous reported results on three benchmark datasets from Computer Vision—CIFAR-10, CIFAR-100, and SVHN. This results from the expanded architecture search space facilitated by using a surrogate. Our method represents an improved paradigm for implementing horizontal scaling of learning algorithms by making an explicit search for diversity considerably more tractable for the same bounded resources.
Data Types, Data Doubts &amp;amp; Data Trusts

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Bayesian Order-Restricted Inference of Multinomial Counts from Small Areas

Body mass index (BMI) can be a useful indicator of health status, and people can fall in different cells. Estimating BMI cell probabilities for small areas can be difficult, due to a lack of available data from national surveys. We have data from a number of counties in the USA, and it is sensible to assume that BMI may be similar across the counties for each cell. Overall, the cell probabilities for each county follow a unimodal order restriction, and so, it is sensible to assume the same for the individual counties (small areas). Moreover, we assume that the counties are similar with some variations. In this setting, it is convenient to use the Bayesian paradigm to adaptively pool the data over areas. Therefore, we use a hierarchical multinomial Dirichlet model with order restrictions, to model the cell counts and the cell probabilities, thereby permitting a borrowing of strength across areas. We provide efficient Gibbs samplers to make inference about the cell probabilities for multinomial Dirichlet models with and without order restrictions (a model with the same pooling structure). To make inference, we compute the posterior distributions of the cell probabilities for both models. In general for most counties, as expected, the posterior distributions of cell probabilities of the model with order restrictions have significantly less variation, as measured by posterior standard deviations and coefficients of variation, than those of the model without order restrictions.
From Memex to Google: The origin and evolution of search engines

Search engines have become one of the main channels for accessing information on the Web.Their widespread use means that the media, companies, institutions or any agent whose objective is to be visible to or to attract digital audiences is obliged to ensure its website is ranked at the top of the results pages.In Spain alone, 88% of people use search engines daily, with Google being the most used to conduct these searches, claiming a national market share of 95% and more than 90% worldwide.Given their current importance, an in-depth understanding of these tools becomes essential and it is worth pondering just how we have reached this current situation.What exactly are the origins of search engines and how has Google come to exercise its quasi-monopoly?The objective of this study is to explore the origin of search engines and to describe the main milestones in their evolution.To do so, a bibliographic review has been carried out using the main academic databases in the social sciences comprising a narrative search, complemented by an examination of the grey literature.The result is a journey through the history of search engines from their origins and subsequent technology developments to the creation of the World Wide Web.Likewise, a study is made of the original search engines and their main characteristics, with a particular emphasis on the path taken by Google given its current position of supremacy.Understanding the past has direct implications not only for our understanding of the present reality of web searches and access to information, but it is also essential for managing the continuous digital transformation to which we are all exposed, which has repercussions for all areas of the economy and society and, of course, for communication.
Multi-Agent with Multi Objective-Based Optimized Resource Allocation on Inter-Cloud

Cloud computing is the ability to provide new technologies and standard cloud services.One of the essential features of cloud computing is the provision of "unlimited" computer resources to users on demand.However, single cloud resources are generally limited and may not be able to cope with the sudden rise in user needs.Therefore, the inter-cloud concept is introduced to support resource sharing between clouds.In this system, each cloud can tap the resources of other clouds when there are not enough resources to meet the demands of the consumer.In cloud computing, allocating the available resources of service nodes to on-demand tasks is an important concern.To achieve this, in this paper, multiagent with multi-objective optimized resource allocation on inter-cloud is proposed.The proposed algorithm is a combination of adaptive tree seed optimization (ATSO) and multi-agent.The proposed approach consists of four agents namely, user interface agent, monitoring agent, scheduler agent, and Executer agent.Initially, the user agent collects the task from the users, and the monitoring agent reports the resource list to the scheduler agent.Based on the resource list, the scheduler agent schedules the task with the help of ATSO.Finally, the executer agent, allocate the task to the resources.The performance of the proposed approach is evaluated using makespan, cost, and resource utilization.
An Al-Based Security System Using Computer Vision and NLP Conversion System

In this 21st century, ensuring the security of a place is one of the most crucial things. We have to use either manual or semi-autonomous entrances and human security guards in most of the security systems. Many sophisticated apartments are still now using a traditional security system to ensure safety. Besides, monitoring only by the camera in a certain area is not a better solution to confirm safety. A lot of paperwork has been proposed to make security system as efficient as possible. But a few methods have been proposed an intelligent security system with integrated hardware and automation. The proposed methodology has emphasized building a robotics ecosystem of security that control entrances to give access to authentic people, making decisions and taking actions in any circumstances automatically by itself. These tasks are being performed based on a collective method of image processing, natural language processing, supervised learning, IoT, automation and web server technology. This robotic system is capable of memorizing previously identified humans with their information, talking with users in a local language, gathering necessary information and detecting everything with the user. By this method, there has been proposed a solution to overcome the limitations of a manual or semi-autonomous security system and provide intelligent AI-based security with a robotic ecosystem at a minimum cost.
A Specialized Shell for Intelligent Systems of Prescribing Medication

This paper analyzes the existing decision support systems for prescription of drug therapy. The main principles of development and architecture of an intelligent clinical decision support system that is implemented as a specialized shell are described. The unique features of the system, as well as information and software components that are part of it, are shown. The presented examples demonstrate all the proposed solutions.
The Construction of Financial Teaching System Platform Based on Digital Intelligence Technology

At present, the competition among companies is becoming increasingly fierce, and the requirements for financial management are getting higher and higher, which also puts forward higher requirements for financial professionals related to finance. Traditional teaching methods can no longer meet the current development needs. Adjust and improve teaching methods, etc. A financial education system platform using digital intelligent technology is a better way. It can effectively simulate units and positions in real business and social environments. Students can learn and receive education in this virtual environment. Recognize the content and job characteristics of each position, improve their learning and execution capabilities, and enable them to better improve their overall awareness and professional skills. Secondly, enhance students' interest in learning and professional skills to lay a solid foundation for future internships. In this regard, research on the financial education system platform based on digital intelligent technology has certain practical significance. This article aims to study the construction of a financial teaching system platform based on digital intelligent technology. And it introduced a form of nonlinear econometric model. After preliminary completion of the system design and optimization test of the real machine platform, the experiment shows that the CPU usage rate of the db of the teaching system platform during the test is about 18%, but after optimization and improvement, the CPU usage rate can reach 13%, which meets the expected requirements of the experiment. Practice has proved that the system has a certain effect and practical significance in practice.
Supply Chain Management Using Blockchain, IoT and Edge Computing Technology

Supply chain management is a backbone of the industry to serve the requirement of routine life. The supply chain process is complex as it involves several organisations across geographical boundaries. It involves various stakeholders with multivariant processes such as product information, manufacturers, logistics, consumers, product safety, etc. These are some centralised system's challenges. Blockchain technology can achieve transparency, decentralisation, and distributed trust to resolve these issues and improve the existing system. With the help of technology, managing goods, services, and information with an efficient system can be designed to achieve high performance. The health sector is a challenge for each country in this situation of pandemic Covid-19, and to perform the smooth operation, there is a need for an end-to-end tracking system, robust, secure, and strong framework. It is required to ensure the security and integrity of data with transparency in supply chain management. This study presented the conceptual framework of blockchain enabling supply chain management to provide the solution in the healthcare industry. This study provides an efficient way of utilising blockchain technology in the supply chain process and enhance the healthcare sector.
Error analysis of a ruled grating and mechanism optimization of a grating ruling machine

To improve the quality and success rate of grating ruling, we theoretically analyze the factors that affect the grating performance according to the Fraunhofer far-field diffraction method. The analysis shows that the yaw angle error of the grating line has a greater impact on the performance index. Based on this result, we simulate and analyze the angle error by the principle of mechanical simulation. Then we determine the thickness of the spring sheet, the number of connecting steel sheets, and the initial closing force of the tension spring. We use the optimized workbench structure of the grating ruling machine to rule three gratings. The diffracted wavefronts are all better than <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>λ<!-- λ --></mml:mi> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3</mml:mn> </mml:mrow> </mml:mrow> </mml:math> , and the success rate is 100%, which means that the experiments verify the effectiveness of the modified workbench structure.
Real-Time Anomaly Detection and Classification from Surveillance Cameras Using Deep Neural Network

With increasing security threats, anomaly detection and classification are highly recommended work nowadays. Anomaly detection and classification from surveillance videos are more complex tasks due to the prevalence of anomalous activity. There are still some exceptional problems that require advanced approaches. Deep learning has recently made it possible to detect and classify anomalies in a critical way. In this paper, we have proposed a fine-tuned ResNet-50 model to learn anomalous patterns by exploiting 14 types of anomalous images. In our approach, we have first augmented the image data before passing it to the model. Instead of a fully connected layer, we have added an average pooling layer, dropout layer, flatten, dense layer, and dense layer followed by an activation function (softmax). We also introduce a new dataset that consists of 10483 real-world anomalous images, with 14 realistic anomalies, including abuse, fire, road accident, robbery, suicide attempt, etc. Increasing the classification performance, such baselines demonstrate that it is incredibly tough for our dataset and opens up more opportunities for future work <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . In terms of accuracy, our proposed model can acquire 100% accuracy for anomaly detection, and for anomaly classification, on average, it acquires 79.69% accuracy with a computational cost of 61.45 milliseconds per frame.
Rainfall Forecasting Using the Group Method of Data Handling Model: A Case Study of Sarawak, Malaysia

Time series forecasting has led to the emergence of various forecasting models applied to arrays of time series problems, such as rainfall forecasting, dengue forecasting, tourism forecasting, and others. The Artificial Neural Network (ANN) is a popular Artificial Intelligence (AI) model extensively employed in much research for time series forecasting due to its nonlinear modeling ability. The group method of data handling (GMDH) is an AI model with the characteristics of heuristic self-organizing capability. This model has shown successful results in many areas. Nowadays, rainfall forecasting remains a vital interest and is still actively researched, where researchers use different soft computing techniques. The ANN has been popularly studied for rainfall forecasting because of its ability to efficiently train a large amount of data and completely detect complex connections between nonlinear dependent and independent variables. However, research on rainfall forecasting using the GMDH model is limited. Hence, this paper designates the GMDH model and its application to rainfall forecasting. The conventional GMDH model uses the polynomial transfer function. The sigmoid transfer function is proven to solve the multicollinearity issue caused by the quadratic polynomial of the GMDH model. Hence, this research tackled the multicollinearity issue of using different transfer functions in GMDH modeling and forecasting. The study compares the results of using polynomial and sigmoid transfer functions for the GMDH model development. This research uses the Malaysia rainfall dataset of the Sarawak regions from 2010 until 2019 as a case study to evaluate the effectiveness of the GMDH models in this research. The results exhibit that the polynomial transfer function is dominant in achieving the smallest RMSE and MSE values in all regions.
Resource Authorization Methods for Edge Computing

To realize the distribution of processing load and prompt response, the concept of edge computing is drawing attention. Under the edge computing environment, server processing is carried out on an edge node located near various devices with a communication module instead of on a central server. These edge nodes can be provided by another entity other than service providers such as network operators. The edge nodes have fewer computing resources than a central server has. Therefore, appropriate dynamic resource management is required to avoid resource exhaustion. For this purpose, authorization techniques, e.g., OAuth, can be applied. In this paper, we consider applying the OAuth protocol for a privilege delegation on edge computing. Firstly, we clarify the authentication flows differ depending on the relationship of edge computing players (edge provider, user, service provider). We then describe the unique problems of resource authorization on edge computing.
GAN Based Image Inpainting Methods: A Taxonomy

Image inpainting is the technique that's able to “repair” or “correct” the image by reconstructing missing regions of an image using AI algorithms. As the techniques of images inpainting become mature in recent years, the demand for image inpainting algorithms rises in the market. It can be used to fill out missing areas in a picture, denoise images, or even remove a specific object from an image. To this end, it is important to propose a survey for image inpainting method, which can provide a comprehensive introduction of this area. The reason for creating this paper is due to the lack of resources that conclude all the methods that are being used in image inpainting. As the field of image inpainting soars, researchers may need documents which explain all the methods that can be used to train their image inpainting models. In the paper, we gathered some most successful methods that are used by many researchers. We explained their model, methods they used, the results of the training, and the advantages of the approaches they used in their research.
Facial Recognition Technologies: A Survey and Comparison of Systems and Practical Applications

Through recent years, facial recognition technology has become increasingly relevant in a widespread area of applications. There are numerous approaches to facial recognition technology, each best-suited for different types of practices. A survey which compares the infrared, thermal, and deep learning methods is performed in this study. Each method is evaluated based on its speed, accuracy, and efficiency and is given a overall percentage of reliability. Further, we examine the advantages and disadvantages of each method and assess what common usage of each method would be in a practical setting. We find a point of commonality between each method type where accuracy and efficiency must strike a balance, further compounded by the practical applications of each method. Our findings show that while there is an ideal method of facial recognition for each individual application, there is no ideal method that applies to every application.
Locality and expectation effects in Hindi preverbal constituent ordering

We investigate the relative impact of two influential theories of language comprehension, viz., Dependency Locality Theory (Gibson, 2000; DLT) and Surprisal Theory (Hale, 2001; Levy, 2008), on preverbal constituent ordering in Hindi, a predominantly SOV language with flexible word order. Prior work in Hindi has shown that word order scrambling is influenced by information structure constraints in discourse. However, the impact of cognitively grounded factors on Hindi constituent ordering is relatively underexplored. We test the hypothesis that dependency length minimization is a significant predictor of syntactic choice, once information status and surprisal measures (estimated from n-gram i.e., trigram and incremental dependency parsing models) have been added to a machine learning model. Towards this end, we setup a framework to generate meaning-equivalent grammatical variants of Hindi sentences by linearizing preverbal constituents of projective dependency trees in the Hindi-Urdu Treebank (HUTB) corpus of written text. Our results indicate that dependency length displays a weak effect in predicting reference sentences (amidst variants) over and above the aforementioned predictors. Overall, trigram surprisal outperforms dependency length and parser surprisal by a huge margin and our analyses indicate that maximizing lexical predictability is the primary driving force behind preverbal constituent ordering choices in Hindi. The success of trigram surprisal notwithstanding, dependency length minimization predicts non-canonical reference sentences having fronted direct objects over variants containing the canonical word order, cases where surprisal estimates fail due to their bias towards frequent structures and word sequences. Locality effects persist over the Given-New preference of subject-object ordering in Hindi. Accessibility and local statistical biases discussed in the sentence processing literature are plausible explanations for the success of trigram surprisal. Further, we conjecture that the presence of case markers is a strong factor potentially overriding the pressure for dependency length minimization in Hindi. Finally, we discuss the implications of our findings for the information locality hypothesis and theories of language production.