Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence and the Metaverse: Present and Future Aspects

Artificial Intelligence (AI) and the metaverse are some of the most prominent technologies of the twenty-first century. Each can enhance people’s lives in many ways, improve many industries, and increase the efficiency of many working processes. The metaverse is a compilation of virtual, augmented, and physical realities. Although “metaverse” terminology has been around for some time, it remains an emerging technology even as it becomes more of an everyday topic. Very soon, the metaverse will be a place where one can work, learn, shop, be entertained, and interact with others in ways that were never before possible. Without machine learning most metaverse experiences will not be possible which allows software applications to become more accurate at predicting outcomes without being explicitly programmed. AI and data science will be at the forefront of converging this combination of technology and will help revolutionize how people interact across the globe. It will lead to innovations, revenue streams, and deeper connections. AI and the metaverse can be applied in industries such as healthcare, gaming, management, marketing, education, and more. Generally, these technologies are looked at separately without considering their influence on each other and their potential for collaboration. In this paper, we talk about how artificial intelligence and the metaverse work together, how they are applied in various industries, and their potential. Before explaining the role of AI in the metaverse, its need to be explained the concept of the metaverse, where and how it is applied, and its potential.
Blockchain Technology in Metaverse: Opportunities, Applications, and Open Problems

The metaverse is the next technological revolution that will mirror our physical life into a virtual world. In this new world, various data patterns are being generated in large volumes, Avatars can perform various transactions Metaverse's major requirements are data reliability, integrity, security, transparency, and decentral integrity, security, transparency, and decentral are major requirements in Metaverse. Blockchain technology is one of the magic technologies able to optimally implement these requirements in the metaverse. This chapter discusses around why Blockchain is a key player in the metaverse. The chapter discusses the main characteristics of Blockchain and how they can be modeled in the metaverse. In addition, it discusses the differences between fungible and non-fungible tokens (NFTs), and how each token type can be processed using three types of Ethereum Request for Comments (ERC) protocols, ERC-20, ERC-721, and ERC-1155. After that, the chapter presents four real Blockchain applications in the metaverse. finally, the chapter is ended with a set of challenges and open problems around utilizing Blockchain technology in the metaverse.
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers

One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Furthermore, we carry out a detailed statistical analysis of the fill probability of orders placed in the order book, including within the bid-ask spread, for assets with different queue dynamics and trading activity.
All-pairwise squared distances lead to more balanced clustering

&lt;abstract&gt;&lt;p&gt;In clustering, the cost function that is commonly used involves calculating all-pairwise squared distances. In this paper, we formulate the cost function using mean squared error and show that this leads to more balanced clustering compared to centroid-based distance functions, like the sum of squared distances in $ k $-means. The clustering method has been formulated as a cut-based approach, more intuitively called &lt;italic&gt;Squared cut (Scut).&lt;/italic&gt; We introduce an algorithm for the problem which is faster than the existing one based on the Stirling approximation. Our algorithm is a sequential variant of a local search algorithm. We show by experiments that the proposed approach provides better overall optimization of both mean squared error and cluster balance compared to existing methods.&lt;/p&gt;&lt;/abstract&gt;
The Future of Metaverse in the Virtual Era and Physical World: Analysis and Applications

The increasing diversity of reported user experiences and the expanding technical capabilities of virtual worlds has led to a splintered definition of what a virtual world is and is not in the academic literature. This chapter defines the term "Metaverse," explains how it came to be, sets it apart from similar concepts, and details its many potential uses. When the actual and the virtual worlds collide, as they do in Augmented and Virtual Reality (AR and VR), the resulting technology is known as Mixed Reality (MR), a subset of VR-related technologies. Also, in this chapter, some important applications based on Metaverse are presented. Ethical issues that are related to the Metaverse are highlighted.
The Threat of the Digital Human in the Metaverse: Security and Privacy

A metaverse is a collection of shared, multi-user, persistent, 3D virtual worlds that are connected to the real world and combined to form a single, eternal virtual reality. The metaverse is transitioning from science fiction to an imminent reality thanks to recent developments in new technologies including blockchain, augmented reality, digital twins, extended reality, and augmented reality. Users enter the metaverse with avatars that they can use to interact with other users as well as the objects, programmes, services, and organisations that make up the metaverse. However, this new mode of communication raises a lot of privacy and security issues. Threats related to the technologies that will be used to realise the metaverse will grow in quantity and quality, and some equally unheard-of threats will also emerge. The privacy and security of users of the metaverse, which we discuss in this chapter, are particularly vulnerable to such threats. MSPs (Metaverse Service Providers) should consider these issues. With ternary-world interactions, we first study an unique distributed metaverse architecture and its important features. The privacy threats to the metaverse are then discussed, along with security countermeasures for these threats. We also examine the primary security concerns associated with the development of the metaverse and data exchange within it.
Grammar Induction for Under-Resourced Languages: The Case of Ch’ol

We apply to the under-resourced language Ch’ol the Womb Grammar Model of grammar induction (WGM), thus called because given appropriate input it can generate grammars for different languages, much as human wombs can generate different races. The WGM is inferential, works for more than just specific tasks and needs neither a pre-specified model family, nor parallel corpora, nor any of the typical models of machine learning. It generates an understudied language’s grammar using representative and correct input sentences in that language, together with its lexicon relevant to that input, all of which is parsed with respect to the correct grammar of a well-studied language, or alternatively, of a “universal” grammar. The errors that inevitably result serve to guide the production of the desired grammar. We present the main framework describing the model and the results of our experiments, inferring Ch’ol grammar from English grammar and suggest some future lines of research in the area.
Proof-Theoretic Foundations of Normal Logic Programs

There are several semantics in logic programming for negation as failure. These semantics can be realized with a combination of induction and coinduction, and this realization can be used to develop a goal-directed method of computing models. In essence, the difference between these semantics is how they resolve the unstratified portions of a program. In this paper, restricting ourselves to the propositional case, we show how the semantics of normal logic programs is a mixture of induction and coinduction, and how we can use coinduction to resolve the cycles (or loops) formed by the rules in a program. We present denotational semantics based on a fixed point of a function and show its equivalence to the use of induction and coinduction. We take a look at the different ways a semantics may resolve cycles, and show how to implement two popular semantics, well-founded and stable models, as well as co-stable model semantics. Finally, we present operational semantics as a parametrized goal-directed execution algorithm that allows us to determine how cycles are resolved.
The Task of Question Answering in NLP: A Comprehensive Review

An important task of natural language processing is a question answering (QA) (NLP). It provides an automated method of pulling the information from a given context. Thus, QA is made up of three separate modules, each of which has a core component in addition to auxiliary components. These three essential elements are answer extraction, information retrieval, and question classification. By classifying the submitted question according to its type, question classification plays a crucial role in QA systems. Information retrieval is crucial for finding answers to questions because, without the presence of the right ones in a document, no further processing can be done to come up with a solution. Last but not least, answer extraction seeks to locate the response to a query posed by the user. This paper sought to provide a comprehensive overview of the various QA methods, assessment criteria, and benchmarking tools that researchers frequently use.
SemWIRet: A Semantically Inclined Strategy for Web Image Recommendation Using Hybrid Intelligence

With a rapid increase of Websites, retrieving information from the Internet has become a very challenging and time-consuming endeavour. There is always room for improvement in terms of yielding and recommending the correct informative measures so that the user would get a very pertinent topic for which the user is searching over the Internet. To ease the work of the user, an enhanced recommendation system is best suited to recommend the optimal solution. Furthermore, relevant articles are displayed according to the user queries. It is highly likely that the relevant search article recommended will be of no use to the user if the recommendation algorithm is not effective. This paper proposes a semantic-infused hybrid intelligence approach to recommendation for user-centric queries relevant using concept and ANOVA cosine similarity and extreme gradient boosting (XGBoost) where the documents are classified. Using the flickr30k dataset, the above experiment was conducted, and 97.16% accuracy was achieved.
UNBIASED ESTIMATION OF THE VANILLA AND DETERMINISTIC ENSEMBLE KALMAN-BUCY FILTERS

In this paper, we consider the development of unbiased estimators for the ensemble Kalman-Bucy filter (EnKBF). The EnKBF is a continuous-time filtering methodology, which can be viewed as a continuous-time analog of the famous discrete-time ensemble Kalman filter. Our unbiased estimators will be motivated from recent work (Rhee and Glynn, Oper. Res., 63:1026-1053, 2015) which introduces randomization as a means to produce unbiased and finite variance estimators. The randomization enters through both the level of discretization and through the number of samples at each level. Our unbiased estimator will be specific to models that are linear and Gaussian. This is due to the fact that the EnKBF itself is consistent, in the large particle limit N &amp;#8594; &amp;#8734;, with the Kalman-Bucy filter, which allows us one derive theoretical insights. Specifically, we introduce two unbiased EnKBF estimators that will be applied to two particular variants of the EnKBF, which are the deterministic and vanilla EnKBF. Numerical experiments are conducted on a linear Ornstein-Uhlenbeck process, which includes a high-dimensional example. Our unbiased estimators will be compared to the multilevel. We also provide a proof of the multilevel deterministic EnKBF, which provides a guideline for some of the unbiased methods.
Cross-scale collaborative network for single image super resolution

Convolution-based networks show impressive performance in Single Image Super-Resolution (SISR) task by making full use of local patterns. However, it requires deeper structure to capture the long-range dependencies which is inefficient. Hence, the Non-Local Attention (NLA) is introduced to search similar patterns globally and effectively exchange information globally. Nevertheless, the naïve NLA only exchanges information in original spatial scale and has quadratic computational complexity with spatial size of input image. Besides, the global fusion of all similar patterns may lead to blurring, which deviates from the goal of SISR to accurately recover image details. Hence, the naïve NLA is not suit for SISR. To solve this problem, we propose a novel Cross-Scale Collaborative Attention (CSCA) which is designed for SISR. Due to similar patterns tend to occur at different locations and scales, we explore self-similarity and cross-scale similarity simultaneously on multi-scale feature pyramid to utilize more rich details for improving the performance of recovery. To guarantee the recovered details are accurate, we aggregate features in each layer of pyramid into groups and only exchange information between the most relevant groups across multi-scale. Hence, CSCA is both efficient and can accurately recover details using the rich information contained in multi-scale. Furthermore, we propose a Cross-Scale Collaborative Network (CSCN) by inserting a few CSCA modules into a simple backbone network. Extensive experiments show the effectivity and efficiency of CSCA, and state-of-the-art performance of CSCN in SISR task.
Beyond the depth of narratives, does the multi-layer monitoring-driven view matter in disclosure? Evidence from MENA countries

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Deep neural network with deformable convolution and side window convolution for image denoising

A noval neural networks with irregular convolution block is proposed for image denoising. In the field of image processing, convolutional neural networks have shown great advantages compared with traditional approaches, however, it is found that standard convolution does not work well on image edge, and it has some drawbacks when dealing with variable noised images. In this paper, we numerically illustrate that the irregular convolution, including deformable convolutional kernel and side window filtering technique, is beneficial for finding effective receptive field and improving image edge. Quantitative and qualitative experimental results are demonstrated, which outperforms classical convolution neural networks in denoising tasks.
Achieving agency within imperfect automation: Working customers and self-service technologies

In this article, we propose to treat agency as something which is accomplished in the entanglement of humans with technologies. This redirects our attention away from the question of what distinguishes humans from smart machines and towards querying how people and automated apparatuses join in processes of mutual sociomaterial engagement. To further our argument, we look at self-service kiosks, which are ubiquitous yet largely overlooked components of mediated environments. We reflect on a participant observation in groceries stores and interviews with customers familiar with self-checkout facilities. They make us aware that operating this equipment is not an individual affair but a joint activity by default, taking place in a temporally regimented setting prone to human errors and malfunction when people try to respond to the terminals’ protocol. This sort of imperfect automation has ambivalent ramifications which rely on the capabilities of users and the capacities of an interface and its underlying operations. Agency, we conclude, thus becomes a matter of viable performance in which humans may act machine-like while machines perform an expanding share of activities.
Of tubes and telegrams: The names of social media platforms and messenger services

Although names play a crucial role in the communication of products, services and companies and their acceptance by the public, virtually no research findings are available to date about the naming strategies used by social media platforms and messenger services. This article explores the naming strategies of the most successful social media platforms and messenger services in terms of the numbers of their users worldwide. For this purpose, 17 brand names were analysed with the help of a taxonomy of brand names that is used in marketing practice. Furthermore, etymological and semantic aspects of the brand names are examined. One finding is that most of the brands use fanciful names, which can best be protected. Additionally, this study highlights the fact that many of the social media platforms and messenger services contain hints to prior media forms in their names. But why do they use these semantically retro references? This study adopts different approaches (remediation, skeuomorphism, invented tradition) to illuminate this retro-naming strategy.
Anchor-based multi-view subspace clustering with graph learning

Multi-view subspace clustering (MVSC) has been drawn wide attentions in the area of pattern recognition and data mining. However, for a multi-view dataset with n samples and V views from k clusters, MVSC commonly requires O(Vn2) memory for storing the view-specific graph matrices and O(n3) time for the eigenvalue decomposition of a shared graph matrix. Hence, most of MVSC methods are difficult to handle the large-scale multi-view data problem. To address this issue, this paper proposes an Anchor-based Multi-View Subspace Clustering with Graph Learning (AMVSCGL) method. Instead of constructing a n×n graph matrix, our method generates a shared coefficient matrix with the size of n×k based on few learned view-specific anchors. Moreover, through further merging a graph learning term, this shared coefficient matrix can simultaneously capture the global and local information among multiple views and few learned view-specific anchors for clustering. Experimental results on seven large-scale multi-view data verify our AMVSCGL’s effectiveness and superiority in comparison with some state-of-the-art methods.
BalCon — resource balancing algorithm for VM consolidation

Cloud providers handle substantial number of requests to create and delete virtual machines (VMs) on a daily basis, where the unknown sequence of requests eventually leads to resource fragmentation. To mitigate this issue, periodic consolidation of VMs into fewer number of physical hosts is an important cost-saving procedure, closely related to the vector bin-packing problem. In this paper, we propose the BalCon algorithm for consolidation that steadily reduces the number of active hosts and keeps migration costs low. BalCon classifies the cluster's state and selects one of three heuristics to balance resources for superior consolidation. To evaluate BalCon's performance with respect to optimality, we introduce integer programming models. BalCon finds 99.7% of the optimal solutions for over 750 problem instances. This outstanding result was achieved due to the Force Step of our algorithm, which is the key improvement detail for common heuristics. We compare BalCon with a modified Sercon heuristic using Huawei and synthetic datasets with two resources for allocation.
Autoencoders with exponential deviation loss for weakly supervised anomaly detection

Weakly supervised anomaly detection aims to detect anomalies using a small number of labeled anomalies and a large amount of unlabeled data. However, existing methods have limitations: unsuitable setting of the anomaly threshold, using an inefficient loss function, and being vulnerable to contaminated data. To address these limitations, this paper proposes a novel framework called the Exponential Deviation Autoencoder (EDAE), which consists of two stages. In the first stage, EDAE pre-trains an autoencoder (AE) to learn a compressed representation of the input data and estimates the anomaly score distribution of the training data to determine an appropriate anomaly threshold. In the second stage, EDAE fine-tunes the AE with a novel Exponential Deviation Loss (EDL) function that provides continuous and nonlinear penalties according to anomaly scores and enables more effective training using labeled anomalies. EDAE also uses batch sampling based on empirical distribution to create batches of data that are more robust to contaminated data. We conduct extensive experiments on various datasets and show that EDAE outperforms state-of-the-art weakly supervised methods with up to a 26% improvement in accuracy.
An individual fairness based outlier detection ensemble

Outlier detection ensembles usually outperform individual detectors. However, the result of the ensemble could still be biased toward a group or individual data. To omit such biases, the notion of fairness was introduced. Individual fairness, in particular, aims at treating similar instances similarly. This paper proposes an individual fairness-based member selection approach (iFselect) for an outlier detection ensemble. We represent individual fairness with iFscore, which considers the relative similarity of the instances in the dataset with their outlier scores. Our method picks up members according to their iFscore from a pool of diverse detectors with varying parameters, giving a balance for performance-fairness trade-off. This enhances its suitability for the unsupervised problem of outlier detection. Moreover, our method avoids the selection of sensitive attributes which otherwise have an associated cost. Experiments on benchmark datasets suggest the superiority of iFselect in fairness, with overall performance being relatively comparable to its state-of-the-art counterparts.