Reviewerly/exHarmony huggingface dataset:
GAMFlow: Global Attention-Based Flow Model for Anomaly Detection and Localization

In industrial areas where anomalous samples are lacking, unsupervised methods with high accuracy are especially important to ensure product quality and stability. An unsupervised method based on a feature extractor and a distribution estimation module has been applied in the industrial field and has achieved good performance. Flow models are usually used as distribution estimation modules. But traditional flow models only focus on feature information in two dimensions, the channel dimension, and the space dimension, while ignoring the cross-dimensional connection between them. To solve this problem, we proposed a flow model with a global attention mechanism for anomaly detection of images. It embeds combined modules of global attention mechanism and convolutional layer in the structure of a reversible neural network, which is capable of global cross-dimensional extraction of image features. In the comparison experiments, our method achieves average image and pixel-level AUC of 0.997 and 0.987 on the MVTec AD dataset and 0.968 and 0.984 on the BTAD dataset, respectively, which outperforms the other traditional methods. In addition, in the detection task, our method also possesses faster inference speeds. This shows that our method has excellent anomaly detection and localization performance, which meets the industrial demand for high-precision anomaly detection and localization.
Rating Text Classification with Weighted Negative Supervision on Classifier Layer

Bidirectional encoder representations from transformers (BERT) gives full play to the advantages of the attention mechanism, improves the performance of sentence representation, and provides a better choice for various natural language understanding (NLU) tasks. Many methods using BERT as the pre-trained model achieve state-of-the-art performance in almost various text classification scenarios. Among them, the multitask learning framework combining the negative supervision and the pre-trained model solves the issue of the model performance degradation that occurs as the semantic similarity of texts conflicts with the classification standards. The current model does not consider the degree of difference between labels, which leads to insufficient difference information learned by the model, and affects classification performance, especially in the rating classification tasks. On the basis of the multi-task learning model, this paper fully considers the degree of difference between labels, which is expressed by using weights to solve the above problems. We supervise negative sam-ples on the classifier layer instead of the encoder layer, so that the classifier layer can also learn the difference information between the labels. Experimental results show that our model can not only performs well in 2-class and multi-class rating text classification tasks, but also performs well in different languages.
Intensifying expletive constructions and their use on social media: Innovative functions of the hashtag #wokeAF in English tweets

The hashtag has seen increasing attention in the linguistics literature, in recognition of its prevalence on social media and in other modes of communication. Here, we report on a diachronic analysis of the hashtag #wokeAF in English-language tweets posted between 2012 and 2022. First, we trace the use of the word woke from verb to adjective, with novel uses arising in African American Vernacular English. Such uses then spread into mainstream standard English, eventually being used in a novel construction: the intensifying expletive ([adjective + as + expletive]). Although examples of the intensifying expletive are listed in the Urban Dictionary, to our knowledge, this is the first linguistic analysis of the construction. Second, we analyse semantic interpretations and syntactic characteristics of the intensifying expletive #wokeAF, by documenting its occurrence in tweets spanning eleven years. Analysis of the discourse and the context in which the hashtag appears allows us to uncover its novel use as a collective noun, which in our data, is linked to a pejorative stance. In general, we find innovation in the semantic scope of the hashtag and versatility in its position and integration within tweets. Given the pervasiveness of the word woke in the public consciousness, as evidenced by its occurrence in the popular press, this article aims to fill a timely gap while providing an interesting example of language innovation online.
The Prevalence of Single Sign-On on the Web: Towards the Next Generation of Web Content Measurement

Much of the content and structure of the Web remains inaccessible to evaluate at scale because it is gated by user authentication. This limitation restricts researchers to examining only a superficial layer of a website: the landing page or public, search-indexable pages. Since it is infeasible to create individual accounts across thousands of webpages, we examine the prevalence of Single Sign-On (SSO) on the web to explore the feasibility of using a few accounts to authenticate to many sites. We find that 58% of the top 10K websites with logins are accessible with popular 3rd-party SSO providers, such as Google, Facebook, and Apple, indicating that leveraging SSO offers a scalable solution to access a large volume of user-gated content.
Projective Measurement-Based Quantum Phase Difference Estimation Algorithm for the Direct Computation of Eigenenergy Differences on a Quantum Computer

Quantum computers are capable of calculating the energy difference of two electronic states using the quantum phase difference estimation (QPDE) algorithm. The Bayesian inference-based implementations for the QPDE have been reported so far, but in this approach, the quality of the calculated energy difference depends on the input wave functions being used. Here, we report the inverse quantum Fourier transformation-based QPDE with Na of ancillary qubits, which allows us to compute the difference of eigenenergies based on the single-shot projective measurement. As proof-of-concept demonstrations, we report numerical experiments for the singlet-triplet energy difference of the hydrogen molecule and the vertical excitation energies of halogen-substituted methylenes (CHF, CHCl, CF2, CFCl, and CCl2) and formaldehyde (HCHO).
Journeying towards best practice data management in biodiversity genomics

Abstract Advances in sequencing technologies and declining costs are increasing the accessibility of large‐scale biodiversity genomic datasets. To maximize the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such datasets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognize the growing need for guidance on comprehensive data management practices that minimize the risks of data loss, maximize efficiency for stand‐alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four fictional personas reflecting differing user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub ( https://genomicsaotearoa.github.io/data‐management‐resources/ ), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in their journey towards best practice. The Hub also provides an opportunity for those biodiversity researchers whose expertise lies beyond genomics and are keen to advance their data management journey. We aim to support the biodiversity genomics community in embedding data management throughout the research lifecycle to maximize research impact and outcomes.
A Conceptual Exploration: Incorporating Physical Education with Metaverse

The term “metaverse” refers to a range of technologies, including chip technology, network communication technology, VR/AR/MR/XR technology, game technology (game engine, game code, multimedia resources), and block chain technology. Augmented reality and virtual reality offer numerous potential application. As the social metaverse becomes more accessible, it is crucial to incorporate additional valuable components. Some experts argue that the use of technology is least essential in the context of physical education. However, challenge persist in the field of physical education. The integration of physical education into metaverse is gaining popularity, as it address the pressing need to overcome the limitations imposed by the lack of physical venues. The evolution of the feasibility of metaverse technologies and their application in sports relies on the utilization of technology definitions and examples. To address the conceptual nature of this essay, an interpretivist research approach has been adopted. This approach emphasizes the analysis of literature rather than data gathering. Additionally, the study incorporates network research and fieldwork to gather educational experiences and metaverse knowledge. This paper delves into the concept of Metaverse integration and explores its potential impact. The metaverse, which is a virtual reality space, has the potential to provide students with an immersive learning experience that enhances their physical education. By incorporating virtual environments, students can simulate real-life situations and learn in a way that mirrors the real world. This paper underscores those significance of metaverse technology in the context to physical education.
Feature descriptors using super-pixels as fuzzy numbers

The objective of this paper is to extract directly local important region descriptors using image super-pixels and fuzzy numbers. Previous works are based on extracting important feature points like corners in an image then region descriptors are formed around these features. Our novel contribution is to consider directly the most discriminative super-pixels as region descriptors. First, each super-pixel is considered as a fuzzy number. Then the alpha-cut which best represents the fuzzy number is obtained. Finally, according to these alpha-cuts and the cardinality of each fuzzy number the region descriptors are formed. Matching is done according to distances between fuzzy numbers. The Palm-print recognition problem was chosen to show the effectiveness of this approach.
9 An innovative application using augmented reality to enhance the teaching-learning process in school education

This chapter presents an application prepared from augmented reality (AR) in the field of school education for normal and mentally weak students. It is observed that schools in rural areas are not able to use information and communications technology tools or computer graphics models for teaching-learning, and when the teacher teaches students in books and explains any image object, the details of objects are not clear to the students due to the unavailability of 3D models. We can use this application in schools in rural areas, where physical subject-wise models are not available or are fewer. Due to the nonavailability of models based on the subject, it is difficult for the students to understand the detailed information related to the subject, so through this application, the detailed information of the subject can be explained very well by showing the model according to the subject to the students. Through this application, it will be easy for those students, whose mental development has not been done very well, who are given education in a special school different from normal schools, in which they can learn sign language as well as objects. In this type of education, it is difficult to identify such objects used in daily life, which they cannot touch, see, or do not have that object in front of them. In that case, an application created in AR will prove to be a boon for students. Because through this application students can understand what they had difficulty with earlier which will help them in living daily life. Therefore, this technology will be helpful in the education of students and will be a better option for the students to learn. It can be used by anyone such as a teacher, parent, or student themselves. The use of this application will be very easy because for this we will be using Android-based devices like mobile or tablet, which is generally available everywhere. This effort and research will enhance the teaching-learning process in school education and will prove to be very helpful for normal students and mentally weak students.
10 How do augmented and virtual reality influences visitor experiences: a case of heritage tourism sites in Rajasthan

Although technology has evolved quickly, it is still incredibly underutilized in many sectors. The new status quo has, nevertheless, increased awareness of technology across many industries, particularly travel and hospitality. As a result, technology is being used in more inventive and profitable ways that help both businesses and consumers. The travel and leisure business is being impacted by technology like augmented and virtual reality (AR/VR) in a wide range of ways. Virtual tours offer alternatives to the pandemic melancholy by enabling users to see locations they have always desired to visit while remaining in their own place. VR and AR solutions are increasingly being used in various tourism sectors, particularly at galleries, historical monuments, amusement parks, and theaters, in Asian cities including Tokyo, Beijing, Kuala Lumpur, Shanghai, Delhi, and Jaipur [6, 44]. The virtual world produced by these innovations is used as a cutting-edge advertising tool to draw visitors to these tourist spots and develop joint marketing destinations [6]. The availability of a wide range of AR and VR tools at the locations also aids the players in coming up with fresh ideas for revitalizing the location to enthrall more visitors and improve their overall quality of service. Therefore, AR and VR techniques are now being utilized more frequently to increase the overall experiences of tourists at a location, making a more interactive and diversified experience simpler and allowing them to engage with visitor attractions in unique ways [17]. However, until recently, relatively few studies have attempted to examine the combined significance of AR and VR approaches from the tourists’ value co-creation perspective. Consequently, this chapter looks into the potential for incorporating AR and VR into the tourism experiences at the well-known historical monuments in Rajasthan with the goal of recommending a framework based on value co-creation. This will also imply that active implementation of AR and VR at historical monuments can help co-create value for the tourist’s experiences before, during, and after their visit. This study will also present a case study on the prominent tourist destinations in Rajasthan where AR and VR technologies are being used to improve the visitor’s overall destination experience.
1 VR in social learning: applications and challenges

The concept of social learning has evolved over the years. As discussed in the work of Laland, social learning is a technique used by all animals and humans to learn from the environment. The new generation of students is raised in a world of information and communication technology. The wealth of knowledge is at their disposal in the form of the Internet. As a result, facilitating social learning is the primary consideration in the design of learning systems. The new social learning systems must assist the learner rather than teach him. The three key functions required are (1) assist in locating the appropriate content; (2) assist learners in connecting with the appropriate people; and (3) motivate/incentivize people to learn. Augmented reality/ virtual reality (VR) can be used to improve social learning among people. The interaction of people with a simulated environment made using computer technology, typically including computer graphics and artificial intelligence, is known as virtual reality. Virtual is something that does not exist in reality. In VR, a person can hold animated or virtual items created using computer graphics and artificial intelligence. This chapter discusses several VR applications for social learning. These applications are in the metaverse, social media, neuropathy and psychology, training, and distance education fields. Each application is covered with references to the most recent research in the field. Additionally highlighted are the social and ethical concerns with using VR for social learning. The conclusions are offered at the end.
A secure energy trading in a smart community by integrating Blockchain and machine learning approach

In today’s smart communities, small-scale energy systems are essential for sustainable development and efficient resource management. However, ensuring the confidentiality, safety, and accurate prediction of energy consumption patterns in energy trading is a major challenge. To address these issues, an innovative solution that synergistically combines two cutting-edge technologies: blockchain and machine learning is proposed. This paper unveils a novel approach that harmoniously merges blockchain with the Recalling-Enhanced Recurrent Neural Network (RERNN) to revolutionize energy trading systems called ‘Blockchain-Enhanced Energy Trading with Recalling-Enhanced Recurrent Neural Network (BET-RERNN).’ Data from IoT-enabled smart devices is securely stored in blockchain blocks, ensuring data integrity and immutability. Blockchain’s decentralized nature creates a trust-less environment for energy trading, protecting the privacy and anonymity of participants while maintaining transparency. At the heart of our system lies the advanced machine-learning capabilities of the RERNN model. By processing the data stored on the blockchain, RERNN accurately predicts optimal power generation for small-scale energy systems, enabling smart communities to make informed decisions and optimize their energy consumption. The BET-RERNN scheme provides a plethora of strengths. First, participants can securely engage in energy trading without compromising sensitive information, fostering a more resilient and efficient market. Second, blockchain technology ensures that all energy-related data is protected from tampering and unauthorized access, ensuring system reliability and trust. An in-depth comparison of RERNN’s performance to traditional General Regression Neural Network (GRNN) and Gradient Boost Decision Tree (GBDT) methods is conducted. To verify the strategy’s effectiveness, MATLAB simulations are employed, demonstrating its real-world applicability and scalability. By combining blockchain and machine learning, a secure and privacy-preserving smart community is established, promoting sustainable energy practices for a greener future.
Hardness of (Semiuniform) MLWE with Short Distributions Using the Rényi Divergence

The module learning with errors (MLWE) problem has attracted considerable attention for its tradeoff between security and efficiency. The quantum/classical worst-case to average-case hardness for the MLWE problem (or more exactly, a family of problems) has been established, but most of the known results require the seed distribution to be the uniform distribution. In the present paper, we show that, using the noise flooding technique based on the Rényi divergence, the search MLWE problem with uniform <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>B</mi> </math> -bounded secret distribution for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>1</mn> <mo>≤</mo> <mi>B</mi> <mo>≪</mo> <mi>q</mi> </math> can still be hard for some seed distributions that are not (even computationally indistinguishable from) the uniform distribution under the standard MLWE assumption. Specifically, we show that if the seed distribution is a semiuniform distribution (namely, the seed distribution can be publicly derived from and has a “small difference” to the uniform distribution), then for suitable parameter choices, the search MLWE problem with uniform bounded secret distribution is hard under the standard MLWE assumption. Moreover, we also show that under the appropriate setting of parameters, the search MLWE problem with uniform bounded noise distribution is at least as hard as the standard MLWE assumption using a different approach than the one used by Boudgoust et al. in [JoC 2023].
Hardness of Module-LWE with Semiuniform Seeds from Module-NTRU

The module learning with errors (MLWE) problem has attracted significant attention and has been widely used in building a multitude of lattice-based cryptographic primitives. The hardness of the MLWE problem has been established for several variants, but most of the known results require the seed distribution (i.e., the distribution of matrix <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi mathvariant="bold">A</mi> </math> ) to be the uniform distribution. In this paper, we show that under the Module-N-th degree Truncated polynomial Ring Units (NTRU) (MNTRU) assumption, the search MLWE problem can still be hard for some distributions that are not (even computationally indistinguishable from) the uniform distribution. Specifically, we show that if the seed distribution is a semiuniform distribution (namely, the seed distribution can be publicly derived from and has a “small difference” to the uniform distribution), then for appropriate settings of parameters, the search MLWE problem is hard under the MNTRU assumption. Moreover, we also show that under the appropriate settings of parameters, the search learning with errors over rings problem with semiuniform seeds can still be hard under the NTRU assumption due to our results for the search MLWE problem with semiuniform seeds being rank-preserving.
Sharing quantum nonlocality in the noisy scenario

It was showed in [ Phys. Rev. Lett. 125 090401 (2020)] that there exist unbounded number of independent Bobs who can share quantum nonlocality with a single Alice by performing sequentially measurements on the Bob’s half of the maximally entangled pure two-qubit state. However, from practical perspectives, errors in entanglement generation and noises in quantum measurements will result in the decay of nonlocality in the scenario. In this paper, we analyze the persistency and termination of sharing nonlocality in the noisy scenario. We first obtain the two sufficient conditions under which there exist n independent Bobs who can share nonlocality with a single Alice under noisy measurements and the noisy initial two qubit entangled state. Analyzing the two conditions, we find that the influences on persistency under different kinds of noises can cancel each other out. Furthermore, we describe the change patterns of the maximal nonlocality-sharing number under the influence of different noises. Finally, we extend our investigation to the case of arbitrary finite-dimensional systems.