Reviewerly/exHarmony huggingface dataset:
A method of image stitching with partition matching and direct detection for rotated image

Aiming at the problem of stitching overlapped images taken under different rotation angles, we present an effective and adaptive method to rotate the image to an appropriate angle before stitching in this paper. Firstly, we take the upper half, lower half, left half, and right half of the target image as the four partitions to be matched. Then, we make feature extractions and match between the reference image and those four partitions of the target image respectively. According to the half part with the most matching pairs, the target image is rotated by a preliminary angle. At the same time, the distribution area of the matching points of the reference image is calculated as the overlapping area. After matching the overlapping areas of the reference image and the rotated target image, we propose a method to detect the direction of the matching pairs to adjust the rotation angle. Finally, the reference image and the rotated target image can be successfully stitched into a wider image. Experimental results show that our method can find a suitable angle to rotate the target image, and can achieve an effective and satisfactory stitching result.
Image super‐resolution based on self‐similarity generative adversarial networks

Self-attention has been successfully leveraged for long-range feature-wise similarities in deep learning super-resolution (SR) methods. However, most of the SR methods only explore the features on the original scale, but do not take full advantage of self-similarities features on different scales especially in generative adversarial networks (GAN). In this paper, self-similarity generative adversarial networks (SSGAN) are proposed as the SR framework. The framework establishes the multi-scale feature correlation by adding two modules to the generative network: downscale attention block (DAB) and upscale attention block (UAB). Specifically, DAB is designed to restore the repetitive details from the corresponding downsampled image, which achieves multi-scale feature restoration through self-similarity. And UAB improves the baseline up-sampling operations and captures low-resolution to high-resolution feature mapping, which enhances the cross-scale repetitive features to reconstruct the high-resolution image. Experimental results demonstrate that the proposed SSGAN achieve better visual performance especially in the similar pattern details.
A Visual Variability and Visuo‐Tactile Coordination Inspired Child Adaptation Mechanism for Wearable Age Group Recognition and Activity Recognition

Not all wearable fitness devices are suitable for children. Due to the decay of activity recognition accuracy and exercise requirement variety caused by the physical differences between adults and children, devices developed for adults cannot provide appropriate exercise guidance to children. Considering the complexity and time‐consuming nature of developing a new children‐specific model, this article proposes a child adaptation mechanism that can be integrated into current adult wearable devices. Two algorithms using unlabeled children's data for age group recognition and activity recognition inspired by the visual variability in cognitive process and visuo‐tactile coordination phenomenon in cortical plasticity are proposed. During an experiment on 30 adults and children, the age group recognition algorithm achieves 93.33% recognition accuracy, and the activity recognition algorithm achieves 88.57% recognition accuracy. This is an 11.16% improvement compared with the baseline and 2.87–4.1% improvement compared with the state‐of‐the‐art transfer learning or self‐adaptation algorithms. The proposed mechanism ameliorates current wearable devices with minimum cost to make them suitable for children and can serve as an alternative to help children learn healthy exercise habits. It is hoped that the work can draw the attention of academia to vulnerable groups, including children, to build a friendlier artificial intelligence network.
Learning the model update with local trusted templates for visual tracking

Existing Siamese trackers usually do not update templates or adopt single-updating strategies. However, historical information cannot be effectively utilized when using these strategies, and model drift from complex tracking challenges cannot be addressed. To address this issue, a novel tracking framework that learns the model update with local trusted templates is proposed in this paper. The authors propose a complementary confidence evaluation method to select local trusted templates in a sliding window. This provides high-confidence historical information. The authors also propose a method including linear learning and deep learning to learn to model updates. Different from traditional update strategies, the authors’ method combines non-linear and linear updates to obtain reliable templates with the most abundant historical information, which solves the complex tracking challenges to a certain extent. Finally, the adaptive fusion response maps of the two strategies determine the final tracking based on the confidence evaluation. Experimental results on NFS, UAVDT, UAV123, UAV20L and VOT2016 show that our method performs favourably when compared with current state-of-the-art methods.
Quality of Service aware secure data transmission model for Internet of Things assisted wireless sensor networks

Abstract Wireless sensor networks (WSNs) are the most critical networks in developing various Internet of Things (IoT) based applications. However, such intelligent devices have drawbacks for energy, memory, processing, and computation resources. In addition, achieving Quality of Service (QoS) with secure data transmission (DT) is a significant challenge for WSNs. This article proposes efficient IoT authentication and energy‐aware safe DT models for achieving better QoS and security with lower energy consumption (EC) in WSNs. Initially, the network randomly deploys the sensor nodes (SNs) and performs the registration of the SNs with the base station (BS). Second, the Entropy‐based Jaccard similarity induced fuzzy C‐means clustering (EJS‐FCM) model clusters the SNs based on the intrinsic qualities of nodes. Then the boosted grasshopper optimization algorithm (BGOA) selects the cluster heads (CHs) optimally by considering major fitness parameters such as communication distance, energy, and distance centroid. Following that, an Adler‐32 hashing‐based authentication scheme performs authentication of every IoT node deployed in the network to provide safer DT. After successful authentication, secure hash elliptical curve cryptography (SHECC) encrypts the data collected from the CHs by incorporating a hashing function both in encryption and decryption, enhancing the IoT data's security level. Finally, in a cloud server (CS), the decentralized blockchain (BC) stores the encrypted data and the authentication credentials of the IoT devices from the BS, which provide additional security to the network. The results of the presented model show its performance efficiency over existing approaches for energy efficiency (EE), QoS, and security.
Improving Classification Accuracy by Optimizing Activation Function for Convolutional Neural Network on Homomorphic Encryption

A secure machine learning technology that performs prediction while encrypting data using homomorphic encryption is being developed. However, Convolutional Neural Networks (CNN) on homomorphic encryption cannot use general non-linear activation functions, Thus, the classification accuracy is low. We proposed a novel method to create an activation function that improves the classification accuracy by performing a pre-training optimization on the coefficients of the polynomial approximation of the Mish function. We confirmed the improvement of classification accuracy for MNIST, Fashion-MNIST, and CIFAR-10 by optimizing the Mish function through pre-training. The classification accuracy can be improved by 4.27% for CIFAR-10. Furthermore, we showed that classification accuracy improves for Fashion-MNIST and CIFAR-10 even when different networks and datasets are optimized by pre-training the activation function. These results show that the activation function of CNNs on a homomorphic encryption can be optimized to improve classification accuracy.
A Certificateless-Based Authentication and Key Agreement Scheme for IIoT Cross-Domain

The Industrial Internet of Things (IIoT) improves productivity and intelligent manufacturing process through revolutionary technology. Due to the complexity of the manufacturing process, cross-domain access is inevitable. Recently, Meng et al. proposed a secure and efficient blockchain-assisted entity authentication mechanism BASA for IIoT cross-domain. In the BASA scheme, the authors utilized identity-based signature (IBS) to realize mutual authentication and the Ephemeral Elliptic Curve Diffie-Hellman (ECDHE) exchange mechanism to negotiate the session key. Due to the inherent key escrow problem of identity-based cryptography (IBC), the key generation center (KGC) can obtain the session key negotiated between two entities distributed in different domains. When KGC is threatened, the security of the session key is worrying. Considering this security concern, based on the BASA scheme, in this article, we first show a secure and efficient certificateless public-key signature (CL-PKS) scheme with anonymity. Then, combined with the ECDHE key exchange mechanism, we give an efficient cross-domain authentication and key agreement scheme CL-BASA with the aid of consortium blockchain. After that, we make security verification by the formal analysis tool, Tamarin, which shows that our CL-BASA is secure. The evaluation demonstrates that our CL-BASA may have a slight disadvantage in storage overhead, but it has obvious advantages than competitor schemes in terms of communication overhead and computational overhead.
On a Framework for Federated Cluster Analysis

Federated learning is becoming increasingly popular to enable automated learning in distributed networks of autonomous partners without sharing raw data. Many works focus on supervised learning, while the area of federated unsupervised learning, similar to federated clustering, is still less explored. In this paper, we introduce a federated clustering framework that solves three challenges: determine the number of global clusters in a federated dataset, obtain a partition of the data via a federated fuzzy c-means algorithm, and validate the clustering through a federated fuzzy Davies–Bouldin index. The complete framework is evaluated through numerical experiments on artificial and real-world datasets. The observed results are promising, as in most cases the federated clustering framework’s results are consistent with its nonfederated equivalent. Moreover, we embed an alternative federated fuzzy c-means formulation into our framework and observe that our formulation is more reliable in case the data are noni.i.d., while the performance is on par in the i.i.d. case.
Kazakhstani womenʼs participation in online marketplaces: Benefits and barriers

Abstract This study aims to investigate womenʼs participation in offline and online marketplaces and identify related factors, particularly the digital divide, access to the internet, and the level of information and communications technology (ICT) proficiency. It discusses the empirical methodology of its two approaches: a descriptive analysis of statistical data and a sociological survey. This study employs quantitative analyses. The respondents to this study were women who used marketplaces in Kazakhstan. The model was tested using the data for 295 respondents. The results reveal the COVID‐19 pandemic affected both the number of online purchases and the structure of those purchases: women began ordering more medicines and food, and less equipment and clothing online. The main factors affecting participation in online marketplaces are income level, access to the internet, and the use of ICT. This study tries to reduce the gap in the literature on the benefits and barriers for women by providing empirical evidence about the influence of COVID‐19 on online marketplaces.
The puzzle of ideography

An ideography is a general-purpose code made of pictures that do not encode language, which can be used autonomously - not just as a mnemonic prop - to encode information on a broad range of topics. Why are viable ideographies so hard to find? I contend that self-sufficient graphic codes need to be narrowly specialized. Writing systems are only an apparent exception: At their core, they are notations of a spoken language. Even if they also encode nonlinguistic information, they are useless to someone who lacks linguistic competence in the encoded language or a related one. The versatility of writing is thus vicarious: Writing borrows it from spoken language. Why is it so difficult to build a fully generalist graphic code? The most widespread answer points to a learnability problem. We possess specialized cognitive resources for learning spoken language, but lack them for graphic codes. I argue in favor of a different account: What is difficult about graphic codes is not so much learning or teaching them as getting every user to learn and teach the same code. This standardization problem does not affect spoken or signed languages as much. Those are based on cheap and transient signals, allowing for easy online repairing of miscommunication, and require face-to-face interactions where the advantages of common ground are maximized. Graphic codes lack these advantages, which makes them smaller in size and more specialized.
Isomorphism of the Categories of Time and Space in the Novel “Falling Man” by D. DeLillo

The purpose of the study is to determine the peculiarities of perception of the category of time by the characters interpreting it as space based on the material of D. DeLillo’s novel “Falling Man”: instead of vertically (chronologically) unfolding, the past, present and future simultaneously merge at one point on a horizontal plane. Scientific originality of the study lies in clarifying the interdependencies between the character’s emotional-volitional state and his/her mental combination of the present, past and future in terms of simultaneity. As a result, it has been found that in the mind of a psychologically traumatised character, all three time layers are characterised by the same degree of simultaneity as individual points in physical space, they “freeze” at the same moment, thus transforming into “the eternal present”. The narrative, which reflects the character’s memory, is modelled by the narrator as a kind of springboard on which narratives of past and present events collide simultaneously and on a selective basis.
Human face aging based on active appearance model using proper feature set

In this paper, a simulation of the human face evolution from youth to old age is presented. In this work, the goal is to maintain a person's appearance and accurately estimate the face at the target age; in such a way that the simulated face should be realistic and have the appearance and texture characteristics of the target age. To achieve this goal, the number of feature points and their arrangement on the face are very important. First, we suggest proper feature points. Then, using the active appearance model (AAM) method and the presented model, templates are obtained as representative of different age groups. To maintain the unique geometric characteristics of the input face and apply these characteristics to the target age template, the proposed feature point pattern and the MLS method have been used. Then, using the active appearance model method and having the target age template contain the geometric characteristics of the input face, and the steps of changing the input face age to reach the target age are presented. Finally, the results of the survey, using two methods of age recognition and real image recognition (separately for man and woman images), show an average of 80.77% (male images) and 81.36% (female images) correct answers of the participants in this Poll.
Deep Hierarchical Super Resolution for Scientific Data

We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform gridwith minimal seam artifacts on octree node boundaries. Our method uses existing state-of-the-art SR models and adds flexibility to upscale input data with varying levels of detail across the domain, instead of only uniform grid data that are supported in previous approaches.The key is to use a hierarchy of SR NNs, each trained to perform 2× SR between two levels of detail, with a hierarchical SR algorithm that minimizes seam artifacts by starting from the coarsest level of detail and working up.We show that our hierarchical approach outperforms baseline interpolation and hierarchical upscaling methods, and demonstrate the usefulness of our proposed approach across three use cases including data reduction using hierarchical downsampling+SR instead of uniform downsampling+SR, computation savings for hierarchical finite-time Lyapunov exponent field calculation, and super-resolving low-resolution simulation results for a high-resolution approximation visualization.
French translation of a dialogue dataset and text-based emotion detection

Chatbots allow computer programs to interact naturally with a user. However, they remain limited due to their lack of sensitivity to the user's state of mind and emotions. This sensitivity will allow the chatbots to provide more accurate answers. Text-based emotion detection has already been explored for the english language (Chatterjee et al., 2019), yet no satisfying french dataset is available. We propose to translate the emotion corpus of multi-party conversation EmotionLines, which is based on the Friends TV show, by exploiting its french broadcasting. Our translation-based dataset generation method is adaptable to any dataset deriving from foreign movies, or TV shows broadcasted in french. Using this translated dataset, we propose a classifier based on BERT, able to detect the user's emotion from text. It takes into account the context of the discussion to improve its inferences.
Data-driven alternating current optimal power flow: A Lagrange multiplier based approach

This paper proposes a data-driven Alternating Current Optimal Power Flow (AC-OPF) method assisted by Lagrange multipliers. Stacked Extreme Learning Machine (SELM) is introduced for AC-OPF learning to avoid the time-consuming training and hyperparameter adjustment process of deep neural networks. Instead of incorporating the prior physical information into the neural networks algorithm, we developed a new neural network structure for the SELM learning based on the Lagrange multipliers of the AC-OPF problem. Case studies of several IEEE benchmark systems demonstrate that the AC-OPF learning performance is improved by introducing additional Lagrange multipliers information, while the proposed method outperforms other alternatives with almost 99% learning accuracy and fast computation.
What are the benefits and opportunities of launching a Metaverse for <scp>NEOM</scp> city?

Abstract It is expected that in , the first phase of the most innovative city in the world will be opened. Seemingly NEOM city's developers have forgotten about an attractive technology called Metaverse, presented almost simultaneously along with the NEOM idea in . This study stipulates that Metaverse can appreciably assist in delivering a broad digital perspective of the future to governments, futurologists, people, and particularly the NEOM developers. Also, this study briefly addresses the benefits and opportunities of releasing Metaverse‐based NEOM for the Kingdom of Saudi Arabia (KSA), engineers, developers, futurologists, and people worldwide. Moreover, it is illustrated that KSA and people worldwide can benefit from the Metaverse version. For example, people can visit the world's dream city as digital tourists. As a result of this revolutionary idea, KSA can earn considerable revenue by land pre‐selling and providing remote services.
Effects of learner control design in an AR-based exhibit on visitors’ museum learning

This study shifts the focus away from demonstrating the existence of the effect toward understanding the mechanism by which the effect of AR operates in museum learning. By uncovering and describing the contingencies of AR from the perspectives of learner control, this study investigates how and when AR affects museum learning experiences, and to give insights into curation with AR. A between-subjects experiment was conducted with 48 college students divided into three groups. This study considered both qualitative and quantitative features of learner control and designed the AR control tools and experiment accordingly, and the findings supported the success of integrating the immersive technology of AR and the theoretical framework of learner control to construct museum exhibits. The results showed that visitors are willing to use the provided tools in museum AR and perform steadily in knowledge acquisition. In addition to offering more learner control in museums, AR promotes positive behaviors and attitudes. This study contributes to the field studies of learner control by linking learner control with the critical dimensions of AR-enhanced museum learning to provide more guidance in exhibit design. Based on the findings, practical suggestions on incorporating learner control in AR-based interactive exhibits are provided.
Quitting thresholds in visual search are impacted by target present detection times but not their variability

Models of visual search posit that target absent responses occur when the quitting threshold for the trial is reached before a target is detected, and that feedback about missed targets allows the quitting threshold to be adaptively set to the difficulty of the search task. While these models may effectively capture processes in lab-based tasks, in real-world searches feedback is often impossible to provide. Instead, observers have little information about their errors, and may only be aware of when they successfully detect the target. We posit that in the absence of feedback the time required to find a target might influence quitting thresholds. In three experiments, we investigate how manipulating the mean time and the standard deviation of time to detect a target influence quitting thresholds in target absent trials. To vary target detection times while holding the search stimuli constant, we used an eye-movement contingent change to surreptitiously introduce a target near fixation at a particular time. Results show that decreasing the mean time to find a target also decreases the number of items inspected and reaction time in target absent trials, the hallmark of a shift in the quitting threshold. By contrast, varying the standard deviation around a fixed mean had no impact on target absent search times. These findings suggest that people are sensitive to the typical time required to find a target in a given task and use that information to flexibly adjust target absent quitting thresholds, but people are not sensitive to the variability.
Quantum-safe Anonymous Hierarchical Identity-Based Encryption with Traceable Identities

Anonymous Identity Based Encryption (AIBET) scheme allows a tracer to use the tracing key to reveal the recipient’s identity from the ciphertext while keeping other data anonymous. This special feature makes AIBET a promising solution to distributed IoT data security since it can preserve both the privacy and the anonymity of a communication network but still give the server the ability to revoke the anonymity of some specific users in necessary situations (such as when their illegal behavior are detected). In this paper, we construct an efficient quantum-safe Hierarchical Identity-Based cryptosystem with Traceable Identities (AHIBET) with fully anonymous ciphertexts. We prove the security of our AHIBET scheme under the Learning with Errors (LWE) problem in the standard model.