Reviewerly/exHarmony huggingface dataset:
How VR Technological Features Prompt Tourists’ Visiting Intention: An Integrated Approach

To achieve sustainable tourism, the tourism development authorities have used advanced technologies to promote destinations, attract visitors, and protect the environment. Accordingly, while the available research on tourism technologies indicates that virtual reality (VR) matters in the visit intention of potential tourists, the mechanism of their inner senses when experiencing VR technologies is overlooked. To fill in this void, this study develops a comprehensive conceptual model for investigating the influence of VR technological features on tourists’ minds, and consequently, their destination visit intention. This study integrates the technology acceptance model, stimuli-organism-response model, and flow theory into a unified conceptual model for a comprehensive understanding of tourists’ behavior. By focusing on tourists’ decision-making and behavior processes, this study sheds new light on how VR stimuli (i.e., vividness and interactivity) could translate into tourists’ attitudes and visiting intentions. This conceptual model is then tested and validated using structural equation modeling on survey data collected from 311 VR users after experiencing virtual tours designed for destinations in Vietnam. The findings indicate that virtual tours affect tourists’ psychological well-being, which translates into visiting intention. Based on the results, this study emphasizes the role of VR as a powerful marketing tool to promote a destination. Furthermore, approaches to enhance the effectiveness of VR tours, particularly, for tourism developers, are proposed. This study thus contributes to the literature in the area of VR tourism, contributing both theoretical and practical knowledge.
Transfer Learning and Analogical Inference: A Critical Comparison of Algorithms, Methods, and Applications

Artificial intelligence and machine learning (AI/ML) research has aimed to achieve human-level performance in tasks that require understanding and decision making. Although major advances have been made, AI systems still struggle to achieve adaptive learning for generalization. One of the main approaches to generalization in ML is transfer learning, where previously learned knowledge is utilized to solve problems in a different, but related, domain. Another approach, pursued by cognitive scientists for several decades, has investigated the role of analogical reasoning in comparisons aimed at understanding human generalization ability. Analogical reasoning has yielded rich empirical findings and general theoretical principles underlying human analogical inference and generalization across distinctively different domains. Though seemingly similar, there are fundamental differences between the two approaches. To clarify differences and similarities, we review transfer learning algorithms, methods, and applications in comparison with work based on analogical inference. Transfer learning focuses on exploring feature spaces shared across domains through data vectorization while analogical inferences focus on identifying relational structure shared across domains via comparisons. Rather than treating these two learning approaches as synonymous or as independent and mutually irrelevant fields, a better understanding of how they are interconnected can guide a multidisciplinary synthesis of the two approaches.
The weaponization of artificial intelligence: What the public needs to be aware of

Technological progress has brought about the emergence of machines that have the capacity to take human lives without human control. These represent an unprecedented threat to humankind. This paper starts from the example of chemical weapons, now banned worldwide by the Geneva protocol, to illustrate how technological development initially aimed at the benefit of humankind has, ultimately, produced what is now called the “Weaponization of Artificial Intelligence (AI)”. Autonomous Weapon Systems (AWS) fail the so-called discrimination principle, yet, the wider public is largely unaware of this problem. Given that ongoing scientific research on AWS, performed in the military sector, is generally not made available to the public domain, many of the viewpoints on this subject, expressed across different media, invoke common sense rather than scientific evidence. Yet, the implications of a potential weaponization of our work as scientists, especially in the field of AI, are reaching further than some may think. The potential consequences of a deployment of AWS for citizen stakeholders are incommensurable, and it is time to raise awareness in the public domain of the kind of potential threats identified, and to encourage legal policies ensuring that these threats will not materialize.
Blockchain-Based Decentralized Identification in IoT: An Overview of Existing Frameworks and Their Limitations

The popularity of the Internet of Things (IoT) and Industrial IoT (IIoT) has caused a rapid increase in connected entities and exposed its lack of adequate Identity Management (IdM). Traditional IdM systems are highly dependent on central authorities; any failure can potentially compromise the entire system. To eliminate the drawback of central authorities and evolve IdM systems to meet increasingly stringent security requirements, the Decentralized Identification approach has been proposed. This approach often relies on blockchain technology to provide a secure and tamper-proof method of managing and verifying identities. Therefore, this article investigates the capabilities of blockchain-based decentralized identification for the IoT domain, with an emphasis on the heterogeneity of online devices. It describes a number of features and specifications of decentralized identification with a specific focus on Self-Sovereign Identity (SSI), a model in which entities own their identities. The article concludes with a discussion of technical aspects as well as potential obstacles and constraints to the implementation of decentralized identification in the context of the Internet of Things.
Adaptive Kernel Kalman Filter

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of the predictive and posterior distributions. This paper introduces a Bayesian filter called the adaptive kernel Kalman filter (AKKF). With this filter, the arbitrary predictive and posterior distributions of hidden states are approximated using the empirical kernel mean embeddings (KMEs) in reproducing kernel Hilbert spaces (RKHSs). In parallel with the KMEs, some particles, in the data space, are used to capture the properties of the dynamical system model. Specifically, particles are generated and updated in the data space, while the corresponding kernel weight mean vector and covariance matrix associated with the feature mappings of the particles are predicted and updated in the RKHSs based on the kernel Kalman rule (KKR). Simulation results are presented to confirm the improved performance of our approach with significantly reduced particle numbers, by comparing with the unscented Kalman filter (UKF), particle filter (PF) and Gaussian particle filter (GPF). For example, compared with the GPF, the proposed approach provides around 5% logarithmic mean square error (LMSE) tracking performance improvement in the bearing-only tracking (BOT) system when using 50 particles.
SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern

Limited by objectively poor lighting conditions and hardware devices, low-light images with low visual quality and low visibility are inevitable in the real world. Accurate local details and reasonable global information play their essential and distinct roles in low-light image enhancement: local details contribute to fine textures, while global information is critical for a proper understanding of the global brightness level. In this paper, we focus on integrating local and global aspects to achieve high-quality low-light image enhancement by proposing the synchronous multi-scale low-light enhancement network (SMNet). A synchronous multi-scale representation learning structure and a global feature recalibration module are adopted in SMNet. Different from the traditional multi-scale feature learning architecture, SMNet carries out the multi-scale representation learning in a synchronous way: we first calculate the rough contextual representations in a top-down manner and then learn multi-scale representations in a bottom-up way to generate representations with rich local details. To acquire global brightness information, a global feature recalibration module (GFRM) is applied after the synchronous multi-scale representations to perceive and exploit proper global information by global pooling and projection to recalibrate channel weights globally. The synchronous multi-scale representation and GFRM compose the basic local-and-global block. Experimental results on mainstream real-world dataset LOL and synthetic dataset MIT-Adobe FiveK show that the proposed SMNet not only leads the way on objective metrics (0.41/2.31 improvement of PSNR on two datasets) but is also superior in subjective comparisons compared with typical SoTA methods. The code had already been uploaded to <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/linshideng/SMNet</uri> .
Gumbel MuZero for the Game of 2048

In recent years, AlphaZero and MuZero have achieved remarkable success in a broad range of applications. AlphaZero masters playing without human knowledge, while MuZero also learns the game rules and environment's dynamics without the access to a simulator during planning, which makes it applicable to complex environments. Both algorithms adopt Monte Carlo tree search (MCTS) during self-play, usually using hundreds of simulations for one move. For stochasticity, Stochastic MuZero was proposed to learn a stochastic model and uses the learned model to perform the tree search. Recently, Gumbel MuZero was proposed to ensure the policy improvement and can thus learn reliably with a small number of simulations. However, Gumbel MuZero used a deterministic model as in MuZero, limiting its performance in stochastic environments. In this paper, we propose to combine Gumbel MuZero and Stochastic MuZero, the first attempt to apply Gumbel MuZero to a stochastic environment. Our experiment on the stochastic puzzle game 2048 demonstrates that the combined algorithm can perform well and achieve an average score of 394,645 with only 3 simulations during training, greatly reducing the computational resource needed for training.
An Adaptive Learning Environment for Programming Based on Fuzzy Logic and Machine Learning

In this paper, we present an Intelligent Tutoring System (ITS), for use in teaching the logic of computer programming and the programming language ‘C’. The aim of the ITS is to adapt the delivered learning material and the lesson sequence to the knowledge level and learning needs of each individual student. The adaptation of the presented ITS is based on fuzzy logic and a machine learning technique. Particularly, the system uses the distance weighted k-nearest neighbor algorithm to detect the learner’s knowledge level and abilities concerning computer programming during her/ his first interaction with the system. Next and during subsequent interactions of the learner with the system, fuzzy logic is used to identify the learner’s current knowledge level and potential misconceptions. The system takes into consideration the knowledge dependencies that exist among the domain concepts of the learning material and, applying fuzzy rules, decides about the learning material that has to be delivered to the learner as well as the lesson sequence. The system has been fully implemented and evaluated through t-tests. The evaluation results show that the combination of machine learning (for initially identifying the student’s learning abilities and needs) with fuzzy logic (for the continuous identification of the learner’s current knowledge level and misconceptions) provides more personalized learning experience, promotes the active participation of students in the learning process and results in decrease in the number of dropouts.
Prostate cancer classification with MRI using Taylor-Bird Squirrel Optimization based Deep Recurrent Neural Network

Prostate cancer is a type of cancer that develops in the prostate. The prostate is a little gland in men that resembles a walnut and secretes seminal fluid, which nourishes and transports sperm. A Taylor-Bird Squirrel Optimization based Deep Recurrent Neural Network (Taylor-BSO based Deep RNN) is created to determine the severity degree of prostate cancer classificationstart the diagnosis process . The Taylor series is combined with the Bird Swarm Algorithm (BSA) and Squirrel Search Algorithm (SSA), respectively, to create the Taylor-BSO. By filtering the MR image using the Hybrid Local and Non-Local Means (HLNLM) filtering model, the noise that was present in the input image is successfully eliminated. The procedure of cancer classification is designed for determining the existence or absence of a tumour using the features that were collected in the segmentation findings. The threshold number, however, categorizes the tumour severity level as either a high-grade or low-grade tumour. .
Image encryption algorithm based on 2D‐CLICM chaotic system

Abstract Image encryption algorithm based on a two‐dimensional Chebyshev‐Logistic‐Infinite Collapse Map (2D‐CLICM) is proposed. A new two‐dimensional chaotic map is designed, and numerical studies show that 2D‐CLICM has a larger chaotic region, better randomness and more complex chaotic behavior. An image encryption algorithm (CLICM‐IE) is developed based on the 2D‐CLICM. The randomly generated key is used to generate more random chaotic sequences and the security is greatly improved. To obtain lower computation and time complexity, the whole process performs row encryption for each image pixel. The Arnold transformation preprocesses the image and then the encryption of image pixels is accomplished by controlling the random confusion and diffusion parts with the sequence generated by 2D‐CLICM. The results show that the CLICM‐IE algorithm is deeply random and unpredictable, and resists a variety of attacks. These results can provide good basis and practical application for the chaos generation and image encryption.
flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX

Across scientific fields, the Bayesian framework is used to account for uncertainties in inference (Ellison, 2004;Lancaster, 2004;Von Toussaint, 2011).However, for models with more than a few parameters, exact inference is intractable.A frequently used strategy is to approximately sample the posterior distribution on a model's parameters with a Markov chain Monte Carlo (MCMC) method.Yet conventional MCMC methods relying on local updates can take a prohibitive time to converge when posterior distributions have complex geometries (see e.g., Rubinstein & Kroese ( 2017)).flowMC is a Python library implementing accelerated MCMC leveraging deep generative modelling as proposed by Gabrié et al. (2022), built on top of the machine learning libraries JAX (Bradbury et al., 2018) and Flax (Heek et al., 2020).At its core, flowMC uses a combination of Metropolis-Hastings Markov kernels using local and global proposed moves.While multiple chains are run using local-update Markov kernels to generate approximate samples over the region of interest in the target parameter space, these samples are used to train a normalizing flow (NF) model to approximate the samples' density.The NF is then used in an independent Metropolis-Hastings kernel to propose global jumps across the parameter space.The flowMC sampler can handle non-trivial geometry, such as multimodal distributions and distributions with local correlations.
A Deterministic Kaczmarz Algorithm for Solving Linear Systems

.We propose a new deterministic Kaczmarz algorithm for solving consistent linear systems \(A{\bf x}={\bf b}\) . Basically, the algorithm replaces orthogonal projections with reflections in the original scheme of Stefan Kaczmarz. Building on this, we give a geometric description of solutions of linear systems. Suppose \(A\) is \(m\times n\) , we show that the algorithm generates a series of points distributed with patterns on an \((n-1)\) -sphere centered on a solution. These points lie evenly on \(2m\) lower-dimensional spheres \(\{\mathbb{S}_{k0},\mathbb{S}_{k1}\}_{k=1}^m\) , with the property that for any \(k\) , the midpoint of the centers of \(\mathbb{S}_{k0},\mathbb{S}_{k1}\) is exactly a solution of \(A{\bf x}={\bf b}\) . With this discovery, we prove that taking the average of \(O(\eta (A)\log (1/\varepsilon ))\) points on any \(\mathbb{S}_{k0}\cup \mathbb{S}_{k1}\) effectively approximates a solution up to relative error \(\varepsilon\) , where \(\eta (A)\) characterizes the eigengap of the orthogonal matrix produced by the product of \(m\) reflections generated by the rows of \(A\) . We also analyze the connection between \(\eta (A)\) and \(\kappa (A)\) , the condition number of \(A\) . In the worst case \(\eta (A)=O(\kappa^2(A)\log m)\) , while for random matrices \(\eta (A)=O(\kappa (A))\) on average. Finally, we prove that the algorithm indeed solves the linear system \(A^{{\tt T}}W^{-1}A{\bf x} = A^{{\tt T}}W^{-1}{\bf b}\) , where \(W\) is the lower-triangular matrix such that \(W+W^{{\tt T}}=2AA^{{\tt T}}\) . The connection between this linear system and the original one is studied. The numerical tests indicate that this new Kaczmarz algorithm has comparable performance to randomized (block) Kaczmarz algorithms.KeywordsKaczmarz algorithmlinear systemsreflectionsMSC codes65F10
Commonsense knowledge graph-based adapter for aspect-level sentiment classification

Attention- and graph-based models are widely applied to existing aspect-level sentiment classification (ALSC) tasks. In spite of the effectiveness, most of these studies ignore commonsense knowledge of aspects. When sentences have the same syntactic structures and opinion words, the sentiment polarities toward aspects can be different. Moreover, no easy and flexible method has been proposed to infuse knowledge into existing ALSC models. For these reasons, we propose a novel commonsense knowledge graph-based adapter (CKGA) for ALSC. Firstly, we link aspects to a knowledge graph end extract an aspect-related sub-graph. Then, a pre-trained language model and the knowledge graph embedding are utilized to encode the commonsense knowledge of entities based on which the corresponding knowledge is extracted with a graph convolutional networks. Specifically, CKGA is an adapter-based model which can be added to existing models in a simple way without modifying the original models. Experimental results on three benchmark datasets illustrate that state-of-the-art ALSC models can be significantly improved with CKGA. Thus, CKGA can leverage external knowledge to enhance the sentiment delivery on the task of ALSC.
Visual-semantic consistency matching network for generalized zero-shot learning

Generalized zero-shot learning aims to classify samples of seen and unseen classes by providing only the labels of seen classes. Most GZSL methods directly associate seen classes' visual features with semantic information or leverage semantic information to synthesize samples of unseen classes to transfer the knowledge of seen classes to unseen classes. However, existing generative methods simply employ visual features extracted from the per-trained CNN backbone, which overlooks that the visual features of similar categories have significant similarities and lack enough discriminating information, leading to poor classification performance when generalizing the knowledge of seen classes to unseen classes. To mitigate this issue, we present a Visual-Semantic Consistency Matching Network (VSCM) for generalized zero-shot learning. Our proposed method employs a conditional VAE to generate the visual features of unseen classes and utilizes a visual-semantic consistency matching network that aligns visual space with semantic space to obtain visual-semantic consistency features. Specifically, we propose a semantic chunking network that teams up with semantic attention and a semantic encoder to guide the visual-semantic consistency matching network to get synthetic semantic information aligned with factual semantic information. The semantic relation network guarantees the consistency between the factual semantic information and the block features, while the semantic independence network measures the independence of the blocks. Finally, we concatenate visual features and synthetic semantic information as visual-semantic consistency features to improve the separability between categories. Extensive experiments on five GZSL benchmark datasets demonstrate the significant generalization performance of our proposed method over the state-of-the-art methods. Our codes have been available at:https://github.com/zzq158/VSCM-GZSL.
Using 360-degree immersive videos to assess multiple transdiagnostic symptoms: A study focusing on fear of negative evaluation, paranoid thoughts, negative automatic thoughts, and craving

Over the last 20 years, virtual reality (VR) has gained a great interest for both assessment and treatment of various psychopathologies. However, due to high costs and material specificity, VR remains disadvantageous for clinicians. Adopting a multiple transdiagnostic approach, this study aims at testing the validity of a 360-degree immersive video (360IV) for the assessment of five common psychological symptoms (fear of negative evaluation, paranoid thoughts, negative automatic thoughts, craving for alcohol and for nicotine). A 360IV was constructed in the Darius Café and included actors behaving naturally. One hundred and fifty-eight adults from the general population were assessed in terms of their proneness towards the five symptoms, were then exposed to the 360IV and completed measures for the five state symptoms, four dimensions of presence (place, plausibility, copresence and social presence illusions) and cybersickness. Results revealed that the five symptoms occurred during the immersion and were predicted by the participants' proneness towards these symptoms. The 360IV was also able to elicit various levels of the four dimensions of presence while producing few cybersickness. The present study provides evidence supporting the use of the 360IV as a new accessible, ecological, and standardized tool to assess multiple transdiagnostic symptoms.The online version contains supplementary material available at 10.1007/s10055-023-00779-y.