Reviewerly/exHarmony huggingface dataset:
The role of perceived risk and information security on customers' acceptance of service robots in the hotel industry

This study proposed and tested a theoretical framework that investigated the influences of perceived risk and information security on hotel customers’ intention to use service robots. In addition, the impacts of self-efficacy, innovativeness, and facilitating conditions on perceived risk and information security were examined. Structural equation modeling (SEM) was used to test the proposed model by utilizing data collected from eleven countries including the United States, United Kingdom, Turkey, Spain, Romania, Japan, Israel, India, Greece, Canada, and Brazil. The study results demonstrated that perceived risk had a negative impact on customers’ intention to use service robots while information security had a positive impact. In addition, the study results indicated that self-efficacy negatively influenced perceived risk, and positively influenced perceived information security; and innovativeness and facilitating condition positively influenced information security. The study findings offer several important contributions to the hospitality robotics technology adoption literature and present valuable implications for hospitality practitioners and service robot vendors.
Effects of resource orchestration, strategic information exchange capabilities, and digital orientation on innovation and performance of hotel supply chains

Supply chain (SC) innovation has become a competitive source for hotels to enhance performance in the turbulent business environment. Drawing on the resource orchestration (RO) and information sharing (IS) theories, we propose an integrated theoretical framework delineating how strategic information exchange (SIE) and RO capabilities and digital orientation of a hotel foster innovation and enhance the performance of its SC. Based on PLS-SEM analysis of data collected from 281 hotels in the United Arab Emirates, our findings indicate that RO and SIE capabilities of hotels influence the performance of SCs, whereas SC innovation mediates the said relationships. However, contrary to the expectations, digital orientation did not moderate the relationships between RO and SIE capabilities and SC innovation of hotels.
SEAM: An integrated activation-coupled model of sentence processing and eye movements in reading

Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2023) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, 2005). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.
Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems

The Bayesian inference approach is widely used to solve inverse problems due to its versatile and natural ability to handle ill-posedness. However, there are often challenges when dealing with situations involving continuous fields or parameters with large-resolution discrete representations. Furthermore, the prior distribution of the unknown parameters is also commonly difficult to determine. Therefore, in this study, an operator learning-based generative adversarial network (OL-GAN) is proposed and integrated into the Bayesian inference framework to address these issues. Compared with classical Bayesian approaches, the distinctive characteristic of the proposed method is that it learns the joint distributions of parameters and responses. By using the trained generative model to handle the prior in Bayes' rule, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithms (e.g., Markov chain Monte Carlo, MCMC) under the proposed framework. More importantly, efficient sampling can be implemented in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection. Furthermore, the generator is resolution-independent due to the incorporation of operator learning. Predictions can thus be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features. It serves as a pivotal approach to combat the curse of dimensionality, enhance model generalization, mitigate data sparsity, and extend the applicability of classical models. Existing research predominantly focuses on domain knowledge-based feature engineering or learning latent representations. However, these methods, while insightful, lack full automation and fail to yield a traceable and optimal representation space. An indispensable question arises: Can we concurrently address these limitations when reconstructing a feature space for a machine learning task? Our initial work took a pioneering step towards this challenge by introducing a novel self-optimizing framework. This framework leverages the power of three cascading reinforced agents to automatically select candidate features and operations for generating improved feature transformation combinations. Despite the impressive strides made, there was room for enhancing its effectiveness and generalization capability. In this extended journal version, we advance our initial work from two distinct yet interconnected perspectives: 1) We propose a refinement of the original framework, which integrates a graph-based state representation method to capture the feature interactions more effectively and develop different Q-learning strategies to alleviate Q-value overestimation further. 2) We utilize a new optimization technique (actor-critic) to train the entire self-optimizing framework in order to accelerate the model convergence and improve the feature transformation performance. Finally, to validate the improved effectiveness and generalization capability of our framework, we perform extensive experiments and conduct comprehensive analyses. These provide empirical evidence of the strides made in this journal version over the initial work, solidifying our framework’s standing as a substantial contribution to the field of automated feature transformation. To improve the reproducibility, we have released the associated code and data by the Github link https://github.com/coco11563/TKDD2023_code.
DeepSecDrive: An explainable deep learning framework for real-time detection of cyberattack in in-vehicle networks

Autonomous driving (AD) technologies are becoming increasingly popular, promising significant enhancements in road safety and efficiency. However, the susceptibility of autonomous vehicles to cyberattacks highlights the critical need for protecting the security and dependability of in-vehicular networks (IVNs). This study proposes a lightweight, efficient, and explainable deep learning framework, DeepSecDrive, for early detection of security threats against IVN. DeepSecDrive presents robust feature extractor units designed with deformable convolutions and a lightweight non-local network (LNLN). The former promotes dynamic change of receptive field to enhance extraction of complex features from the traffics of in-vehicle networks, while the latter enables learning contextual representations and non-local relationships in vehicular data. DeepSecDrive integrates Shapley Additive exPlanations to construct multi-level interpretability units, providing local and global explanations for model decisions in terms of feature contributions to the overall performance. Proof-of-concept experiments are conducted to evaluate the efficacy and durability of DeepSecDrive on real-world IVN attacks from Car-Hacking dataset, and the experimental results demonstrate the ability of our model to outperform state-of-the-art detection methods.
Recovering sign bits of DCT coefficients in digital images as an optimization problem

Recovering unknown, missing, damaged, distorted, or lost information in DCT coefficients is a common task in multiple applications of digital image processing, including image compression, selective image encryption, and image communication. This paper investigates the recovery of sign bits in DCT coefficients of digital images, by proposing two different approximation methods to solve a mixed integer linear programming (MILP) problem, which is NP-hard in general. One method is a relaxation of the MILP problem to a linear programming (LP) problem, and the other splits the original MILP problem into some smaller MILP problems and an LP problem. We considered how the proposed methods can be applied to JPEG-encoded images and conducted extensive experiments to validate their performances. The experimental results showed that the proposed methods outperformed other existing methods by a substantial margin, both according to objective quality metrics and our subjective evaluation.
Path planning of unmanned vehicles based on adaptive particle swarm optimization algorithm

Path planning technology is the basis of autonomous driving of unmanned vehicles. However, there are some problems in the traditional path planning technology. For example, high-quality global paths can't be generated quickly; Lacking of security verification ability; The performance of dynamic obstacle avoidance is poor. Therefore, this paper proposes a path planning method of unmanned vehicles based on adaptive particle swarm optimization algorithm (APSO). Firstly, a map simplification strategy (MSS) is proposed. The grid map is preprocessed by map simplification strategy to reduce the search space and time of path planning algorithm; Secondly, an APSO algorithm is proposed. The algorithm coordinates the search of particles through three adaptive factors and Levy flight strategy. Then, a security checking strategy is proposed. Security checking strategy can be used to verify the safety of global path; Finally, a dynamic obstacle avoidance strategy based on behavior is proposed. Vehicles can independently analyze the types of collision and adopt corresponding obstacle avoidance strategies. The simulation results show that MSS-APSO algorithm and APSO algorithm surpass original algorithms and comparison algorithms; MSS-APSO algorithm has strong applicability in real map environment; The obstacle avoidance strategy has great obstacle avoidance ability and real-time performance; The map simplification strategy can improve iterations of the algorithm and quality of the global path.
Non-fungible tokens: The missing ingredient for sustainable supply chains in the metaverse age?

Non-fungible tokens (NFTs), unique digital assets on a blockchain that can represent ownership of digital or physical items, are potentially ground-breaking in their capacity for transforming the sustainability of supply chains. This paper explores and discusses how NFTs, analysed through the lens of the Technology Organisation Environment (TOE) framework, can drive supply chain sustainability and overcome the barriers to Blockchain Technology (BCT) adoption. We elucidate the unique value proposition of NFTs and explore the potential of 'phygital products' for linking digital NFTs to physical goods. Based on the existing literature and a review of real-world case studies, we propose that NFTs can (1) incentivise supply chain stakeholders to engage in more sustainable practices; (2) enhance customer willingness to pay for sustainable products; (3) provide anti-counterfeit measures; (4) support circular business model growth. A sustainable 'Mint-to-Order' production strategy starting with the creation (minting) of an NFT is proposed based on a synthesis of the findings. The use of NFTs has significant implications for both digital and physical product offerings and sustainable supply chain management (SSCM) practice making a compelling case for adoption. This paper is one of the first to explore the potential of using NFTs to drive sustainability in supply chains forming an essential component of a metaverse nexus and delineating a future research agenda.
A comprehensive review of elderly fall detection using wireless communication and artificial intelligence techniques

Falls among older adults substantially affect mobility, health, and mortality. However, advancements in wireless and internet-of-things technologies have led to the development of fall detection and rescue systems. These systems offer a solution to mitigate the impact of falls by swiftly delivering emergency services to individuals, thereby reducing the risk of loss of life, injuries, and associated healthcare expenses. This paper aims to review current elderly fall detection systems (FDSs) comprehensively. The assessment examines FDSs explicitly designed for older individuals, considering fall detection methods, system architecture, wireless communications, sensor types, performance metrics, challenges, limitations, and more. In addition, a taxonomy and comprehensive review, accompanied by comparative analysis, have been conducted to categorize FDSs into traditional and artificial intelligence-based methods. The artificial intelligence techniques containing machine learning and deep learning methods for detecting elderly falls were critically reviewed and compared. Moreover, the deep learning-based systems have shown high accuracy in fall detection during the review. By conducting a comparative analysis among various FDSs, this review aims to aid researchers in identifying the most accurate and appropriate method for detecting falls in elderly individuals. In conclusion, this review assists researchers in making informed decisions and enhances the reliability and usability of elderly fall detection systems by addressing significant challenges and limitations.
CPT: Color-based Prompt Tuning for pre-trained vision-language models

Vision-Language Pre-training (VLP) models have shown promising capabilities in grounding natural language in image data, facilitating a broad range of cross-modal tasks. However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for large amounts of labeled data to stimulate the visual grounding capability of VLP models for downstream tasks. To address the challenge, we present Color-based Prompt Tuning (CPT), a novel paradigm for tuning VLP models, which reformulates visual grounding into a fill-in-the-blank problem with color-based co-referential markers in image and text, maximally mitigating the gap. In this way, CPT enables strong few-shot and even zero-shot visual grounding capabilities of VLP models. Comprehensive experimental results show that CPT achieves state-of-the-art performance on zero/few-shot visual grounding (e.g., 75.1 zero-shot accuracy in RefCOCO evaluation), outperforming fine-tuned and other prompt-tuned models by a large margin. Moreover, CPT can also be easily extended to achieve promising zero/few-shot performance on other vision-language tasks, such as visual relation detection, visual commonsense reasoning and visual question answering. We make the data and codes publicly available at https://github.com/thunlp/CPT.
Surface codes, quantum circuits, and entanglement phases

Surface codes---leading candidates for quantum error correction (QEC)---and entanglement phases---a key notion for many-body quantum dynamics---have heretofore been unrelated. Here we establish a link between the two. We map two-dimensional (2D) surface codes under a class of incoherent or coherent errors (bit flips or uniaxial rotations) to $(1+1)\mathrm{D}$ free-fermion quantum circuits via Ising models. We show that the error-correcting phase implies a topologically nontrivial area law for the circuit's 1D long-time state $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$. Above the error threshold, we find a topologically trivial area law for incoherent errors and logarithmic entanglement in the coherent case. In establishing our results, we formulate 1D parent Hamiltonians for $|{\mathrm{\ensuremath{\Psi}}}_{\ensuremath{\infty}}\ensuremath{\rangle}$ via linking Ising models and 2D scattering networks, the latter displaying respective insulating and metallic phases and setting the 1D fermion gap and topology via their localization length and topological invariant. We expect our results to generalize to a duality between the error-correcting phase of ($d+1$)D topological codes and $d$-dimensional area laws; this can facilitate assessing code performance under various errors. The approach of combining Ising models, scattering networks, and parent Hamiltonians can be generalized to other fermionic circuits and may be of independent interest.
Securing Cloud Infrastructure in IaaS and PaaS Environments

Cloud computing has revolutionized IT infrastructure deployment and management, but it also presents security and resilience challenges. The study delves into the principles and strategies of cloud security to safeguard cloud environments and guarantee business continuity. It explains the concepts of infrastructure as a service (IaaS) and platform as a service (PaaS), their benefits and challenges, and the complex web of security principles within the cloud, including the shared responsibility model, best practices, and identity and access management. The guide explores cloud threats, focusing on common threats and emerging trends. It covers data security, network security measures, and security monitoring. It emphasizes integrating security into DevOps, securing CI/CD pipelines, and infrastructure as code (IaC) security. It covers disaster recovery, business continuity, cloud backup strategies, high availability, and cloud-based solutions, enabling organizations to effectively manage cloud security and resilience.
Improved artificial potential field method for mobile robot path planning

Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.
Non-Uniform Illumination Underwater Image Restoration via Illumination Channel Sparsity Prior

Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform Illumination underwater image restoration. Technically, the illumination channel sparsity prior is built on the observation that the illumination channel of a uniform-light underwater image in HSI color space contains few pixels whose intensity is very low. Then according to the Retinex theory, we design a variational model with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm term, constraint term, and gradient term, by integrating the proposed ICSP into an extended underwater image formation model. Such three regularizations are effective in enhancing the brightness, correcting color distortion, and revealing structures and fine-scale details. Meanwhile, we exploit a fast numerical algorithm on the base of the alternating direction method of multipliers (ADMM) to accelerate solving this optimization problem. We also collect a benchmark dataset, namely NUID that contains 925 real underwater images of different non-uniform illumination. Extensive experiments demonstrate that our proposed method is effective in terms of quantitative and qualitative comparisons, ablation studies, convergence analysis, and applications. The code and dataset are available at https://github.com/Hou-Guojia/ICSP.
A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection

Feature selection (FS) is an important data preprocessing technique that selects a small subset of relevant features to improve learning performance. However, it is also challenging due to its large search space. Recently, a competitive swarm optimizer (CSO) has shown promising results in FS because of its potential global search ability. The main idea of CSO is to select two solutions randomly and then let the loser (worse fitness) learn from the winner (better fitness). Although such a search mechanism provides a high population diversity, it is at risk of generating unqualified solutions since the winner’s quality is not guaranteed. In this work, we propose a constrained evolutionary mechanism for CSO, which verifies the quality of all the particles and lets the infeasible (unqualified) solutions learn from the feasible (qualified) ones. We also propose a novel local search and a size-change operator that guide the population to search for smaller feature subsets with similar or better classification performance. A surrogate model, based on support vector machines, is proposed to assist both local search and the size-change operator to explore a massive number of potential feature subsets without requiring excessive computational resource. Results on 24 real-world datasets show that the proposed algorithm can select smaller feature subsets with higher classification performance than state-of-the-art evolutionary computation (EC) and non-EC benchmark algorithms.