Reviewerly/exHarmony huggingface dataset:
Tagging before Alignment: Integrating Multi-Modal Tags for Video-Text Retrieval

Vision-language alignment learning for video-text retrieval arouses a lot of attention in recent years. Most of the existing methods either transfer the knowledge of image-text pretraining model to video-text retrieval task without fully exploring the multi-modal information of videos, or simply fuse multi-modal features in a brute force manner without explicit guidance. In this paper, we integrate multi-modal information in an explicit manner by tagging, and use the tags as the anchors for better video-text alignment. Various pretrained experts are utilized for extracting the information of multiple modalities, including object, person, motion, audio, etc. To take full advantage of these information, we propose the TABLE (TAgging Before aLignmEnt) network, which consists of a visual encoder, a tag encoder, a text encoder, and a tag-guiding cross-modal encoder for jointly encoding multi-frame visual features and multi-modal tags information. Furthermore, to strengthen the interaction between video and text, we build a joint cross-modal encoder with the triplet input of [vision, tag, text] and perform two additional supervised tasks, Video Text Matching (VTM) and Masked Language Modeling (MLM). Extensive experimental results demonstrate that the TABLE model is capable of achieving State-Of-The-Art (SOTA) performance on various video-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC and DiDeMo.
Lexical diversity as a predictor of complexity in textbooks on the Russian language

The parametric model of the text as a research problem is of paramount importance in modern linguistics and education, since it opens up new approaches to understanding the processes of comprehending texts of various types. In the current study, 17 Russian language textbooks for elementary school were employed to identify correlations between lexical diversity indices and other complexity predictors. The total volume of the corpus compiled for the study is 439,938 words. The two-stage research algorithm included the evaluation of the reference values of text features at the basic level (word length, sentence length, the number of unique, non-repeating words and the number of word forms), evaluation and subsequent contrasting of complexity predictors, i.e. lexical diversity and readability indices. All calculations were performed with the automatic text analyzer RuLingva. The study revealed a positive dynamic of readability and no evidence of lexical diversity increase across grades. An average level of vocabulary diversity and overlaps of every 4th word in the text are fixed. No indication of correlation between text readability and lexical diversity is found. The obtained results can be useful to researchers, textbook authors, and teachers selecting textbooks. The prospects are seen in implementing functional and epidigmatic stratification of the vocabulary of the Russian textbooks under study.
The effects of Experience-Technology Fit (ETF) on consumption behavior: Extended Reality (XR) visitor experience

Purpose Traditionally, Task–Technology Fit (TTF) theory has been applied to examine the usefulness of technology in the work environment. Can the same approach (based on experience rather than tasks) be applied to non-work, cultural heritage environments? This is the question the authors ask in this study. This study proposes a new variation of TTF based on the experience economy model, namely Experience–Technology Fit (ETF), for the non-work environment, in particular, in the context of cultural heritage, where visitor experience is enhanced by extended reality technology, which combines immersive technologies and artificial intelligence. Design/methodology/approach Employing a quantitative survey method, the empirical analysis seeks to determine the influence of Mixed Reality (MR) characteristics (interactivity, vividness), Voice User Interface (VUI) characteristics (speech recognition, speech synthesis) and experience economy factors (education, entertainment, esthetic, escape) on satisfaction, revisit intention and actual purchase to propose a new ETF model. Findings VUI, MR, and experience factors were significantly associated with ETF; when combined with MR-based experience, ETF was significantly associated with satisfaction. This study’s findings further demonstrate the relationship between users' satisfaction when engaging with MR-based experience and revisit intention, while purchase intention was significantly associated with the actual purchase. Originality/value The novel contribution of this study is the proposal of the EFT model, a new variation of TTF based on the experience economy model. Overall, this study expands the applications of TTF to an experience-oriented business, thereby broadening the authors’ understanding of technological success with a specific focus on the technology fit of Extended Reality (XR) in the context of cultural heritage.
Improving adversarial robustness of medical imaging systems via adding global attention noise

Recent studies have found that medical images are vulnerable to adversarial attacks. However, it is difficult to protect medical imaging systems from adversarial examples in that the lesion features of medical images are more complex with high resolution. Therefore, a simple and effective method is needed to address these issues to improve medical imaging systems’ robustness. We find that the attackers generate adversarial perturbations corresponding to the lesion characteristics of different medical image datasets, which can shift the model’s attention to other places. In this paper, we propose global attention noise (GATN) injection, including global noise in the example layer and attention noise in the feature layers. Global noise enhances the lesion features of the medical images, thus keeping the examples away from the sharp areas where the model is vulnerable. The attention noise further locally smooths the model from small perturbations. According to the characteristic of medical image datasets, we introduce Global attention lesion-unrelated noise (GATN-UR) for datasets with unclear lesion boundaries and Global attention lesion-related noise (GATN-R) for datasets with clear lesion boundaries. Extensive experiments on ChestX-ray, Dermatology, and Fundoscopy datasets show that GATN improves the robustness of medical diagnosis models against a variety of powerful attacks and significantly outperforms the existing adversarial defense methods. To be specific, the robust accuracy is 86.66% on ChestX-ray, 72.49% on Dermatology, and 90.17% on Fundoscopy under PGD attack. Under the AA attack, it achieves robust accuracy of 87.70% on ChestX-ray, 66.85% on Dermatology, and 87.83% on Fundoscopy.
An ecosystem approach to Web3.0: a systematic review and research agenda

Purpose This paper reviews the extant research on Web3.0 published between 2003 and 2022. Design/methodology/approach This study uses a topic modeling procedure latent Dirichlet allocation to uncover the research themes and the key phrases associated with each theme. Findings This study uncovers seven research themes that have been featured in the existing research. In particular, the study highlights the interaction among the research themes that contribute to the understanding of a number of solutions, applications and use cases, such as metaverse and non-fungible tokens. Research limitations/implications Despite the relatively small data size of the study, the results remain significant as they contribute to a more profound comprehension of the relevant field and offer guidance for future research directions. The previous analysis revealed that the current Web3.0 technology is still encountering several challenges. Building upon the pioneering research in the field of blockchain, decentralized networks, smart contracts and algorithms, the study proposes an exploratory agenda for future research from an ecosystem approach, targeting to enhance the current state of affairs. Originality/value Although topics around Web3.0 have been discussed intensively among the crypto community and technological enthusiasts, there is limited research that provides a comprehensive description of all the related issues and an in-depth analysis of their real-world implications from an ecosystem perspective.
Volumetric Benchmarking of Error Mitigation with Qermit

The detrimental effect of noise accumulates as quantum computers grow in size. In the case where devices are too small or noisy to perform error correction, error mitigation may be used. Error mitigation does not increase the fidelity of quantum states, but instead aims to reduce the approximation error in quantities of concern, such as expectation values of observables. However, it is as yet unclear which circuit types, and devices of which characteristics, benefit most from the use of error mitigation. Here we develop a methodology to assess the performance of quantum error mitigation techniques. Our benchmarks are volumetric in design, and are performed on different superconducting hardware devices. Extensive classical simulations are also used for comparison. We use these benchmarks to identify disconnects between the predicted and practical performance of error mitigation protocols, and to identify the situations in which their use is beneficial. To perform these experiments, and for the benefit of the wider community, we introduce<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>Q</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi></mml:math>– an open source python package for quantum error mitigation. Qermit supports a wide range of error mitigation methods, is easily extensible and has a modular graph-based software design that facilitates composition of error mitigation protocols and subroutines.
Barrier identification, analysis and solutions of blockchain adoption in construction: a fuzzy DEMATEL and TOE integrated method

Purpose Blockchain has the potential to facilitate a paradigm shift in the construction industry toward effectiveness, transparency and collaboration. However, there is currently a paucity of empirical evidence from real-world construction projects. This study aims to systematically review blockchain adoption barriers, investigate critical ones and propose corresponding solutions. Design/methodology/approach An integrated method was adopted in this research based on the technology–organization–environment (TOE) theory and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach. Blockchain adoption barriers were first presented using the TOE framework. Then, key barriers were identified based on the importance and causality analysis in the fuzzy DEMATEL. Several suggestions were proposed to facilitate blockchain diffusion from the standpoints of the government, the industry and construction organizations. Findings The results highlighted seven key barriers. Specifically, the construction industry is more concerned with environmental barriers, such as policy uncertainties (E2) and technology maturity (E3), while most technical barriers are causal factors, such as “interoperability (T4)” and “smart contracts' security (T2)”. Practical implications This study contributes to a better understanding of the problem associated with blockchain implementation and provides policymakers with recommendations. Originality/value Identified TOE barriers lay the groundwork for theoretical observations to comprehend the blockchain adoption problem. This research also applied the fuzzy method to blockchain adoption barrier analysis, which can reduce the uncertainty and subjectivity in expert evaluations with a small sample.
Quantifying Egocentric Distance Perception in Virtual Reality Environment

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Implementation of Augmented Reality in Dental Surgery Using HoloLens 2: An In Vitro Study and Accuracy Assessment

Computer-guided dynamic navigation systems have elevated dental implant surgery into a more patient-friendly and efficient procedure. However, commercially available systems use displays as an output for guidance. This implies that surgeons have to take their eyes off the surgery site frequently in order to accurately adjust surgical instruments. The solution for such an issue is to use augmented reality (AR) head-mounted devices to showcase all necessary guiding instructions without any distractions. In this work, the implementation of augmented reality in dental surgery using the HoloLens 2 AR headset for the assessment of accuracy is described. The experiments were performed in vitro with two implementation steps, evaluating calibration and perspective of the virtual dental model alignment accuracies using a newly created marker. The calibration results showed overall accuracy and precision surpassing the 1 mm threshold in distance-related measurements. Dependency on the side on which the marker was observed and the movement of the user’s head were considered to be the most influential factors regarding the perspective of the virtual dental model alignment accuracy estimate.
Product Question Answering in E-Commerce: A Survey

Product question answering (PQA), aiming to automatically provide instant responses to customer's questions in E-Commerce platforms, has drawn increasing attention in recent years. Compared with typical QA problems, PQA exhibits unique challenges such as the subjectivity and reliability of user-generated contents in E-commerce platforms. Therefore, various problem settings and novel methods have been proposed to capture these special characteristics. In this paper, we aim to systematically review existing research efforts on PQA. Specifically, we categorize PQA studies into four problem settings in terms of the form of provided answers. We analyze the pros and cons, as well as present existing datasets and evaluation protocols for each setting. We further summarize the most significant challenges that characterize PQA from general QA applications and discuss their corresponding solutions. Finally, we conclude this paper by providing the prospect on several future directions.
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective

We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis.Zero-shot learning aims to train a model on a given task such that it can address new learning tasks without any additional training.Our approach converts zero-shot learning into multiple-choice tasks, avoiding problems in commonly used large-scale generative models such as FLAN.It not only adds generalization ability to models but also significantly reduces the number of parameters.Our method shares the merits of efficient training and deployment.Our approach shows state-of-the-art performance on several benchmarks and produces satisfactory results on tasks such as natural language inference and text classification.Our model achieves this success with only 235M parameters, which is substantially smaller than state-of-the-art models with billions of parameters.The code and pre-trained models are available at https:
Enabling supra-aortic vessels inclusion in statistical shape models of the aorta: a novel non-rigid registration method

Statistical Shape Models (SSMs) are well-established tools for assessing the variability of 3D geometry and for broadening a limited set of shapes. They are widely used in medical imaging due to their ability to model complex geometries and their high efficiency as generative models. The principal step behind these techniques is a registration phase, which, in the case of complex geometries, can be a critical issue due to the correspondence problem, as it necessitates the development of correspondence mapping between shapes. The thoracic aorta, with its high level of morphological complexity, poses a multi-scale deformation problem due to the presence of several branch vessels with varying diameters. Moreover, branch vessels exhibit significant variability in shape, making the correspondence optimization even more challenging. Consequently, existing studies have focused on developing SSMs based only on the main body of the aorta, excluding the supra-aortic vessels from the analysis. In this work, we present a novel non-rigid registration algorithm based on optimizing a differentiable distance function through a modified gradient descent approach. This strategy enables the inclusion of custom, domain-specific constraints in the objective function, which act as landmarks during the registration phase. The algorithm’s registration performance was tested and compared to an alternative Statistical Shape modeling framework, and subsequently used for the development of a comprehensive SSM of the thoracic aorta, including the supra-aortic vessels. The developed SSM was further evaluated against the alternative framework in terms of generalisation, specificity, and compactness to assess its effectiveness.
One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

Cross-domain NER is a challenging task to address the low-resource problem in practical scenarios. Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain. Owing to the mismatch issue among entity types in different domains, previous approaches normally tune all parameters of PLMs, ending up with an entirely new NER model for each domain. Moreover, current models only focus on leveraging knowledge in one general source domain while failing to successfully transfer knowledge from multiple sources to the target. To address these issues, we introduce Collaborative Domain-Prefix Tuning for cross-domain NER (CP-NER) based on text-to-text generative PLMs. Specifically, we present text-to-text generation grounding domain-related instructors to transfer knowledge to new domain NER tasks without structural modifications. We utilize frozen PLMs and conduct collaborative domain-prefix tuning to stimulate the potential of PLMs to handle NER tasks across various domains. Experimental results on the Cross-NER benchmark show that the proposed approach has flexible transfer ability and performs better on both one-source and multiple-source cross-domain NER tasks.
AI Renaissance, artificial intelligence, information overload, human-computer interaction, decision-making

Objective: This paper aims to explore the concept of AI as a modern-day Renaissance movement, triggered by the proliferation of the internet and advancements in artificial intelligence technologies. It delves into the transformative impact of AI on human-computer interactions and decision-making processes.&#x0D; Results: O’Leary's (1997) early notion of a Renaissance movement sparked by the internet's ubiquity finds resonance in the emergence of the AI renaissance. AI technologies such as natural language processing, machine learning, heuristic language processing, and neural networks have integrated into intricate networked computing environments. These technologies facilitate the handling, retrieval, and analysis of vast amounts of data available on the World Wide Web. Given the overwhelming volume of data, direct human analysis has become impractical, necessitating AI-driven support for efficient data utilization. In today's competitive and tech-driven landscape, the time available for decision-making has diminished, prompting reliance on intelligent agents and delegating decision-making tasks to these digital surrogates.&#x0D; Conclusions: The contemporary AI renaissance signifies a paradigm shift in human-computer dynamics. The convergence of AI technologies with the internet's vast information landscape has created a symbiotic relationship, redefining traditional computer roles. AI-enabled tools not only manage the deluge of data but also extend decision-making capabilities, optimizing efficiency in an increasingly fast-paced world. This transformative movement transcends conventional computing boundaries and has paved the way for a new era of human-machine interaction.
Rise of artificial general intelligence: risks and opportunities

Artificial intelligence is making extraordinary progress with an unprecedented rate, reaching and surpassing human capabilities in many tasks previously considered unattainable by machines, as language translation, music composition, object detection, medical diagnoses, software programming, and many others. Some people are excited about these results, while others are raising serious concerns for possible negative impacts in our society. This article addresses several questions that are often raised about intelligent machines: Will machines ever surpass human intellectual capacities? What will happen next? What will be the impact in our society? What are the jobs that artificial intelligence puts at risk? Reasoning about these questions is of fundamental importance to predict possible future scenarios and prepare ourselves to face the consequences.
A Real-Time Path Planning Method for Urban Low-Altitude Logistics UAVs

To solve the problem of poor real-time performance in path planning algorithms for unmanned aerial vehicles (UAVs) in low-altitude urban logistics, a path planning method combining modified Beetle Antennae Search (BAS) with the Simulated Annealing (SA) algorithm is proposed. Firstly, based on the requirements of task execution and constraints of UAV flight, a fitness function for real-time search of waypoints is designed while ensuring the safety and obstacle avoidance of the UAV. Then, to improve the search accuracy and real-time performance, determining the initial search direction in the BAS algorithm is improved, while the search step size and antennae sensing length are updated in real-time according to the distance between the UAV and the obstacle. Finally, the SA algorithm is combined with the BAS algorithm to update the waypoints, expanding the search range of each waypoint, avoiding the process of updating the waypoints from becoming trapped in the local optimal waypoints. Meanwhile, the effectiveness of the next waypoint is evaluated based on the Metropolis criterion. This paper generates a virtual urban logistics distribution environment based on the density and distribution of urban buildings, and compares the performance of algorithms in obstacle-sparse, obstacle-moderate, and obstacle-dense environments. The simulation results demonstrate that the improved method in this paper has a more significant capacity for environmental adaptation. In terms of the path length, waypoints, safety obstacle avoidance, and smoothness, the planned path outperforms the original BAS method. It satisfies the needs of real-time path planning for UAVs involved in urban low-altitude logistics.