Reviewerly/exHarmony huggingface dataset:
Quantum Algorithm for Dynamic Programming Approach for DAGs and Applications

In this paper, we present a quantum algorithm for the dynamic programming approach for problems on directed acyclic graphs (DAGs). The running time of the algorithm is $$O(\sqrt{\hat{n}m}\log\hat{n})$$ , and the running time of the best known deterministic algorithm is $$O(n+m)$$ , where $$n$$ is the number of vertices, $$\hat{n}$$ is the number of vertices with at least one outgoing edge; $$m$$ is the number of edges. We show that we can solve problems that use OR, AND, NAND, MAX, and MIN functions as the main transition steps. The approach is useful for a couple of problems. One of them is computing a Boolean formula that is represented by Zhegalkin polynomial, a Boolean circuit with shared input and non-constant depth evaluation. Another two are the single source longest paths search for weighted DAGs and the diameter search problem for unweighted DAGs.
Concept Parser With Multimodal Graph Learning for Video Captioning

Conventional video captioning methods are either stage-wise or simple end-to-end. While the former might introduce additional noise when exploiting off-the-shelf models to provide extra information, the latter suffers from lacking high-level cues. Therefore, a more desired framework should be able to capture multi-aspects of videos consistently. To this end, we present a concept-aware and task-specific model named CAT that accounts for both low-level visual and high-level concept cues, and incorporates them effectively in an end-to-end manner. Specifically, low-level visual and high-level concept features are obtained from the video transformer and concept parser of CAT. And a concept loss is further introduced to regularize the learning process of concept parser w.r.t. generated pseudo ground truth. To combine multi-level features, a caption transformer is later introduced in CAT, where visual and concept features are the inputs and caption is its output. In particular, we make critical design choices in the caption transformer to learn to exploit these cues with a multi-modal graph. This is achieved by a graph loss that enforces effective learning of intra and inter correlations between multi-level cues. Extensive experiments on three benchmark datasets demonstrate that CAT achieves 2.3 and 0.7 improvements in the CIDEr metric on MSVD and MSR-VTT compared to the state-of-the-art method SwinBERT and also achieves a competitive result on VATEX.
Automating Dependency Updates in Practice: An Exploratory Study on GitHub Dependabot

Dependency management bots automatically open pull requests to update software dependencies on behalf of developers. Early research shows that developers are suspicious of updates performed by dependency management bots and feel tired of overwhelming notifications from these bots. Despite this, dependency management bots are becoming increasingly popular. Such contrast motivates us to investigate Dependabot, currently the most visible bot on GitHub, to reveal the effectiveness and limitations of state-of-art dependency management bots. We use exploratory data analysis and a developer survey to evaluate the effectiveness of Dependabot in keeping dependencies up-to-date, interacting with developers, reducing update suspicion, and reducing notification fatigue. We obtain mixed findings. On the positive side, projects do reduce technical lag after Dependabot adoption and developers are highly receptive to its pull requests. On the negative side, its compatibility scores are too scarce to be effective in reducing update suspicion; developers tend to configure Dependabot toward reducing the number of notifications; and 11.3% of projects have deprecated Dependabot in favor of other alternatives. The survey confirms our findings and provides insights into the key missing features of Dependabot. Based on our findings, we derive and summarize the key characteristics of an ideal dependency management bot which can be grouped into four dimensions: configurability, autonomy, transparency, and self-adaptability.
Human Action Recognition Based on Vision Transformer and L2 Regularization

In recent years, the field of human action recognition has been the focus of computer vision, and human action recognition has a good prospect in many fields, such as security state monitoring, behavior characteristics analysis and network video image restoration. In this paper, based on attention mechanism of human action recognition method is studied, in order to improve the model accuracy and efficiency in VIT network structure as the framework of feature extraction, because video data includes characteristics of time and space, so choose the space and time attention mechanism instead of the traditional convolution network for feature extraction, In addition, L2 weight attenuation regularization is introduced in model training to prevent the model from overfitting the training data. Through the test on the human action related dataset UCF101, it is found that the proposed model can effectively improve the recognition accuracy compared with other models.
From Parking Meters to Vending Machines: A Study of Usability Issues in Self-Service Technologies

This paper describes a mixed-methods usability study of seven diverse Self-Service Technologies (SSTs). SSTs mediate many of our everyday interactions with individuals, businesses, and government organizations. Parking meters, transport ticket machines, electric vehicle recharge points, and fast-food ordering kiosks are all likely familiar examples of this category of technology, promising convenient access to products and services. Despite their ubiquity, many SSTs suffer from severe usability issues, the nature of which have not been explored to date by the HCI community. This study evaluates the interactions between users and a broad sample of SSTs, details the usability issues that occurred, explores their connections and consequences, and presents a set of design considerations that may lead to their remediation.
How users cognitively appraise and emotionally experience the metaverse: focusing on social virtual reality

Purpose Despite the increasing interest in the metaverse—immersive three-dimensional virtual worlds wherein personalized avatars interact with one another—little is known about how users cognitively appraise and emotionally experience it. To fill this gap, the present study explores the emotional, behavioral and social consequences of users' cognitive appraisals, while focusing on social virtual reality (VR) as a representative entry point to the metaverse. Design/methodology/approach Drawing on cognitive appraisal theory against the backdrop of a framework for classifying emotions, this study develops and tests a theoretical model to account for users' continuance intention and its consequences in the social VR context based on two-wave panel survey data collected from 216 users of social VR platforms, including AltspaceVR, VRChat, Bigscreen and Rec Room. Findings The results of the first survey showed that perceived opportunity was more strongly influenced by technological opportunity than social opportunity, whereas perceived threat was more strongly affected by social threat than technological threat. Integrating the data collected from the first survey with those of the second survey, we also found that users' continuance intention positively influenced both their behavioral engagement and social self-efficacy. Originality/value By adopting a longitudinal approach, this study provides insights that may be valuable to researchers and practitioners who seek to use social VR for business purposes. This study also contributes to the metaverse literature by conceptualizing and operationalizing the opportunity and threat factors of social VR and identifying salient emotions that users experience in this context. Finally, this study has practical implications for addressing the social and technological features that may cause adverse user experiences in social VR.
Omnidirectional visual computing: Foundations, challenges, and applications

Omnidirectional images and videos are widespread due to the popularization of devices for capturing and visualization. Unlike pinhole-based imagery, omnidirectional media lie on the surface of a sphere, have a 360° × 180°field of view, and store the light intensities from an entire environment. Notably, applications involving immersive augmented, mixed, and virtual reality experiences benefit from the 360° content. Although defined on the spherical domain, omnidirectional images are often mapped to a (multi-)planar representation, which results in distorted images and degrades the performance of most traditional visual computing algorithms designed to work on the plane. This paper reviews the spherical camera model, the most common capturing devices, and popular (multi-)planar representations of omnidirectional media. It also approaches the main challenges of omnidirectional visual computing, focusing on the deep learning paradigm, and tackles four visual computing applications that strongly explore the potential of omnidirectional imagery.
Geometric attacks resistant double zero-watermarking using discrete Fourier transform and fractional-order Exponent-Fourier moments

Zero-watermarking algorithms provide the means of effective, lossless copyright protection, but the existing ones can only resist a limited variety of geometric attacks. To overcome this limitation, a novel zero-watermarking algorithm resistant to an extensive variety of geometric attacks is suggested, in which fractional-order Exponent-Fourier moments (FrEFMs) and discrete Fourier transform (DFT) are used as complementary geometric invariant features to construct zero-watermarks. Relying on the rotation, scaling, aspect ratio changing and cropping invariance of FrEFMs amplitude and the perfect translation invariance of DFT amplitude, our algorithm can effectively resist a wider variety of geometric and common image processing attacks, which has been experimentally illustrated in this work. Therefore, the proposed zero-watermarking algorithm is superior to other cutting-edge ones.
Deep image compression using scene text quality assessment

Image compression is a fundamental technology for Internet communication engineering. However, a high compression rate with general methods may degrade images, resulting in unreadable texts. In this paper, we propose an image compression method for maintaining text quality. We developed a scene text image quality assessment model to assess text quality in compressed images. The assessment model iteratively searches for the best-compressed image holding high-quality text. Objective and subjective results showed that the proposed method was superior to existing methods. Furthermore, the proposed assessment model outperformed other deep-learning regression models.
Graph Convolutional Neural Networks for Link Prediction in Social Networks

Social networks are complex systems that require specialized techniques to analyze and understand their structure and dynamics. One important task in social network analysis is link prediction, which involves predicting the likelihood of a new link forming between two nodes in the network. Graph convolutional neural networks (GCNNs) have recently emerged as a powerful approach for link prediction, leveraging the graph structure and node features to learn effective representations and predict links between nodes. This chapter provides an overview of recent advances in GCNNs for link prediction in social networks, including various GCNN architectures, feature engineering techniques, and evaluation metrics. It discusses the challenges and opportunities in applying GCNNs to social network analysis, such as dealing with sparsity and heterogeneity in the data and leveraging multi-modal and temporal information. Moreover, this also provides reviews of several applications of GCNNs for link prediction in social networks.
Taxonomy of Styles, Strategies, and Tactics in E-Negotiations

Negotiation are essential in every day’s life, increasingly via electronic media. Due to a huge amount of negotiation styles, strategies, and tactics presented in the literature, it is unclear which combinations of strategies and tactics for a concrete style can be applied in (electronic) negotiations. Therefore, we conducted a systematic literature review identifying combinations of negotiation styles, strategies, and tactics in electronic negotiations. The findings were consolidated into a taxonomy and patterns of the combinations were generated.
Protecting Vulnerable Consumers Within the Metaverse

This chapter explores risks related to the metaverse and vulnerable consumers. Expansion of the virtual world could worsen inequalities, particularly for minorities, if not appropriately cared for using public policy and marketing techniques. Addressing accessibility, affordability, and digital literacy are crucial to avoid exclusion and economic marginalization. Privacy, data protection, and the rights of virtual entities are also important considerations. Stakeholders must develop ethical guidelines and regulations that protect vulnerable populations, requiring collaboration across multiple fields.
Adaptive weighted multiscale retinex for underwater image enhancement

Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance.
Artificial neural networks-based simulation of obstacle detection with a mobile robot in a virtual environment

Mobile robot navigation is primarily a task that occurs in a real environment. However, simulating obstacles and robot movements in a virtual environment can provide significant advantages and yield good results, as demonstrated in this paper. By employing artificial neural networks (ANNs), it is possible to develop a trained system in a virtual environment that can detect obstacles using data collected from various sensors. In this study, infrared (IR) sensors and a camera were utilized to gather information from the virtual environment. The MatLab Simulink software package was used as a tool to train the artificial neural networks. Detection and avoidance of obstacles were simulated in the RobotinoSIM virtual environment.
Infrared Image Caption Based on Object-Oriented Attention

With the ongoing development of image technology, the deployment of various intelligent applications on embedded devices has attracted increased attention in the industry. One such application is automatic image captioning for infrared images, which involves converting images into text. This practical task is widely used in night security, as well as for understanding night scenes and other scenarios. However, due to the differences in image features and the complexity of semantic information, generating captions for infrared images remains a challenging task. From the perspective of deployment and application, to improve the correlation between descriptions and objects, we introduced the YOLOv6 and LSTM as encoder-decoder structure and proposed infrared image caption based on object-oriented attention. Firstly, to improve the domain adaptability of the detector, we optimized the pseudo-label learning process. Secondly, we proposed the object-oriented attention method to address the alignment problem between complex semantic information and embedded words. This method helps select the most crucial features of the object region and guides the caption model in generating words that are more relevant to the object. Our methods have shown good performance on the infrared image and can produce words explicitly associated with the object regions located by the detector. The robustness and effectiveness of the proposed methods were demonstrated through evaluation on various datasets, along with other state-of-the-art methods. Our approach achieved BLUE-4 scores of 31.6 and 41.2 on KAIST and Infrared City and Town datasets, respectively. Our approach provides a feasible solution for the deployment of embedded devices in industrial applications.
An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification

To address the issue of low accuracy in insulator object detection within power systems due to a scarcity of image sample data, this paper proposes a method for identifying insulator objects based on improved few-shot object detection through feature reweighting. The approach utilizes a meta-feature transfer model in conjunction with the improved YOLOv5 network to realize insulator recognition under conditions of few-shot. Firstly, the feature extraction module of the model incorporates an improved self-calibrated feature extraction network to extract feature information from multi-scale insulators. Secondly, the reweighting module integrates the SKNet attention mechanism to facilitate precise segmentation of the mask. Finally, the multi-stage non-maximum suppression algorithm is designed in the prediction layer, and the penalty function about confidence is set. The results of multiple prediction boxes are retained to reduce the occurrence of false detection and missing detection. For the poor detection results due to a low diversity of sample space, the transfer learning strategy is applied in the training to transfer the entire trained model to the detection of insulator targets. The experimental results show that the insulator detection mAP reaches 29.6%, 36.0%, and 48.3% at 5-shot, 10-shot, and 30-shot settings, respectively. These findings serve as evidence of improved accuracy levels of the insulator image detection under the condition of few shots. Furthermore, the proposed method enables the recognition of insulators under challenging conditions such as defects, occlusion, and other special circumstances.
Combined use of multiple external and internal standards in LA-ICP-MS analysis of bulk geological samples using lithium borate fused glass

We describe a method for laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) elemental analysis of geological materials using low-dilution Li-borate fused glass wavelength dispersive X-ray fluorescence (WDXRF) pellets, with samples, drift monitor and 18 reference materials (RMs) identically prepared. After analysis for 46 elements by WDXRF, LA-ICP-MS intensities from samples and RMs are collected, and background corrected with Iolite software. HALite , a new software application, was developed to derive the elemental compositions from the LA-ICP-MS net signals. In HALite , elements are drift-corrected using polynomial functions, and flux-fused RM element sensitivities are calculated from known mass fractions. Multiple internal standard (IS) elements are used to model each sample's laser response. Analyte mass fractions in unknowns are determined using the calibrated sensitivity correlation models for multiple IS elements. Either the WDXRF mass fractions or the initial round of calculated LA-ICP-MS mass fractions are used to calculate weighted mean sensitivities. Validation experiments with flux-fused RMs run as unknowns yield results with less than 5–10% total relative uncertainty for most analytes. We derive equations which allow calculation of the precision and total uncertainty as a function of mass fraction for each analyte element. Supplementary material : Table 1 - RMs used; Table 2 - Operating parameters; Table 3 - Model v. accepted mass fractions; Table 4 - NIST 610 v. multiple IS models; Table 5 - Fitting parameters; Appendix 1 - HALite description; Appendix A - Summary calibration graphs; Appendix B - Validation results; Appendix C - WDXRF comparisons; Appendix D - Repeatability uncertainties; Appendix E - RM uncertainties; and Appendix F - Total uncertainties for this article are available at https://doi.org/10.6084/m9.figshare.c.6639885
Digital Assessment in Technology-Enriched Education: Thematic Review

Digital assessment has become relevant as part of the digital learning process, as technology provides not only teaching and learning but also assessment, including productive feedback. With the rapid development of educational technology and the expansion of related research, there is a lack of research-based clarification of aspects of digital assessment without considering the impact of temporary pandemic solutions. The purpose of this thematic review is to summarize key features in studies over a specified period of time (2018–2021); consequently, it does not offer completely new knowledge, but captures essential knowledge of the last few years before the pandemic to avoid losing a significant aspect of digital assessment due to temporary pandemic solutions. The review results in a description of digital assessment that includes its conditions, opportunities and challenges, as well as other characteristics. The findings confirm the importance of digital assessment in the modern educational process and will increase the understanding of digital assessment among those involved in education (administrators, educators and researchers), inviting them to consider possible pedagogical principles. Furthermore, these findings are now comparable to and should be supplemented with post-pandemic insights and knowledge.
A Virtual Reality Laboratory for Blended Learning Education: Design, Implementation and Evaluation

Launched during the pandemic, the EU-funded JANUS project aimed to ensure the continuity of student workshops at universities using a virtual reality (VR) robotics laboratory. With the return to normality, the project has been redesigned to capitalise on the positive outcomes of the experience. The VR lab provides safe and unrestricted access to the labs and experiments with the machines, reducing the consequences of student mistakes and improving the user experience by allowing the experiment to be repeated from different angles, some of which are impossible to access in the real lab. In addition, integration with an interactive learning platform called “ViLLE” allows for continuous assessment of the learning experience. Self-evaluation of the material taught and learned can be integrated with the execution of the exercises that pave the way for Kaizen. Two VR workshops for the blended learning of robotics were developed during the JANUS project. Their evaluation reported favourable responses from the students whose learning performance was indirectly measured.