Reviewerly/exHarmony huggingface dataset:
Are you talking to me? An Audio Augmented Reality conversational guide for cultural heritage

Augmented Reality (AR) technologies are increasingly utilized as a means of stimulating immersive experiences to cultural site visitors, mainly through visual superimposition of interactive digital elements onto the physical world. Recent research has investigated the use of Audio AR (AAR) in heritage sites, wherein visitors listen to spatially registered sound which could be attributed to 'talking' physical artefacts. A parallel trend in the audience engagement programs of cultural institutions involves the employment of AI chatbots which are engaged in dialogues with followers or visitors to provide meaningful responses to a number of user questions. Herein, we present Exhibot, an intelligent audio guide system aiming at enhancing the user experience of cultural site visitors. Exhibot involves the combination of AAR and chatbot technologies to enable natural visitor-exhibit interaction, while also leveraging IoT devices to contextualize the delivered information. The key contribution of the proposed system lies in the interplay of AAR, chatbot and IoT technologies to create immersive learning experiences in the context of an integrated cultural guide system. Exhibot has undergone field trials to validate its usability and utility in realistic operational conditions. As a case study, we have chosen the statue of a prominent politician situated at a central square in Heraklion, Greece. The evaluation results indicated a very positive attitude of users, which is attributed both to the sense of immersion evoked by the AAR-powered storytelling and the natural human-like conversation enabled by the chatbot.
Tamper-proof access control for IoT clouds using enclaves

Internet of Things (IoT) devices rely on cloud computing for processing user-sensitive data, like health recordings and geolocalization. In this case, security primitives like cryptography and certificate-based authentication does not prevent the cloud provider from acting against the privacy policy. This paper presents a framework for clouds to execute arbitrarily complex processing tasks over IoT data while maintaining the access control policies over the client’s control. We rely on a memory enclave to enforce that the cloud follows personal and customizable access policies and analyzed the security properties of our scheme. The performance evaluation reveals that these robust security improvements come with a latency overhead of just 0.1 ms, confirming the system’s viability. The system leverages multi-threaded processing inside an enclave to process thousands of client messages per second, achieving high scalability. This work also contributes with a microbenchmark that identifies how much each step of an enclave application influences the performance and evaluates the enclave viability for performing realistic IoT data processing.
Improving generalization of convolutional neural network through contrastive augmentation

Data augmentation is widely used to improve the generalization ability of convolutional neural networks in the image domain. The conventional augmentation schemes, e.g., single augmentation (including RandAug, Mixup, and CutMix) or batch augmentation, only optimize the network from pairs of augmented images and corresponding labels, leading to a loss of discriminative representation learning. To this end, we propose contrastive augmentation (CA), to learn discriminative representation by capturing the contrastive semantics among augmented samples. Specifically, the proposed CA method explicitly regularizes the similarity of augmented sample pairs to enable the model learn contrastive semantics. Equipped with CA at the training stage, the model simultaneously learns classification representation and contrastive semantics, which makes representation discriminative and does not incur additional inference computational costs. On conventional and fine-grained classification tasks, the experimental results show that the proposed method can effectively improve the generalization capability of convolutional neural networks, including ResNet, EfficientNet, MobileNet, ShuffleNet, and VAN. The compatibility experiment shows that our contrastive augmentation can be combined with other augmentation techniques, e.g., Mixup and CutMix, to further improve the model's performance.
Improved robustness of vision transformers via prelayernorm in patch embedding

Vision Transformers (ViTs) have recently demonstrated state-of-the-art performance in various vision tasks, replacing convolutional neural networks (CNNs). However, because ViT has a different architectural design than CNN, it may behave differently. To investigate whether ViT has a different performance or robustness, we tested ViT and CNN under various imaging conditions in practical vision tasks. We confirmed that for most image transformations, ViT's robustness was comparable or even better than that of CNN. However, for contrast enhancement, ViT performed particularly poorly. We show that this is because positional embedding in ViT's patch embedding can work improperly when the color scale changes. We demonstrate that the use of PreLayerNorm, a modified patch embedding structure, ensures the consistent behavior of ViT. Results demonstrate that ViT with PreLayerNorm exhibited improved robustness in the contrast-varying environments.
Smoothing group <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mn>1</mml:mn><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> regularized discriminative broad learning system for classification and regression

This paper presents the framework of the smoothing group L1/2 regularized discriminative broad learning system for pattern classification and regression. The core idea is to improve the sparseness of the standard broad learning system and improve performance on recognition and generalization. First, the ε-dragging technique is introduced into the standard broad learning system to relax regression targets and enlarge distances between categories. Then, we integrate the group L1/2 regularization to optimize the network architecture to achieve sparsity. For the original group L1/2 regularization, the objective function is non-convex and non-smooth, which is hard for theoretical analysis. Therefore, we propose a simple and effective smoothing technique, i.e.,smoothing group L1/2 regularization, which can effectively eliminate the deficiency of the original group L1/2 regularization. As a result, the final weights projection matrix has a compact form and shows discriminative power capability. In addition, the alternating direction method of multipliers was adopted to optimize the algorithm. The simulation results show that the proposed algorithm has redundancy control capability and improved performance on recognition and generalization. The simulation results proves the efficiency of the theoretical analysis.
Multi-label image recognition for electric power equipment inspection based on multi-scale dynamic graph convolution network

There exists many types of power equipments in power system operation and inspection scenarios. Among them, different types of equipments often have common representations, thus these various types of power equipments with similar characteristics would bring certain challenges to power equipment defect recognition. The traditional multi-label image recognition is usually with low recognition accuracy. The main reason is that the relationship between each label in the image is ignored. Furthermore, graph convolutional neural networks rely on the modeling ability of graphs to further improve the accuracy of recognition. To this end, we propose a multi-label image recognition model for electric power equipment inspection based on multi-scale dynamic graph convolutional network. In our model, multi-scale image features are extracted through a multi-scale feature extraction network firstly, and then the label relevance of a specific image is adaptively learned through combining the dynamic graph convolutional network. Finally, a self-built dataset of electrical equipment defects is used to conduct experimental results for comparison and validation. According to the analysis of our experimental results, our proposed model shows a better performance. The average accuracy of our model can reach 88.1%, which increases by 0.8% and 31.8% compared with the original model and the baseline model, respectively, showing the effectiveness and superiority of the proposed method.
Consumer-to-consumer conflicts and brand moderation strategies during COVID-19 service failures: a framework for international marketers

Purpose Since the start of the COVID-19 pandemic, one dark social-media phenomenon in particular has experienced a significant rise: consumer-to-consumer (C2C) conflicts, i.e. consumers who verbally attack each other in response to COVID-19 service failures. The aim of this paper is to uncover the sources of such conflicts and to gain an insight into the corresponding conflict moderation strategies that international brands adopt. Design/methodology/approach The methodology consists of non-participatory netnographic observations of 13 national, international, and global online brand communities (OBCs) on Facebook. The authors use purposeful sampling to collect relevant data on conflict sources and brand moderation strategies during COVID-19 service failures and a hybrid approach to thematic analysis to derive distinct themes from these data. Findings The paper identifies five C2C conflict sources: brand attack, brand dissatisfaction, brand skepticism, brand contention and brand defense; these are then classified as having either an individualistic (self-oriented) or collectivistic (other-oriented) orientation. The authors also uncover several moderation strategies: non-engaging, automated, bolstering, asserting (direct, indirect) and informing (factual, empathetic, apologetic), which are broadly categorized into two levels based on their passive vs active approach and authoritative vs cooperative orientation. The paper further highlights that brands adapt their moderation strategies to specific sources of C2C conflicts, thereby producing a range of OBC outcomes. Practical implications The study's empirically informed framework comprising sources of undesirable conflicts and brand moderation strategies offers a practical tool that can aid marketing managers in nurturing civil C2C engagement and interactive behaviors in their OBCs. By adopting our framework, brand and marketing practitioners can tailor their communication strategies toward different sources of C2C conflict and minimize their adverse consequences, thus, fostering an overall constructive OBC engagement. Originality/value The authors offer a novel framework to international marketing research, consisting of C2C conflict sources and corresponding moderation strategies that take place in response to service failures during the COVID-19 pandemic. These insights, in turn, inform international marketers about new ways of transforming the dark side of OBCs into a source of competitive advantage based on real-world brand practice.
Trace elements of sulfides in the Dengjiashan Pb–Zn deposit from West Qinling, China: Implications for mineralization conditions and genesis

Dengjiashan is a large‐scale Pb–Zn deposit discovered in the western part of the Xicheng ore field within the West Qinling metallogenic belt. Information regarding the distribution and occurrence of trace elements in ore minerals and mineralization, as well as the genesis of this deposit, remains scarce. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) analysis and elemental mapping were used to determine the distribution and occurrence of trace elements in the sulfide minerals of the Dengjiashan deposit, as well as delineate the process of its genesis. Our results showed that the trace elements enriched in the different sulfides were significantly different. Sphalerite is the main carrier mineral of the scattered elements Cd, Ge and Ga. Based on the trace element content and ratio, principal component analysis and colour of the sphalerite and pyrite, the deposit likely formed in a medium‐temperature environment. The comparative analysis of trace elements from multiple deposits of different origins, combined with the geological characteristics and distribution of sulfide trace elements, suggests that Dengjiashan is a sedimentary exhalative (SEDEX)‐type deposit. As the metallogenic process had a closer relationship to medium‐low temperature hot brine and later tectonic metamorphic hydrothermal fluid, than with magmatic hydrothermal fluid, the deposit is likely of non‐magmatic SEDEX–hot brine superimposed transition origin. This study provides additional insight on the distribution of trace elements in the sulfide minerals of the Dengjiashan deposit, as well as the processes that led to its formation, and may further facilitate ongoing and future key metal prospecting and exploration efforts.
Spatial data trusts: an emerging governance framework for sharing spatial data

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes, backed by the confidence of clear and unambiguous data governance. Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust. The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications, addressing some longstanding barriers to data sharing, such as location privacy and data sovereignty. This paper introduces and explores the concept of a ‘spatial data Trust’ by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data. The work identifies five key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in the field, including spatial data infrastructures (SDIs), location privacy, and spatial data quality. The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.
Role of Multimedia Innovative Technology in Green Banking

Purpose: Progression is an unpreventable reality of presence, and banking is no exclusion. Cash transformers and moneylenders from times gone past are great agents today. Cash held in trust became store taking, and money advancing became credit making; over an extended time, banks transformed into a need, and the occupation of banks, transformed into a critical piece of monetary reality. Banks' turn of events and headway has been mind-boggling, with the latest frenzy being intuitive media banking. The chapter additionally framed the amazing open doors and dangers for banks because of the presentation of innovations and how banks are making the most of the open doors and endeavouring to cure the risks. The financial area in India is a lifesaver for the country. Indian banks could become the fifth most prominent on earth by 2020 and the third most prominent by 2025.Methodology: This study has given auxiliary information. Furthermore, it's gathered from the holding bank of India concerning utilisation by various banks. Which utilised graphic review including mean mode middle.Finding: After the review, we find that sight, sound, and green banking have become fantastic assets for the baking area. During COVID-19, the utilisation of mixed media expanded in contrast to a year ago.Significance: The review featured sight and sound and green banking sealed spine in COVID -19 and is utilised expanded after this pandemic.
Firefly search algorithm based on leader strategy

In the firefly search algorithm, individuals will move toward a brighter direction according to the light intensity factor in the iteration, which has a high convergence speed. It faces the problem of unbalanced exploration and exploitation. At the same time, the diversity of the algorithm is insufficient. Especially when dealing with complex benchmark functions or engineering problems, it cannot show excellent performance. This paper proposes a firefly search algorithm based on the leader and follower population model and combines the Brownian motion strategy and Levy flight strategy (FA-BMLA). Using the different performances of the Brownian motion strategy and Levy flight strategy in exploration and exploitation, adding an adaptive parameter switching that the FA-BMLA can accord to different situations change its strategy. By testing 20 benchmark functions such as single-mode, multi-mode, and CEC2014, the results are compared with advanced algorithms such as the firefly search algorithm and its variants, whale optimization algorithm, and salp swarm algorithm, and the results are evaluated by the nonparametric test method. Numerical results and statistical experiments show that the FA-BMLA has better solution quality, convergence accuracy, and stability. The FA-BMLA is applied to solve the three engineering problems of pressure vessel, welding beam, and rolling bearing. The data results and experimental comparisons show that FA-BMLA can obtain an accurate solution to engineering problems.
The effect of robot anthropomorphism on revisit intentions after service failure: a moderated serial mediation model

Purpose The purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure. Design/methodology/approach This study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design. Findings Study 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold. Originality/value This study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.
DCServCG: A data-centric service code generation using deep learning

Modern software development paradigms, including Service-Oriented Architecture (SOA), tend to make use of available services e.g., web service Application Programming Interfaces (APIs) to generate new software. Thus, for the further advancement of SOA, the development of accurate automatic tasks, such as service discovery and composition, is necessary. Most of these automated tasks rely heavily on web service metadata annotation. The lack of machine-readable documentation and structured metadata reduces the accuracy and volume of automatic data annotation, negatively affecting the performance of automated SOA tasks. This study aims to propose automatic code completion for improving web service-based systems by identifying and capturing service usage collected from public repositories that share Open Source Software (OSS). To this end, a Data-Centric Service Code Generation (DCServCG) model is proposed to improve old-fashioned, general-purpose code generators that neglect essential service-based code characteristics e.g., sequence overlap and bias issues. DCServCG takes advantage of the data-centric concept, i.e., conditional text generation, to overcome the mentioned issues. We have evaluated the approach from the point of view of language modeling metrics. The obtained results indicate that the usage of the data-centric approach reduces perplexity by 1.125. Moreover, the DCServCG model uses de-noising and conditional text generation, which is trained on the transformer by distilling the knowledge, DistilGPT2 (82M parameters) trained faster and its perplexity is 0.363 lower than ServCG (124M parameters) without de-noising and conditional text generation, which lower perplexity value indicates better model generalization performance.
Real-Time UAV Correlation Filter Based on Response-Weighted Background Residual and Spatio-Temporal Regularization

Correlation filter (CF) is widely used in unmanned aerial vehicle (UAV) tracking because of its efficient performance. However, due to the existence of edge effects, CF will be confused so that the peak of the response is no longer obvious, resulting in tracking drift and template degradation, thereby degrading CF performance. To handle the problem, we propose a new CF for this problem. First, we introduce a response-weighted background residual term to make CF learn the background in a targeted manner according to the strength of the response. Secondly, a history filter model is constructed and a spatio-temporal regularization term is introduced to improve the robustness of CF. Finally, we conduct experiments on two challenging UAV benchmarks, DTB70 and UAV123_10fps. The results show that our tracker achieves state-of-the-art performance compared with 15 other SOTA trackers, and can run at 58 FPS on a single CPU, meeting the needs of UAV real-time tracking.
A Multimodal Spam Filtering System for Multimedia Messaging Service

Filtering systems that detect spam by a single modality have increased over the past few years for Short Message Services (SMS). With the growth of marketing via Multimedia Messaging Services (MMS), a spammer can evade detection by injecting multimodal junk information into MMS to decrease systems recognition based on a single modality. Due to this situation, we propose in this paper, a powerful spam MMS filtering system, with a multi-modality fusion technique, it can detect spam whether it is hidden in text or images. The architecture of the proposed system combines along Short-Term Memory (LSTM) model and a Convolutional Neural Network (CNN) model to filter spam MMS. Based on the text and image portions of an MMS, the LSTM and the CNN calculate two classification probabilities. Afterwards, to determine whether the MMS is spam or not, these values are incorporated into a fusion model. Based on the results of the experiments, the overall accuracy is 98.56% for the MMS dataset, 96.97% for the text-based dataset, and 97.98% for the image-based dataset. We have compared the proposed system with some relevant ones and we have found that it performs better in many criteria such as precision, f1-score, recall, and accuracy and gives an improvement between 1 and 5% in smishing detection.