Reviewerly/exHarmony huggingface dataset:
A New Mapping Protocol for Laser Ablation (with <scp>Fast‐Funnel</scp> ) Coupled to a <scp>Time‐of‐Flight</scp> Mass Spectrometer ( <scp>LA‐FF‐ICP‐ToF‐MS</scp> ) for the Rapid, Simultaneous Quantification of Multiple Minerals

Although in situ analysis by LA‐ICP‐MS is considered a rapid technique with minimal sample preparation and data reduction, mapping areas of millimetres in size using a small beam (&lt; 15 μm) can be time consuming (several hours) when a quadrupole ICP‐MS is used. In addition, fully quantitative imaging using internal standardisation by LA‐ICP‐MS is challenging in samples with more than one mineral phase present due to varying ablation rates. A new protocol for the quantification of multiple coexisting phases, mapped at a rate of about 12 mm 2 h ‐1 and a resolution of 12 μm × 12 μm per pixel, is presented. The protocol allows mapping of most atomic masses, ranging from 23 Na to 238 U, using a time‐of‐flight mass spectrometer (ICP‐ToF‐MS, TOFWERK) connected to a 193 nm excimer laser. A fast‐funnel device was successfully used to increase the aerosol transport speed, reducing the time usually required for mapping by a factor of about ten compared with a quadrupole ICP‐MS. The lower limits of detection for mid and heavy masses are in the range 0.1–10 μg g ‐1 , allowing determination of trace to ultra‐trace elements. The presented protocol is intended to be a routine analytical tool that can provide greater access to the spatial distribution of major and trace elements in geological materials.
Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed.
Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

With the advent of Reinforcement Learning (RL) and its continuous progress, state-of-the-art RL systems have come up for many challenging and real-world tasks. Given the scope of this area, various techniques are found in the literature. One such notable technique, Multiple Deep Q-Network (DQN) based RL systems use multiple DQN-based-entities, which learn together and communicate with each other. The learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully designed. As more complex DQNs come to the fore, the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training, need for high resources, more training time, and difficulty in fine-tuning leading to performance issues. Taking a cue from the parallel processing found in the nature and its efficacy, we propose a lightweight ensemble based approach for solving the core RL tasks. It uses multiple binary action DQNs having shared state and reward. The benefits of the proposed approach are overall simplicity, faster convergence and better performance compared to conventional DQN based approaches. The approach can potentially be extended to any type of DQN by forming its ensemble. Conducting extensive experimentation, promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks, and Atari 2600 games as compared to recent techniques. The proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task, 259.2 on the LunarLander-v2 task, and state-of-the-art results on four out of five Atari 2600 games.
Is GPT-3 All You Need for Visual Question Answering in Cultural Heritage?

The use of Deep Learning and Computer Vision in the Cultural Heritage domain is becoming highly relevant in the last few years with lots of applications about audio smart guides, interactive museums and augmented reality. All these technologies require lots of data to work effectively and be useful for the user. In the context of artworks, such data is annotated by experts in an expensive and time consuming process. In particular, for each artwork, an image of the artwork and a description sheet have to be collected in order to perform common tasks like Visual Question Answering. In this paper we propose a method for Visual Question Answering that allows to generate at runtime a description sheet that can be used for answering both visual and contextual questions about the artwork, avoiding completely the image and the annotation process. For this purpose, we investigate on the use of GPT-3 for generating descriptions for artworks analyzing the quality of generated descriptions through captioning metrics. Finally we evaluate the performance for Visual Question Answering and captioning tasks.
Boosting transferability of physical attack against detectors by redistributing separable attention

The research on attack transferability is of great importance as it can guide how to conduct an adversarial attack without knowing any information about target models. However, it remains challenging for adversarial examples to maintain a good attack transferability performance, especially for the black-box attack implemented in the physical world. To enhance black-box transferability of physical attacks on object detectors, we present a novel adversarial learning method to produce adversarial patches by redistributing separable attention maps. Concretely, we first develop smoothed multilayer attention maps by introducing serial composite transformations, which could suppress model-specific noise on the one hand, and cover objects to be concealed at various resolutions on the other hand. Besides, our method resorts to a scalable mask to separate object attention from the background and adjust their distribution with a novel loss function. Extensive experiments show that our approach outperforms state-of-the-art methods in both the digital space and the physical world. Our code is available at https://github.com/zhangyu13a/transPhyAtt.
ParAlg: A Paraphasia Algorithm for Multinomial Classification of Picture Naming Errors

A preliminary version of a paraphasia classification algorithm (henceforth called ParAlg) has previously been shown to be a viable method for coding picture naming errors. The purpose of this study is to present an updated version of ParAlg, which uses multinomial classification, and comprehensively evaluate its performance when using two different forms of transcribed input.A subset of 11,999 archival responses produced on the Philadelphia Naming Test were classified into six cardinal paraphasia types using ParAlg under two transcription configurations: (a) using phonemic transcriptions for responses exclusively (phonemic-only) and (b) using phonemic transcriptions for nonlexical responses and orthographic transcriptions for lexical responses (orthographic-lexical). Agreement was quantified by comparing ParAlg-generated paraphasia codes between configurations and relative to human-annotated codes using four metrics (positive predictive value, sensitivity, specificity, and F1 score). An item-level qualitative analysis of misclassifications under the best performing configuration was also completed to identify the source and nature of coding discrepancies.Agreement between ParAlg-generated and human-annotated codes was high, although the orthographic-lexical configuration outperformed phonemic-only (weighted-average F1 scores of .78 and .87, respectively). A qualitative analysis of the orthographic-lexical configuration revealed a mix of human- and ParAlg-related misclassifications, the former of which were related primarily to phonological similarity judgments whereas the latter were due to semantic similarity assignment.ParAlg is an accurate and efficient alternative to manual scoring of paraphasias, particularly when lexical responses are orthographically transcribed. With further development, it has the potential to be a useful software application for anomia assessment.https://doi.org/10.23641/asha.22087763.
Hybrid Transformer Based Feature Fusion for Self-Supervised Monocular Depth Estimation

With an unprecedented increase in the number of agents and systems that aim to navigate the real world using visual cues and the rising impetus for 3D Vision Models, the importance of depth estimation is hard to understate. While supervised methods remain the gold standard in the domain, the copious amount of paired stereo data required to train such models makes them impractical. Most State of the Art (SOTA) works in the self-supervised and unsupervised domain employ a ResNet-based encoder architecture to predict disparity maps from a given input image which are eventually used alongside a camera pose estimator to predict depth without direct supervision. The fully convolutional nature of ResNets makes them susceptible to capturing per-pixel local information only, which is suboptimal for depth prediction. Our key insight for doing away with this bottleneck is to use Vision Transformers, which employ self-attention to capture the global contextual information present in an input image. Our model fuses per-pixel local information learned using two fully convolutional depth encoders with global contextual information learned by a transformer encoder at different scales. It does so using a mask-guided multi-stream convolution in the feature space to achieve state-of-the-art performance on most standard benchmarks.
An optimized wavelet neural networks using cuckoo search algorithm for function approximation and chaotic time series prediction

Although the practicability of using wavelet neural networks (WNNs) in nonlinear function approximation has been addressed extensively, selecting the optimal number of hidden nodes and their appropriate initial locations remains a great challenge for WNNs' initialization. The cuckoo search algorithm (CSA) is used in this study for optimizing WNNs. The position of the cuckoo eggs represents the translation of the wavelet hidden nodes, which are optimized based on the egg-laying and breeding strategy of cuckoos. The solutions from the CSA are assigned as initial translation vectors for the WNNs and subsequently evaluated on a few benchmarking functions and real-world applications. Performance assessment demonstrates its superior approximation capability than the existing methods used for WNNs initialization.
From “prepare for the unknown” to “train for what's coming”: A digital twin-driven and cognitive training approach for the workforce of the future in smart factories

Despite on-the-job assistance technology is getting popular in the Smart Operator 4.0 literature, non-routine work also requires highly-skilled and pre-trained workers to prevent serious errors and keep high efficiency and safety levels. The question that motivates this study is: 'is it possible to train industrial workers for what exactly is coming instead of preparing them for a large set of scenarios - even very unlikely ones?'. This work proposes: (i) a structured On-the-Job Training strategy for non-routine tasks, called 'training-on-the-go' or 'prescriptive training', according to which a Prescriptive Analytics module schedules the training sessions not long before the actual performance, but only when and if needed; (ii) a proof-of-concept of a game-based training system where virtual scenes and context of an industrial site are faithfully recreated thanks to digital twin data and models; (iii) the use of evolutionary-based fuzzy cognitive maps (E-FCM) for the extraction of the workers' implicit procedural knowledge and for the comparison of mental models of experienced vs. inexperienced workers to assess potential misconceptions or flaws in their decision-making process. This work contributes to the evolution of worker training paradigms and systems from the perspective of human-centric cyber-physical production systems and aims for current gaps in workforce training, i.e. poor timeliness and effectiveness, limited context and industrial information integration, and scarce focus on the experts' implicit knowledge. An application study with a non-routine task on an offshore oil platform demonstrates how the proposed system facilitates knowledge transfer, offers situational awareness and sustains the workforce competence development process.
S-Type Random k Satisfiability Logic in Discrete Hopfield Neural Network Using Probability Distribution: Performance Optimization and Analysis

Recently, a variety of non-systematic satisfiability studies on Discrete Hopfield Neural Networks have been introduced to overcome a lack of interpretation. Although a flexible structure was established to assist in the generation of a wide range of spatial solutions that converge on global minima, the fundamental problem is that the existing logic completely ignores the probability dataset’s distribution and features, as well as the literal status distribution. Thus, this study considers a new type of non-systematic logic termed S-type Random k Satisfiability, which employs a creative layer of a Discrete Hopfield Neural Network, and which plays a significant role in the identification of the prevailing attribute likelihood of a binomial distribution dataset. The goal of the probability logic phase is to establish the logical structure and assign negative literals based on two given statistical parameters. The performance of the proposed logic structure was investigated using the comparison of a proposed metric to current state-of-the-art logical rules; consequently, was found that the models have a high value in two parameters that efficiently introduce a logical structure in the probability logic phase. Additionally, by implementing a Discrete Hopfield Neural Network, it has been observed that the cost function experiences a reduction. A new form of synaptic weight assessment via statistical methods was applied to investigate the effect of the two proposed parameters in the logic structure. Overall, the investigation demonstrated that controlling the two proposed parameters has a good effect on synaptic weight management and the generation of global minima solutions.
Automatic Ship Object Detection Model Based on YOLOv4 with Transformer Mechanism in Remote Sensing Images

Despite significant advancements in object detection technology, most existing detection networks fail to investigate global aspects while extracting features from the inputs and cannot automatically adjust based on the characteristics of the inputs. The present study addresses this problem by proposing a detection network consisting of three stages: preattention, attention, and prediction. In the preattention stage, the network framework is automatically selected based on the features of the images’ objects. In the attention stage, the transformer structure is introduced. Taking into account the global features of the target, this study combines a self-attention module in the transformer model and convolution operation to integrate image features from global to local and for detection, thus improving the ship target accuracy. This model uses mathematical methods to obtain results of predictive testing in the prediction stage. The above improvements are based on the You Only Look Once version 4 (YOLOv4) framework, named “Auto-T-YOLO”. The model achieves the highest accuracy of 96.3% on the SAR Ship Detection dataset (SSDD) compared to the other state-of-the-art (SOTA) model. It achieves 98.33% and 91.78% accuracy in the offshore and inshore scenes, respectively. The experimental results verify the practicality, validity, and robustness of the proposed model.
Testing of Lubricants for DIC Tests to Measure the Forming Limit Diagrams of Aluminum Thin Sheet Materials

We investigated lubricants and thin teflon foils that can be applied in the formability testing of the thin aluminum sheets used in the electronics and automotive industries. For the tests, thirteen different industrial lubricants (oils and greases) (i.e., L1–L13) and two Teflon films (i.e., 0.08 and 0.22 mm thick) were applied. The authors conducted an Erichsen test, and the thickness reduction of the discs was measured first. In forming-limit curve (FLC) tests, it is crucial that the stresses are localized in the central area of the specimen during forming and that biaxial deformation is maintained throughout, if possible. We aimed to achieve and fulfill this task. To perform this measurement, the GOM ARAMIS measuring system was utilized. It is an optical measuring system based on the digital image correlation (DIC) principle, capable of measuring both stresses and displacements in real time. A specific validation method was also developed to qualify the DIC system. We concluded that there was a 5% difference in Erichsen indentation (IE) number diagrams between the best (L12) and worst (L4) cases for the lubricants and oils tested, which is a significant difference for thin plates. We found that this value could be increased and improved by using Teflon discs. Furthermore, the localization of stress maxima, i.e., the centering of cracks in the specimen, could be achieved by combining Teflon discs and L12 lubricant (with appropriate layer order), which significantly aids in the recording of standard FLC diagrams. Using foils is also advantageous because they are readily available, have no expiry date, and are of less environmental concern.
Reversible Data Hiding in Encrypted Images with Extended Parametric Binary Tree Labeling

Images uploaded to the cloud may be confidential or related to personal private information, so they need to be encrypted before uploading to the cloud storage. At the service provider side, appending additional information is usually required for transmission or database management. Reversible data hiding in encrypted images (RDHEI) serves as a technical solution. Recent RDHEI schemes successfully utilize the spatial correlation between image pixel values to vacate spare room for data hiding, however, the data payload can be further improved. This paper proposes a RDHEI scheme based on extended parameter binary tree labeling, which replaces non-reference pixel values with their prediction errors in a reduced length to vacate space. We further encode the prediction error of non-embeddable pixels to fit the space left from labeling. Thus, the space required to store the pixel bits replaced by labeling codes is saved. Experimental results show that the data payload of the extended parametric binary tree labeling outperforms state-of-the-art schemes. The embedding rates for the commonly applied datasets, including Bossbase, BOWS-2, and UCID, are 3.2305 bpp, 3.1619 bpp, and 2.8113 bpp, respectively.
A holistic approach to augmented reality-related research in tourism: through bibliometric analysis

Purpose With its potential to significantly improve the tourism sector, augmented reality (AR), an emerging technology, is a growing force in the field. It has attracted significant academic attention and expanded publishing options over the past 20 years. However, so far, no review has provided a comprehensive overview of AR-related research in tourism. This work uses the VOS Viewer and Bibliometrix R-package software to present a bibliometric review of the scientific literature in the area of AR-related tourism research in order to fill this gap. Design/methodology/approach The theme of AR in tourism was searched in the Scopus database. The original search yielded 327 papers in total, which were then filtered based on the parameters. A total of 190 relevant documents were generated by the refining process. For the publications pertaining to AR in tourism, the most cited papers, prominent authors, productive journals and countries, co-authorship among countries, trending topics, thematic map, co-occurrence analysis of keywords and bibliographic coupling of documents were all looked into. Findings This report shows a recent rise in research on AR in the tourism industry. The top 10 studies in the topic had a combined total of 1749 citations, while the authors found three papers with more than 200 Scopus citations. Till the date of extraction, 555 writers have contributed to the field of AR in tourism, with Tom Dieck M.C., Chung N. and Jung T. receiving the most citations. According to co-authorship analysis, the UK collaborates with other countries the most. Thematic map has thrown insight into the niche themes and emerging themes in the field. Coupling analysis has revealed four major research streams in the field of AR in tourism. Research limitations/implications This study can be considered as a first study to utilize a bibliometric method to address this research gap, identifying the top documents, journals and, most importantly, the future research areas identified through the thematic map analysis of keywords and bibliographic coupling. Practical implications Historical growth within this discipline was made clear by an in-depth analysis of the studied themes in this area. Hence, the discovery of relatively new subjects offers scholars the chance to plan their research in this area and thereby enhance the literature. The study is also beneficial for practitioners in the tourism industry. Originality/value This work examined previously published publications on AR in tourism using a bibliometric analytic technique. Additionally, it contributes to the current knowledge accumulation in learning about the applicability of AR in tourism and enlightens tourism scholars on the structure of the field.
Hyperledger Fabric Blockchain and ROS 2 Integration for Autonomous Mobile Robots

Trust is increasingly becoming a key consideration in the design of autonomous robotic systems. In industrial applications, security and trust in the system are requirements for widespread adoption. Blockchain technologies have emerged as a potential solution to address identity management and secure data aggregation and control. However, the vast majority of works to date utilize Ethereum and smart contracts that are not scalable or well suited for industrial applications. This paper presents what is, to the best of our knowledge, the first integration of ROS 2 with the Hyperledger Fabric blockchain. With a framework that leverages Fabric smart contracts and ROS 2 through a Go application, we delve into the potential of using blockchain for controlling robots, and gathering and processing their data. We demonstrate the applicability of the proposed framework to an inventory management use-case where different robots are used to detect objects of interest in a given area. Designed to meet the requirements of distributed robotic systems, we show that the performance of the robots is not impacted significantly by the blockchain layer. At the same time, we provide examples for developing other applications that integrate Fabric smart contracts with ROS 2. Our results pave the way for further adoption of blockchain technologies in autonomous robotic systems for building trustable data sharing.