Reviewerly/exHarmony huggingface dataset:
Fast, Accurate, But Sometimes Too-Compelling Support: The Impact of Imperfectly Automated Cues in an Augmented-Reality Head-Mounted Display on Visual Search Performance

While the visual search for targets in a complex scene might benefit from using augmented-reality (AR) head-mounted display (HMD) technologies by helping to efficiently direct human attention, imperfectly reliable automation support could manifest in occasional errors. The current study examined the effectiveness of different HMD cues that might support visual search performance and their respective consequences following automation errors. A total of 56 participants searched a three-dimensional environment containing 48 objects in a room, in order to locate a target object that was viewed prior to each trial. They searched either unaided or assisted by one of the three HMD types of cues: an arrow pointing to the target, a plan-view minimap highlighting the target, and a constantly visible icon depicting the appearance of the target object. The cue was incorrect in 17% of the trials for one group of participants and 100% correct for the second group. Through both analysis and modeling of both search speed and accuracy, the results indicated that the arrow and minimap cues depicting location information were more effective than the icon cue depicting visual appearance, both overall, and when the cue was correct. However, there was a tradeoff on the infrequent occasions when the cue erred. The most effective AR-based cue led to a greater automation bias in which the cue was more often blindly followed without careful examination of the raw images. The results speak to the benefits of AR and the need to examine potential costs when AR-conveyed information may be incorrect because of imperfectly reliable systems.
Solutions for Software Requirement Risks Using Artificial Intelligence Techniques

Software projects have a very high probability of failure, and a major reason for this is a poor requirement engineering process. Requirement elicitation and documentation are the primary and most vital steps in the software development life cycle. There is a lack of a model or framework that deals with these risks in parallel, and a lot of work is needed to manage them holistically. This chapter includes the identification of factors that affect successful software development and explores various sources of requirement risks. It intends to create an intelligent framework for managing VUCA risks in software requirements. The relationship between VUCA and terrorism risks identification at an early stage using fuzzy logic, ANFIS, and intelligent Bayesian network models. The framework includes a model for volatile requirement prioritization and a model for requirement ambiguity and uncertainty management.
Macroscopic maximally-entangled-state preparation between two atomic ensembles

We develop a scheme to prepare a macroscopic maximally entangled state between two atomic ensembles using adaptive quantum nondemolition (QND) measurements. The quantum state of the system is evolved using a sequence of QND measurements followed by adaptive unitaries such that the desired measurement outcome is obtained with asymptotically unit probability. This procedure is repeated in $z$- and $x$-spin bases alternately such that the state converges deterministically towards the maximally entangled state. Up to a local spin-basis rotation, the maximally entangled state has zero total spin angular momentum, i.e., it is a singlet state. Our protocol does not perform postselection and works beyond the Holstein-Primakoff regime for the atomic spin degrees of freedom, producing genuine macroscopic entanglement.
Architecturing Binarized Neural Networks for Traffic Sign Recognition

Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving. While the use of deep learning is well-known in traffic signs classification due to the high accuracy results obtained using convolutional neural networks (CNNs) (state of the art is 99.46%), little is known about binarized neural networks (BNNs). Compared to CNNs, BNNs reduce the model size and simplify convolution operations and have shown promising results in computationally limited and energy-constrained devices which appear in the context of autonomous driving. This work presents a bottom-up approach for architecturing BNNs by studying characteristics of the constituent layers. These constituent layers (binarized convolutional layers, max pooling, batch normalization, fully connected layers) are studied in various combinations and with different values of kernel size, number of filters and of neurons by using the German Traffic Sign Recognition Benchmark (GTSRB) for training. As a result, we propose BNNs architectures which achieve an accuracy of more than $$90\%$$ for GTSRB (the maximum is $$96.45\%$$ ) and an average greater than $$80\%$$ (the maximum is $$88.99\%$$ ) considering also the Belgian and Chinese datasets for testing. The number of parameters of these architectures varies from 100k to less than 2M. The accompanying material of this paper is publicly available at https://github.com/apostovan21/BinarizedNeuralNetwork .
Anonymous conference key agreement in linear quantum networks

Sharing multi-partite quantum entanglement between parties allows for diverse secure communication tasks to be performed. Among them, conference key agreement (CKA) - an extension of key distribution to multiple parties - has received much attention recently. Interestingly, CKA can also be performed in a way that protects the identities of the participating parties, therefore providing <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mi>y</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi></mml:math>. In this work, we propose an anonymous CKA protocol for three parties that is implemented in a highly practical network setting. Specifically, a line of quantum repeater nodes is used to build a linear cluster state among all nodes, which is then used to anonymously establish a secret key between any three of them. The nodes need only share maximally entangled pairs with their neighbours, therefore avoiding the necessity of a central server sharing entangled states. This linear chain setup makes our protocol an excellent candidate for implementation in future quantum networks. We explicitly prove that our protocol protects the identities of the participants from one another and perform an analysis of the key rate in the finite regime, contributing to the quest of identifying feasible quantum communication tasks for network architectures beyond point-to-point.
<scp>DCELANM‐Net</scp>: Medical image segmentation based on dual channel efficient layer aggregation network with learner

Abstract Segmenting medical images is a principal component of computer vision. The UNet model framework has taken over as the standard framework for this activity across a wide range of medical picture segmentation applications. Due to convolutional neural networks (CNNs) convolution operation limitations, the model's global modeling ability is not absolutely perfect. Moreover, a single convolution operation cannot gather feature information at various scales, which will have an impact on the quality of the global feature extraction as well as the localization of local details. The DCELANM‐Net structure, which this article offers, is a model that ingeniously combines a Dual Channel Efficient Layer Aggregation Network (DCELAN) and a Micro Masked Autoencoder (Micro‐MAE). On the one hand, for the DCELAN, the features are more effectively fitted by deepening the network structure, which in turn can successfully learn and fuse the features, helping to locate the local feature information more accurately; and the utilization of each layer of channels is more effectively improved by widening the network structure and residual connections. We adopt Micro‐MAE as the learner of the model. In addition to being straightforward in its methodology, it also includes a self‐supervised learning method, which has the benefit of being incredibly scalable for the model. This scalable method enables the generalization of high‐volume models, and the models can show good scaling behavior. It is also shown that Micro‐MAE is a powerful and adaptable learner that we can incorporate it into our network design to improve the model's accuracy and stability for tasks involving medical picture segmentation. Superior metrics and good generalization are demonstrated by DCELANM‐Net on the datasets Kvasir‐SEG and CVC‐ClinicDB. In the experiments, we set DCELANM‐S and DCELANM‐L to represent different sizes of the model, and since DCELANM‐L has the best performance, DCELANM‐L is determined as the base model for all experiments, called DCELANM.
Collaborative generative learning activities in immersive virtual reality increase learning

Learning in immersive virtual reality (VR) can be highly motivating, but the perceptual richness and activity can distract students from the core instructional message. Generative learning activities may mitigate some of the limitations of immersive learning by helping learners focus on making sense of the key material. The main aim of this study was to examine whether learners benefit from being prompted to engage in individual or collaborative generative learning activities while immersed in a VR lesson compared to viewing the same lesson with no breaks. High school students (N = 164) viewed a biology lesson adapted for immersive VR about how our cells are structured and function. For the experimental conditions, the lesson was split up into four parts, with a pause after each part where learners summarized the main points and created a virtual representation of a cell; this was performed individually or collaboratively within the VR environment. Based on generative learning theory, we predicted that students engaging in individual and collaborative generative learning activities would display significantly higher posttest scores than the control group. Based on theories of computer-supported collaborative learning, we examined whether the collaborative generative activity group would achieve higher posttest scores than the individual generative activity group. The results showed that adding collaborative generative activities to a VR lesson was more effective at improving learning than adding individual generative activities. These results are consistent with collaborative cognitive load theory and demonstrate the value of adding collaborative generative learning activities to immersive VR lessons.
An aspect-level sentiment analysis dataset for therapies on Twitter

The dataset described is an aspect-level sentiment analysis dataset for therapies, including medication, behavioral and other therapies, created by leveraging user-generated text from Twitter. The dataset was constructed by collecting Twitter posts using keywords associated with the therapies (often referred to as treatments). Subsequently, subsets of the collected posts were manually reviewed, and annotation guidelines were developed to categorize the posts as positive, negative, or neutral. The dataset contains a total of 5364 posts mentioning 32 therapies. These posts are further categorized manually into 998 (18.6%) positive, 619 (11.5%) negatives, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. The potential use of this dataset lies in the development of automatic systems that can detect users' sentiments toward therapies based on their posts. While there are other sentiment analysis datasets available, this is the first that encodes sentiments associated with specific therapies. Researchers and developers can utilize this dataset to train sentiment analysis models, natural language processing algorithms, or machine learning systems to accurately identify and analyze the sentiments expressed by consumers on social media platforms like Twitter.
Effects of contemporary technologies, such as blockchain and artificial intelligence (AI) in enhancing consumers’ trustworthiness of online reviews

ABSTRACTThe research note discusses the potential effects of contemporary technologies, such as blockchain and artificial intelligence (AI), in enhancing consumers' trustworthiness of online reviews in the tourism and hospitality context. The study postulates that three characteristics of blockchain – decentralization, immutability, and timestamping – ensure transparency in the online review process. Using a socio-technical systems approach, the current study provides insights into how blockchain technology, AI agents, and human factors interact to facilitate such transparency and thereby enhance consumers' trustworthiness of online reviews for their decision-making. Therefore, the study proposes that perceived transparency mediates the relationship between these three blockchain characteristics and the trustworthiness of online reviews. In proposing an investigation of the characteristics of blockchain and its interaction with AI in online reviews, the study highlights that researchers and practitioners pay attention to a specific characteristic of contemporary technologies to actualize potential benefits.该研究报告讨论了区块链和人工智能等当代技术在旅游和酒店业背景下提高消费者在线评论可信度的潜在影响. 该研究假设区块链的三个特征——去中心化、不变性和时间戳——确保了在线审查过程的透明度. 目前的研究采用社会技术系统的方法,深入了解区块链技术、人工智能代理和人为因素如何相互作用,以促进这种透明度,从而提高消费者对在线评论决策的可信度. 因此,该研究提出,感知透明度介导了这三个区块链特征与在线评论可信度之间的关系. 该研究建议对区块链的特征及其与人工智能在在线评论中的互动进行调查,强调研究人员和从业者关注当代技术的一个特定特征,以实现潜在利益.KEYWORDS: Blockchainartificial intelligenceonline reviewtrustworthinesstransparencysocio-technical system Disclosure statementNo potential conflict of interest was reported by the author(s).
Can Automated Metadata Extraction Make Scientific Data More Navigable?

FAIR principles require that scientific data be findable, discoverable, and reusable by users. To enable FAIRness, practioners of a science repository will often construct a rich, searchable index of metadata derived from the data. Unfortunately, manual metadata annotation methods do not scale to the many data files generated by many projects; and instead automated extraction systems are needed to scalably parse these files—often with nonstandard schema requiring specialized parsing strategies—and deposit representative metadata into a search index. In this work, we evaluate whether, and the extent to which, automatically extracted metadata make research repositories more navigable. We present a two-part user study conducted with scientists at two U.S. national laboratories from projects spanning spectroscopy and battery modeling. We constructed research indexes automatically by using the Xtract metadata extraction system. In the first part of our study, we learned about each user's role and identified key navigation concerns for scientists. We found that participants wished to navigate for purposes of discovery, retrieval, and organization. In the second part, participants completed simulated research data navigation tasks crafted to reflect real-world navigability concerns. We found that regardless of the interface used, participants consistently solved navigation tasks with high degrees of confidence and correctness, and significantly ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.2\mathrm{X}-50\times$</tex> ) faster than via their alternative methods (e.g., manual directory scans or designing a customized navigational tool).
A Linked Data and Ontology-Based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry

The provision of suitable training materials is crucial in preparing construction workers for the safety risks inherent in their work environments. However, retrieving relevant training resources for specific work tasks and worksites poses challenges due to the dispersed, unstructured, and multifarious nature of available content. This paper proposes a linked data and ontology-based system framework for effectively sharing and accessing educational safety contents from heterogeneous sources in a standardized way. The main technical solutions in the framework include an ontology that leverages the Resource Description Framework and Ontology Web Language to structure and publish educational safety training contents based on the linked data principles, and a content retrieval method which leverages the SPARQL protocol and RDF query language. Experimental results confirm that the proposed approach outperforms existing repositories in terms of search time, search result precision, and accuracy. It is envisaged that the framework would aid safety trainers in sharing, searching, and retrieving didactically appropriate materials to prepare workers for the unique safety challenges of their jobsite environments.
ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

Remote sensing images (RSI) are useful for various tasks such as Earth observation and climate change. However, RSI may suffer from stripe noise due to physical limitations in sensor systems. Therefore, image destriping is essential, since stripe noise may cause serious problems in real applications. In this paper, we shall present a new Alternating Direction method of multipliers (ADMM)-based Optimization Model, called ADOM for stripe noise removal in RSI. First, we formulate an optimization function for finding stripe noise components from the observed image for stripe noise removal, and then optimization process for solving the optimization function in order to extract stripe noise component. In the optimization process, we shall propose a weight-based detection strategy for efficient stripe noise component capture, and an ADMM-based acceleration strategy for fast stripe noise removal. In the weight-based detection strategy, we effectively detect stripe noise similar to the image details by using weighted norm generated by adjusting norm and group norm weights based on the momentum coefficient and residual parameter. In the ADMM-based acceleration strategy, we accelerate optimization process by using two control strategies: evidence-based starting point control and momentum-based step-size control. The former provides a starting point for more accurately finding stripe noise component, and the latter accelerates convergence by using the momentum coefficient while providing optimization stability by exploiting the damping coefficient. Our experimental results show that ADOM achieves better performance for both of simulated and real image data sets compared to the other destriping models.
HPO-empowered machine learning with multiple environment variables enables spatial prediction of soil heavy metals in coastal delta farmland of China

Machine learning (ML) models have been widely used for predicting spatial variability of soil heavy metals. However, it is impossible to explore the entire hyperparameter space of ML models by artificially trial-and-error experimentation. Here, an auto hyperparameter optimization-based machine learning (HPO-ML) method with three search algorithms and random forest (RF) and extreme gradient boosting (XGBoost) models was developed to predict the heavy metal content in soil with multiple environmental variables. The tree-structured Parzen estimator (TPE) algorithm outperformed other search algorithms in identifying the optimal hyperparameters of RF and XGBoost models. The model prediction results showed that the TPE-XGBoost had the highest accuracy for predicting the As (RMSE = 3.06 mg kg−1 and R2 = 70.35%), Cd (RMSE = 0.10 mg kg−1 and R2 = 75.43%), Cr (RMSE = 13.86 mg kg−1 and R2 = 82.11%), Ni (RMSE = 3.19 mg kg−1 and R2 = 75.20%), Pb (RMSE = 3.75 mg kg−1 and R2 = 74.79%), and Zn (RMSE = 6.83 mg kg−1 and R2 = 70.05%) contents. The TPE-XGBoost mapping result showed that areas with high concentrations of soil heavy metals were concentrated in the central and eastern areas (As), the mainstream of the Yellow River (Cd), the northeast area (Cr), the ancient watercourse of the Yellow River (Ni and Pb), and the central and northeastern areas (Zn). The SHapley additive explanation (SHAP) and structural equation model (SEM) were used to interpret the drivers of environmental variables. It is found that the variables with the highest contributions were CO, PM2.5, O3, PC3, PC1, and PC4 for predicting the As, Cd, Cr, Ni, Pb, and Zn contents, respectively, and there was a significant source-receptor coupling path. The results demonstrate the feasibility of using the HPO-ML approach in hyperparameter-limited conditions, which providing data-driven pathways and options to support the high-quality development of agriculture and the protection of farmland soil ecosystem.
Logical Multilateralism

Abstract In this paper we will consider the existing notions of bilateralism in the context of proof-theoretic semantics and propose, based on our understanding of bilateralism, an extension to logical multilateralism. This approach differs from what has been proposed under this name before in that we do not consider multiple speech acts as the core of such a theory but rather multiple consequence relations. We will argue that for this aim the most beneficial proof-theoretical realization is to use sequent calculi with multiple sequent arrows satisfying some specific conditions, which we will lay out in this paper. We will unfold our ideas with the help of a case study in logical tetralateralism and present an extension of Almukdad and Nelson’s propositional constructive four-valued logic by unary operations of meaningfulness and nonsensicality. We will argue that in sequent calculi with multiple sequent arrows it is possible to maintain certain features that are desirable if we assume an understanding of the meaning of connectives in the spirit of proof-theoretic semantics. The use of multiple sequent arrows will be justified by the presence of congruentiality-breaking unary connectives.
SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]

Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Supervised Learning for Earth Observation-Sentinel-1/2</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SSL4EO</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S12</i> ) to assemble a large-scale, global, multimodal, and multiseasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pretraining for a set of representative methods: momentum contrast (MoCo), self-distillation with no labels (DINO), masked autoencoders (MAE), and data2vec, and multiple downstream applications, including scene classification, semantic segmentation, and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pretrained models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhu-xlab/SSL4EO-S12</uri> .