Reviewerly/exHarmony huggingface dataset:
Innovation dynamics in the age of artificial intelligence: introduction to the special issue

In this paper, we discuss the impact of artificial intelligence (AI) on innovation dynamics and argue that AI has affected innovation dynamics in at least two distinct ways. First, innovation using AI has unique dynamics that are characterised by data playing a central role and the increasing importance of external collaboration; however, data security and privacy issues also present new risks to external collaboration. When innovating in AI, collaboration with customers and competitors is critical, yet there are considerable risks associated with data sharing. Second, unique organisational challenges emerge during the diffusion of AI innovations, because adopting AI in an organisation not only results in the need for additional employee competencies but also challenges organisational power structures. We also discuss the merits of AI as a general purpose technology and argue that conclusions about AI in relation to innovation dynamics are likely to change when generative AI is widely adopted.
Constructions and equivalence of Sidon spaces

Abstract Sidon spaces have been introduced by Bachoc et al. (in: Mathematical Proceedings of the Cambridge Philosophical Society, Cambridge University Press, 2017) as the q -analogue of Sidon sets. The interest on Sidon spaces has increased quickly, especially after the work of Roth et al. (IEEE Trans Inform Theory 64(6):4412–4422, 2017), in which they highlighted the correspondence between Sidon spaces and cyclic subspace codes. Up to now, the known constructions of Sidon Spaces may be divided in three families: the ones contained in the sum of two multiplicative cosets of a fixed subfield of $$\mathbb {F}_{q^n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>n</mml:mi> </mml:msup> </mml:msub> </mml:math> , the ones contained in the sum of more than two multiplicative cosets of a fixed subfield of $$\mathbb {F}_{q^n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:msup> <mml:mi>q</mml:mi> <mml:mi>n</mml:mi> </mml:msup> </mml:msub> </mml:math> and finally the ones obtained as the kernel of subspace polynomials. In this paper, we will mainly focus on the first class of examples, for which we provide characterization results and we will show some new examples, arising also from some well-known combinatorial objects. Moreover, we will give a quite natural definition of equivalence among Sidon spaces, which relies on the notion of equivalence of cyclic subspace codes and we will discuss about the equivalence of the known examples.
On a Symmetric Image Cryptosystem Based on a Novel One-Dimensional Chaotic System and Banyan Network

In this paper, a Banyan network with high parallelism and nonlinearity is used for the first time in image encryption to ensure high complexity and randomness in a cipher image. To begin, we propose a new 1-D chaotic system (1-DSCM) which improves the chaotic behavior and control parameters’ structure of the sin map. Then, based on 1-DSCM, a Banyan network, and SHA-256 hash function, a novel image encryption algorithm is conducted. Firstly, a parameter is calculated using SHA-256 hash function and then employed to preprocess the plaintext image to guarantee high plaintext sensitivity. Secondly, a row–column permutation operation is performed to gain the scrambled image. Finally, based on the characteristic of DNA encoding, a novel DNA mapping is constructed using an N=4 Banyan network and is used to diffuse the scrambled image. Simulation results show that the 1-DSCM has excellent performance in chaotic behavior and that our encryption algorithm exhibits strong robustness against various attacks and is suitable for use in modern cryptosystems.
Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption

Abstract Digital pathology (DP), or the digitization of pathology images, has transformed oncology research and cancer diagnostics. The application of artificial intelligence (AI) and other forms of machine learning (ML) to these images allows for better interpretation of morphology, improved quantitation of biomarkers, introduction of novel concepts to discovery and diagnostics (such as spatial distribution of cellular elements), and the promise of a new paradigm of cancer biomarkers. The application of AI to tissue analysis can take several conceptual approaches, within the domains of language modelling and image analysis, such as Deep Learning Convolutional Neural Networks, Multiple Instance Learning approaches, or the modelling of risk scores and their application to ML. The use of different approaches solves different problems within pathology workflows, including assistive applications for the detection and grading of tumours, quantification of biomarkers, and the delivery of established and new image-based biomarkers for treatment prediction and prognostic purposes. All these AI formats, applied to digital tissue images, are also beginning to transform our approach to clinical trials. In parallel, the novelty of DP/AI devices and the related computational science pipeline introduces new requirements for manufacturers to build into their design, development, regulatory and post-market processes, which may need to be taken into account when using AI applied to tissues in cancer discovery. Finally, DP/AI represents challenge to the way we accredit new diagnostic tools with clinical applicability, the understanding of which will allow cancer patients to have access to a new generation of complex biomarkers.
Smart Gesture Controlled Systems Using IoT

A new technology called a smart gesture-controlled system enables users to interact with devices and systems by moving their hands or body. It interprets specific user motions into commands that control the system or device utilizing sensors, machine learning algorithms, and software interfaces. This technology has the potential to alter how we interact with technology by making it more approachable to a wider range of users. Gesture recognition systems have been used in human-computer interaction for many years. On the other hand, recent developments in sensors and machine learning algorithms have made it possible to create highly accurate and responsive gesture detection systems.
Controlling Computer Features Through Hand Gesture

This chapter introduces an AI-driven hand gesture recognition system designed to enhance computer settings control, prioritizing improved accessibility and user experiences. Leveraging machine learning algorithms trained on a dataset of relevant hand gestures (e.g., volume and brightness control), this project emphasizes data analysis for trend identification and system refinement. Successful outcomes could stimulate further research and innovation, potentially revolutionizing accessibility and user experience solutions. Ultimately, this endeavor aims to empower computer users with a more intuitive and accessible means of adjusting settings, contributing significantly to human-computer interaction advancement.
A Mobile Device Application for Visualizing Molecular Symmetry and Orbitals in Augmented Reality

Molecular symmetry and orbitals are two important chemical concepts which can be difficult for students to visualize in three dimensions; as such, instructors have traditionally adopted a variety of approaches to teach them including the use of physical models and digital renderings/resources. This paper describes a new mobile device application (app) called leARnCHEM which leverages advances in augmented reality to enable students to visualize and interact with these items in a novel way. The app is available for free on both android and iOS devices and is capable of both marker and markerless rendering of molecules. The app development and features, as well as preliminary implementation and feedback from undergraduate students, is described. We expect this app to be a valuable resource for teaching and learning symmetry or orbitals as it is interactive and engaging, is freely accessible to anyone with a mobile device, and can be used essentially anywhere.
Artificial Intelligence in Aviation: New Professionals for New Technologies

Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) to transform operations and improve efficiency and safety. However, the aviation industry requires qualified graduates with relevant AI competencies to meet this demand. This study analyzed aviation engineering bachelor’s programs at European universities to determine if they are preparing students for AI integration in aviation by incorporating AI-related topics. The analysis focused on program descriptions and syllabi using semantic annotation. The results showed a limited focus on AI and machine learning competencies, with more emphasis on foundational digital skills. Reasons include the newness of aviation AI, its specialized nature, and implementation challenges. As the industry evolves, dedicated AI programs may emerge. But currently, curricula appear misaligned with stated industry goals for AI adoption. The study provides an analytical methodology and competency framework to help educators address this gap. Producing graduates equipped with AI literacy and collaboration skills will be key to aviation’s intelligent future.
Implications of Brewer's Rule in Data Warehouse Design

Distributed systems cannot achieve consistency, availability, and partition tolerance, according to Brewer's Rule (CAP theorem). Brewer's Rule affects distributed data warehouses. Brewer's Rule improves data warehouse architecture, according to this study. Database research Brewer's Rule reliability, accessibility, and tolerance for partitions. Consistency—the condition that all distributed system nodes access the same data simultaneously—is examined. Distributed transactions, two-phase commit, and consensus methods let data warehouses achieve consistency. A distributed system must be available even if some nodes fail, the study says. Replication, load balancing, and failover provide high availability in data warehouses. Finally, partition tolerance—the need for a distributed system to operate after network partitions—is examined. Data sharding, distributed caching, and data replication help data warehousing systems achieve partition tolerance. Brewer's Rule helps improve data warehouse architecture for use cases and business needs. The study also discusses trade-offs companies must make when improving consistency, availability, and partition tolerance. To maintain dependable and performant data warehousing solutions, organizations must balance these trade-offs. Organizations designing and implementing distributed data warehouses must consider Brewer's Rule. Organizations may construct high-performing, dependable data warehouse systems that fulfill their business needs by understanding how consistency, availability, and partition tolerance affect data warehouse architecture.
Lithological Unit Classification Based on Geological Knowledge-Guided Deep Learning Framework for Optical Stereo Mapping Satellite Imagery

Lithological unit classification (LUC) refers to the classification of different types of rocks within an area, and it has been widely used in many fields, such as resource surveys and infrastructure planning. However, traditional field surveys require a lot of resources and time. Since remote sensing technology can rapidly acquire information without regional limitations, many researchers have focused on classifying lithological units with remote sensing images. However, in an area covered by vegetation, the beneficial information directly provided by remote sensing images is limited. Moreover, lithological interpretation often requires geological prior knowledge for guidance, which cannot be provided by remote sensing images. Thus, this study designed a dual-branch deep learning model to extract geological prior knowledge from geological information, and improve the accuracy of LUC. In the process of feature transmission of the model, a Dense Attention residual - Atrous Spatial Pyramid Pooling (DA-ASPP) module was proposed to maximize the preservation of lithological units’ features. The DA-ASPP integrates the idea of dense connection into ASPP for multiscale object feature preservation and adds residual structure into the channel attention mechanism to screen out the representative features of lithological units. The study area was located in southeastern Hubei Province, China, with seven categories of lithological units. A total of seven deep-learning networks were compared. The proposed method achieved a mean Intersection over Union (IOU) of 44.61% with a Macro-F1 of 56.54%, which were better than those of comparison models. Moreover, the visualization results demonstrated the superiority of the proposed model in LUC.
An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem

The ant colony algorithm faces dimensional catastrophe problems when solving the large-scale traveling salesman problem, which leads to unsatisfactory solution quality and convergence speed. To solve this problem, an adaptive ant colony optimization for large-scale traveling salesman problem (AACO-LST) is proposed. First, AACO-LST improves the state transfer rule to make it adaptively adjust with the population evolution, thus accelerating its convergence speed; then, the 2-opt operator is used to locally optimize the part of better ant paths to further optimize the solution quality of the proposed algorithm. Finally, the constructed adaptive pheromone update rules can significantly improve the search efficiency and prevent the algorithm from falling into local optimal solutions or premature stagnation. The simulation based on 45 traveling salesman problem instances shows that AACO-LST improves the solution quality by 79% compared to the ant colony system (ACS), and in comparison with other algorithms, the PE of AACO-LST is not more than 1% and the Err is not more than 2%, which indicates that AACO-LST can find high-quality solutions with high stability. Finally, the convergence speed of the proposed algorithm was tested. The data shows that the average convergence speed of AACO-LST is more than twice that of the comparison algorithm. The relevant code can be found on our project homepage.
Thermal Image Super-Resolution Based on Lightweight Dynamic Attention Network for Infrared Sensors

Infrared sensors capture infrared rays radiated by objects to form thermal images. They have a steady ability to penetrate smoke and fog, and are widely used in security monitoring, military, etc. However, civilian infrared detectors with lower resolution cannot compare with megapixel RGB camera sensors. In this paper, we propose a dynamic attention mechanism-based thermal image super-resolution network for infrared sensors. Specifically, the dynamic attention modules adaptively reweight the outputs of the attention and non-attention branches according to features at different depths of the network. The attention branch, which consists of channel- and pixel-wise attention blocks, is responsible for extracting the most informative features, while the non-attention branch is adopted as a supplement to extract the remaining ignored features. The dynamic weights block operates with 1D convolution instead of the full multi-layer perceptron on the global average pooled features, reducing parameters and enhancing information interaction between channels, and the same structure is adopted in the channel attention block. Qualitative and quantitative results on three testing datasets demonstrate that the proposed network can superior restore high-frequency details while improving the resolution of thermal images. And the lightweight structure of the proposed network with lower computing cost can be practically deployed on edge devices, effectively improving the imaging perception quality of infrared sensors.
Design Patterns for Situated Visualization in Augmented Reality

Situated visualization has become an increasingly popular research area in the visualization community, fueled by advancements in augmented reality (AR) technology and immersive analytics. Visualizing data in spatial proximity to their physical referents affords new design opportunities and considerations not present in traditional visualization, which researchers are now beginning to explore. However, the AR research community has an extensive history of designing graphics that are displayed in highly physical contexts. In this work, we leverage the richness of AR research and apply it to situated visualization. We derive design patterns which summarize common approaches of visualizing data in situ. The design patterns are based on a survey of 293 papers published in the AR and visualization communities, as well as our own expertise. We discuss design dimensions that help to describe both our patterns and previous work in the literature. This discussion is accompanied by several guidelines which explain how to apply the patterns given the constraints imposed by the real world. We conclude by discussing future research directions that will help establish a complete understanding of the design of situated visualization, including the role of interactivity, tasks, and workflows.
AGLC-GAN: Attention-based global-local cycle-consistent generative adversarial networks for unpaired single image dehazing

Image dehazing is a critical image pre-processing task to estimate the haze-free images corresponding to the input hazy images. Despite the recent advances, the task of image dehazing remains challenging, especially in the unsupervised scenario. Several efforts can be found in the literature to dehaze images in a supervised set-up, where a huge number of paired (clear and hazy images) images are required for training. The supervised approaches often become biased towards the nature of haze present in the training hazy images, and produce less realistic images for query hazy images. We propose an Attention-based Global–Local Cycle-consistent Generative Adversarial Network (AGLC-GAN) for Unpaired Single Image Dehazing. The proposed CycleGAN-based AGLC-GAN model contains a dehazing generator encapsulating an autoencoder-like network with an attention mechanism comprising channel attention and pixel attention to deal with uneven haze intensity across the image. We use a global–local consistent discriminator to identify spatially varying haze and improve the stability of the discriminator. We adopt cyclic perceptual consistency loss to maintain consistency in the feature space. A dynamic feature enhancement module and an adaptive mix-up module are included in the proposed generator to dynamically obtain more spatially structured features and hence, adaptively preserve the flow of shallow features. Furthermore, we extensively experiment with the proposed model on multiple benchmark datasets for evaluating the efficacy of removing haze. The results of the experiments conducted in the study, demonstrate a significant quantitative and qualitative improvement over the existing methods for unpaired image dehazing. 1
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution

Current Scene text image super-resolution approaches primarily focus on extracting robust features, acquiring text information, and complex training strategies to generate super-resolution images. However, the upsampling module, which is crucial in the process of converting low-resolution images to high-resolution ones, has received little attention in existing works. To address this issue, we propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. Unlike previous graph attention mechanisms, our approach achieves 2-3 orders of magnitude improvement in efficiency and memory utilization by eliminating the dependency on sparse adjacency matrices and introducing a sliding window approach for efficient parallel computation. Additionally, we introduce the MLP-based Sequential Residual Block (MSRB) for robust feature extraction from text images, and a Local Contour Awareness loss (ℒlca) to enhance the model's perception of details. Comprehensive experiments on TextZoom demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy. For single-stage and multi-stage strategies, we achieved improvements of 0.7% and 2.6%, respectively, increasing the performance from 52.6% and 53.7% to 53.3% and 56.3%. The code is available at https://github.com/wenyu1009/RTSRN.
Integrating VideoMAE based model and Optical Flow for Micro- and Macro-expression Spotting

The task of interval localization of macro- and micro-expression in long videos has a wide range of applications in the field of human-computer interaction. Compared with macro-expression, micro-expression has shorter duration, lower intensity, and smaller number of samples, which make them more difficult to spot accurately in long videos. In this paper, we propose a pre-trained model combined with the optical flow method to improve the accuracy and robustness of macro- and micro-expression spotting. Firstly, self-supervised pre-training is performed on rich unlabeled data based on VideoMAE. Then, multiple models are trained on the datasets SAMM-LV and CAS(ME)³ for macro- and micro-expression with different fine-grains. Finally, different lengths of slices are generated based on the models with different fine-grains, and the optimal matching method through the combination of model fine-grainedness and slice lengths is explored. At the same time, macro- and micro-expression generating regions were spotted using the optical flow method, fused with the model outputs to supplement the spatio-temporal information not captured by the model and to exclude the interference of non-interested regions. We evaluated the performance of our method on the MEGC2023 testset (consisting of 10 long videos from SAMM and 20 long videos from CAS(ME)3) and won first place in the MEGC2023 Challenge. The results demonstrate the effectiveness of the method.
FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information

Recently, Fourier frequency information has attracted much attention in Low-Light Image Enhancement (LLIE). Some researchers noticed that, in the Fourier space, the lightness degradation mainly exists in the amplitude component and the rest exists in the phase component. By incorporating both the Fourier frequency and the spatial information, these researchers proposed remarkable solutions for LLIE. In this work, we further explore the positive correlation between the magnitude of amplitude and the magnitude of lightness, which can be effectively leveraged to improve the lightness of low-light images in the Fourier space. Moreover, we find that the Fourier transform can extract the global information of the image, and does not introduce massive neural network parameters like Multi-Layer Perceptrons (MLPs) or Transformer. To this end, a two-stage Fourier-based LLIE network (FourLLIE) is proposed. In the first stage, we improve the lightness of low-light images by estimating the amplitude transform map in the Fourier space. In the second stage, we introduce the Signal-to-Noise-Ratio (SNR) map to provide the prior for integrating the global Fourier frequency and the local spatial information, which recovers image details in the spatial space. With this ingenious design, FourLLIE outperforms the existing state-of-the-art (SOTA) LLIE methods on four representative datasets while maintaining good model efficiency. Notably, compared with a recent Transformer-based SOTA method SNR-Aware, FourLLIE reaches superior performance with only 0.31% parameters. Code is available at https://github.com/wangchx67/FourLLIE