Reviewerly/exHarmony huggingface dataset:
Evolution Through Large Models

This chapter pursues the insightInsight that large language modelsLarge language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic programming (GP). Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), inEvolution through Large Models the main experiment ELM combined with MAP-ElitesMAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the SodaraceSodarace domain, which the original LLM had never seen in pretraining. These examples then help to bootstrapBootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrapBootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endednessOpen-endedness, deep learning, and reinforcement learningReinforcement Learning. These implications are explored here in depth in the hope of inspiring new directions of research now opened up by ELM.
Artificial Intelligence in Education: Awareness, Readiness and Practice of Using Artificial Intelligence Technologies in Professional Activities by University Faculty

Daily artificial intelligence (AI) technologies are being implemented deeper and deeper in all spheres of human life, including education. At the same time, the degree of integration of AI tools into the pedagogical process largely depends on the awareness, readiness and practice of using the entire AI arsenal by teachers in their professional activities. The purpose of this study is to identify the awareness of university faculty in the use of AI tools in teaching activities, as well as to determine the degree of their readiness and practice of using AI technology in the educational process. The survey participants were 426 teachers from 18 universities of the Russian Federation. The results of the survey showed that the integration of AI tools in education is currently at an early stage. In general, university faculty lack a systematic understanding of the organizational, teaching and learning potential of AI tools. Many educators have segmental knowledge, related to a greater extent directly to their professional activities in teaching specific disciplines, and are not able to create a complete picture of the possibilities of AI in education. At the same time, most university professors express a neutral attitude or readiness to use AI tools in teaching activities. The experience of practical application of AI tools in the pedagogical process is limited to a few cases of using specific technologies in teaching specific aspects of disciplines.
Disentangled Representation Learning for Multimedia

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form. The process of separating underlying factors of variation into variables with semantic meaning benefits in learning explainable representations of data, which imitates the meaningful understanding process of humans when observing an object or relation. As a general learning strategy, DRL has demonstrated its power in improving the model explainability, controllability, robustness, as well as generalization capacity in a wide range of scenarios such as computer vision, natural language processing, data mining etc. In this tutorial, we comprehensively present DRL from various aspects including motivations, definitions, methodologies, evaluations, applications and model designs for multimedia. We discuss works on DRL based on two well-recognized definitions, i.e., Intuitive Definition and Group Theory Definition. We further categorize the methodologies for DRL into four groups, i.e., Traditional Statistical Approaches, Variational Auto-encoder Based Approaches, Generative Adversarial Networks Based Approaches, Hierarchical Approaches and Other Approaches. We also analyze principles to design different DRL models that may benefit different tasks in practical multimedia applications. Finally, we point out challenges in DRL as well as potential research directions deserving future investigations. We believe this tutorial may provide insights for promoting the DRL research in the multimedia community.
Understanding AI tool engagement: A study of ChatGPT usage and word-of-mouth among university students and office workers

This study aims to explore the determinants of user behaviors toward an artificial intelligence (AI) tool, ChatGPT, focusing on university students and office workers. In this study, we present a comprehensive model to understand user engagement with AI tools, specifically focusing on ChatGPT. The model is grounded on four primary stages, each containing distinct variables: 1) fundamental (comprising perceived intelligence and system quality), 2) knowledge and service (covering knowledge acquisition, application, personalization, and trust), 3) gain with user tendency (encompassing utilitarian benefits, individual impact, satisfaction, and personal innovativeness), and 4) behavior (including behavioral intention, continued usage, and Word-of-Mouth (WOM)). A total of 13 variables have been examined. A survey was conducted on a sample of 645 university students and office workers, and the collected data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal significant associations between perceived intelligence and knowledge management, and personalization. System quality also significantly impacts knowledge management and personalization. Knowledge acquisition and application were found to significantly affect utilitarian benefits and individual impact, but not satisfaction. Personalization significantly influenced utilitarian benefits, individual impact, and satisfaction. Trust significantly impacts behavioral intention. Utilitarian benefits and individual impact had a positive effect on satisfaction, behavioral intention, and WOM. Personal innovativeness was significantly associated with behavioral intention. Behavioral intention significantly affected usage and WOM, while usage did not significantly associate with WOM. Among control variables, only age affects behavioral intention. This study also confirmed the indirect effects and conducted a multi-group analysis (MGA) between students and workers. MGA results show that there are significant differences in three relationships (personalization-satisfaction, utilitarian benefits-WOM, and behavioral intention-WOM) between students and workers. This research extends the understanding of AI tool usage and provides theoretical and practical insights for researchers, practitioners, and policymakers in AI and related fields.
Matrix Diffractive Deep Neural Networks Merging Polarization into Meta‐Devices

Abstract The all‐optical diffractive deep neural networks (D 2 NNs) framework as a hardware platform is demonstrated to implement various advanced functional meta‐devices with high parallelism and high processing speed. However, the design methodology merging trainable polarization modulation neurons into the D 2 NNs, which potentially possess higher integration and more task‐loading capacity, is not yet fully explored. Here, the matrix diffractive deep neural networks (M‐D 2 NNs) are proposed to deploy polarization‐sensitive Jones matrix metasurfaces into the all‐optical polarization multiplexing networks to perform sophisticated inference tasks as well as inverse designs for advanced functional meta‐devices. Three polarization multiplexing meta‐devices with advanced functionalities are implemented by the M‐D 2 NNs, that is, high task‐capacity integration classification, non‐interleaved high‐efficiency Jones matrix eight‐channel regulation, and custom‐polarization information cryptographic multiplexing. The M‐D 2 NNs are demonstrated to provide a new strategy to merge polarization into electromagnetic and optical field modulators by Jones matrix metasurfaces, which may drive the evolution of all‐optical networks toward multi‐task integration and more advanced functional devices.
Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.
Mobile money innovation and global value chain participation: Evidence from developing countries

This study provides empirical insights on the effect of mobile money innovation on global value chain participation. Applying quantile estimation technique on the data of 90 developing economies between 2011 and 2018, we document that mobile money innovation has significant positive effect on participation in global value chains. Furthermore, our results reveal that economic and governance factors such as financial development, human capital development, tariff and distance are crucial variables that influence participation in global value chain. We recommend that friendly trade policies and investment in socioeconomic infrastructures and human capital development would help in facilitating the wide-spread adoption of mobile money innovations and enhance GVC participation.
DS-YOLOv8-Based Object Detection Method for Remote Sensing Images

The improved YOLOv8 model (DCN_C2f+SC_SA+YOLOv8, hereinafter referred to as DS-YOLOv8) is proposed to address object detection challenges in complex remote sensing image tasks. It aims to overcome limitations such as the restricted receptive field caused by fixed convolutional kernels in the YOLO backbone network and the inadequate multi-scale feature learning capabilities resulting from the spatial and channel attention fusion mechanism’s inability to adapt to the input data’s feature distribution. The DS-YOLOv8 model introduces the Deformable Convolution C2f (DCN_C2f) module in the backbone network to enable adaptive adjustment of the network’s receptive field. Additionally, a lightweight Self-Calibrating Shuffle Attention (SC_SA) module is designed for spatial and channel attention mechanisms. This design choice allows for adaptive encoding of contextual information, preventing the loss of feature details caused by convolution iterations and improving the representation capability of multi-scale, occluded, and small object features. Moreover, the DS-YOLOv8 model incorporates the dynamic non-monotonic focus mechanism of Wise-IoU and employs a position regression loss function to further enhance its performance. Experimental results demonstrate the excellent performance of the DS-YOLOv8 model on various public datasets, including RSOD, NWPU VHR-10, DIOR, and VEDAI. The average mAP@0.5 values achieved are 97.7%, 92.9%, 89.7%, and 78.9%, respectively. Similarly, the average mAP@0.5:0.95 values are observed to be 74.0%, 64.3%, 70.7%, and 51.1%. Importantly, the model maintains real-time inference capabilities. In comparison to the YOLOv8 series models, the DS-YOLOv8 model demonstrates significant performance improvements and outperforms other mainstream models in terms of detection accuracy.
Discovering causal relations and equations from data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws, and principles that are invariant, robust, and causal has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventions on the system under study. With the advent of big data and data-driven methods, the fields of causal and equation discovery have developed and accelerated progress in computer science, physics, statistics, philosophy, and many applied fields. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for data-driven causal and equation discovery, point out connections, and showcase comprehensive case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is revolutionised with the efficient exploitation of observational data and simulations, modern machine learning algorithms and the combination with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.
A robust regressor model for estimating solar radiation using an ensemble stacking approach based on machine learning

Of late, with the rapid advancement in solar power generation, some difficulties have cropped up due to solar intermittency, necessitating an accurate forecast of Global Horizontal Irradiance. To address this challenge, this work proposes a feature selection method based on the variance inflation factor combined with mutual information (VIF-MI). In addition, this article also proposes an improved stack ensemble with an extra tree (SE-ET) regressor in which the machine learning models such as Decision Tree, Random Forest, Light Gradient Boost Machine, Cat Boost, and Extreme Gradient Boost are taken as base learners and the ridge regressor along with the extra tree regressor are taken as meta learners. The prediction performance is evaluated by comparing the outcome of each considered model and bagging with the proposed method. For the VIF-MI method, the proposed SE-ET exhibits the best estimation performance and good reduction of error, such as in mean absolute error by 16–60%, in root mean squared error by 7–64%, and in mean absolute percentage error by 20–75% as compared to the other models. Conclusively, it may be inferred that the ensemble technique minimizes the prediction error and can help plan for using sporadic solar resources more effectively.
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators

Generative adversarial networks (GANs) have recently made great progress in blind image super-resolution (SR) with their superiority in learning mappings between manifolds, which benefits the reconstruction of image's textural details. Recent works have largely focused on designing more realistic degradation models, or constructing a more powerful generator structure but neglected the ability of discriminators in improving visual performances. In this paper, we present A-ESRGAN, a GAN model for blind SR tasks featuring an attention U-Net based, multi-scale discriminator that can be seamlessly integrated with other generators. To our knowledge, this is the first work to introduce attention U-Net structure as the discriminator of GAN to solve blind SR problems. And the paper also gives an interpretation of the mechanism behind multi-scale attention U-Net that brings performance breakthrough to the model. Experimental results demonstrate the superiority of our A-ESRGAN over state-of-the-art level performance in terms of quantitative metrics and visual quality. The code can be find in https://github.com/stroking-fishes-ml-corp/A-ESRGAN .
Efficient collective action for tackling time-critical cybersecurity threats

Abstract The latency reduction between the discovery of vulnerabilities, the build-up, and the dissemination of cyberattacks has put significant pressure on cybersecurity professionals. For that, security researchers have increasingly resorted to collective action in order to reduce the time needed to characterize and tame outstanding threats. Here, we investigate how joining and contribution dynamics on Malware Information Sharing Platform (MISP), an open-source threat intelligence sharing platform, influence the time needed to collectively complete threat descriptions. We find that performance, defined as the capacity to characterize quickly a threat event, is influenced by (i) its own complexity (negatively), by (ii) collective action (positively), and by (iii) learning, information integration, and modularity (positively). Our results inform on how collective action can be organized at scale and in a modular way to overcome a large number of time-critical tasks, such as cybersecurity threats.
Human Pose Estimation Using Deep Learning: A Systematic Literature Review

Human Pose Estimation (HPE) is the task that aims to predict the location of human joints from images and videos. This task is used in many applications, such as sports analysis and surveillance systems. Recently, several studies have embraced deep learning to enhance the performance of HPE tasks. However, building an efficient HPE model is difficult; many challenges, like crowded scenes and occlusion, must be handled. This paper followed a systematic procedure to review different HPE models comprehensively. About 100 articles published since 2014 on HPE using deep learning were selected using several selection criteria. Both image and video data types of methods were investigated. Furthermore, both single and multiple HPE methods were reviewed. In addition, the available datasets, different loss functions used in HPE, and pretrained feature extraction models were all covered. Our analysis revealed that Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are the most used in HPE. Moreover, occlusion and crowd scenes remain the main problems affecting models’ performance. Therefore, the paper presented various solutions to address these issues. Finally, this paper highlighted the potential opportunities for future work in this task.
Composite Quantum Simulations

In this paper we provide a framework for combining multiple quantum simulation methods, such as Trotter-Suzuki formulas and QDrift into a single Composite channel that builds upon older coalescing ideas for reducing gate counts. The central idea behind our approach is to use a partitioning scheme that allocates a Hamiltonian term to the Trotter or QDrift part of a channel within the simulation. This allows us to simulate small but numerous terms using QDrift while simulating the larger terms using a high-order Trotter-Suzuki formula. We prove rigorous bounds on the diamond distance between the Composite channel and the ideal simulation channel and show under what conditions the cost of implementing the Composite channel is asymptotically upper bounded by the methods that comprise it for both probabilistic partitioning of terms and deterministic partitioning. Finally, we discuss strategies for determining partitioning schemes as well as methods for incorporating different simulation methods within the same framework.
Motion planning around obstacles with convex optimization

From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics. Planners based on optimization can design trajectories in high-dimensional spaces while satisfying the robot dynamics. However, in the presence of obstacles, these optimization problems become nonconvex and very hard to solve, even just locally. Thus, when facing cluttered environments, roboticists typically fall back to sampling-based planners that do not scale equally well to high dimensions and struggle with continuous differential constraints. Here, we present a framework that enables convex optimization to efficiently and reliably plan trajectories around obstacles. Specifically, we focus on collision-free motion planning with costs and constraints on the shape, the duration, and the velocity of the trajectory. Using recent techniques for finding shortest paths in Graphs of Convex Sets (GCS), we design a practical convex relaxation of the planning problem. We show that this relaxation is typically very tight, to the point that a cheap postprocessing of its solution is almost always sufficient to identify a collision-free trajectory that is globally optimal (within the parameterized class of curves). Through numerical and hardware experiments, we demonstrate that our planner, which we name GCS, can find better trajectories in less time than widely used sampling-based algorithms and can reliably design trajectories in high-dimensional complex environments.
Lightweight You Only Look Once v8: An Upgraded You Only Look Once v8 Algorithm for Small Object Identification in Unmanned Aerial Vehicle Images

In order to solve the problems of high leakage rate, high false detection rate, low detection success rate and large model volume of small targets in the traditional target detection algorithm for Unmanned Aerial Vehicle (UAV) aerial images, a lightweight You Only Look Once (YOLO) v8 algorithm model Lightweight (LW)-YOLO v8 is proposed. By increasing the channel attention mechanism Squeeze-and-Excitation (SE) module, this method can adaptively improves the model’s ability to extract features from small targets; at the same time, the lightweight convolution technology is introduced into the Conv module, where the ordinary convolution is replaced by the GSConv module, which can effectively reduce the model computational volume; on the basis of the GSConv module, a single aggregation module VoV-GSCSPC is designed to optimize the model structure in order to achieve a higher computational cost-effectiveness. The experimental results show that the LW-YOLO v8 model’s mAP@0.5 metrics on the VisDrone2019 dataset are more favorable than those on the YOLO v8n model, improving by 3.8 percentage points, and the computational amount is reduced to 7.2 GFLOPs. The LW-YOLO v8 model proposed in this work can effectively accomplish the task of detecting small targets in aerial images from UAV at a lower cost.