Reviewerly/exHarmony huggingface dataset:
Twitter Newcomers: Uncovering the Behavior and Fate of New Accounts Through Early Detection and Monitoring

There is a significant body of literature concerning the analysis of Twitter accounts, yet the behavior of newly created accounts remains relatively unexplored. In this study, we introduce a novel approach to detect Twitter accounts right after registration and explore their behavioral patterns. In a two-week period in April 2020, our technique identified over 500,000 accounts before they even started interacting with the platform. Each account was monitored for 21 days by sampling profile information and timelines at scheduled intervals, retrieving over 8 million tweets. An additional sample of profile information was collected approximately two years after creation, in May 2022. One of the key findings of our study is the lack of sustained and genuine engagement from new accounts. Indeed, a large proportion of them (almost 25%) were suspended by Twitter in the first 21 days, and the evaluation conducted after two years reveals that only a tiny fraction of the remaining enabled accounts seem to be active and genuine users (3.8% of the initial sample). Additionally, despite the early suspensions enforced by Twitter, it turns out that some short-lived accounts still managed to have a substantial impact on the total volume of content and interactions from new accounts. Overall, our findings may have important implications for understanding the dynamics of new accounts’ behavior as well as Twitter’s suspension policy prior to the recent change in ownership. This could stimulate further research to evaluate the impact of the ongoing changes introduced by the new administration.
Real-time optimal control of automatic sewing considering fabric geometric shapes

This study aims to improve the sewing accuracy and robustness of a control system for automating sewing operations. In a previous study, we experimentally demonstrated a control system for automating the butt seaming of two fabric pieces (one curved and another straight) into a 3D shape. However, because the control method was obtained through trial and error, there were problems, such as low sewing accuracy and low robustness, including sewing failure when changing the sewing machine feed rate. Therefore, this study improved the sewing accuracy using a real-time optimal control. Furthermore, robustness was enhanced using the feed rate and geometric shapes of the fabrics. The experimental results confirm that the proposed method improved sewing accuracy and robustness.
Virtual Museum Scene Design Based on VRAR Realistic Interaction under PMC Artificial Intelligence Model

Abstract At present, there are problems of insufficient conservation and heritage and single display channels in the field of museums. In the background of digital era, innovative new museum display mode and interaction mode can bring new exhibition experience for visitors. In this paper, we study the key technologies such as 3D scene visualization, facility and equipment simulation, and human-computer interaction simulation. Based on virtual reality and augmented reality, somatosensory interactive digital technologies, an interaction engine under multiple classifiers is proposed, an online virtual museum design strategy is built, and an all-round, multi-angle, three-dimensional interactive museum display platform is constructed. A quantitative analysis method based on the policy consistency index model is proposed to conduct user measurement and post-use evaluation of the conceived virtual museum. The application results show that the design platform can intuitively display the complex environment of an intelligent digital museum, support the development of immersive virtual roaming and virtual touch, and provide a new way to visit a new type of interactive museum.
Analysis of the improvement path of English listening and speaking ability based on big data technology

Abstract In current English learning, listening, and speaking is an important part of English learning. To cultivate students’ English listening and speaking ability and meet the demand for language communication improvement today, this paper uses big data technology as the main support to recognize, noise reduction and feature extraction of input speech using speech recognition algorithm, noise elimination algorithm and feature extraction algorithm, respectively. Obtain data related to students’ English listening and speaking abilities to form systematic and complete student big data. Through preprocessing and mining, it provides a basis for decision making in English listening and speaking teaching. According to the results of the analysis of students’ English listening and speaking ability, the percentage of students who liked English listening very much in the pre-test was 25% of the class, and the percentage of students who thought they could be optimistic about the hardships in learning was 18%. After practicing the improvement path, the percentages of students increased to 38% and 29%, respectively. The mean value of listening at the pre-test was 20.23, which was lower than the mean value of listening at the post-test, 21.72. The above experimental results are sufficient to show that the development of the pathway in this paper can make students more motivated and enthusiastic in learning English listening and speaking, and thus improve their English listening and speaking skills.
Image embedding for denoising generative models

Abstract Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of embedding an image into the latent space of Denoising Diffusion Models, that is finding a suitable “noisy” image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images.
A Comprehensive Survey of RGB-Based and Skeleton-Based Human Action Recognition

With the advancement of computer vision, human action recognition (HAR) has shown its broad research worth and application prospects in a wide range of fields such as intelligent security, automatic driving and human-machine interaction.Based on the type of data captured by cameras and sensors, e.g., RGB, depth, skeleton, and infrared data, HAR methods can be classified into RGB-based and skeleton-based.RGB data is easy and inexpensive to obtain, but RGB-based methods need to cope with a large amount of irrelevant background information and are easily affected by factors such as lighting and shooting angle.The skeleton-based methods eliminate the impact of background variables and require little computational work due to their skeleton-focused features, but they lack the context data necessary for HAR.This paper gives a thorough survey of these two approaches, covering deep learning methods, handcrafted feature extraction methods, common datasets, challenges, and future research directions.The skeleton-based action recognition methods section specifically presents the most well-liked 2D and 3D pose estimation algorithms.This survey aims to give researchers new to the area or engaged in a long-term study a selection of datasets and algorithms, as well as an overview of the present issues and expected future directions in the field.
Noise resistance of next-generation reservoir computing: a comparative study with high-order correlation computation

Abstract Reservoir computing (RC) methods have received more and more attention and applications in chaotic time series prediction with their simple structure and training method. Recently, the next-generation reservoir computing (NG-RC) method has been proposed by Gauthier et al. (Nat Commun 12:5564, 2021) with less training cost and better time series predictions. Nevertheless, in practice, available data on dynamic systems are contaminated with noise. Though NG-RC is shown highly efficient in learning and predicting, its noise resistance captivity is not clear yet, limiting its use in practical problems. In this paper, we study the noise resistance of the NG-RC method, taking the well-known denoising method, the high-order correlation computation (HOCC) method, as a reference. Both methods have similar procedures in respect of function bases and regression processes. With the simple ridge regression method, the NG-RC method has a strong noise resistance for white noise, even better than the HOCC method. Besides, the NG-RC method also shows a good prediction ability for small colored noise, while it does not provide correct reconstruct dynamics. In this paper, other than reconstruction parameters, four numerical indicators are used to check the noise resistance comprehensively, such as the training error, prediction error, prediction time, and auto-correlation prediction error, for both the short-time series and long climate predictions. Our results provide a systematic estimation of NG-RC’s noise resistance capacity, which is helpful for its applications in practical problems.
Personalized and motion-based human activity recognition with transfer learning and compressed deep learning models

Human activity recognition (HAR) enables the recognition of the activities of daily living using signals from motion sensors integrated into mobile and wearable devices. One of the challenges is the uniqueness of each individual with his/her different characteristics. A general model trained without user data may perform poorly on specific users. Another challenge is running deep learning (DL) models on mobile and wearable devices due to their limited resources. In this paper, to cope with these two challenges, we use transfer learning to build personalized models and model compression for running DL algorithms. We examine the impact of different DL architectures, the number of layers to be fine-tuned, the amount of user training data, and the transfer to new datasets on the performance of HAR. We compare the performance of the transferred models with general and user-specific models in terms of F1 score, training and inference time.
Trace-based Deductive Verification

Contracts specifying a procedure’s behavior in terms of pre- and postconditions are essential for scalable software verification, but cannot express any constraints on the events occurring during execution of the procedure. This necessitates to annotate code with intermediate assertions, preventing full specification abstraction. We propose a logic over symbolic traces able to specify recursive procedures in a mod- ular manner that refers to specified programs only in terms of events. We also provide a deduction system based on symbolic execution and induction that we prove to be sound relative to a trace semantics. Our work generalizes contract-based to trace-based deductive verification by extending the notion of state-based contracts to trace-based contracts.
An Interactive SMT Tactic in Coq using Abductive Reasoning

A well-known challenge in leveraging automatic theorem provers, such as satisfiability modulo theories (SMT) solvers, to discharge proof obligations from interactive theorem provers (ITPs) is determining which axioms to send to the solver together with the con- jecture to be proven. Too many axioms may confuse or clog the solver, while too few may make a theorem unprovable. When a solver fails to prove a conjecture, it is unclear to the user which case transpired. In this paper we enhance SMTCoq — an integration between the Coq ITP and the cvc5 SMT solver — with a tactic called abduce aimed at mitigating the uncertainty above. When the solver fails to prove the goal, the user may invoke abduce which will use abductive reasoning to provide facts that will allow the solver to prove the goal, if any.
Gold source and ore-forming process of the Linglong gold deposit, Jiaodong gold province, China: Evidence from textures, mineral chemical compositions and sulfur isotopes of pyrite

The Linglong gold orefield is located northwest of the Jiaodong gold province, in the eastern North China Craton (NCC) area. It consists of over 10 gold mines with a total gold resource of approximately 1000 t, and gold mineralization occurs in gold-quartz veins. The Linglong gold deposit underwent four stages of ore-forming processes namely: Pyrite-quartz (stage I) – milky-white quartz vein with euhedral pyrites, Quartz-pyrite (stage II) – smoky quartz veins with euhedral-subhedral pyrites, Polymetallic sulfides (stage III) – quartz veins with large amounts of polymetallic sulfides (pyrite, pyrrhotite, sphalerite, galena), hematite, and native gold, and the Quartz–carbonate (stage IV) – calcite-quartz veins. This study focuses on the investigation of pyrites from three main mineralization stages (I-III), exploring their textures, in-situ major and trace element concentrations, and in-situ sulfur isotopic compositions. The study examined pyrite through various methods including BSE and EMPA major element, LA-ICP-MS trace element, and LA-MC-ICP-MS sulfur isotope analyses. Pyrite from three distinct stages of ore-forming processes were analyzed. Pyrites from stage I (Py-I) exhibited euhedral crystals with homogeneous brightness in the BSE, lacked mineral inclusions, porous microtextures, and oscillatory zones. Py-I possessed low contents of Au, Ag, As, Bi, Cu, Sb, Zn, Co, and Ni. Pyrites from stage II (Py-II) showed fine-coarse grains and euhedral-subhedral features with slight fractures, similar to Py-I, however, had the higher concentrations of As and Sb. Pyrites from stage III (Py-III) were found in the quartz-polymetallic sulfide veins and exhibited subhedral to anhedral features and more significant concentrations of metallic elements, particularly Au and As. The δ34S values of Py-I, Py-II and Py-III were in the ranges of 6.76‰–7.47‰, 6.11‰–7.27‰ and 6.07‰–8.33 ‰, respectively. Analysis of the texture and geochemical composition of pyrite suggested that invisible gold was primarily present in pyrite in the form of solid solution (Au+), accompanied by a small amount of nano-form gold. The ore-forming fluids that led to this mineralization were mostly derived from mantle-derived mafic magma mixed with some minor metamorphic sedimentary material. A decrease in fluids temperature, water-rock interactions and fluid boiling led to the depositions of sulfides and gold. In the Late Mesozoic, lithospheric delamination of the eastern block of the NCC triggered decompression and melting of the upwelling asthenospheric mantle resulting in partial melting of the lithospheric mantle. This lithospheric mantle-derived mafic magma provided the ore-forming fluids for large-scale gold mineralization in the Jiaodong gold province.
Local pseudo-attributes for long-tailed recognition

Existing long-tailed recognition methods focus on learning global image representation by re-weighing, re-sampling, or global representation learning. However, we observe that solving real-world long-tailed recognition problems requires a fine-grained understanding of local parts within the image in order to avoid confusion among images with similar global configurations. We propose a novel self-supervised learning framework based on local pseudo-attributes (LPA) that are learned via clustering of local features without any human annotations. Such pseudo-attributes are often more balanced compared to image-level class labels. Our method outperforms the state-of-the-art on various long-tailed image classification datasets, such as CIFAR100-LT, iNaturalist, and ImageNet-LT.
Oscillation and Oscillating Structures in Syntax

Summary Linguistic expressions are very often indeterminate and have several possible readings. Syntactic indeterminacy may result from different underlying structures, from polysemous elements, or from so-called oscillation. Unlike the former two, oscillation cannot be reduced to one or more discrete interpretations in a given context, it is fundamentally non-resolvable. While oscillating structures do not hamper successful communication, they present a problem for linguistic analyses and categorizations. The relevant literature does not address oscillation systematically; if anything, it is treated in connection with problems of categorization. In this paper, oscillation is perceived as a phenomenon sui generis . We first give a definition of oscillation against the backdrop of other indeterminate structures and outline their relevance for a proper understanding of older and less formal varieties. Then we propose a tentative typology of oscillating structures and their potential triggers in selected Slavic languages.
A general steganographic framework for neural network models

In this paper, we propose a general steganographic framework for neural networks to achieve covert communication. Firstly, we design a baseline steganographic method to embed secret data into convolutional layers of a given neural network (cover network) during the process of network training. With a network containing secret data (stego network), matrix multiplication is used to encode the parameters of convolutional layers for data extraction. Based on the baseline steganographic method, we respectively extended multi-source and multi-channel steganographic schemes for different scenarios, in which multiple agents are allowed to embed or extract secret data at the sender or receiver side. The multiple senders or receivers at the same side are ignorant of each other's existence, ensuring the independence and security of communication between each pair of sender and receiver. Experimental results verified the universality of the baseline method and the effectiveness of the extended schemes, including capacity, security, and robustness.
Towards a DISSECT-CF extension for simulating function-as-a-service

Serverless computing is a new way of delivering cloud services that abstracts a user from the burden of managing resources and infrastructure. Although a few serverless simulators have been developed, they do not compare to services that are offered by commercial providers. Developing a comprehensive serverless simulation framework is essential towards evaluating scenarios reliant on the serverless paradigm. This paper focuses on introducing a serverless computing model to the DIScrete event baSed Energy Consumption simulaTor for Clouds and Federations (DISSECT-CF) simulator to enable simulating realistic function-as-a-service (FaaS) solutions. This new model is capable of imitating serverless providers in terms of cost model, associating triggers, extracting performance metrics, and customising the configurations of functions.
Object measurement in real underwater environments using improved stereo matching with semantic segmentation

Accurately measuring distances in underwater environments is critical for applications such as underwater exploration and maintenance of underwater structures, but is limited by underwater environmental factors. To address this challenge, semantic segmentation and visual stereo matching are promising methods to achieve this goal. In this paper, we proposed a combination of Retinex and Curvature filter for image enhancement, and a modified SGBM (Semi-Global Block Matching) method using dilated SAD(sum of absolute differences) window and the dynamic disparity range obtained using semantic segmentation results to accelerate stereo matching, while also providing insight into the content of the sensed scene. By using the method we proposed, the measurement error is significantly reduced, even in extreme environments with low visibility and complex backgrounds. It showed strong adaptability when dealing with irregular shape objects, and sped up the calculation speed of the algorithm by reducing the calculation area. In the comparison experiment, we found that the error of SGBM with our image enhancement was reduced to 22.8%. By using our stereo matching algorithm, the error was further reduced to 6.2%, demonstrating its superiority for underwater measurement tasks. Our approach offered an efficient solution for autonomous distance measurement in challenging underwater environments.
Web-based support for higher school teachers: insights from a survey and theoretical analysis

This article delves into the theoretical aspects of utilizing Web 2.0 technology in higher education. The study examines the responses of 87 participants to identify the types of activities conducted by higher education teachers and determine the Web 2.0 tools that can enhance their instructional practices. The authors conduct a comprehensive theoretical analysis of existing research and resources that focus on the theoretical foundations of incorporating Web tools in higher education. The research presents the common characteristics of online courses and discusses principles for the effective functioning and physical placement of online systems in the Web space. Based on the findings, the study concludes that promoting online courses aimed at familiarizing Mathematics teachers with the technical capabilities of creating educational content using Web 2.0 technology is highly feasible. This article contributes valuable insights into the theoretical underpinnings of Web 2.0 technology in higher education and offers recommendations for integrating these tools into instructional practices.
Ecohydrological insight: Solar farms facilitate carbon sink enhancement in drylands

Solar farms are critical to tackling climate change and achieving carbon neutrality. Besides producing renewable energy, a solar farm modifies microclimates and changes water distribution, consequently affecting local carbon sequestration capacity (CSC). Yet, how the CSC of an ecosystem responds to these changes after solar farm construction remains inadequately understood. Herein, the SOFAR model was adopted to reveal the effects of large-scale solar farms (LSFs) on CSC in arid northern China, with a series of numeric experiments along a climate gradient (with precipitation ranging from 70 to 500 mm yr-1). The results show that relative to pristine vegetation background, CSC was non-linearly increased by averages of 3.49-6.68%, 4.43-10.25%, 5.07-9.71% and 5.6% each year after the installation of LSFs in hyper-arid climates (with aridity index or AI = 0.04-0.05), arid climates (AI = 0.14-0.16), semi-arid climates (AI = 0.21-0.3) and semi-humid climates (AI = 0.55), respectively. The increase in available water for plants growing under the drip lines of photovoltaic panels (PVs) in LSFs is confirmed to be the overwhelming factor responsible for CSC enhancement. Although biases remain in the estimation of increased CSC in hyper- and semi-humid regions due to the high variability of climate (e.g., extreme drought events) and serious radiation reduction beneath PVs, it is certain that solar farms facilitate CSC without increasing external land use. These results will deepen our understanding of the feedback between solar farms and ambient environments and be meaningful for vegetation management in solar farms, especially in the context of climate change and carbon neutrality aims.