Reviewerly/exHarmony huggingface dataset:
Design of Substation Carbon Emission Prediction Model Based on Cloud Model

In order to achieve the goal of energy conservation and emission reduction, all walks of life are taking active actions. Power system, as the main dispatching application of energy, is in full swing in the monitoring and management of carbon emissions and other related studies. The purpose of this paper is to study the design of substation carbon emission prediction model based on the cloud model. First, the basic concepts and numerical characteristics of the cloud model are introduced. Secondly, it introduces the principle of carbon emission prediction method based on cloud model and the common carbon emission prediction method. Combining the real-time substation carbon emission data simulation with the cloud model prediction, the prediction results were compared with the BP neural network, and two average error values were used to measure the prediction results. It was found that the cloud model had a higher accuracy in predicting carbon emission, which verified its feasibility and superiority.
A Novel Spatio-Temporal-Wise Network for Action Recognition

Action recognition is a challenging task that requires understanding the temporal relationships between frames. However, capturing and processing spatio-temporal and motion features is computationally expensive, making it difficult to apply to practical situations. We propose a novel approach called the Spatio-Temporal-Wise (STW) network to address this problem. The STW network inserts STW blocks, consisting of a Spatio-Temporal Fusion Module and a Temporal-Wise Module, into an existing 2D CNN. This approach requires very little additional computational overhead but brings huge performance improvements in recognizing human actions. The proposed method is evaluated on several public datasets, including Something-Something v1 & v2, Kinetics-400, UCF101, and HMDB51. STW achieved comparable or better performance on these datasets compared to state-of-the-art methods. Notably, the STW network improves recognition accuracy by 26.6% and 34.6% on the Something-Something v1 & v2 datasets, respectively, with less than 2% additional computational overhead. The results demonstrate that the STW network can significantly improve performance in action recognition tasks while requiring only a small additional computational overhead, which represents a promising direction for developing more efficient and effective approaches to handling temporal reasoning in action recognition, which may have important applications in the future.
Southwestern Australia Acid-Saline Mineralogy: Observations From Reflectance Spectroscopy

Acid-saline sediments in shallow-lake environments in southwestern Australia host complex mineralogical suites representing long-term weathering and modern extreme acid-saline chemistry. It is not known whether large-scale reflectance spectroscopy datasets from watersheds across the Yilgarn Craton show regional mineralogical variability. This study assesses over 2500 spectra from datasets of shallow subsurface sediments by using automatic minima detections for spectral features of interest. Integrating microscopic and spectral observations show that proportionally similar mineral assemblages exist in a variety of recognizable textures. Spectral results indicate that iron oxide and phyllosilicates are the most detected mineral types, alongside less common alunite, jarosite, and gypsum. The difference in detection abundance by percentage for mineral groups, particularly iron oxides, accounts for the largest regional variation observed in spectra. Spectral feature characteristics show significant regional variation for iron oxide, alunite, and hydrated mineral absorptions. The spectral observations align with previous mineralogical observations of these landscapes and the expected weathering evolution for granitoids, where Al-phyllosilicates and iron oxides are most prevalent. Observations of these mineral suites enhance understanding of regional differences and similarities in chemical precipitates and products of water–rock interaction in extreme acid-saline environments and have implications for studying sediments and rocks on Mars.
Integrating Relational Structure to Heterogeneous Graph for Chinese NL2SQL Parsers

The existing models for NL2SQL tasks are mainly oriented toward English text and cannot solve the problems of column name reuse in Chinese text data, description in natural language query, and inconsistent representation of data stored in the database. To address this problem, this paper proposes a Chinese cross-domain NL2SQL model based on a heterogeneous graph and relative position attention mechanism. This model introduces relational structure information defined by the expert to construct initial heterogeneous graphs for database schemas and natural language questions. The heterogeneous graph is pruned based on natural language questions, and the multi-head relative position attention mechanism is used to encode the database schema and natural language questions. The target SQL statement is generated using a tree-structured decoder with predefined SQL syntax. Experimental results on the CSpider dataset demonstrate that our model better aligns database schema with natural language questions and understands the semantic information in natural language queries, effectively improving the matching accuracy of Chinese multi-table SQL statement generation.
Hybrid-disturbance-observer-based interaction control for a fully actuated UAV with tether-based positioning system

Purpose This paper aims to tackle the problem for a fully actuated unmanned aerial vehicle (FUAV) to perform physical interaction tasks in the Global Positioning System-denied environments without expensive motion capture system (like VICON) under disturbances. Design/methodology/approach A tether-based positioning system consisting of a universal joint, a tether-actuated absolute position encoder and an attitude sensor is designed to provide reliable position feedback for the FUAV. To handle the disturbances, including the tension force caused by the taut tether, model uncertainties and other external disturbances such as aerodynamic disturbance, a hybrid disturbance observer (HDO) combining the position-based method and momentum-based technology with force sensor feedback is designed for the system. In addition, an HDO-based impedance controller is built to allow the FUAV interacting with the environment and meanwhile rejecting the disturbances. Findings Experimental validations of the proposed control algorithm are implemented on a real FUAV with the result of nice disturbance rejection capability and physical interaction performance. Originality/value A cheap alternative to indoor positioning system is proposed, with which the FUAV is able to interact with external environment and meanwhile reject the disturbances under the help of proposed hybrid disturbance observer and the impedance controller.
Your phrases matter: Third waves in research approaches and new contexts for formulaic language

This study reports on new contexts in which formulaic language has been used in the years since 2013 when the last synthesis was carried out. The background presents an old but still useful definition and lists themes under which research was arranged in 2013 and which continue to be used.This study has a particular emphasis on the relevance of formulaic language to people living with dementia.Section 3, identifying new directions, reviews new 'third waves' of research priorities in several fields in which formulaic sequences play a major role, including sociolinguistic variation, corpus-based and corpus-driven analyses, pragmatics, human-computer interaction, and psycholinguistics, all of which are relevant to speech-language therapists. Section 4, outreach and expansions, illustrates new contributions from cognitively impaired person-to-person exchanges in online environments, recent examinations of infant- and pet-directed speech incorporating formulaic language, and online graphic explorations such as emojis. Section 5 focuses on growth of research in theoretical and clinical applications by Van Lancker Sidtis, as illustrated by references to her recent work.The paper's main contribution is to summarize the work on formulaic language over the last 10 years, to indicate its continued importance and relevance in ordinary conversation, and especially in allowing people living with dementia to continue to interact with others.The paper concludes by suggesting that more focus be placed on the analysis of formulaic language with an emphasis on its relevance for speech-language therapists and other clinicians.What is already known on the subject Research has been growing since the late 1970s and early 1980s on non-propositional language (as opposed at that time to the Chomskyan paradigm) and especially on lexical bundles, idioms, second language acquisition and multiword expressions. Studies beginning with Hughlings Jackson (1874) have been annotated through early 2012 (Wray, 2013). What this study adds This study examines 'third waves' in pragmatics, sociolinguistics and areas of neurology and speech perception contributing to what Van Lancker Sidtis (2021) calls the third wave of acceptance of the range and depth of formulaic sequences in ordinary or familiar language. What are the clinical implications of this work? Conversations with pet robots or web-based composition with emojis are but two of the developing areas built on formulaic sequences currently being used for communication interventions with persons living with dementia or other major neurocognitive disorder. Overviews of major contributions in theory and social contexts by Wray (2020, 2021) and theoretical and cognitive applications by Van Lancker Sidtis (2021) detail new areas for the study of formulaic sequences and their contributions to a range of neurocognitive disorders.
<scp>MEMOR</scp>: A database of archeological human remains collections from Flanders, Belgium

The aim of this article is to describe a newly created open access database of archeological human remains collections from Flanders, Belgium. The MEMOR database (www.memor.be) was created to provide an overview of the current practices of loans, reburial, and the research potential of human skeletons from archeological sites currently stored in Flanders. In addition, the project aimed to provide a legal and ethical framework for the handling of human remains and was created around stakeholder involvement from anthropologists, geneticists, contract archeologists, the local, regional and national government agencies, local and national government, universities, and representatives of the major religions. The project has resulted in the creation of a rich database with many collections available for study. The database was created using the open-source Arches data management platform that is freely available for organizations worldwide to configure in accordance with their individual needs and without restrictions on its use. Each collection is linked to information about the excavation and the site the remains originate from, its size and time period. In addition, a research potential tab reveals whether any analyses were performed, and whether excavation notes are available with the assemblage. The database currently contains 742 collections, ranging in size from 1 to over 1000 individuals. New collections will continue to be added when new assemblages are excavated and studied. The database can also be expanded to include human remains collections from other regions and other material categories, such as archaeozoological collections.
Dynamical analysis of a novel chaotic system and its application to image encryption

This study presents a novel four-dimensional chaotic system derived from the Lorenz-Haken equation. The paper investigates the system’s dissipative properties, equilibrium point, Poincaré map, 0–1 test, Lyapunov exponent, complexity, and bifurcation diagram to introduce the system’s dynamics. The research also examines the chaotic behavior of the system under various parameters, demonstrating the existence of symmetric attractors and offset boosting. To demonstrate the system’s feasibility, circuit simulation is performed using Multisim software, and the hardware implementation is carried out using a Field Programmable Gate Array (FPGA). An image encryption algorithm is designed by integrating matrix dislocation, backward diffusion, and deoxyribonucleic acid (DNA) encryption with the discretized chaotic system. The simulation results show that the algorithm has a good encryption effect. NIST tests show that the sequence has good randomness, correlation coefficient, and information entropy tests show that the encrypted image has strong randomness, and key space and key sensitivity prove that the system has a good ability to resist exhaustive attacks. The use of simulated differential attacks, cropping attacks, and noise attacks has demonstrated the high robustness of the system. The use of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index proves that the encryption algorithm does not affect image quality. Further proving the excellent performance of the proposed encryption algorithm.
Rethinking Multi-Object Tracking Based on Re-Identification and Appearance Model Management

Conventional multi-object tracking (MOT) methods often fail to track objects that repeatedly disappear and reappear in the scene due to occlusion or moving off the screen. Previous studies have used motion and appearance information to address these issues but have limitations in dealing with complex scenarios. To overcome these limitations, we propose a post-hoc framework for MOT, a so-called rethinking MOT. The proposed framework efficiently combines MOT and re-identification methods to handle tracking failures in conventional MOT methods. To this end, we proposed a track reliability checking method to determine unreliable tracks and performed re-identification to correct incorrect tracking results. For re-identification, we proposed appearance aspect-ratio matching that is practical and effective. In addition, we designed an online appearance model management for accurate and efficient re-identification. The proposed framework does not require any offline training stages, such as feature or metric learning, but it is very practical for comparing appearances in real time. In addition, it is a flexible and pluggable framework that can adopt any baseline methods for MOT and Re-id. To validate the proposed methods, we collected 12 new challenging indoor datasets. Unlike the existing benchmark MOT datasets, we consider complex indoor sequences where targets reappear many times in the scene. The experimental results reveal that the proposed methods are promising for MOT under various challenging sequences and outperform state-of-the-art methods. Compared to baseline of the proposed rethinking MOT, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$IDF_{1}$ </tex-math></inline-formula> score was increased by 36.9% in own dataset and 50.4% in the benchmark dataset.
“People’s libraries” of the Society of zealots of Russian historical enlightenment at the turn of the 19th and 20th centuries

At the beginning of the reign of Nicholas II, representatives of the conservativeoriented élite and their supporters from other social strata established the Society of Zealots of Russian Historical Enlightenment in memory of Emperor Alexander III with the aims of maintaining the ideological course of the Emperor (who died in 1894), as well as preventing the return to the reforms in the spirit of Alexander II. One of the directions of its activity was propaganda of political values of Alexander III among common people. For this purpose, in the late 1890s the society began to organise “people’s libraries”, where rural and urban residents could read the relevant ideological literature and where educational talks would be held with them within the ideological guidelines of the Society. The Society’s leadership was careful in deciding who among its members would be entrusted with the organisation of such libraries. To prevent the libraries from receiving ideologically undesirable literature, a complex centralised system of supplying them with both already published editions and new ones was developed. In the fi rst years of its implementation, before the political crisis of 1905–1906 and following liberal reforms, this project of the Society can generally be regarded as successful. An extensive network of libraries was established throughout Russia, and these librarians managed to handle the demands of their users. However, this stage witnessed a growing interest of readers in current events and the necessity to provide libraries with periodicals. Readers were mainly interested in fi ction and popular historical works, while spiritual literature was less asked for, except for the period of Lent. Later, during the liberalisation of the internal political life of the country, these trends could not but create diffi culties for libraries in terms of maintaining their ideological orientation.
Conspiracy Theories of Human Trafficking: Knowledge and Perceptions Among a Diverse College Population

This is the first study to assess belief in myths and conspiracy theories about human trafficking (HT) among a diverse college student population. Four hundred and three college students (74% Latinx and 70% females) from a Hispanic serving institute in the southeast U.S. participated in a study to assess the extent to which they endorse HT myths and conspiracy theories, the role of identity factors in HT myth and conspiratorial beliefs, and overall HT knowledge. Results indicate that many endorsed myths of HT, had limited HT training and mixed levels of HT knowledge, and most believed in at least one conspiracy theory. Men and younger adults were most susceptible to belief in conspiracy theories. Understanding the beliefs of this population and their past exposure to HT facts can further aid in planning educational interventions to raise awareness of HT, debunk conspiracy theories, help identify victims, and potentially prevent future victimization. Improving the efforts of anti-trafficking educational efforts and addressing conspiracy theories of HT contributes to UN Sustainable Development Goal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all, and build effective, accountable, and inclusive institutions at all levels.
Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration

Structured pruning is a well-established technique for compressing neural networks, making them suitable for deployment in resource-limited edge devices. This study presents an efficient loss-aware automatic selection of structured pruning (LAASP) criteria for slimming and accelerating deep neural networks. The majority of pruning methods employ a sequential process consisting of three stages, 1) training, 2) pruning, and 3) fine-tuning, whereas the proposed pruning technique adopts a pruning-while-training approach that eliminates the first stage and integrates the second and third stages into a single cycle. The automatic selection of magnitude or similarity-based filter pruning criteria from a specified pool of criteria and the specific pruning layer at each pruning iteration is guided by the network's overall loss on a small subset of training data. To mitigate the abrupt accuracy drop due to pruning, the network is retrained briefly after each reduction of a predefined number of floating-point operations (FLOPs). The optimal pruning rates for each layer in the network are automatically determined, eliminating the need for manual allocation of fixed or variable pruning rates for each layer. Experiments on the VGGNet, ResNet, and MobileNet models on the CIFAR-10 and ImageNet benchmark datasets demonstrate the effectiveness of the proposed method. In particular, the ResNet56 and ResNet110 models on the CIFAR-10 dataset significantly improve the top-1 accuracy compared to state-of-the-art methods while reducing the network FLOPs by 52%. Furthermore, pruning the ResNet50 model on the ImageNet dataset reduces FLOPs by more than 42% with a negligible 0.33% drop in the top-5 accuracy. The source code of this study is publicly available on GitHub: https://github.com/ghimiredhikura/laasp.
Machine Vision Based Wireless Link Layer Anomaly Characterization

As the number of wireless end and edge devices increases, so does the volume of data to be monitored in view of predicting or detecting malfunctions. Furthermore, as the networks become more complex, the more context can be provided around a certain anomaly, fault or malfunction, the easier will be to establish mitigation actions in a fast and efficient manner. While detecting and classifying shapes of anomalous link behaviour from time series has already been investigated, the precise localization in time and characterization in duration and amplitude from actual data traces that would provide more context related to the anomaly is outstanding.
Multilingual digital terminology: Introduction to the special issue

Journal Article Multilingual digital terminology: Introduction to the special issue Get access Federica Vezzani, Federica Vezzani Writing - original draft, Writing - review & editing Department of Linguistics and Literary Studies, University of Padova, Via Elisabetta Vendramini 13, Padova 35137, Italy Email: federica.vezzani@unipd.it https://orcid.org/0000-0003-2240-6127 Search for other works by this author on: Oxford Academic Google Scholar Giorgio Maria Di Nunzio Giorgio Maria Di Nunzio Writing - original draft, Writing - review & editing Department of Information Engineering, University of Padova, Via Gradenigo 6a, Padova 35131, Italy https://orcid.org/0000-0001-9709-6392 Search for other works by this author on: Oxford Academic Google Scholar Digital Scholarship in the Humanities, Volume 38, Issue Supplement_1, June 2023, Pages i1–i5, https://doi.org/10.1093/llc/fqad028 Published: 21 June 2023
Becoming Digital? University Learning and Teaching in the Digital Information Ecology

Digital technologies have brought with them a new information ecology. Information scarcity has been replaced by ubiquity, the ability to contest expertise has increased dramatically, the control of information flows has been redistributed, computers are increasingly capable of ‘intelligence’, and the way humans connect with those machines, that information and each other has been totally recast. But we are still educating people for the past—learning outcomes, activities and assessments continue to draw largely on pre-digital traditions. For example, despite the fact that information is far more accessible, it’s not uncommon for learning to still focus on content and its recall, rather than higher-order information and people skills. This chapter explores what is required to navigate the information ecology created by digital technologies and considers how those same digital tools can be used to connect people, praxis and information to construct the learning that our students need for today’s work and world.