Miracle-dz/UniFAR-FTUs huggingface dataset:
LXPER Index: a curriculum-specific text readability assessment model for EFL students in Korea

Automatic readability assessment is one of the most important applications of Natural Language Processing (NLP) in education. Since automatic readability assessment allows the fast selection of appropriate reading material for readers at all levels of proficiency, it can be particularly useful for the English education of English as Foreign Language (EFL) students around the world. However, most readability assessment models are developed for the native readers of English and have low accuracy for texts in non-native English Language Training (ELT) curriculum. We introduce LXPER Index, which is a readability assessment model for non-native EFL readers in the ELT curriculum of Korea. To measure LXPER Index, we use the mixture of 22 features which we prove to be significant in text readability assessment. We also introduce the Text Corpus of the Korean ELT Curriculum (CoKEC-text), which is the first collection of English texts from a non-native country’s ELT curriculum with each text’s target grade level labeled. In addition, we assembled the Word Corpus of the Korean ELT Curriculum (CoKEC-word), which is a collection of words from the Korean ELT curriculum with word difficulty labels. Our experiments show that our new model, trained with CoKEC-text, significantly improves the accuracy of automatic readability assessment for texts in the Korean ELT curriculum. The methodology used in this research can be applied to other ELT curricula around the world.
Drifting through the medium: kicks and self-propulsion of binaries within accretion discs and other environments

Compact binaries are within the reach of gravitational and electromagnetic wave detectors, and are important for our understanding of astrophysical environments and the composition of compact objects. There is a vast body of work devoted to the evolution of such binaries in background media, such as in common-envelope evolution, accretion disks and dark matter mini-spikes. Here, we explore further gravitationally-bound binaries evolving within an environment. We show that dissipative effects such as gravitational drag and accretion impart a momentum to the center of mass of asymmetric binaries. We numerically evolve the binaries in a Newtonian setup and show that, depending on the medium density, the center of mass can accelerate to high speeds -- in some cases $300\, {\rm km/s}$ or more -- during inspiral, with potentially observable signatures. Our numerical results are fully consistent with an \textit{analytical} result for the CM evolution at first order in the medium density.
Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring

Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay. Since obtaining a large quantity of pre-graded essays to a particular prompt is often difficult and unrealistic, the task of cross-prompt AES is vital for the development of real-world AES systems, yet it remains an under-explored area of research. Models designed for prompt-specific AES rely heavily on prompt-specific knowledge and perform poorly in the cross-prompt setting, whereas current approaches to cross-prompt AES either require a certain quantity of labelled target-prompt essays or require a large quantity of unlabelled target-prompt essays to perform transfer learning in a multi-step manner. To address these issues, we introduce Prompt Agnostic Essay Scorer (PAES) for cross-prompt AES. Our method requires no access to labelled or unlabelled target-prompt data during training and is a single-stage approach. PAES is easy to apply in practice and achieves state-of-the-art performance on the Automated Student Assessment Prize (ASAP) dataset.
Collecting the Public Perception of AI and Robot Rights

Whether to give rights to artificial intelligence (AI) and robots has been a sensitive topic since the European Parliament proposed advanced robots could be granted "electronic personalities. " Numerous scholars who favor or disfavor its feasibility have participated in the debate. This paper presents an experiment (N=1270) that 1) collects online users' first impressions of 11 possible rights that could be granted to autonomous electronic agents of the future and 2) examines whether debunking common misconceptions on the proposal modifies one's stance toward the issue. The results indicate that even though online users mainly disfavor AI and robot rights, they are supportive of protecting electronic agents from cruelty (i.e., favor the right against cruel treatment). Furthermore, people's perceptions became more positive when given information about rights-bearing non-human entities or myth-refuting statements. The style used to introduce AI and robot rights significantly affected how the participants perceived the proposal, similar to the way metaphors function in creating laws. For robustness, we repeated the experiment over a more representative sample of U.S. residents (N=164) and found that perceptions gathered from online users and those by the general population are similar.
Distinct distances on hyperbolic surfaces

For any cofinite Fuchsian group $\Gamma\subset {\rm PSL}(2, \mathbb{R})$, we show that any set of $N$ points on the hyperbolic surface $\Gamma\backslash\mathbb{H}^2$ determines $\geq C_{\Gamma} \frac{N}{\log N}$ distinct distances for some constant $C_{\Gamma}>0$ depending only on $\Gamma$. In particular, for $\Gamma$ being any finite index subgroup of ${\rm PSL}(2, \mathbb{Z})$ with $\mu=[{\rm PSL}(2, \mathbb{Z}): \Gamma ] 0$.
Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

Heterogeneous domain adaptation (HDA) transfers knowledge across source and target domains that present heterogeneities e.g., distinct domain distributions and difference in feature type or dimension. Most previous HDA methods tackle this problem through learning a domain-invariant feature subspace to reduce the discrepancy between domains. However, the intrinsic semantic properties contained in data are under-explored in such alignment strategy, which is also indispensable to achieve promising adaptability. In this paper, we propose a Simultaneous Semantic Alignment Network (SSAN) to simultaneously exploit correlations among categories and align the centroids for each category across domains. In particular, we propose an implicit semantic correlation loss to transfer the correlation knowledge of source categorical prediction distributions to target domain. Meanwhile, by leveraging target pseudo-labels, a robust triplet-centroid alignment mechanism is explicitly applied to align feature representations for each category. Notably, a pseudo-label refinement procedure with geometric similarity involved is introduced to enhance the target pseudo-label assignment accuracy. Comprehensive experiments on various HDA tasks across text-to-image, image-to-image and text-to-text successfully validate the superiority of our SSAN against state-of-the-art HDA methods. The code is publicly available at https://github.com/BIT-DA/SSAN.
Reinforced Epidemic Control: Saving Both Lives and Economy

Saving lives or economy is a dilemma for epidemic control in most cities while smart-tracing technology raises people's privacy concerns. In this paper, we propose a solution for the life-or-economy dilemma that does not require private data. We bypass the private-data requirement by suppressing epidemic transmission through a dynamic control on inter-regional mobility that only relies on Origin-Designation (OD) data. We develop DUal-objective Reinforcement-Learning Epidemic Control Agent (DURLECA) to search mobility-control policies that can simultaneously minimize infection spread and maximally retain mobility. DURLECA hires a novel graph neural network, namely Flow-GNN, to estimate the virus-transmission risk induced by urban mobility. The estimated risk is used to support a reinforcement learning agent to generate mobility-control actions. The training of DURLECA is guided with a well-constructed reward function, which captures the natural trade-off relation between epidemic control and mobility retaining. Besides, we design two exploration strategies to improve the agent's searching efficiency and help it get rid of local optimums. Extensive experimental results on a real-world OD dataset show that DURLECA is able to suppress infections at an extremely low level while retaining 76\% of the mobility in the city. Our implementation is available at this https URL.
Cluster-Driven Mesh Denoising

Feature-preserving mesh denoising has received noticeable attention recently. Many methods often design great weighting for anisotropic surfaces and small weighting for isotropic surfaces, to preserve sharp features. However, they often disregard the fact that small weights still pose negative impacts to the denoising outcomes. Furthermore, it may increase the difficulty in parameter tuning, especially for users without any background knowledge. In this paper, we propose a novel clustering method for mesh denoising, which can avoid the disturbance of anisotropic information and be easily embedded into commonly-used mesh denoising frameworks. Extensive experiments have been conducted to validate our method, and demonstrate that it can enhance the denoising results of some existing methods remarkably both visually and quantitatively. It also largely relaxes the parameter tuning procedure for users, in terms of increasing stability for existing mesh denoising methods.
A Learning-from-Observation Framework: One-Shot Robot Teaching for Grasp-Manipulation-Release Household Operations

A household robot is expected to perform various manipulative operations with an understanding of the purpose of the task. To this end, a desirable robotic application should provide an on-site robot teaching framework for non-experts. Here we propose a Learning-from-Observation (LfO) framework for grasp-manipulation-release class household operations (GMR-operations). The framework maps human demonstrations to predefined task models through one-shot teaching. Each task model contains both high-level knowledge regarding the geometric constraints and low-level knowledge related to human postures. The key idea is to design a task model that 1) covers various GMR-operations and 2) includes human postures to achieve tasks. We verify the applicability of our framework by testing an operational LfO system with a real robot. In addition, we quantify the coverage of the task model by analyzing online videos of household operations. In the context of one-shot robot teaching, the contribution of this study is a framework that 1) covers various GMR-operations and 2) mimics human postures during the operations.
Hyperspectral Image Classification With Spatial Consistence Using Fully Convolutional Spatial Propagation Network

In recent years, deep convolutional neural networks (CNNs) have demonstrated impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. However, the existing CNN-based models operate at the patch level, in which a pixel is separately classified into classes using a patch of images around it. This patch-level classification will lead to a large number of repeated calculations, and it is hard to identify the appropriate patch size that is beneficial to classification accuracy. In addition, the conventional CNN models operate convolutions with local receptive fields, which cause the failure of contextual spatial information modeling. To overcome these aforementioned limitations, we propose a novel end-to-end, pixel-to-pixel, fully convolutional spatial propagation network (FCSPN) for HSI classification. Our FCSPN consists of a 3-D fully convolution network (3D-FCN) and a convolutional spatial propagation network (CSPN). Specifically, the 3D-FCN is first introduced for reliable preliminary classification, in which a novel dual separable residual (DSR) unit is proposed to effectively capture spectral and spatial information simultaneously with fewer parameters. Moreover, the channel-wise attention mechanism is adapted in the 3D-FCN to grasp the most informative channels from redundant channel information. Finally, the CSPN is introduced to capture the spatial correlations of HSIs via learning a local linear spatial propagation, which allows maintaining the HSI spatial consistency and further refining the classification results. Experimental results on three HSI benchmark data sets demonstrate that the proposed FCSPN achieves state-of-the-art performance on HSI classification.
Going Cognitive: A Demonstration of the Utility of Task-General Cognitive Architectures for Adaptive Robotic Task Performance

It has been claimed that a main advantage of cognitive architectures (compared to other types of specialized robotic architectures) is that they are task-general and can thus learn to perform any task as long as they have the right perceptual and action primitives. In this paper, we provide empirical evidence for this claim by directly comparing a high-performing custom robotic architecture developed for the standardized robotic "FetchIt! " challenge task to a hybrid cognitive robotic architecture that allows for online one-shot task learning and task modifications through natural language instructions. The results show that there is no disadvantage of running the hybrid architecture (i.e., no significant difference in overall performance or computational overhead compared to the custom architecture) while adding the flexibility of online one-shot task instruction and modification not available in the custom architecture.
Examining the relevance of cultural ecosystem services in forest management in Europe

The ecosystem services framework has become one of the most important paradigms in forest planning and management as a way to link the multiple provisioning, regulating, and cultural services derived from ecosystems and their benefits to human wellbeing. Recently, there have been multiple efforts in emphasizing the importance of cultural ecosystem services (CES). However, the consideration of CES in management models remains a challenge. In the current context of increased demand for the joint supply of multiple ecosystem services, we aim to evaluate which CES are currently promoted by European landowners, what their the future could be, and which factors support or hinder these processes. Our findings are based on a survey of 1182 forest landowners from 25 European countries. By using a mixed-method approach that combines descriptive, ordination, and cluster analysis with qualitative data, our results reveal that CES are currently of high relevance in European forests and forest owners and managers have a generally positive attitude toward further promotion of CES through forest management. There is a high degree of synergy between CES, which suggests that many of them could be jointly promoted. Our analysis further identifies the existence of four differentiated types of forest owners based on their attitudes and management in relation to CES. These groups also differ in regard to the challenges and barriers they perceive in relation to forest CES. These diverse perspectives among Europe’s landowners suggest the need for different strategies that satisfy the diverse context-related social-ecological needs required to further promote CES in European forests.
K-SPIN: Efficiently Processing Spatial Keyword Queries on Road Networks

A significant proportion of all search volume consists of local searches. As a result, search engines must be capable of finding relevant results combining both spatial proximity and textual relevance with high query throughput. We observe that existing techniques answering these <italic>spatial keyword queries</italic> use keyword aggregated indexing, which has several disadvantages on road networks. We propose K-SPIN, a versatile framework that instead uses <italic>keyword separated indexing</italic> to delay and avoid expensive operations. At first glance, this strategy appears to have impractical pre-processing costs. However, by exploiting several useful observations, we make the indexing cost not only viable but also light-weight. For example, we propose a novel <inline-formula><tex-math notation="LaTeX">$\rho$</tex-math><alternatives><mml:math><mml:mi>ρ</mml:mi></mml:math><inline-graphic xlink:href="abeywickrama-ieq1-2894140.gif"/></alternatives></inline-formula>-Approximate Network Voronoi Diagram (NVD) with one order of magnitude less space cost than exact NVDs. By carefully exploiting features of the K-SPIN framework, our query algorithms are up to two orders of magnitude more efficient than the state-of-the-art as shown in our experimental investigation on various queries, parameter settings, and real road network and keyword datasets.
Commodity storage and the autocorrelation of prices

For storable commodities with stochastic supply, intertemporal arbitrage can smooth the effects of temporary gluts and, when stocks are available, temporary shortages. Qualitative features of the price behavior of some important commodities have long been seen to be consistent with such arbitrage. In this paper we show that speculative arbitrage in simple models in the tradition of Gustafson (1958) can induce the degree of correlation and variation observed in widely-studied price histories of seven major commodities.
The Influences Of Trait Conscientiousness And Religiosity On The Teachers Emotion Regulation

Emotion regulation is the ability to recognize, monitor, and evaluate the emotional responses. Especially, on the intensity and form of their reactions in achieving a goal. This research aims to find the factors that most influence the regulation of emotions. This matter is very important to comprehend the mechanism of emotion regulation, especially in teachers. The design of this research is the causal relationship study. The sample of this research amounted to 377 of the Public Elementary School teachers throughout the city of Padang. The sampling technique used is the cluster random sampling, which took samples from the 11 sub-districts in the city of Padang. The data analysis method used is the partial least squares path modeling to test the influence of trait conscientiousness and religiosity on the emotion regulation of teachers. The results of the data analysis show that all paths in the model are not removed. The conscientiousness has a significant influence on emotion regulation, and the religiosity also has a significant influence on emotion regulation, and the conscientiousness has a significant influence on the religiosity. The evaluation of the whole part of the calculation of the suitability of this model gives the conclusion that the proposed hypothesis model has a good level of model suitability. This research model has a high enough model suitability. The accuracy of the model is 48.1% which explains that the contribution of the model to explain the structural relationship of the three variables researched is 48.1% and the rest is explained by other variables that are not involved in the model. The religiosity's role is as a mediator in the relationship of conscientiousness and emotion regulation. The allegation of religiosity as a mediator is quite strong, this is because the correlation coefficient between the conscientiousness and religiosity is 0.462. The addition of the religiosity variable as a mediating variable explains that the addition of this variable will make an additional contribution as an explanation of the emotion regulation. The coefficient of determination on the emotion regulation variable is 34.3%.
Hydrogeology, Engineering Geology and Geotechnics FIELD AND LABORATORY TESTING OF TIME DOMAIN REFLECTOMETRY CABLES FOR LANDSLIDE MONITORING M.sc

Due to the large number of landslides and mass move ents that hit Italy every year, mitigation and monitoring of critical situations ar ise as crucial aspects of engineering geology. Time Domain Reflectometry (TDR) is a monit ring technique that could integrate or even substitute inclinometer, a standa rd tool to measure slope displacements. Even if the use of TDR cables is not extremely recent in geotechnical monitoring, it still poses interesting challenges i n sensor tuning and results interpretation. The work presented here has the obj ective to find a quantitative correlation between TDR response and cable deformat ion. Two approaches have been followed: laboratory and in situ tests. Laboratory testing aims at finding a relationship between deformation type and TDR response; in situ testing features a net of TDR cables coupled to inclinometers. Our work aims at s howing that TDR can effectively replace inclinometer sounding if a period of tuning with reference measures is available.
Growth and feed utilization efficiencies of seawater Atlantic salmon, Salmo salar L., fed a diet containing supplementary enzymes

Supplementary enzymes were added to a diet in order to increase its digestibility and improve the growth achieved by seawater Atlantic salmon, Salmo salar L. The supplementary enzymes were selected to hydrolyse proteins and carbohydrates. Three diets, with fishmeal as the only source of protein (C1), with fishmeal and soybean meal (C2) and diet C2 with supplementary enzymes (C3) were fed to seawater salmon (100g) for 12 weeks at nominal tank rations equivalent to 2·5% body weight per day. Consumption rates of individual fish were measured by radiography. The salmon fed C3 had higher rates of food consumption, significantly higher final weights and rates of growth and lower food conversion efficiencies and maintenance ration than those fed C2. Salmon fed C1 had similar food conversion efficiencies to the C3 fed fish. These results indicate that the addition of supplementary enzymes to diets containing soybean meal improves the growth and food conversion efficiency of salmon.
MODELING THERMAL EXPANSION OF A LARGE AREA EXTRUSION DEPOSITION ADDITIVELY MANUFACTURED PARTS USING A NON-HOMOGENIZED APPROACH

Interest in the use of large area extrusion deposition additive manufacturing (LAEDAM) to create tools for creation of composites is on the rise, due to its ability to create complex shapes rapidly. To ensure the parts created from the tool meet geometric standards, it is important to understand the thermal expansion of the printed part. Which is a challenge as LAEDAM imparts a non-uniform fiber orientation to the deposited material. A non-uniform fiber orientation in the deposited material creates a non-homogeneous cross section at a given position. Due to this heterogeneity, the coefficient of thermal expansion (CTE) also varies according to the position in the cross section. Previous modelling attempts of LAEDAM parts have employed a homogenized approach. This work experimentally characterizes CTE variations across the cross section of a bead using thermomechanical analysis and uses this as a non-homogenized input at the bead level for a finite element model. Predictions from this finite element model are then be compared to strain maps measured using 2-D digital image correlation of large-scale printed parts (127 mm cubes).