Reviewerly/exHarmony huggingface dataset:
Physical representation of female character in children’s novels by children

Literary work is a form and result of creative works of art whose objects are humans and their lives use language as a medium. Especially children's literature by children, basically has its own advantages. The storyline is unique and interesting and builds the expression of the child's world. This study aims to describe the physical aspects of female characters in children's novels by children. This paper is a qualitative research with a descriptive approach. The approach used in this study is a psychological literacy approach. The technique of collecting research data was done by reading carefully accompanied by marking. The analytical technique used is a symbolic hermeneutic technique. Based on the results of the study, the findings of this study relate to the physical aspects of female characters in children's novels which include physical aspects in terms of gender, physical aspects in terms of age, physical aspects in terms of facial characteristics, the physical aspect in terms of the clothes used, and the physical aspect in terms of the state of the body (senses). Key words: physical, representation, children's novel, and children's work
Applying the ‘BUCS’ strategy in grade IV of Elementary School to learn main ideas and supporting details

This study aims to improve learning activities and the ability to determine the main and supporting ideas through the application of the BUCS earning strategy that includes Baca (read), Uraikan (transform), Cermati (observe), Sampaikan (convey) in grade IV of Islamic Elementary School (IES) SDIT Nurul ‘Ilmi, Medan. Classroom action research employed in this study refers to the Kemmis Taggart model whose cycle includes planning, observation, and reflection. The research subjects were 21 students of fourth graders of the IES, whiles the objects studied were learning activities and the ability to determine the main ideas and supporting details in reading texts in the Indonesian language. Data were collected through observation sheets and tests and were analyzed by using qualitative descriptive analysis. The results show that (1) the means for learning activities of Grade IV students at SDIT Nurul ‘Ilmi likely indicated a modest increase from 72% to 77%. (2) Besides, students’ skills in identifying main ideas and supporting details likewise experienced a noticeable upturn. The means for this variable in the pretest was 57, which then escalated to 61 in cycle 1 and 89 in cycle 2, respectively.English translation. Keywords: BUCS learning strategy, main ideas and supporting details, elementary school
Teknik Equivalence Partitions untuk Pengujian Aplikasi Manajemen Kas dan Inventaris Berbasis Web

Performing checks on software specifications requires testing and validation. The discrepancy in the data entry process was caused by the validation process not being optimized. One of the consequences is the process of failure when inputting data. As a result, the application can be dangerous for future system users. Therefore, it is necessary to improve the quality and accurate verification. When the testing process finds errors that were previously unknown, then the test is called a good test. One of the testing method is the black box testing method with the Equivalence Partition technique. Equivalence Partition is a technique used to reduce the number of test cases that exist during testing, by entering data on each form that is in the application in each menu, then testing and grouping based on the validity by the system functionality.
Impacts of Training Service Quality on Student Satisfaction and Word-of-mouth in Online English Training in HoChiMinh city

This article aims at investigate components that comprise services quality and the impacts of service quality on student satisfaction and word-of-mouth in online English training. The combined method of quantitative and qualitative research was used, data from 502 students were collected and analyzed using structural equation modeling (SEM) with help of AMOS program. The findings show that the quality of service provided by online English training has a direct influence on student satisfaction. The service quality of online English teaching has an immediate and indirect influence on student word-of-mouth. Student satisfaction has a direct influence on word-of-mouth marketing. Moreover, the research points out that service quality of online English training consists of six components: (1) responsiveness; (2) reliability; (3) empathy; (4) assurance; (5) privacy and (6) website design. Based on these results, some administration implications are suggested for educational administrators to enhance student satisfaction and word-of-mouth in online English training regarding service quality and the six components mentioned.
Social prioritisation in scene viewing is not automatic: Evidence from the effects of a spatial memory load

When free-viewing scenes, participants tend to preferentially fixate social elements (e.g. people). We investigated whether this bias is automatic by testing whether it would be disrupted by increasing the demands of a secondary dual-task: holding a set of (1 or 6) spatial locations in memory, presented either simultaneously or sequentially. Following a retention interval, participants judged whether a test location was present in the to-be-remembered stimuli. During the retention interval participants free-viewed scenes containing a social element (e.g. a person) and a non-social element (object) that served as regions of interest. In order to assess the impact of physical salience, the non-social element was presented in both an unaltered baseline version, and in a version where its salience was artificially increased. The results showed that the preference to look at social elements decreased when the demands of the spatial memory task were increased from 1 to 6 locations, regardless of presentation mode (simultaneous or sequential). This reduction in the social prioritisation effect was accompanied by an increased tendency to fixate on the centre of the screen. The results place important boundary conditions on the social prioritisation effect and suggest clear limits to its automaticity.
SiamLight: lightweight networks for object tracking via attention mechanisms and pixel-level cross-correlation

Abstract Despite Siamese-based trackers have achieved great success in recent years, researchers have focused more on the accuracy of trackers than their complexity, which leads to their inapplicability in some scenarios, and the real-time speed can be greatly limited. In this work, we propose a lightweight network method called SiamLight for object tracking. MobileNet-V3 is selected as the backbone network. The PG-corr module is added as the feature fusion module, a strategy that decomposes the template feature into spatial and channel kernels, reducing the matching regions and suppressing the effect of similar interference. In addition, we also add the CSM module, which carries out attention to the channel and spatial simultaneously. CSM module not only reduces the number of parameters but also ensures that it can be integrated into existing network architectures as a plug-and-play module. Finally, multiple separable convolution blocks are added to the classification and regression branches to meet our lightweight parameters and Flops requirements. The experiments on LaSOT, VOT2018, VOT2019, OTB100, and UAV123 benchmarks show that the method has fewer Flops and parameters than state-of-the-art trackers.
Mapping soil organic carbon fractions for Australia, their stocks and uncertainty

Abstract. Soil organic carbon (SOC) is the largest terrestrial carbon pool. SOC is composed of a continuum set of compounds with different chemical composition, origin and susceptibilities to decomposition, that are commonly separated into pools characterised by different responses to anthropogenic and environmental disturbance. Here we map the contribution of three SOC fractions to the total SOC content of Australia’s soils. The three SOC fractions: mineral-associated organic carbon (MAOC), particulate organic carbon (POC) and pyrogenic organic carbon (PyOC), represent SOC composition with distinct turnover rates, chemistry, and pathway formation. Data for MAOC, POC, and PyOC were obtained with near- and mid-infrared spectral models calibrated with measured SOC fractions. We transformed the data using an isometric log-ratio transformation (ilr) to account for the closed compositional nature of SOC fractions. The resulting, back-transformed ilr components were mapped across Australia. SOC fraction stocks for the 0–30 cm were derived with maps of total organic carbon concentration, bulk density, coarse fragments and soil thickness. Mapping was done by quantile regression forest fitted with the ilr transformed data and a large set of environmental variables as predictors. The resulting maps along with the quantified uncertainty show the unique spatial pattern of SOC fractions in Australia. MAOC dominated the total SOC with an average of 59 % ± 17.5 %, whereas 28 % ± 17.5 % was PyOC and 13 % ± 11.1 % was POC. The allocation of TOC into the MAOC fractions increased with depth. SOC vulnerability (i.e., POC/[MAOC + PyOC]) was greater in areas with Mediterranean and temperate climate. TOC and the distribution among fractions were the most influential variables on SOC fraction uncertainty. Further, the diversity of climatic and pedological conditions suggests that different mechanisms will control SOC stabilisation and dynamics across the continent, as shown by the model covariates importance metric. We estimated the total SOC stocks (0–30 cm) to be 12.7 Pg MAOC, 2 Pg POC and 5.1 Pg PyOC, which is consistent with previous estimates. The maps of SOC fractions and their stocks can be used for modelling SOC dynamics and forecasting changes in SOC stocks as response to land use change, management, and climate change.
EMPHATIC CONSTRUCTIONS DISLOCAZIONE A DESTRA AND DISLOCAZIONE A SINISTRA IN COMEDIES OF THE 16th CENTURY TUSCAN AUTHORS

The modern Italian language of informal communication is characterized by a rather active use of emphatic syntactic means, in particular the constructions right dislocation (dislocazione a destra) and left dislocation (dislocazione a sinistra), which are considered innovative. But it is quite difficult to determine the real time of occurrence of these phenomena in the speech of Italian speakers: the number of reliable written samples of oral informal communication of the past is very small, and in the case of communication that took place several centuries ago, there are no such sources at all. Comedies are very valuable material for analysis, as comic theatre is closely connected with the phenomenon of «parlato recitato» - colloquial speech in stage performance, which uses a complex of linguistic means associated with informal oral discourse. So, it is possible to trace examples of relatively accurately reproduced verbal communication of past centuries in the texts of comedies. The presence of emphatic constructions allows us to confirm the hypothesis that the Italian language nowadays does not develop new constructions and structures, but rather undergoes a process of restandartization, in which phenomena that were considered characteristic of colloquial usage change their position and become normative.
MODELING OF BILINGUAL THESAURUS OF INVESTMENT TERMS

The paper is devoted to the principles of compiling a bilingual dictionary of investment terms. The terminology system in the field of the investment activity on the equity market is constantly and intensively updated with new terms. The lack of such type of dictionaries, which are essential for linguists, translators and other professionals in linguistics as well as investors and traders, makes the compiling of the dictionary very relevant. In the article, a preparatory stage of thesaurus modeling, including analysis of methodological base and identification of methods, is described. According to the parametric approach underlying the creation of a thesaurus, a number of key parameters of dictionary structures is identified. The algorithm for practical stage of working with linguistic material on the example of modeling a dictionary entry of specified dictionary is also presented.
Gas-pressure tuning of wavelength of photon pair emitted by Four-Wave-Mixing in Nanofibers

We present experimental results demonstrating the possibility to tune the wavelength of the photon pair emitted through four wave mixing in a nanofiber, using the pressure of a gas surrounding the nanofiber. Using Argon, a shift of idler wavelength of -1.1nm/bar is measured demonstrating fine adjustment possibility of emission wavelength, allowing to choose between different WDM channels.
Balancing Value Underestimation and Overestimation with Realistic Actor-Critic

Model-free deep reinforcement learning (RL) has been successfully applied to challenging continuous control domains. However, poor sample efficiency prevents these methods from being widely used in real-world domains. This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. To learn such policies, we introduce uncertainty punished Q-learning, which uses uncertainty from the ensembling of multiple critics to build various confidence-bounds of Q-function. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25\% performance improvement on the most challenging Humanoid environment compared to SAC.
1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track

This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All the original labels are projected to a 256-class unified label space, and the model is trained using a cross-entropy loss. Without significant hyperparameter tuning or any specific loss weighting, our solution ranks the first place on all the testing semantic segmentation benchmarks from multiple domains (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposed method can serve as a strong baseline for the multi-domain segmentation task and benefit future works. Code will be available at https://github.com/lambert-x/RVC_Segmentation.
Design and Fabricate Personal Health Sensing Devices

With the development of low-cost electronics, rapid prototyping techniques, as well as widely available mobile devices (e.g. mobile phones, smart watches), users are able to develop their own basic interactive functional applications, either on top of existing device platforms, or as stand-alone devices. However, the boundary for creating personal health sensing devices, both function prototyping and fabrication -wise, are still high. In this paper, I present my works on designing and fabricating personal health sensing devices with rapid function prototyping techniques and novel sensing technologies. Through these projects and ongoing future research, I am working towards my vision that everyone can design and fabricate highly-customized health sensing devices based on their body form and desired functions.
Optimization for Amortized Inverse Problems

Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwithstanding, the existing optimization approaches use gradient descent largely without adapting to the non-convex nature of the problem and can be sensitive to initial values, impeding further performance improvement. In this paper, we propose an efficient amortized optimization scheme for inverse problems with a deep generative prior. Specifically, the optimization task with high degrees of difficulty is decomposed into optimizing a sequence of much easier ones. We provide a theoretical guarantee of the proposed algorithm and empirically validate it on different inverse problems. As a result, our approach outperforms baseline methods qualitatively and quantitatively by a large margin.
Efficient Directed Graph Sampling via Gershgorin Disc Alignment

Graph sampling is the problem of choosing a node subset via sampling matrix $\mathbf{H} \in \{0,1\}^{K \times N}$ to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb{R}^K$, $K < N$, so that the target signal $\mathbf{x} \in \mathbb{R}^N$ can be reconstructed in high fidelity. While sampling on undirected graphs is well studied, we propose the first sampling scheme tailored specifically for directed graphs, leveraging a previous undirected graph sampling method based on Gershgorin disc alignment (GDAS). Concretely, given a directed positive graph $\mathcal{G}^d$ specified by random-walk graph Laplacian matrix $\mathbf{L}_{rw}$, we first define reconstruction of a smooth signal $\mathbf{x}^*$ from samples $\mathbf{y}$ using graph shift variation (GSV) $\|\mathbf{L}_{rw} \mathbf{x}\|^2_2$ as a signal prior. To minimize worst-case reconstruction error of the linear system solution $\mathbf{x}^* = \mathbf{C}^{-1} \mathbf{H}^\top \mathbf{y}$ with symmetric coefficient matrix $\mathbf{C} = \mathbf{H}^\top \mathbf{H} + \mu \mathbf{L}_{rw}^\top \mathbf{L}_{rw}$, the sampling objective is to choose $\mathbf{H}$ to maximize the smallest eigenvalue $\lambda_{\min}(\mathbf{C})$ of $\mathbf{C}$. To circumvent eigen-decomposition entirely, we maximize instead a lower bound $\lambda^-_{\min}(\mathbf{S}\mathbf{C}\mathbf{S}^{-1})$ of $\lambda_{\min}(\mathbf{C})$ -- smallest Gershgorin disc left-end of a similarity transform of $\mathbf{C}$ -- via a variant of GDAS based on Gershgorin circle theorem (GCT). Experimental results show that our sampling method yields smaller signal reconstruction errors at a faster speed compared to competing schemes.
Functional Simulation of Real-Time Quantum Control Software

Modern quantum computers rely heavily on real-time control systems for operation. Software for these systems is becoming increasingly more complex due to the demand for more features and more real-time devices to control. Unfortunately, testing real-time control software is often a complex process, and existing simulation software is not usable or practical for software testing. For this purpose, we implemented an interactive simulator that simulates signals at the application programming interface level. We show that our simulation infrastructure simulates kernels 6.9 times faster on average compared to execution on hardware, while the position of the timeline cursor is simulated with an average accuracy of 97.9% when choosing the appropriate configuration.
Anatomy of the Electroweak Phase Transition for Dark Sector induced Baryogenesis

We investigate the electroweak phase transition patterns for a recently proposed baryogenesis model with CP violation originated in the dark sector. The model includes a complex scalar singlet-Higgs boson portal, a $U(1)_l$ gauge lepton symmetry with a $Z^\prime$ gauge boson portal and a fermionic dark matter particle. We find a novel thermal history of the scalar sector, featuring a $Z_2$ breaking singlet vacuum in the early Universe driven by a dark Yukawa coupling, that induces a one-step strongly first order electroweak phase transition. We explore the parameter space that generates the observed matter-antimatter asymmetry and dark matter relic abundance, while being consistent with constraints from electric dipole moment and collider searches, and dark matter direct detection bounds. The complex singlet can be produced via the Higgs portal and decays into Standard Model particles after traveling a certain distance. We explore the reach for long-lived singlet scalars at the {\unit[13]{TeV}} Large Hadron Collider with $\mathcal{L}=\unit[139]{fb^{-1}}$ and show its impact on the parameter space of the model. Setting aside currently unresolved theoretical uncertainties, we estimate the gravitational wave signatures detectable at future observatories.
A DirectX-Based DICOM Viewer for Multi-User Surgical Planning in Augmented Reality

Preoperative medical imaging is an essential part of surgical planning. The data from medical imaging devices, such as CT and MRI scanners, consist of stacks of 2D images in DICOM format. Conversely, advances in 3D data visualization provide further information by assembling cross-sections into 3D volumetric datasets. As Microsoft unveiled the HoloLens 2 (HL2), which is considered one of the best Mixed Reality (XR) headsets in the market, it promised to enhance visualization in 3D by providing an immersive experience to users. This paper introduces a prototype holographic XR DICOM Viewer for the 3D visualization of DICOM image sets on HL2 for surgical planning. We first developed a standalone graphical C++ engine using the native DirectX11 API and HLSL shaders. Based on that, the prototype further applies the OpenXR API for potential deployment on a wide range of devices from vendors across the XR spectrum. With native access to the device, our prototype unravels the limitation of hardware capabilities on HL2 for 3D volume rendering and interaction. Moreover, smartphones can act as input devices to provide another user interaction method by connecting to our server. In this paper, we present a holographic DICOM viewer for the HoloLens 2 and contribute (i) a prototype that renders the DICOM image stacks in real-time on HL2, (ii) three types of user interactions in XR, and (iii) a preliminary qualitative evaluation of our prototype.
Auxiliary task discovery through generate-and-test

In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data efficiency by forcing the agent to learn auxiliary prediction and control objectives in addition to the main task of maximizing reward, and thus producing better representations. Typically these tasks are designed by people. Meta-learning offers a promising avenue for automatic task discovery; however, these methods are computationally expensive and challenging to tune in practice. In this paper, we explore a complementary approach to the auxiliary task discovery: continually generating new auxiliary tasks and preserving only those with high utility. We also introduce a new measure of auxiliary tasks usefulness based on how useful the features induced by them are for the main task. Our discovery algorithm significantly outperforms random tasks, hand-designed tasks, and learning without auxiliary tasks across a suite of environments.
An Improved Correction Term for Dimension Reduction in Quantum Key Distribution

The dimension reduction method enables security proofs of quantum key distribution (QKD) protocols that are originally formulated in infinite dimensions via reduction to a tractable finite-dimensional optimization. The reduction of dimensions is associated with a correction term in the secret key rate calculation. The previously derived correction term is loose when the protocol measurements are nearly block-diagonal with respect to the projection onto the reduced finite-dimensional subspace. Here, we provide a tighter correction term. It interpolates between the two extreme cases where all measurement operators are block-diagonal, and where at least one has maximally large off-diagonal blocks. This new correction term can reduce the computational overhead of applying the dimension reduction method by reducing the required dimension of the chosen subspace.