Reviewerly/exHarmony huggingface dataset:
Dependency-length minimization and its limits: A possible role for a probabilistic version of the final-over-final condition

A prominent principle in explaining a range of word-order regularities is dependency locality, which minimizes the linear distances (dependency lengths) between a head and its dependents.However, it remains unclear to what extent language users in fact observe locality when producing sentences under diverse conditions of cross-categorical harmony (such as the placement of verbal and nominal heads on the same vs.different sides of their dependents), dependency direction (head-final vs. head-initial), and parallel vs. hierarchical dependency structures (e.g.multiple adjectives dependent on the same head vs. nested genitive dependents).Using forty-five dependency-annotated corpora of diverse languages, we find that after controlling for harmony and conditioning on dependency types, dependency-length minimization (DLM) is inversely correlated with the overall presence of head-final dependencies.This anti-DLM effect in sentences with more head-final dependencies is specifically associated with an accumulation of dependents in parallel structures and with disharmonic orders in hierarchical structures.We propose a detailed interpretation of these results and tentatively suggest a role for a probabilistic principle that favors embedding head-initial (e.g.VO) structures inside equally head-initial and thereby lengthminimizing structures (e.g.relative clauses after the head noun), while head-final (OV) structures have a less pronounced preference for harmony and DLM.This is in line with earlier findings in research on the Greenbergian word-order universals and with a probabilistic version of what has been suggested more recently as the finalover-final condition.
A Seismic Phase Recognition Algorithm Based on Time Convolution Networks

Over recent years, frequent earthquakes have caused huge losses in human life and property. Rapid and automatic earthquake detection plays an important role in earthquake warning systems and earthquake operation mechanism research. Temporal convolution networks (TCNs) are frameworks that use expansion convolution and expansion, which have large and temporal receptive fields and can adapt to time series data. Given the excellent performance of temporal convolution networks using time series data, this paper proposes a deep learning framework based on the temporal convolution network model, which can be used to detect and obtain the accurate start times of seismic phases. In addition, a convolutional neural network (CNN) was added to the temporal convolution network model to automatically extract the deep features of seismic waves and the expansion convolution of each level was added to optimize its structure, which not only reduced the experimental parameters but also produced high-precision seismic phase detection results. Finally, the model was compared to the TCN, CNN-LSTM, SELD-TCN and the traditional AR-AIC methods. Our experimental results showed that the S-TCN method demonstrated great advantages in the accuracy and performance of seismic phase detection.
ALOHA Simulation to Determine Consequence Scenarios on Transportation Liquefied Natural Gas (LNG) in DKI Jakarta Province

The mobility of using LNG is based on its advantages as a substituent fuel for gasoline and diesel, where LNG has low emission properties. The process of mobilizing the distribution of LNG has the potential for accidents when it takes place. The study was conducted to determine the hazards that can occur and the safe distance for evacuation in the event of a leak using ALOHA simulation. The research process is carried out by studying literature, collecting data, determining the month and time of release, and continuing with a simulation of the existing data. The data analysis technique was carried out by determining the tank temperature and water dispersion model at 31 SPBG. Fireball simulation results at 8:00 a.m. 2:00 p.m, and 10:00 p.m have safe distances at 812 meters, 812 meters, and 815 meters. Fire column simulation results at 8:00 a.m. 2:00 p.m, and 10:00 p.m have safe distances at 29 meters, 29 meters, and 28 meters. Vapor cloud simulation results at 8:00 a.m. 2:00 p.m, and 10:00 p.m have Lower Explosive Limits (LEL) at 169 meters, 160 meters, and 243 meters. Thus the ALOHA simulation can represent the safe distance of evacuation and scenarios in the event of an accident.
Hubungan Motivasi Belajar dengan Hasil Belajar IPS Siswa Kelas IV

The purpose of this study is 1) to describe students' learning motivations. 2) to describe the student's learning outcomes. 3) To find out how much the relationship between student learning motivation and student learning outcomes. This research method is a correlation research with quantitative approaches. The data analysis technique used is Pearson Product Moment Correlation (PPM). The results showed that, 1) the level of motivation to learn in natural resource utilization materials wisely is classified as high based on the calculation of the percentage of student grades with an overall average of 79.24. 2) Students' learning outcomes on natural resource utilization materials wisely are classified as high with an overall average of 86.24. So from the results of research that has been done there is a relationship between learning motivation and the results of learning IPS cognitive realm in the material utilization of natural resources wisely.
Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing

Cloud computing has become popular in data sharing mainly because it has huge storage capacity and computing power. Securing the privacy of sensitive data for cloud-based data sharing is vital. Currently, there are various conditional proxy re-encryption (UPRE) schemes that have been proposed to resolve the privacy issue. Nevertheless, the existing UPRE schemes cannot allow the proxy (e.g., the cloud server) to transfer the outsourced encrypted data under the data owner’s public key of any homomorphic encryption scheme into the encrypted data under the data user’s public key of a homomorphic encryption scheme (possibly different from the data owner). The transformation of outsourced encrypted data between homomorphic encryption schemes is more suitable for the real data sharing in clouds. Consequently, we present the notion of universal conditional proxy re-encryption (UCPRE) to solve the issue of flexible transformation of outsourced encrypted data between homomorphic encryption schemes in cloud-based data sharing. UCPRE is lightweight in the sense that it only requires the re-encrypted key generation and re-encryption algorithms. We give the definition of UCPRE and prove that it is HRA secure without random oracle. Finally, we show that our UCPRE is efficient and rational compared to other existing CPRE schemes by instantiating our UCPRE.
Impact of Business Intelligence on Strategic Entrepreneurship: The Mediating Role of Organizational Agility

The purpose of the study is to examine the impact of Business Intelligence (BI) on Strategic Entrepreneurship (SE) with the existence of Organizational Agility (OA) in Jordanian entrepreneurial companies. The researchers used the quantitative method, as it has an empirical nature. The questionnaire was adopted as a data collection tool. In total, 209 questionnaires were disturbed electronically among 16 entrepreneurial companies operating in King Hussein Business Park (KHBS) in Amman, Jordan. The analyzed results showed a high positive impact of BI on SE, which is seen evident in the results where BI dimensions (data warehousing, data mining, and On-Line Analytic Processing (OLAP)), have a statistically significant impact on SE and its dimensions (entrepreneurial mindset, applying creativity &amp; developing innovation, opportunities identification, and acceptance of risk). Additionally, OA depicted a partial mediation between BI and SE. Furthermore, all main and sub hypotheses were significant and accepted, since the p-value of each analysis is less than 0.05. Finally, the researchers recommend applying BI in entrepreneurial companies that do not adopt it, and it should be given a high priority as it has been proven that BI increases the chances of identifying opportunities, accepting risks, and, moreover, developing innovative projects.
News Category Dataset

People rely on news to know what is happening around the world and inform their daily lives. In today's world, when the proliferation of fake news is rampant, having a large-scale and high-quality source of authentic news articles with the published category information is valuable to learning authentic news' Natural Language syntax and semantics. As part of this work, we present a News Category Dataset that contains around 210k news headlines from the year 2012 to 2022 obtained from HuffPost, along with useful metadata to enable various NLP tasks. In this paper, we also produce some novel insights from the dataset and describe various existing and potential applications of our dataset.
Towards Faithful Model Explanation in NLP: A Survey

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to understand. This has given rise to numerous efforts towards model explainability in recent years. One desideratum of model explanation is faithfulness, i.e. an explanation should accurately represent the reasoning process behind the model's prediction. In this survey, we review over 110 model explanation methods in NLP through the lens of faithfulness. We first discuss the definition and evaluation of faithfulness, as well as its significance for explainability. We then introduce recent advances in faithful explanation, grouping existing approaches into five categories: similarity-based methods, analysis of model-internal structures, backpropagation-based methods, counterfactual intervention, and self-explanatory models. For each category, we synthesize its representative studies, strengths, and weaknesses. Finally, we summarize their common virtues and remaining challenges, and reflect on future work directions towards faithful explainability in NLP.
A Depth-Progressive Initialization Strategy for Quantum Approximate Optimization Algorithm

The quantum approximate optimization algorithm (QAOA) is known for its capability and universality in solving combinatorial optimization problems on near-term quantum devices. The results yielded by QAOA depend strongly on its initial variational parameters. Hence, parameters selection for QAOA becomes an active area of research as bad initialization might deteriorate the quality of the results, especially at great circuit depths. We first discuss on the patterns of optimal parameters in QAOA in two directions: the angle index and the circuit depth. Then, we discuss on the symmetries and periodicity of the expectation that is used to determine the bounds of the search space. Based on the patterns in optimal parameters and the bounds restriction, we propose a strategy which predicts the new initial parameters by taking the difference between previous optimal parameters. Unlike most other strategies, the strategy we propose does not require multiple trials to ensure success. It only requires one prediction when progressing to the next depth. We compare this strategy with our previously proposed strategy and the layerwise strategy on solving the Max-cut problem, in terms of the approximation ratio and the optimization cost. We also address the non-optimality in previous parameters, which is seldom discussed in other works, despite its importance in explaining the behavior of variational quantum algorithms.
Enhancing Listening Skills of EFL Indonesian Learners through Busuu Application

Students identify what they hear and know while actively participating in the listening process, utilizing linguistic expertise to understand the information The goal of this study was to improve second-year students' listening skills and understand how they use Busuu to learn English. This study included 25 students in fourth semester at one of the universities in Indonesia. Purposive sampling was used to select the sample, which included students at fourth semester of Department of English Language Education as well as students who had passed the Literal Listening, Interpretive Listening, and Critical Listening subjects. The findings reveal that in normality test the significance value of pretest and posttest was better than 0.05 (0.56 &gt; 0.05, 0.86 &gt; 0.05), it implies that the data on listening learning outcomes is normally distributed. The paired sample t-test results showed that sig value was 0.001. This result showed that the students’ performance improved in pretest and posttest from the treatment in the classroom. They were excited and experienced fun in the classroom especially using Busuu application. The study recommends to use Busuu to improve students' listening skills.
Using Screenshot Attachments in Issue Reports for Triaging

In previous work, we deployed IssueTAG, which uses the one-line summary and the description fields of the issue reports to automatically assign them to the stakeholders, who are responsible for resolving the reported issues. Since its deployment on January 12, 2018 at Softtech – the software subsidiary of the largest private bank in Turkey, IssueTAG has made a total of 301,752 assignments (as of November 2021). One observation we make is that a large fraction of the issue reports submitted to Softtech has screenshot attachments and, in the presence of such attachments, the reports often convey less information in their one-line summary and the description fields, which tends to reduce the assignment accuracy. In this work, we use the screenshot attachments as an additional source of information to further improve the assignment accuracy, which, to the best of our knowledge, has not been studied before for automatic issue assignments. In particular, we develop a number of multi-source assignment models, which use both the issue reports and the screenshot attachments, as well as a number of single source models, which use either the issue reports or the screenshot attachments, and empirically evaluate them on real issue reports. Compared to the currently deployed single-source model in the field, the best multi-source model improved the assignment accuracy from 0.848 to 0.855 at an acceptable overhead cost, reducing the overall 3.3 percentage-point deficit between the human triagers and the deployed system by 0.7 points.
A Multitask Learning Approach for Named Entity Recognition by Exploiting Sentence-Level Semantics Globally

Named entity recognition (NER) is one fundamental task in natural language processing, which is usually viewed as a sequence labeling problem and typically addressed by neural conditional random field (CRF) models, such as BiLSTM-CRF. Intuitively, the entity types contain rich semantic information and the entity type sequence in a sentence can globally reflect the sentence-level semantics. However, most previous works recognize named entities based on the feature representation of each token in the input sentence, and the token-level features cannot capture the global-entity-type-related semantic information in the sentence. In this paper, we propose a joint model to exploit the global-type-related semantic information for NER. Concretely, we introduce a new auxiliary task, namely sentence-level entity type sequence prediction (TSP), to supervise and constrain the global feature representation learning process. Furthermore, a multitask learning method is used to integrate the global-type-related semantic information into the NER model. Experiments on the four datasets in different languages and domains show that our final model is highly effective, consistently outperforming the BiLSTM-CRF baseline and leading to competitive results on all datasets.
Remote Teaching during COVID-19 Emergency: Teaching and Assessment Strategies and the Role of Previous Training

The Emergency Remote Teaching experience during the COVID-19 pandemic resulted in schools reviewing teaching methods and strategies. The Italian Society for Educational Research (SIRD) conducted a survey in which more than 16,000 Italian teachers took part. The aim of this study is to be a first step in analysing the data from the Tuscany and Umbria regions by comparing them with national data in relation to the teaching and assessment methods and strategies used, and with the previous teacher training in distance learning. Secondly, the paper aims to deepen the link between the use of certain teaching and assessment methods, the previous training, and the quality of the distance learning experience perceived by teachers. For this purpose, some items and scales from the questionnaire used in the SIRD survey and some categories from the qualitative analysis of the responses to the open-ended questions were selected. The study, first and foremost, revealed some peculiarities of the two regions, including Tuscany’s lower evaluation of the effectiveness of the experience. On the national level, a positive relationship was found between teachers using interactive teaching strategies and “alternative” assessment activities and the higher perception of both effectiveness for student learning and adequate performance assessment. Finally, the role of previous training is underlined as useful only if it is not generally technology-centred, but rather related to teaching and assessment methods in virtual learning environments.
A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution

The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.
Expandable Fully Actuated Aerial Vehicle Assembly: Geometric Control Adapted from an Existing Flight Controller and Real-World Prototype Implementation

An assembly composed of multiple aerial vehicles can realize omnidirectional motion with six degrees of freedom. Such an assembly has a heavier payload capacity and better fault tolerance compared with a single aircraft. Thus, such assemblies have the potential to become an ideal platform for manipulation. This paper investigates the controller design and prototype implementation for an expandable aerial vehicle assembly (AVA). The proposed AVA is composed of multiple sub-aircraft connected together via spherical joints at their center of mass. Each sub-aircraft can rotate around the spherical joint. The system dynamics of such an AVA can be separated into a slowly varying system and a fast varying system. The design criteria for a controller for this type of AVA was analyzed based on the similarity between the slowly varying system and a fully actuated rigid aircraft. This can reduce the design procedure for the controller and increase the expandability of the AVA. The stability criteria were carefully analyzed by considering the tracking error of each sub-aircraft. As an example, the controller of the AVA was designed using trajectory linearization control on the manifold, since the configuration space of the aircraft is a non-Euclidean space. A prototype composed of three quadrotors was implemented. The real-time expandable communication protocol among the different sub-aircraft was designed based on the CAN bus. Furthermore, the software and the hardware of the real-world prototype were developed. Both simulation and real-world tests were conducted, which validated the feasibility of the control design and the software implementation for an expandable assembly containing multiple aerial vehicles.
Three-Dimensional Image Analysis and Training Method of Action Characteristics of Sanda Whip Leg Technique Based on Wavelet Transform

The whip leg technique is an indispensable offensive action for high-level Sanda athletes in the competition. In order to improve the effective guidance of the action of Wushu Sanda whip leg, based on wavelet transform, the three-dimensional image analysis of Sanda whip leg technical action characteristics, to explore the movement speed of each link of the attacking leg, the contraction form, and force sequence of major muscles or muscle groups and put forward correct and reasonable movement techniques, is expected to provide theoretical basis and reference for coaches and athletes in the training of Sanda whip leg movement. Based on this, this paper attempts to use the three-dimensional image analysis system and surface electromyography acquisition system from the perspective of wavelet transform to collect the speed of the attacking leg movement and the parameters related to muscle discharge and to quantitatively analyze and evaluate its movements. The sequence of activities of the whip leg strike is hip joint-knee joint-hip joint-knee joint-ankle joint. It is suggested to strictly follow the principle of link movement in practice, and the completion of the whole movement should be relaxed and natural.
Digital open repositories: reliability evaluation based on ISO 16363 criteria

Open science is constituted of open access, open data, open reproducible search, open science assessment; open science policies; open science tools; open education; open licensing, citizen science, digital preservation, and open innovation. In this research, we intend to approach open access, constituted by open publication in digital repositories (DRs). DRs are the locus of preservation of digital objects, but how do we ensure that a document stored in a DR will be available for long-term access? The reliability of DRs relates to the long-term preservation of their digital records. The ISO 16363:2012 Space data and information transfer systems – Audit and certification of trustworthy digital repositories establish a set of attributes, responsibilities, and requirements that allow the audit and certification of repositories. The objective of the research is to evaluate the institutional repository of the Federal University of Espírito Santo, the Institutional Repository of the Federal Institute of Amazonas, and the Digital Repository of the Federal University of Rio Grande do Sul according to the requirements demanded by ISO 16363:2012. This article is qualitative research with an exploratory and descriptive objective. We sent a survey to the repository collaborators to conduct the self-assessment according to the ISO's reliability criteria. Long-term preservation and authenticity of the information in digital media become necessary for DRs to be effectively trusted.