Reviewerly/exHarmony huggingface dataset:
Causality-Based Visual Analysis of Questionnaire Responses

As the final stage of questionnaire analysis, causal reasoning is the key to turning responses into valuable insights and actionable items for decision-makers. During the questionnaire analysis, classical statistical methods (e.g., Differences-in-Differences) have been widely exploited to evaluate causality between questions. However, due to the huge search space and complex causal structure in data, causal reasoning is still extremely challenging and time-consuming, and often conducted in a trial-and-error manner. On the other hand, existing visual methods of causal reasoning face the challenge of bringing scalability and expert knowledge together and can hardly be used in the questionnaire scenario. In this work, we present a systematic solution to help analysts effectively and efficiently explore questionnaire data and derive causality. Based on the association mining algorithm, we dig question combinations with potential inner causality and help analysts interactively explore the causal sub-graph of each question combination. Furthermore, leveraging the requirements collected from the experts, we built a visualization tool and conducted a comparative study with the state-of-the-art system to show the usability and efficiency of our system.
KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios.To address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients.Experimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction.Our KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.
Semantic segmentation with cross convolution and multi-layer feature refinement

Multi-level features and contextual information have been proven effective in semantic segmentation. As a common solution, dilated convolution with multiple dilation rates is widely adopted by various computer vision tasks. However, since a large number of pixels are not involved in convolutional calculation, dilated convolution suffers from the information loss problem. Another important aspect for image segmentation is that most feature fusion methods combine different level features directly, which ignores the semantic gap between shallow layer features and deep layer features. In this work, we propose the multi-scale cross convolution to alleviate the information loss problem. Cross convolution conducts convolutional operations in horizontal and vertical direction with different kernels. By combining cross convolution with dilated convolutions using different convolution kernels, more pixels are engaged in convolutional operation to capture multi-scale features. To address the issue of semantic gap between multi-layer features, a feature fusion scheme is developed in which a dual attention mechanism is applied to conduct feature refinement in both spatial and channel dimensions. Comprehensive experiments are conducted to evaluate the proposed method on Cityscapes and ADE20K datasets. Experimental results demonstrate that the cross convolution and feature fusion method improve segmentation performance significantly and achieve competitive performance over state-of-the-art approaches.
我が国の洋釘類輸入黎明期における輸入のあり方と中村重兵衛の関わり

This article examined the career of Nakamura Jubei, a nail dealer who was involved in the import of Western nails in the early Meiji period, his involvement in the import of Western nails, and the trading house that was responsible for the import. The following points become clear.
The vulnerability of flood disaster to the students secondary school at Jampea Island

Flood is one of the conditions when overflowing water inundated the lower areas. The occurrence of floods always has both positive and negative impacts. If the flood occurs in residential areas, it will have a more negative impact. Therefore, people should have preparedness to prevent the effects of the flood. In 2022, a flood occurred in the Jampea Islands, Selayar Islands regency, and affected 90% of the population. It was caused by the high rainfall intensity that resulted in the higher river discharge and overflowing into settlements. It was unclear how much loss was caused by the flood, but this phenomenon has traumatized the community. This island has been occupied for hundreds of years. However, it has never been a flood that the water inundated 90% of the island's land. Since the flood is possible to occur when the high rainfall intensity happens. Hence, this research was conducted to find out how is community preparedness in disaster mitigation. The community was focused on students at the secondary school level (SMA and SMK). Students were selected by considering their readiness to be more proactive in disaster mitigation through learning practices and socialization in the family environment. The data were collected through an online questionnaire and observation, then analyzed with a diagram presentation system. The results found that the aspect of students' knowledge was still classified as low. While the institutional aspect is still relatively moderate. Therefore, the capacity of students still needs to be strengthened to prevent the magnitude of the risk of flooding.
Loosely-Stabilizing Algorithm on Almost Maximal Independent Set

The maximal independent set (MIS) problem is one of the most fundamental problems in the field of distributed computing. This paper focuses on the MIS problem with unreliable communication between processes in the system. We propose a relaxed notion of MIS, named almost MIS (ALMIS), and show that the loosely-stabilizing algorithm proposed in our previous work can achieve exponentially long holding time with logarithmic convergence time and space complexity regarding ALMIS, which cannot be achieved at the same time regarding MIS in our previous work.
Multi-secret Sharing Scheme for Zero Trust Environment

The zero-trust concept of “never trust, always verify” injected new vitality into the development of network security. However, the related research on zero trust is still preliminary, and the research on secure data transmission is relatively less. How to achieve secure data transmission in zero trust environment is a problem to be solved. Aiming the data transmission scenario of private data secret sharing among agents, this paper proposed a secure data transmission scheme based on multi-secret sharing to solve the problems of identity comparison confirmation of participants and adjust the access structure according to the security value of trust evaluation in existing secret sharing schemes. The proposed scheme adopts the secret sharing design without the trusted third party, which avoids the security risk of trusted third party disclosure in the traditional secret sharing scheme. Secondly, in the system parameter setting phase, the identity of participants and zero-trust identity database are compared to combine the characteristics of zero-trust “identity authentication”, to improve the security of secret sharing. Finally, during secret distribution, zero-trust trust evaluation security values are used to adjust the access structure based on zero-trust ‘trust evaluation’ to ensure secure data transmission.
Error‐bounded Image Triangulation

Abstract We propose a novel image triangulation method to reduce the complexity of image triangulation under the color error‐bounded constraint and the triangle quality constraint. Meanwhile, we realize a variety of visual effects by supporting different types of triangles (e.g., linear or curved) and color approximation functions (e.g., constant, linear, or quadratic). To adapt to these discontinuous and combinatorial objectives and constraints, we formulate it as a constrained optimization problem that is solved by a series of tailored local remeshing operations. The feasibility and practicability of our method are demonstrated over various types of images, such as organisms, landscapes, portraits and cartoons. Compared to state‐of‐the‐art methods, our method generates far fewer triangles for the same color error or much smaller color errors using the same number of triangles.
The Zelensky Effect, by Olga Onuch and Henry E. Hale, Hurst Publishers, 2022, 424pp., $24.95 (hardcover), ISBN 9781787388635.

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Robust steganography in practical communication: a comparative study

Abstract To realize the act of covert communication in a public channel, steganography is proposed. In the current study, modern adaptive steganography plays a dominant role due to its high undetectability. However, the effectiveness of modern adaptive steganography is challenged when being applied in practical communication, such as over social network. Several robust steganographic methods have been proposed, while the comparative study between them is still unknown. Thus, we propose a framework to generalize the current typical steganographic methods resisting against compression attack, and meanwhile empirically analyze advantages and disadvantages of them based on four baseline indicators, referring to as capacity, imperceptibility, undetectability, and robustness. More importantly, the robustness performance of the methods is compared in the real application, such as on Facebook, Twitter, and WeChat, which has not been comprehensively addressed in this community. In particular, the methods modifying sign of DCT coefficients perform more superiority on the social media application.
Stud Hybrid Tasmanian Devil - Grey Wolf Optimization: A novel Bio-Inspired Optimization Algorithm for Learning-to-Rank

&lt;p&gt;Building ranking models used in Information Retrieval (IR) is one of the main problems of learning-to-Rank. Tasmanian Devil Optimization algorithm is a recently proposed meta-heuristic optimization algorithm based on the Tasmanian Devils’ predation mechanism. In this paper, grey wolf operator and stud mechanism are introduced into the TDO optimization process to improve the ability of exploitation and achieve better performance. The proposed Stud Hybrid Tasmanian Devil - Grey Wolf Optimization (SHTDO) combines Grey Wolf Optimizer and TDO with a Stud Selection and Crossover Operator, thus helping to enhance the exploration and exploitation as well as the motion under both strategies. Also, to validate the algorithm on the different fields, widely accepted benchmark and dataset was used for testing. The results show that our algorithm obtains higher ranking performance than TDO, which makes it more suitable for solving optimization problems, especially LTR tasks.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Hubungan Interaksi Sosial Dengan Hasil Belajar Ilmu Pengetahuan Sosial di SDN Maccini Sombala Makassar

The main problem of lack of social interaction among students is known to be one of the research problems. The research objective was to analyze the relationship between social interaction and social studies learning outcomes at SDN Maccini Sombala. This study uses a mixed strategy (Mixed Methods). The quantitative respondents used a probability sampling of 30 people, while the qualitative used a purposive sampling of 3 people. Data collection techniques are questionnaires, observation, documentation and interviews. The data analysis technique is descriptive statistics. The results of the study show that there is a strong relationship between social interaction and learning outcomes. This can be seen from the results of the measurement of r count which is more prominent than 0.997 ≥ 0.374 or r count ≥ rtable, from the information handling that has been carried out shows that the interaction and learning outcomes at level IV have a strong relationship. The conclusion is that social interaction plays an important role in improving student learning outcomes.
NJUNLP’s Submission for CCMT 2023 Quality Estimation Task

Quality Estimation is a task aiming to estimate the quality of translations without relying on any references. This paper describes our submission for CCMT 2023 quality estimation sentence-level task for English-to-Chinese (EN-ZH). Due to the challenges of costly annotations and small dataset sizes in the QE field, many researchers have attempted to leverage rich parallel corpora for unsupervised learning through methods such as uncertainty quantification, and data augmentation. Existing mainstream unsupervised QE methods exhibit good diversity and variability, so we test these methods individually as well as their ensemble effect.
Video Dehazing Using Dark Channel Prior and Type-2 Fuzzy Sets

Dehazing refers to the methods designed for haze removal from the videos to increase visibility of objects. Dark Channel Prior (DCP) approach and haze veil analysis approach are two popular methods in the literature for haze removal. The DCP approach to remove haze from video frames is employed in his paper. The quality of the haze removed frames is increased by sharpening the edges of the objects in the image by using type-2 fuzzy sets. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) parameters are used to test the effectiveness of the proposed approach. Video samples are gathered from the Kaggle dataset, with a particular emphasis on the vehicle objects in the scene. Results obtained after applying the proposed approach to several video frames show the efficacy of the method compared to other dehazing approaches.
IDENTIFYING 21ST-CENTURY SKILLS GAP IN THE ESL/EFL MALAYSIAN POSTGRADUATE EDUCATION SYSTEM

Abstract: Literacy in the 21st century entails creating and approving knowledge and data. Due to the abundance of information available, users must be able to sort through the uncertainty and be able to verify opinions from several aspects. Those who are pursuing postgraduate studies in particular need to be critical thinkers because simply understanding the material is not enough. Nonetheless, adjusting and transferring digital skills may be difficult for postgraduate overseas students as they have to use their linguistic skills to address issues with online navigation. The current study intends to look into how Malaysian and Chinese English language postgraduate students integrate and apply some 21st-century abilities to their research. One public university in Malaysia received voluntary responses from 207 respondents (96 postgraduate students from Malaysia and 111 overseas students from China) to a series of 21st century skills survey questions that were adapted from Kelly, Knowles, and Euisuk Sung (2019). The survey consists of four relevant 21st century skills that are identified as important to postgraduate students, which are collaborative, critical thinking, communication as well as creativity and innovation. The findings show that Creativity and Innovation has the lowest mean score. This study emphasizes the importance of having a variety of digital abilities for these postgraduate ESL/EFL students to succeed in the digital world. This study offers insights into the difficulties and possibilities for the development of digital skills in various cultural contexts by examining the skills gap between ESL/EFL students of Malaysia and China.&#x0D; Keywords: 21st century skills; digital literacy; critical thinking; communication; creativity and innovation
Write a Tested HTTP Image Generator API

One of the core reasons to use GoLang is that its energy consumption is well below many other mainstream languages.
Studi Literatur: Keefektifan Peningkatan Literasi Dan Numerasi Melalui Program Kampus Mengajar Pada Jenjang Sekolah Dasar

The low literacy and numeracy in Indonesia is a&#x0D; national priority agenda that is always trying to be improved. The government seeks to provide solutions by inviting students from universities throughout Indonesia to collaborate to improve literacy and numeracy as well as technology adaptation at the elementary school level through the Independent Learning Campus policy, namely the Teaching Campus. The activities carried out are in the form of teaching, assisting school administration, and assisting technology adaptation. The purpose of this study is to present a literature review on the effectiveness of the Teaching Campus program in the form of increasing literacy and numeracy at the elementary school level. The results of the study show the effectiveness of increasing literacy and numeracy through the Teaching Campus program at the elementary school level. This is evident from the realization of tasks given by the government to Teaching Campus students which include: (1) Pre-Assignment, (2) Assignment, (3) Final Assignment. In addition to the 3 main tasks, Campus Teaching students have main tasks in the field such as: (1) assisting the teaching and learning process (KBM), (2) Adapting Technology, (3) School Administration.
The potential of Jamblang Bark Actived Carbon (Syzygium cumini) in Reducing Lead Heavy Metal (Pb) Levels

Jamblang wood, also known as jambolin or jambolan, is a plant that is widespread in various tropical regions, including Indonesia. Jamblang bark has great potential as a source of raw material for making active carbon because it is rich in organic compounds, cellulose, and lignin, which can provide an ideal pore structure to capture and bind heavy metals. The literature search process was carried out on various databases (PubMed, Web of Sciences, EMBASE, Cochrane Libraries, and Google Scholar) regarding the potential of jamblang bark (Syzygium cumini) activated carbon in reducing lead heavy metal (Pb) levels. This study follows the preferred reporting items for systematic reviews and meta-analysis (PRISMA) recommendations. Activated carbon produced from jamblang bark (Syzygium cumini) has significant potential in reducing levels of heavy metal lead (Pb) in various wastewater treatment settings, drinking water treatment, and recovery of heavy metals from industrial waste.
WSA: A Unified Framework for Word and Sentence Autocompletion in Interactive Machine Translation

Interactive machine translation (IMT) is a process that leverages collaboration between human translators and machine translation models to produce high-quality translations and improve translation efficiency. Automatic completion is a critical function of IMT, which generates alternative translation for inappropriate words or segments based on human feedback for the translation. There are two limitations in previous research on autocompletion. Firstly, they treat these two types of autocompletion tasks as independent tasks, thus ignoring the potential correlation between them. The second limitation is that they only focus on completing translations based on human feedback, ignoring the role of the initial translation. In this paper, we propose a novel word and sentence autocompletion (WSA) method, which jointly models word and sentence autocompletion tasks. By means of joint modeling, the proposed method not only enables the generation of translations at both word and sentence level, but also increases the accuracy of the translations. In addition, we further improve the accuracy of translation autocompletion by utilizing the initial translation to enhance the semantic representation of the source sentence. Experimental results show that our method significantly outperforms the baselines by 10.34% in accuracy for word-level task and 1.86 BLEU score for sentence-level task.
Exploring Factors Affecting Information Literacy in Elementary, Middle, and High School Students Using Random Forest

Objectives The main purpose of this study is to identify factors that improve information competency in elemen-tary, middle, and high school students and quantitatively evaluate their influence. For this purpose, the Random Forest algorithm among the ensemble series of machine learning was utilized. Random Forest is an ensemble technique based on decision trees that reduces the risk of overfitting and has high prediction accuracy and stabil-ity, so it is used in various research and practice. In this study, various variables that affect information competency were extracted based on survey data from elementary, middle, and high school students. Complex interactions between variables were analyzed, and key variables that had a particularly significant impact on information capa-bilities were identified. In addition, Shap Value was used to quantitatively evaluate the influence of each major var-iable, with the goal of clearly understanding the relative importance and influence of each variable.&#x0D; Methods To fulfill the objectives of this study, we used publicly available data from the National Information Agency, extracting data from 1,469 elementary, middle, and high school students for model training and evaluation. A total of 20 explanatory variables were selected, including information utilization, social relationships, and the level of usage of information-sharing services.&#x0D; Results The final model built in this study showed performance metrics of Accuracy 0.721, Precision 0.716, Recall 0.723, and F1 Score 0.711. The key influencing variables were identified as digital device usage efficacy, attitudes toward digital technology, information utilization, social relationships and information-sharing services, social capital, information production and sharing levels, usage level of lifestyle services, and digital enablers.&#x0D; Conclusions The advent of the digital society due to the Fourth Industrial Revolution has made information literacy an essential core competency, playing a crucial role in enhancing individual learning capabilities as well as general competitiveness in everyday life. According to the results of this study, in order to improve the information literacy of elementary, middle, and high school students, education in software and hardware (SW·HW), training in in-formation reproduction through digital device usage, and fostering a positive attitude toward digital technology are necessary.