Reviewerly/exHarmony huggingface dataset:
“Bu Kış Kimse Üşümeyecek” Kitabının İngilizce Çevirisinin Makine Çevirisi İle Karşılaştırılması: Google Çeviri’nin Yazın Çevirisinde Kullanılabilirliği Üzerine Bir İnceleme 

Technological innovations and advancements appear in all areas of life, and as a reflection of this, affect the field of Translation Studies, too.Computer-Assisted Translation (CAT) tools and Machine Translation (MT) systems, which provide convenience to human translators, have significantly accelerated the translation process.Unlike CAT tools, where the translators can create their own database and terminology, Google Translate (GT), a Neural Machine Translation (NMT) system, has become more preferred by both amateur and professional translators, due to its free and simple user interface.For this reason, the use of GT, whose popularity has increased in recent years and whose translation quality has been improving according to text types, will be examined in children's books in the context of literary translation.For this purpose, the book "Bu Kış Kimse Üşümeyecek" by Feridun Oral, the English translation of the book by a human translator and the English translation by GT were chosen as the research objects of this study.Translations were analyzed and evaluated descriptively in terms of linguistic transfer, context and translation quality.At the end of the study, evaluations on the usability of machine translation in literary translation, especially in children's literature translation, and recommendations for the researchers who want to do research or practice in this fie ld are presented.
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Abstract Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well‐balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors

A method to perform offline and online speaker diarization for an unlimited number of speakers is described in this paper. End-to-end neural diarization (EEND) has achieved overlap-aware speaker diarization by formulating it as a multi-label classification problem. It has also been extended for a flexible number of speakers by introducing speaker-wise attractors. However, the output number of speakers of attractor-based EEND is empirically capped; it cannot deal with cases where the number of speakers appearing during inference is higher than that during training because its speaker counting is trained in a fully supervised manner. Our method, EEND-GLA, solves this problem by introducing unsupervised clustering into attractor-based EEND. In the method, the input audio is first divided into short blocks, then attractor-based diarization is performed for each block, and finally, the results of each block are clustered on the basis of the similarity between locally-calculated attractors. While the number of output speakers is limited within each block, the total number of speakers estimated for the entire input can be higher than the limitation. To use EEND-GLA in an online manner, our method also extends the speaker-tracing buffer, which was originally proposed to enable online inference of conventional EEND. We introduce a block-wise buffer update to make the speaker-tracing buffer compatible with EEND-GLA. Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND. The experimental results demonstrate that EEND-GLA can perform speaker diarization of an unseen number of speakers in both offline and online inferences.
A statistics-based performance testing methodology: a case study for the I/O bound tasks

This research demonstrates the applicability of the established statistical methodology developed for experimental data analysis within physics, biology, and other natural science disciplines to the analysis of computing performance measurements. The methodology is based on the randomization of test samples and test conditions as well as usage of statistical hypothesis testing to determine if the performance differs. The hypothesis testing method is selected based on the statistical distribution of the data-Student’s t-test for the normal distribution and non-parametric tests, such as the Wilcoxon signed-rank test, for other distributions. Multiple comparisons problem is taken into account using the Bonferroni correction. Though other important natural science methods exist-different kinds of blinded experiments and analysis could be beneficial too-they could be too expensive for such contexts without additional justifications. The method will be applied to a model problem: a comparison of the performance of three implementations of web crawlers, compiled with different options and different compilers. Experimental cases were intentionally selected with both large and minor (but definite) performance differences to show how statistical methods can help determine the appearance of the performance difference and confidently discuss its extent to take into account benefits or drawbacks of the various setups.
2DHeadPose: A simple and effective annotation method for the head pose in RGB images and its dataset

Head pose estimation is one of the essential tasks in computer vision, which predicts the Euler angles of the head in an image. In recent years, CNN-based methods for head pose estimation have achieved excellent performance. Their training relies on RGB images providing facial landmarks or depth images from RGBD cameras. However, labeling facial landmarks is complex for large angular head poses in RGB images, and RGBD cameras are unsuitable for outdoor scenes. We propose a simple and effective annotation method for the head pose in RGB images. The novelty method uses a 3D virtual human head to simulate the head pose in the RGB image. The Euler angle can be calculated from the change in coordinates of the 3D virtual head. We then create a dataset using our annotation method: 2DHeadPose dataset, which contains a rich set of attributes, dimensions, and angles. Finally, we propose Gaussian label smoothing to suppress annotation noises and reflect inter-class relationships. A baseline approach is established using Gaussian label smoothing. Experiments demonstrate that our annotation method, datasets, and Gaussian label smoothing are very effective. Our baseline approach surpasses most current state-of-the-art methods. The annotation tool, dataset, and source code are publicly available at https://github.com/youngnuaa/2DHeadPose.
Embracing Uniqueness: Generating Radiology Reports via a Transformer with Graph-based Distinctive Attention

Automatically generating radiology reports has recently made great progress, which reduces the workload of radiologists. The high similarity among radiology images in training datasets forces existing methods to focus more on common medical facts existing in many images, such as pleural effusion and heart size. However, distinctive medical facts in individual images, such as edema and soft tissue that are rare in the whole dataset, receive little attention or are even ignored. This result leads to reports generated by existing methods that lack descriptions of distinctive medical facts in individual images. In this paper, we propose TRGD, a Transformer with graph-based distinctive attention, to generate high-quality radiology reports automatically. In TRGD, we first design a graph network to extract the features representing common medical facts. Then we lower their proportion in the global semantic features representing complete medical facts to increase the influence of distinctive medical facts. Experimental results on two datasets IU X-Ray and MIMIC-CXR demonstrate that, by sufficiently focusing on distinctive medical facts in individual images, TRGD indeed generates high-quality radiology reports and achieves competitive results with state-of-the-art methods.
Precision-Driven Multi-Target Path Planning and Fine Position Error Estimation on a Dual-Movement-Mode Mobile Robot Using a Three-Parameter Error Model

The multi-target path planning problem is a universal problem to mobile robots and mobile manipulators. The two movement modes of forward movement and rotation are universally implemented in integrated, commercially accessible mobile platforms used in logistics robots, construction robots, etc. Localization error in multi-target path tracking is one of the crucial measures in mobile robot applications. In this article, a precision-driven multi-target path planning is first proposed. According to the path's odometry error evaluation function, the precision-optimized path can be discovered. Then, a three-parameter odometry error model is proposed based on the dual movement mode. The error model describes localization errors in terms of the theoretical motion command values issued to the mobile robot, the forward moving distances, and the rotation angles. It appears that the three error parameters follow the normal distribution. The error model is finally validated using a mobile robot prototype. The error parameters can be identified by analyzing the actual moving trajectory of arbitrary movements. The experimental localization error is compared to the simulated localization error in order to validate the proposed error model and the precision-driven path planning method. The OptiTrack motion capture device was used to capture the prototype mobile robot's pose and position data.
The Combination of Face Identification and Action Recognition for Fall Detection

Falls are a very common unexpected accident that result in serious injuries such as broken bones, head injury. Detecting falls, taking fall patients to the emergency room, and sending notification to their family in time is very important. In this paper, we propose a method that combines face recognition and action recognition for fall detection. Specifically, we identify seven basic actions that take place in elderly daily life based on skeleton data extracted using YOLOv7-Pose model. Two deep models which are Spatial Temporal Graph Convolutional Network (ST-GCN), and Long Short-Term Memory (LSTM) are employed for action recognition on the skeleton data. The experimental results on our dataset show that ST-GCN model achieved an accuracy of 90% that is 7% higher than the LSTM model.
Evaluation of Online Learning Platforms in Latin America

Introduction: The use of online learning platforms has made a huge contribution to the online learning process. A key tool in this world is videoconferencing, which allows the efficient advancement of knowledge of a specific subject during synchronized timing. Objective: With this proposed context, the objective of this investigation is to determine which online video-conference platform has more benefits to be applied in Latin America. Method: A study was carried out with a quantitative method, transversal temporality, and a correlational and comparative process. There was a sample of 272 participants between 12 and 55 years old. Results and conclusion: It was found that the platform with the most benefits according to the analysis of gender, age, and profession is ZOOM. These results allow us to analyze the usefulness of this platform and its benefits in the learning process in Latin America. Doi: 10.28991/ESJ-2022-SIED-018 Full Text: PDF
Relationship between Teacher Work Motivation and Well-being at Different Stages of covid-19 Lockdown

Based on empirical research, the authors show that the first wave of the COVID-19 lockdown (March – June, 2020) had some negative impact on teacher well-being, in particular on such its component as physical health and well-being.&#x0D; It has been found that the types of teacher work motivation have different relationships with teacher well-being at different stages of the COVID-19 lockdown.&#x0D; The authors substantiate the necessity of developing teacher autonomous work motivation, in particular its identified and integrated types, to promote teacher well-being during the COVID-19 pandemic.
The impact of human expert visual inspection on the discovery of strong gravitational lenses

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies.
Efficient decision tree training with new data structure for secure multi-party computation

We propose a secure multi-party computation (MPC) protocol that constructs a secret-shared decision tree for a given secret-shared dataset. The previous MPC-based decision tree training protocol (Abspoel et al. 2021) requires $O(2^hmn log n)$ comparisons, being exponential in the tree height $h$ and with $n$ and $m$ being the number of rows and that of attributes in the dataset, respectively. The cause of the exponential number of comparisons in $h$ is that the decision tree training algorithm is based on the divide-and-conquer paradigm, where rows are padded after each split in order to hide the number of rows in the dataset. We resolve this issue via secure data structure that enables us to compute an aggregate value for every group while hiding the grouping information. By using this data structure, we can train a decision tree without padding to rows while hiding the size of the intermediate data. We specifically describes a decision tree training protocol that requires only $O(hmn log n)$ comparisons when the input attributes are continuous and the output attribute is binary. Note that the order is now linear in the tree height $h$. To demonstrate the practicality of our protocol, we implement it in an MPC framework based on a three-party secret sharing scheme. Our implementation results show that our protocol trains a decision tree with a height of 4 in 404 seconds for a dataset of $2^{20}$ rows and 11 attributes.
The key challenges of blockchain implementation in maritime sector: summary from literature and previous research findings

The concept of transforming maritime industry into a digital platform with real-time communication has rapidly emerged within industry discussions. A big part of the topic was tied around blockchain technology that due to its decentralization feature can simplify the complexity of the supply chain network and interconnect its actors. Nevertheless, only a few studies have investigated the feasibility in detail, yet not covered limitations other than those generally discussed. The purpose of the study is to identify and summarize the challenges of blockchain implementation in the maritime industry and within maritime ports. Based on the literature and previous research findings, 18 challenges to the blockchain implementation were identified and categorized into four dimensions: human factor, operational, organizational, and technological. The findings show that different priorities among ports, low level of digitalization, scalability of blockchain systems, and unwillingness to change business routine are important challenges.
Ontology-aware Prescription Recommendation in Treatment Pathways Using Multi-evidence Healthcare Data

For care of chronic diseases (e.g., depression, diabetes, hypertension), it is critical to identify effective treatment pathways that aim to promptly update the medication following the change of patient state and disease progression. This task is challenging because the optimal treatment pathway for each patient needs to be personalized due to the significant heterogeneity among individuals. Therefore, it is naturally promising to investigate how to use the abundant electronic health records to recommend effective and safe prescriptions. However, prescription recommendation needs to consider multiple aspects of life-critical evidence, such as the information relevance in terms of medical concepts, the health condition in terms of diagnosis history, and the further constraint in terms of side information (e.g., patient demographics and drug side effects). To this end, in this article, we propose a novel prescription recommendation framework named OntoPath to predict the next drug in disease treatment pathways, by building an ontology-aware hierarchical-attention model that integrates multiple medical evidence from domain knowledge guidance, medical history profiling, and side information utilization. Specifically, our method can be characterized from three aspects: (1) by incorporating the longitudinal diagnosis history, we enrich the profiling of patients in terms of comprehensive health conditions, which can largely influence a drug’s outcome on individual patients; (2) using the hierarchical disease and drug ontology structures, we are able to model the domain-specific relevance between patients and drugs at multiple levels of granularity and achieve in-depth collaborative filtering; (3) we introduce a pre-training stage to enhance the discriminativeness of network representations, which helps us obtain a premium model initialization to further boost the final recommendation training. We perform extensive experiments on a large-scale depression cohort with over 37,000 patients from a real-world medical claims database. The quantitative and qualitative results demonstrate the effectiveness of OntoPath through the consistent outperformance over state-of-the-art prescription recommendation baselines and the interpretation of model mechanism in case studies.
The effectiveness of designing and evaluating i-STAR applications in pediatric nursing courses

To apply the App to the curriculum could improve students' motivation and concentration, and also strengthen the effectiveness of professional knowledge of nursing students. Objectives: To construct and evaluate the effectiveness of smartphone applications (i-STAR) in pediatric nursing courses. Methods: Quasi-experimental study and two-group repeated-measure design were used to evaluate the effectiveness of smartphone applications (i-STAR) in pediatric nursing courses. The “i” means “interactive”, students had to log into App and finish four steps, including “Situation(S), Task(T), Action(A), and Reflection(R). A total of 163 nursing students in the third grade of associate's degree in nursing (ADN) participated in the study. The experimental group consisted of 77 participants, whereas the control group consisted of 86 participants. The instruments included the clinical reasoning readiness scale (CRRS), self-directed learning instrument (SDLI), and learning satisfaction of students. The 1st phase was the development, construction, and testing of the “i-STAR App”, including hyperbilirubinemia, pneumonia, and urinary tract infection (UTI). In the 2nd phase, students operated and applied the App in the classroom. The 3rd phase was to evaluate the effectiveness of the “i-STAR App” in the course. Results: The average scores of CRRS and SDLI were not significantly different between the two groups in the T0 andT1 (p > .05). The experimental group of CRRS and SDLI were better than the control group in T0-T1, T1-T2, and T0-T2 (p < .001). The experimental group showed better CRRS and SDLI scores than the control group in T0-T1, and T0-T2 with Repeated measures ANOVA. Also, the experimental group showed prolonged learning effectiveness. The experimental group (41.22 ± 6.08) was more satisfied than the control group (37.29 ± 7.40) (p < .05), although their academic performance was about the same (p > .05). Conclusion: “i-STAR App” could enhance students' interest in pediatric nursing courses, strengthen nursing students' clinical reasoning and self-directed learning ability.