Reviewerly/exHarmony huggingface dataset:
FBC: Fusing Bi-Encoder and Cross-Encoder for Long-Form Text Matching

Semantic text matching has a wide range of applications in natural language processing. Recently proposed models that have achieved excellent results on short text matching tasks are not well suited to long-form text matching problems due to input length limitations and increased noise. On the other hand, long-form texts contain a large amount of information at different granularities after encoding, which cannot be fully interacted and utilized by existing methods. To address above issues, we propose a novel long-form text-matching framework which fuses Bi-Encoder and Cross-Encoder (FBC). Specially, it first employs an entity-driven key sentence extraction method to obtain the crucial content of the text and filter out noise. Subsequently, it integrates Bi-Encoder and Cross-Encoder to better capture semantic features and matching signals. Extensive experiments on several publicly available datasets demonstrate the effectiveness of our approach, compared with strong baselines. Furthermore, our model exhibits greater stability and accuracy in determining the matching relationship between documents describing the same event, which outperforms previously established approaches. The code is released at https://github.com/CSU-NLP-Group/FBC.
Comparison of Error Rate Prediction Methods in Binary Logistic Regression Model for Balanced Data

Binary Logistic Regression is one of the statistical methods that can be used to see the relations between dependent variable with some independent variables, where the dependent variable split into two categories, namely the category declaring a successful event and the category declaring a failed event. The performance of binary logistic regression can be seen from the accurary of the model. Accuracy can be measured by predicting the error rate. One method that can be used to predict error rate is cross validation. The cross validation method works by dividing the data into two parts, namely testing data and training data. Cross validation has several learning methods that are commonly used, namely Leave One Out (LOO), Hold out, and K-fold cross validation. LOO has unbiased estimation of accuracy but take a long time, hold out can avoid overfitting and works faster because no iterations, and k-fold cross validation has smaller error rate prediction. Meanwhile, data cases with different correlation are useful to find out the different correlations effect performance of error rate prediction method. In this study uses artificially generated data with a normal distribution, including univariate, bivariate, and multivariate datasets with various combination of mean differences and correlation. Considering these factors, this study focuses on comparing the three cross validation methods for predicting error rate prediction in binary logistic regression. This study finds out that k-fold cross validation method is the most suitable method to predict errors in logistic regression modeling for balanced data.
Meningkatkan Motivasi Dan Prestasi Belajar Akuntansi Melalui Model Discovery Learning Pada Siswa Kelas XI AK. 1 SMK St. Willibrodus Betun Tahun Pelajaran 2022/ 2023

SMK St. Willibrodus Betun is actively pursuing various educational projects in the field of teacher accreditation. Due to a lack of creativity and innovation in teaching strategies, students are less motivated to learn. The aim of this research is to facilitate academic progress among Grade 11 students at a faster pace. The above objectives will be achieved through the use of the Learning Discovery Model which is available for students of class XI AK.1 for the 2022-2023 academic year at SMK St. Willibrodus Betun. The purpose of this study was to determine the effect of the Discovery Learning model on student learning outcomes and skills. This study used a sample of 22 students of class XI Ak. 1. The results showed that the average score of students who were classified as "Good" in Cycle II increased from 74.54 in Cycle I to 80.45, and student achievement in Cycle II increased from 72.73% to 86.36% . Based on the results of the study it can be concluded that the application of the Discovery Learning Model has the potential to improve academic achievement of students in Accounting class XI Ak. 1 at SMK St. Willibrordus Betun. This research focuses on the utilization of the Discovery Learning model as a means of increasing academic achievement in the context of Accounting education. Keywords of interest include Accounting education, academic achievement, and the Discovery Learning model.
On Contrastive Explanations for Tree-Based Classifiers

We define contrastive explanations that are suited to tree-based classifiers. In our framework, contrastive explanations are based on the set of (possibly non-independent) Boolean characteristics used by the classifier and are at least as general as contrastive explanations based on the set of characteristics of the instances considered at start. We investigate the computational complexity of computing contrastive explanations for Boolean classifiers (including tree-based ones), when the Boolean conditions used are not independent. Finally, we present and evaluate empirically an algorithm for computing minimum-size contrastive explanations for random forests.
Multi-Source Domain Adaptation Through Dataset Dictionary Learning in Wasserstein Space

This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose a novel MSDA framework based on dictionary learning and optimal transport. We interpret each domain in MSDA as an empirical distribution. As such, we express each domain as a Wasserstein barycenter of dictionary atoms, which are empirical distributions. We propose a novel algorithm, DaDiL, for learning via mini-batches: (i) atom distributions; (ii) a matrix of barycentric coordinates. Based on our dictionary, we propose two novel methods for MSDA: DaDil-R, based on the reconstruction of labeled samples in the target domain, and DaDiL-E, based on the ensembling of classifiers learned on atom distributions. We evaluate our methods in 3 benchmarks: Caltech-Office, Office 31, and CRWU, where we improved previous state-of-the-art by 3.15%, 2.29%, and 7.71% in classification performance. Finally, we show that interpolations in the Wasserstein hull of learned atoms provide data that can generalize to the target domain.
Impact of Social Networking Application TikTok on Student Career Orientation (A Case Study: Students in 3 Majors at Universities in Hanoi City)

More people are using social media to share information nowadays. As a result, the information on social media applications can quickly spread to a large number of individuals. Therefore, social media has become a communication channel that assists employers and businesses in finding and attracting young talent. Vice versa, it also helps people looking for jobs with career orientation. This article will focus on analyzing the main contents of career orientations on TikTok, one of the most popular social media apps today, for students. Alongside the positive aspects of information dissemination, there is still a prevalence of subjective and misleading information regarding careers and job choices on current social media apps. The article will identify the general viewpoints of students on career orientation on TikTok, point out the differences in views among students between three majors, and propose solutions to help them receive selective and accurate career orientation information on social media apps like TikTok. This is a research topic that addresses practical needs as an increasing number of young people today choose social media apps as a place for sharing, learning, and career orientation in the future.
Satisfaction and quality of life of nurses satisfaction and quality of life of nurses in a public hospital during the COVID-19 pandemic

Objective: To evaluate the satisfaction and quality of life (QL) of nurses working in a public hospital. Method: This is a cross-sectional study developed during COVID-19 with nurses who had 6 months of work, between October 2020 and February 2021, at a State Oncological Reference Hospital in São Luís-MA. A questionnaire was applied on sociodemographic characteristics and the WHOQOL-bref and Employee Satisfaction Index (ESI) instruments. A normality test was performed followed by the Spearman correlation coefficient with a significance level of 5%. Results: 71 nurses participated in the survey and the average QL scores in all domains are regular (3.64) and employee satisfaction, in all domains, is low (2.50). A weak positive correlation was observed in all domains of the WHOQOL-bref and in the autonomy of the ESI (physical - r=0.482; psychological - r=0.285; social - r=0.318; and environment - r=0.251), while weak negative correlation was observed between the professional status and the psychological (r=-0.312) and the social (r=-0.374) domain and work and environment requirements (r=-0.295). Conclusion and implications for practice: The nurses showed regular and low QL, satisfied and knowing the reasons helps managers to propose interventions to reduce the QL benefit and to improve the care provided.
DIWS-LCR-Rot-hop++: A Domain-Independent Word Selector for Cross-Domain Aspect-Based Sentiment Classification

The Aspect-Based Sentiment Classification (ABSC) models often suffer from a lack of training data in some domains. To exploit the abundant data from another domain, this work extends the original state-of-the-art LCR-Rot-hop++ model that uses a neural network with a rotatory attention mechanism for a cross-domain setting. More specifically, we propose a Domain-Independent Word Selector (DIWS) model that is used in combination with the LCR-Rot-hop++ model (DIWS-LCR-Rot-hop++). DIWS-LCR-Rot-hop++ uses attention weights from the domain classification task to determine whether a word is domain-specific or domain-independent, and discards domain-specific words when training and testing the LCR-Rot-hop++ model for cross-domain ABSC. Overall, our results confirm that DIWS-LCR-Rot-hop++ outperforms the original LCR-Rot-hop++ model under a cross-domain setting in case we impose an optimal domain-dependent attention threshold value for deciding whether a word is domain-specific or domain-independent. For a target domain that is highly similar to the source domain, we find that imposing moderate restrictions on classifying domain-independent words yields the best performance. Differently, a dissimilar target domain requires a strict restriction that classifies a small proportion of words as domain-independent. Also, we observe information loss which deteriorates the performance of DIWS-LCR-Rot-hop++ when we categorize an excessive amount of words as domain-specific and discard them.
Obstacle Detection of Pump Truck Based on Knowledge Distillation

Pump truck is an indispensable machine for infrastructure construction. In order to speed up its intelligentization and solve the obstacle avoidance problem of pump truck boom operation, this paper proposes a YOLO-V5 (You Only Look Once-Version 5), Obstacle detection of pump truck based on knowledge distillation, ODPTKD. Firstly, ODPTKD changed the random affine transformation at the input end into new data augmentation, and improved the network structure of YOLO-v5. Then, the temperature coefficient T is used to soften teachers’ output knowledge, and the cross entropy between the students and teachers’ networks is used to get the loss function. Finally, we will use the gradient descent to conduct knowledge distillation and update the parameters of the student model. According to experimental results, MAP of ODPTKD model is 5.1% higher than that of the model without distillation, and the network size is only 34.1% of that of teachers’ network. Compared with the model without data augmentation, the MAP of ODPTKD model is 15.2% higher. What’s more, the ODPTKD model effectively improves the accuracy of target detection, with high real-time frame rate, small memory occupied by the model, favorable for embedded deployment and strong robustness to noise and other interferences.
User Emotion Status Recognition in MOOCs Study Environment Based on Eye Tracking and Video Feature Fusion

Emotion is an essential aspect that influences the learning outcome and MOOCs learning experience. This research investigates the possibility of employing the fusion characteristics of video and users' eye tracking features to assess MOOC learners' emotions. In the experiment, we gathered eye tracking data from MOOCs students. We applied OpenSmile and OpenCV to extract the video features. And a fusion data set with the eye-tracking data and video features was established in the experiment. Machine learning method is utilized to explore the optimal model using fused feature data sets, and the impacts of multimodal features on emotion recognition are discussed. Our findings demonstrate that the fusion feature can achieve an accuracy rate of 86.3%, which outperformed the single-modal feature sets. According to the study, this technique can be used to learn students' emotion status in their MOOC study and predict the students' affective perception on MOOCs videos.
Explanation for User Trust in Context-Aware Recommender Systems for Search-As-Learning

Learning through web browsing, often termed Search-as-Learning (SaL), can create information overload, due to thousands of search results. SaL can be made more efficient by developing context-aware tools that recommend items to the user and minimize information overload. However, to use context-aware recommender systems (CARS) users need to trust it. Literature has proposed explanations as a feature that helps to build trust. We investigate the impact of explanation on user trust and user experience for using CARS for SaL. Our study results show that people trust a CARS without explanation more during the first use, but for a CARS with explanations, user trust is significant only after multiple uses. Through interviews, we also uncovered the interesting paradox that even though users do not perceive that explanations add to their learning outcomes, they still prefer to use a CARS with explanations over one without.
Automatic Temperature Parameter Tuning for Reinforcement Learning Using Path Integral Policy Improvement

In this article, we propose a novel variant of path integral policy improvement with covariance matrix adaptation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{PI}^{2}$</tex-math> </inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{CMA}$</tex-math> </inline-formula> ), which is a reinforcement learning (RL) algorithm that aims to optimize a parameterized policy for the continuous behavior of robots. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{PI}^{2}$</tex-math> </inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{CMA}$</tex-math> </inline-formula> has a hyperparameter called the temperature parameter, and its value is critical for performance; however, little research has been conducted on it and the existing method still contains a tunable parameter, which can be critical to performance. Therefore, tuning by trial and error is necessary in the existing method. Moreover, we show that there is a problem setting that cannot be learned by the existing method. The proposed method solves both problems by automatically adjusting the temperature parameter for each update. We confirmed the effectiveness of the proposed method using numerical tests.
Optimally Mitigating Backdoor Attacks in Federated Learning

Federated learning (FL) is a distributed, privacy-preserving learning paradigm where a joint model is trained on private data stored on client devices. Data owners (clients) train models locally and then submit them to an aggregation server for incorporation into the joint model. Malicious clients can apply training time attacks, e.g., backdoor attacks, by submitting maliciously trained models. Prior work has shown that Differential Privacy (DP) can provide certified robustness to backdoor attacks; however, there are limited studies regarding DP parameter selection as a function of the model architecture. In this work, we show empirically that larger models (i.e., with more parameters) require stronger DP parameter settings to mitigate backdoor attacks. Furthermore, we present a framework that alters the FL training algorithm to preserve certified accuracy round-by-round and show empirically that it is superior to a model trainer selecting DP parameters ahead of time before training begins and with incomplete information about the attacker. Although tools from DP are used in our proposed framework, it is focused on backdoor attack mitigation and does not provide privacy guarantees.
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscle. The key challenge of automating this task with machine learning is the sparsity of clinical labels, i.e., only a few landmark pixels in a high-dimensional image are annotated, leading many prior works to heavily rely on isotropic label smoothing. However, such a label smoothing strategy ignores the anatomical information of the image and induces some bias. To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD). Our main contributions are: 1) a hierarchical graph representation learning framework for multi-resolution landmark detection via GNNs; 2) induced hierarchical supervision at different levels of granularity using a multi-level loss. We evaluate our model on a public and a private dataset under the in-distribution (ID) and out-of-distribution (OOD) settings. For the ID setting, we achieve the state-of-the-art mean absolute errors (MAEs) of 1.46 mm and 1.86 mm on the two datasets. Our model also shows better OOD generalization than prior works with a testing MAE of 4.3 mm.
MulHiST: Multiple Histological Staining for Thick Biological Samples via Unsupervised Image-to-Image Translation

The conventional histopathology paradigm can provide the gold standard for clinical diagnosis, which, however, suffers from lengthy processing time and requires costly laboratory equipment. Recent advancements made in deep learning for computational histopathology have sparked lots of efforts in achieving a rapid chemical-free staining technique. Yet, existing approaches are limited to well-prepared thin sections, and invalid in handling more than one stain. In this paper, we present a multiple histological staining model for thick tissues (MulHiST), without any laborious sample preparation, sectioning, and staining process. We use the grey-scale light-sheet microscopy image of thick tissues as model input and transfer it into different histologically stained versions, including hematoxylin and eosin (H&E), Masson's trichrome (MT), and periodic acid-Schiff (PAS). This is the first work that enables the automatic and simultaneous generation of multiple histological staining for thick biological samples. Moreover, we empirically demonstrate that the AdaIN-based generator offers an advantage over other configurations to achieve higher-quality multi-style image generation. Extensive experiments also indicated that multi-domain data fusion is conducive to the model capturing shared pathological features. We believe that the proposed MulHiST can potentially be applied in clinical rapid pathology and will significantly improve the current histological workflow.
Mining Negative Temporal Contexts for False Positive Suppression in Real-Time Ultrasound Lesion Detection

During ultrasonic scanning processes, real-time lesion detection can assist radiologists in accurate cancer diagnosis. However, this essential task remains challenging and underexplored. General-purpose real-time object detection models can mistakenly report obvious false positives (FPs) when applied to ultrasound videos, potentially misleading junior radiologists. One key issue is their failure to utilize negative symptoms in previous frames, denoted as negative temporal contexts (NTC) [15]. To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow. By aggregating extracted contexts, we endow the model with the ability to suppress FPs by leveraging NTC. We call the resulting model UltraDet. The proposed UltraDet demonstrates significant improvement over previous state-of-the-arts and achieves real-time inference speed. We release the code, checkpoints, and high-quality labels of the CVA-BUS dataset [9] in https://github.com/HaojunYu1998/UltraDet .
Patients and Slides are Equal: A Multi-level Multi-instance Learning Framework for Pathological Image Analysis

In current pathology image classification, methods mostly rely on patch-based multi-instance learning (MIL), which only considers the relationship between patches and slides. However, in clinical medicine, doctors use slide-level labels to summarize patient-level labels as a diagnostic result, indicating the involvement of three levels of patch, slide, and patient in actual pathology image analysis, which we refer to as the multi-level multi-instance learning (ML-MIL) problem. To address this issue, we propose a novel and general framework called Patients and Slides are Equal (P &SrE), inspired by the doctor's diagnostic process of repeatedly confirming labels at the patient and slide level. In this framework, we treat patients and slides as instances at the same level and use transformers and attention mechanisms to build connections between them. This allows for interaction between patient-level and slide-level information and the correction of their respective features to achieve better classification performance. We evaluate our method on two datasets using two state-of-the-art MIL methods as baselines. The results show that our method improves the performance of the baselines on both slide and patient levels. Our method provides a simple and effective solution to the common problem of ML-MIL in medical clinical scenarios and has broad potential applications.
High-Quality Virtual Single-Viewpoint Surgical Video: Geometric Autocalibration of Multiple Cameras in Surgical Lights

Occlusion-free video generation is challenging due to surgeons' obstructions in the camera field of view. Prior work has addressed this issue by installing multiple cameras on a surgical light, hoping some cameras will observe the surgical field with less occlusion. However, this special camera setup poses a new imaging challenge since camera configurations can change every time surgeons move the light, and manual image alignment is required. This paper proposes an algorithm to automate this alignment task. The proposed method detects frames where the lighting system moves, realigns them, and selects the camera with the least occlusion. This algorithm results in a stabilized video with less occlusion. Quantitative results show that our method outperforms conventional approaches. A user study involving medical doctors also confirmed the superiority of our method.