Reviewerly/exHarmony huggingface dataset:
Toward a shallow discourse parser for Turkish

Abstract One of the most interesting aspects of natural language is how texts cohere, which involves the pragmatic or semantic relations that hold between clauses (addition, cause-effect, conditional, similarity), referred to as discourse relations. A focus on the identification and classification of discourse relations appears as an imperative challenge to be resolved to support tasks such as text summarization, dialogue systems, and machine translation that need information above the clause level. Despite the recent interest in discourse relations in well-known languages such as English, data and experiments are still needed for typologically different and less-resourced languages. We report the most comprehensive investigation of shallow discourse parsing in Turkish, focusing on two main sub-tasks: identification of discourse relation realization types and the sense classification of explicit and implicit relations. The work is based on the approach of fine-tuning a pre-trained language model (BERT) as an encoder and classifying the encoded data with neural network-based classifiers. We firstly identify the discourse relation realization type that holds in a given text, if there is any. Then, we move on to the sense classification of the identified explicit and implicit relations. In addition to in-domain experiments on a held-out test set from the Turkish Discourse Bank (TDB 1.2), we also report the out-domain performance of our models in order to evaluate its generalization abilities, using the Turkish part of the TED Multilingual Discourse Bank. Finally, we explore the effect of multilingual data aggregation on the classification of relation realization type through a cross-lingual experiment. The results suggest that our models perform relatively well despite the limited size of the TDB 1.2 and that there are language-specific aspects of detecting the types of discourse relation realization. We believe that the findings are important both in providing insights regarding the performance of the modern language models in a typologically different language and in the low-resource scenario, given that the TDB 1.2 is 1/20th of the Penn Discourse TreeBank in terms of the number of total relations.
An Empirical Investigation of Taiwanese Teachers’ Technological Pedagogical Content Knowledge in an Initiative to Adopt Digitization during Covid-19 Pandemic

The COVID-19 pandemic presents a new opportunity to accelerate the integration of digital and information technology with the concept of technology teaching content knowledge (TPACK) in education. The Ministry of Education of Taiwan has introduced the digitization of humanities and social sciences (H&amp;SS) courses since 2017, and 189 projects in three categories have been chosen and subsidized by 2022. This study coded and categorized the curriculum content of these projects in accordance with the TPACK framework and used data mining techniques to investigate the TPACK of teachers involved in the implementation of these subsidized digital H&amp;SS courses. By counting technical knowledge (TK), content-knowledge (CK), and technology-content-pedagogy knowledge (TCPK) in digital H&amp;SS courses, we show that before the pandemic, humanities courses emphasized digital technology content with pedagogy (TCP), pedagogy (P), and technology with content (TC), while during the pandemic they focused more on content (C). Social sciences courses shifted from stressing technological pedagogy (TP) and C before the pandemic to increased emphasis on TC during the pandemic. Overall, teachers prioritized P, TCP, and TC across disciplines. Additional findings include differences in TPACK focus between fields, thorough TPACK descriptions in social sciences courses, and emphasis on technology (T) in scientific methods courses both before and during the pandemic. Analysis of P, TC, and TCP topics revealed gaps in skills and technology-specific pedagogy, thus shedding light on the strategies for developing digital teaching competencies of university teachers and how to develop them. The study also provides recommendations for future implementation of digital H&amp;SS programs.
Deriving Ground Truth Labels for Regression Problems Using Annotator Precision

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels.
<i>Quelle folle aventure!</i> Adjective positioning in native French and translated American TV shows and movies

Abstract This paper analyzes adjective positioning in French through the lens of language contact. Though some adjectives are nearly always either before their noun (pre-nominal) or after (post-nominal), others – usually adjectives that depict a positive or negative value – can be found in either position without any detectable semantic change. Our aim is to investigate the possible influence of English on the positioning of these adjectives by using a corpus of native French TV media as well as media translated from English. The categorical pre-nominal pattern of English might be causing a higher rate of pre-nominal adjectives to be used in translated media in comparison to native media. Our findings indicate that the translated corpus shows a preference for pre-nominal adjectives compared to the native French corpus. It was also revealed that some adjectives seem more flexible in translated media. Thus, while French media prefer one position over the other for a specific adjective, the translated media will more easily place the same adjective on either side of the noun. Therefore, we conclude that language contact has a role to play in the positioning of adjectives in TV media in French.
Musical Live Coding in Relation to Interactivity Variations

This article explores the similarities and differences between live coding and traditional music performances. The focus is on how bodily movements are expressed and whether pre-reflective processes may be activated during a live coding performance. While reports from practitioners vary on percepts of embodiment, the community is missing a theoretical background that reflects on practice. Understanding pre-reflective processes in live coding can benefit performance practices and tool development. As a live coder, I reflect on personal experiences and explore what I call ‘interactivity variations’, a term to denote different gestural manners of interactions during a performance. I observe patterns of embodiment among various live coders who use diverse performance systems from online videos. Out of 11 examples of performance systems, two cases demonstrate interactivity variations that can activate pre-reflective processes while another exploits direct manipulation. I present some implications for the patterns of bodily movement during a live coding performance and discuss how descriptive and prescriptive notation can be important and potentially influence our sensorimotor network. The article contributes a first account of a sensorimotor theory on live coding performances, reflecting on practice and embodied music cognition by presenting an aesthetic analysis of 11 online video examples.
GCA-Net: Global Cross Aware Network for salient object detection

In recent years, significant progress has been made in salient object detection. Nevertheless, there remains a need for further improvements in the effective combination of local and global perspectives. Combining global perception with the local focus can enhance the ability to capture the integrity of protruding objects while achieving accurate segmentation. In this paper, we introduce a newly proposed salient object detection network named GCA-Net, which is built upon three essential components. Firstly, we utilize atrous convolution to effectively aggregate contextual information and refine high-level semantic information via spatial pooling. Secondly, we employ 1-dim crossover operations to achieve global perception while minimizing computational effort. Lastly, We have innovatively integrated a contour-aware loss into our salient object detection task to constrain our model’s predictions, leading to more accurate segmentation. After evaluating our model on the established salient object detection benchmark dataset, we conducted extensive experiments to demonstrate the exceptional performance of our method. As a result, our method achieved 1st in both Sm and MAE.
Study on Lightweight and Efficient Deep Image Compression Method Based on MBConv

There have been many studies on image compression using deep learning methods. The most of these studies have focused mainly on increasing the image compression ratio. However, for effective implementation in a wide range of mobile devices, it is crucial to also develop lightweight network architectures that can cope with the limited computational resources and power constraints of these devices. We have proposed three image compression methods based on the MBConv (Mobile inverted Bottleneck Convolution) architecture, which is proposed for image recognition tasks in mobile devices. The main goal of this research is to improve the performance of image compression while at the same time reducing the computational complexity of the network. To validate the effectiveness of the proposed method, we constructed a network based on previous research and conducted a series of experiments. Then, using Rate-Distortion curves, we compared the performance of the proposed method with that of a comparison method. The results showed that all three proposed methods improve the peak signal-to-noise ratio (PSNR), an objective evaluation metric, especially at medium and low bit rates. In addition, the proposed methods were shown to successfully reduce the number of parameters in the network by up to 22%.
Detail-Preserving Video-based Virtual Try-On (DPV-VTON)

Virtual Try-on systems enable the try-on of a desired clothing on a target person image. These systems have led to vast research and have attracted commercial interest. However, the existing techniques are image-based systems limited to using an in-shop target clothing from a pre-defined dataset. To address this, we propose a video-based virtual try-on network DPV-VTON, that simulates the try-on using the target cloth extracted from the fashion videos on a target person image, while preserving the details and the characteristics. The core of the DPV-VTON pipeline is made up of (i) Best Frame Selection (BFS) module that extracts the best frame from the video (ii) Clothing Extraction module (CEM) extracts the target clothing from the selected best frame and generates a binary mask. (iii) A virtual try-on module synthesizes a final virtual try-on. Experiments on the existing benchmark datasets and a curated video dataset demonstrate that DPV-VTON generates photo-realistic and visually promising results. The proposed model obtains the lowest FID, LPIPS and the highest SSIM scores compared to the existing systems.
Recognition and Detection of UAV Based on Transfer Learning

With the increasing application scenarios of UAVs in industry, agriculture, military and other fields, the potential threats to national security and public security cannot be ignored. In addition, effective UAV detection and/or tracking is becoming an increasingly important security service. This paper integrates deep learning and image processing technology to conduct research in this context. In this paper, a transfer learning based UAV detection model (YOLOV5-UAV) is proposed. In order to reduce the influence of the amount of supervised data and the imbalance of target distribution on the performance of the model, the dataset is constructed based on self-shot videos and Internet downloaded videos in different natural scenes, combined with Mosaic data enhancement and adaptive scaling techniques. Therefore, the problem of data security is also effectively solved. Furthermore, real-time tests were carried out in two different time periods, namely day and night, from multiple scales, multiple perspectives and multiple natural scenes, for purpose of verifying the validity of the model. The applicability of different detection models is compared and analyzed for small target, moving background and weak contrast between UAV and background. The results show that YOLOV5-UAV model has a good performance in both detection accuracy and detection speed.
Simulating Quantum Turing Machine in Augmented Reality

As quantum computing theory is attracting attention from researchers nowadays, visualizing the quantum computing process is necessary for fundamental quantum computing education and research. Especially, connecting traditional computational theory with advanced quantum computing concepts is an extremely important step in learning and understanding quantum computing. In this paper, we propose a practical interactive interface for simulating Quantum Turing Machine (QTM) in Augmented Reality (AR) that combines the traditional Turing machine computational model with the quantum computing simulation. Through such an interface, users can use a C-like script to represent a QTM and simulate such QTM in an immersive augmented reality platform through the Vuforia AR engine. After validating our proposed QTM AR simulator through a series of experiments, we show the great potential to apply our QTM AR simulator to quantum computing education through an interactive visualization interface in augmented reality.
DEEPS: A novel framework for image quality improvement of X-ray non-destructive testing

Imaging quality has always been the most critical issue during the analog to digital development of the X-ray non-destructive testing (NDT). Due to the complicated structure of evaluated objects, it is still difficult for human eyes to clearly distinguish foreground objects from the background within the X-ray NDT image. Meanwhile, noise signals of the radiation imaging equipment also causes several shortcomings, such as blurring of key features. Therefore, without upgrading the hardware of industrial inspection systems, there is a strong demand to improve the quality of X-ray NDT images, particularly on content and edges. In this paper, we proposed a novel framework, termed as dual enhancement effectiveness plus sharpening (DEEPS), for quality improvement of X-ray NDT image. Specifically, an alternative implementation of multi-scale retinex with color restoration (MSRCR) is firstly employed to extract and enhance the essential information of the image. Then the combination of contrast limited adaptive histogram equalization (CLAHE) and gamma mapping is sequentially implemented to enhance the contrast and lighting. The output of our proposed framework is finally derived after an unsharp mask (USM) loop. Qualitative and quantitative comparison against the classic methods with Peak Signal-To-Noise Ratio (PSNR), Entropy and Contrast to Improve Index (CII) demonstrate the effectiveness of our proposed framework that effectively improve image quality while preserve the edge information without introducing artefact. In addition, after parallel optimization, our method can be further utilized for real time applications of X-ray non-destructive testing scenarios.
Secure Image Retrieval Based on Deep Learning in Cloud Computing

The rapid growth of multimedia data and the limited storage and computing capacity of local devices motivate the outsourcing service of cloud storage. For the outsourced images, users usually encrypt them to protect their privacy and require the ability to retrieve them later. Therefore, how effectively managing and retrieving massive encrypted images in cloud servers becomes a challenging problem. In this paper, we propose a deep learning-based image retrieval scheme for cloud computing. Firstly, we use a Convolutional Neural Network (CNN) to extract feature descriptors and represent each image as two parts: image category and image feature. Then, we construct an encrypted tree-based index structure to improve retrieval efficiency. At the same time, we use the Learning With Errors (LWE)-based secure k-Nearest Neighbor (kNN) algorithm and random matrix to protect the security of two parts of the descriptors. The feasibility of our scheme is proved through security analysis. Finally, we conduct empirical experiments on the Caltech256 image dataset, and the results show that our scheme can achieve high retrieval efficiency and accuracy while ensuring image security.
Object Detection in Real Open Environment

Although object detection has achieved impressive progress and has a broad impact, it still encounters significant challenges in accurately detecting objects in open-world scenarios. The data in real-world open scenes often exhibit characteristics of limited annotations, such as very few annotated samples or even unknown classes with no annotations. Our studies mainly focus on applying detection to various open scenes and addressing the challenges of sparse samples and unknown classes. The comprehensive research aims to develop more powerful and efficient methods for object detection in the open world, making them more suitable for real-world applications.
Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning

Digraph Representation Learning aims to learn representations for directed homogeneous graphs (digraphs). Prior work is largely constrained or has poor generalizability across tasks. Most Graph Neural Networks exhibit poor performance on digraphs due to the neglect of modeling neighborhoods and preserving asymmetry. In this paper, we address these notable challenges by leveraging hyperbolic collaborative learning from multi-ordered partitioned neighborhoods and asymmetry-preserving regularizers. Our resulting formalism, Digraph Hyperbolic Networks (D-HYPR), is versatile for multiple tasks including node classification, link presence prediction, and link property prediction. The efficacy of D-HYPR was meticulously examined against 21 previous techniques, using 8 real-world digraph datasets. D-HYPR statistically significantly outperforms the current state of the art. We release our code at https://github. com/hongluzhou/dhypr.
ProofBuddy: A Proof Assistant for Learning and Monitoring

Proof competence, i.e. the ability to write and check (mathematical) proofs, is an important skill in Computer Science, but for many students it represents a difficult challenge. The main issues are the correct use of formal language and the ascertainment of whether proofs, especially the students' own, are complete and correct. Many authors have suggested using proof assistants to assist in teaching proof competence, but the efficacy of the approach is unclear. To improve the state of affairs, we introduce ProofBuddy: a web-based tool using the Isabelle proof assistant which enables researchers to conduct studies of the efficacy of approaches to using proof assistants in education by collecting fine-grained data about the way students interact with proof assistants. We have performed a preliminary usability study of ProofBuddy at the Technical University of Denmark.
Quantifying Consistency and Information Loss for Causal Abstraction Learning

Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models. In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. We consider four measures suited for different tasks, analyze their properties, and propose algorithms to evaluate and learn causal abstractions. Finally, we illustrate the flexibility of our setup by empirically showing how different measures and algorithmic choices may lead to different abstractions.
Using Federated Learning in Anomaly Detection and Analytics on Real-time Streaming Data of Healthcare

In order to reduce mortality in critical care units, it is essential to monitor a critically ill patient consistently in a hospital's intensive care unit. According to Harvard University research, medical errors cause nearly 5,000,000 deaths in India annually. The third most common cause of mortality, medical error is one of the top 10 killers. In the neonatal ICU, there are 0.7 medical errors per patient, compared to 1.5 errors per child in a pediatric emergency room. The prevalence of HF appears to be high in India, with estimates ranging from 1.3 million to 4.6 million, and an annual incidence of 491 600-1.8 million. Heart failure is a clinical illness that develops as the cardiac condition deteriorates. The prognosis and course of therapy for HF patients depend heavily on early diagnosis. Low staff-to-patient ratios, a lack of established standard medical protocol, and infrequent monitoring and evaluation of patient safety and quality indicators are the main causes of medical errors. Since human error is the main cause of medical errors, the appropriate technology may be used to reduce them. The goal of this project is to make use of the Federated Learning approach in health care to solve the problem of clinical deterioration and ultimately save lives.
Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages (Extended Abstract)

Causal discovery from observational data provides candidate causal relationships that need to be validated with ad-hoc experiments. Such experiments usually require major resources, and suitable techniques should therefore be applied to identify candidate relations while limiting false positives. Local causal discovery provides a detailed overview of the variables influencing a target, and it focuses on two sets of variables. The first one, the Parent-Children set, comprises all the elements that are direct causes of the target or that are its direct consequences, while the second one, called the Markov boundary, is the minimal set of variables for the optimal prediction of the target. In this paper we present RAveL, the first suite of algorithms for local causal discovery providing rigorous guarantees on false discoveries. Our algorithms exploit Rademacher averages, a key concept in statistical learning theory, to account for the multiple-hypothesis testing problem in high-dimensional scenarios. Moreover, we prove that state-of-the-art approaches cannot be adapted for the task due to their strong and untestable assumptions, and we complement our analyses with extensive experiments, on synthetic and real-world data.