Reviewerly/exHarmony huggingface dataset:
CogDL: A Comprehensive Library for Graph Deep Learning

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL1, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.
Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance

People often use disease or symptom terms on social media and online forums in ways other than to describe their health. Thus the NLP health mention classification (HMC) task aims to identify posts where users are discussing health conditions literally, not figuratively. Existing computational research typically only studies health mentions within well-represented groups in developed nations. Developing countries with limited health surveillance abilities fail to benefit from such data to manage public health crises. To advance the HMC research and benefit more diverse populations, we present the Nairaland health mention dataset (NHMD), a new dataset collected from a dedicated web forum for Nigerians. NHMD consists of 7,763 manually labelled posts extracted based on four prevalent diseases (HIV/AIDS, Malaria, Stroke and Tuberculosis) in Nigeria. With NHMD, we conduct extensive experiments using current state-of-the-art models for HMC and identify that, compared to existing public datasets, NHMD contains out-of-distribution examples. Hence, it is well suited for domain adaptation studies. The introduction of the NHMD dataset imposes better diversity coverage of vulnerable populations and generalisation for HMC tasks in a global public health surveillance setting. Additionally, we present a novel multi-task learning approach for HMC tasks by combining literal word meaning prediction as an auxiliary task. Experimental results demonstrate that the proposed approach outperforms state-of-the-art methods statistically significantly (p < 0.01, Wilcoxon test) in terms of F1 score over the state-of-the-art and shows that our new dataset poses a strong challenge to the existing HMC methods.
Time-manipulation Attack: Breaking Fairness against Proof of Authority Aura

As blockchain-based commercial projects and startups flourish, efficiency becomes one of the critical metrics in designing blockchain systems. Due to its high efficiency, Proof of Authority (PoA) Aura has become one of the most widely adopted consensus solutions for blockchains. Our research finds over 4,000 projects have used Aura and its variants. In this paper, we provide a rigorous analysis of Aura. We propose three types of time-manipulation attacks, where a malicious leader simply needs to modify the timestamp in its proposed block or delay it to extract extra benefits. These attacks can easily break the legal leader election, thus directly harming the fairness of the block proposal. We apply our attacks to a mature Aura project called OpenEthereum. By repeatedly conducting our attacks1 over 15 days, we find that an adversary can gain on average 200% mining rewards of their fair shares. Furthermore, such attacks can even indirectly break the finality of blocks and the safety of the system. Based on the deployment of Aura as of September 2022, the potentially affected market cap is up to 2.13 billion USD. As a by-product, we further discuss solutions to mitigate such issues and report our observations to official teams.
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks

Multiscale features are of great importance in modern convolutional neural networks, showing consistent performance gains on numerous vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multiscale representation ability. However, the design of plug-and-play blocks is getting more and more complex, and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search (NAS). Specifically, we design a new search space PPConv and develop a search algorithm consisting of one-level optimization, zero-one loss, and connection existence loss. PP-NAS minimizes the optimization gap between super-net and subarchitectures and can achieve good performance even without retraining. Extensive experiments on image classification, object detection, and semantic segmentation verify the superiority of PP-NAS over state-of-the-art CNNs (e.g., ResNet, ResNeXt, and Res2Net). Our code is available at https://github.com/ainieli/PP-NAS.
Quantum computing: a new paradigm for ecology

A global technology arms race is underway to build evermore powerful and precise quantum computers. Quantum computers have the potential to tackle certain quantitative problems quicker than classical computers. The current focus of quantum computing is on pushing the boundaries of fundamental quantum information and commercial applications in industrial sectors, financial services, and other profit-led sectors, particularly where improvements in optimisation and sampling can improve increased economic return. We believe that ecologists could exploit the computational power of quantum computers because the statistical approaches commonly used in ecology already have proven pathways on quantum computers. Moreover, quantum computing could ultimately leapfrog our understanding of complex ecological systems, if the hardware, opportunity, and creativity of quantitative ecologists all align.
THE CONCEPT OF TRANSFORMATION OF THE EDUCATIONAL PARADIGM, FOCUSED ON THE NEEDS AND LIFELONG LEARNING

In the current context, the transformation of digital education during the Fourth Industrial Revolution is undergoing a complex process. In particular, various needs constitute the “ecology of educational demand”: from the point of view of personal development, this includes the needs for preferences of interests, talent structure, lifelong learning and career development, which mainly involves diversity and the ability to adapt and employability. The purpose of the article is to analyze the theoretical and practical aspects of the “educational ecology” model in the context of the transformation of the education paradigm of the Fourth Industrial Revolution. Research methodology. Methods of system analysis and synthesis, axiological, hermeneutical, general philosophical – analysis and synthesis, generalization, logical and historical, comparative. The result of the study. The analysis of the transformation of the educational paradigm focused on the needs is carried out. The directions of implementation of the concept of “open education and lifelong learning” are shown. The directions of forming a common digital ecosystem are developed. The reconstruction of the modern infrastructure system with digital intelligence is revealed. The article reveals the restructuring of the development system of scientific and technological talents with the help of digital intelligence. The object of research is the formation and development of the model of “educational ecology” as a complex social, economic and cultural phenomenon. The subject of the study is the impact of the Fourth Industrial Revolution on the formation of the “educational ecology” model in the context of the transformation processes of our time. It is proved that the development of digital intelligence reflects the fundamental transformation of society and the economy to a new paradigm, causing innovation and reconstruction of industrial organization models, modern infrastructure systems, systems for training scientific and technical personnel and models for managing social development.
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

The unlabeled data are generally assumed to be normal data in anomaly detection (AD) based on semi-supervised learning. This assumption, however, can degrade detection performance when distributions of unlabeled and labeled data are different. To solve the problem, we propose a semi-supervised AD algorithm using KL divergence (SAD-KL) estimating the KL divergence of PDFs of the local outlier factors (LOFs) of the labeled normal and the unlabeled data. We show that the PDFs of the LOFs follow Burr distribution and the SAD-KL shows superior detection probability over the existing algorithms even though it takes less learning time.
RRANet: low-light image enhancement based on Retinex theory and residual attention

Low-light image enhancement is to restore the image acquired under insufficient light conditions to the normal exposure image. The low-light image enhancement method based on Retinex theory is a common method. The image is decomposed into the light component and the reflection component, and the corresponding enhancement is done respectively and then fused to achieve the purpose of image enhancement. However, most of the commonly used decomposition and enhancement networks in this field are designed by stacking convolution or up/down sampling, which lacks the guidance of relevant semantic information, resulting in the loss of many details in the decomposed and enhanced images. In order to alleviate the above problems, we propose a low-light image enhancement model based on Retinex theory and residual attention. Under the guidance of semantic information provided by channel domain and spatial domain, it can obtain smoother and less noisy images in the decomposition stage. In the image enhancement stage, the image texture and color can be restored with high quality. Moreover, we design loss functions that are more suitable for decomposition and enhancement tasks to constrain the learning tasks of different networks. In addition, we designed a residual block fused with dual attention unit, which can stably extract richer image features and suppress the generation of noise. Finally, we compare our model with the mainstream methods in recent years on public datasets. Extensive experimental results show that our model is superior to the mainstream methods, showing excellent performance and potential.
&lt;em&gt;How and Why Do Researchers Reference Data&lt;/em&gt;? A Study of Rhetorical Features and Functions of Data References in Academic Articles

Data reuse is a common practice in the social sciences. While published data play an essential role in the production of social science research, they are not consistently cited, which makes it difficult to assess their full scholarly impact and give credit to the original data producers. Furthermore, it can be challenging to understand researchers’ motivations for referencing data. Like references to academic literature, data references perform various rhetorical functions, such as paying homage, signaling disagreement, or drawing comparisons. This paper studies how and why researchers reference social science data in their academic writing. We develop a typology to model relationships between the entities that anchor data references, along with their features (access, actions, locations, styles, types) and functions (critique, describe, illustrate, interact, legitimize). We illustrate the use of the typology by coding multidisciplinary research articles (n = 30) referencing social science data archived at the Inter-university Consortium for Political and Social Research (ICPSR). We show how our typology captures researchers’ interactions with data and purposes for referencing data. Our typology provides a systematic way to document and analyze researchers’ narratives about data use, extending our ability to give credit to data that support research.
ICT Skills – Study concerning students from seventh to ninth grade in Viana do Castelo district

The present study presents the results of an investigation within the scope of educational assessment. The main objective of this study is to assess the digital competencies of students from the 7th to 9th school years in the district of Viana do Castelo. Data were collected through a questionnaire, validated, composed by the dimensions Information and Informal Literacy, Communication and Collaboration, Creation of Digital Content, Security and Problem Solving. The sample consists of 861 respondents. After analysing the results, we consider it relevant to reflect on the trends that contribute to the importance of ICT in the young population. Similarly, it is possible to state that the students in our sample have a reasonable amount of general knowledge, skills and aptitudes regarding digital competences. However, when it comes to surfing the Internet, browsing and filtering information, as well as storing and retrieving information, adding and reformulating files, solving everyday life problems with technology, working with technological innovations and solving digital gaps, most students showed little mastery of these topics. Contrary to being in the same grade or not, it is the gender that shows a significant amount of statistically significant results, regarding digital knowledge, skills and competencies. It can be seen that most students do not ask their family members for help with school tasks, which leads to the conclusion that the acquisition of digital skills is not achieved within the household.
On the performance of machine learning fairness in image classification

In recent years, computer vision has made significant strides in enabling machines to perform a wide range of tasks, from image classification and segmentation to image generation and video analysis. It is a rapidly evolving field that aims to enable machines to interpret and understand visual information from the environment. One key task in computer vision is image classification, where algorithms identify and categorize objects in images based on their visual features. Image classification has a wide range of applications, from image search and recommendation systems to autonomous driving and medical diagnosis. However, recent research has highlighted the presence of bias in image classification algorithms, particularly with respect to human-sensitive attributes such as gender, race, and ethnicity. Some examples are computer programmers being predicted better in the context of men in images compared to women, and the accuracy of the algorithm being better on greyscale images compared to colored images. This discrepancy in identifying objects is developed through correlation the algorithm learns from the objects in context known as contextual bias. This bias can result in inaccurate decisions, with potential consequences in areas such as hiring, healthcare, and security. In this paper, we conduct an empirical study to investigate bias in the image classification domain based on sensitive attribute gender using deep convolutional neural networks (CNN) through transfer learning and minimize bias within the image context using data augmentation to improve overall model performance. In addition, cross-data generalization experiments are conducted to evaluate model robustness across popular open-source image datasets.
Enhancing Flight Delay Prediction through Feature Engineering in Machine Learning Classifiers: A Real Time Data Streams Case Study

The process of creating and selecting features from raw data to enhance the accuracy of machine learning models is referred to as feature engineering. In the context of real-time data streams, feature engineering becomes particularly important because the data is constantly changing and the model must be able to adapt quickly. A case study of using feature engineering in a flight information system is described in this paper. We used feature engineering to improve the performance of machine learning classifiers for predicting flight delays and describe various techniques for extracting and constructing features from the raw data, including time-based features, trend-based features, and error-based features. Before applying these techniques, we applied feature pre-processing techniques, including the CTAO algorithm for feature pre-processing, followed by the SCSO (Sand cat swarm optimization) algorithm for feature extraction and the Enhanced harmony search for feature optimization. The resultant feature set contained the 9 most relevant features for deciding whether a flight would be delayed or not. Additionally, we evaluate the performance of various classifiers using these engineered features and contrast the results with those obtained using raw features. The results show that feature engineering significantly improves the performance of the classifiers and allows for more accurate prediction of flight delays in real-time.
A rotation-based feature and Bayesian hierarchical model for the forensic evaluation of handwriting evidence in a closed set

Forensic handwriting examiners are often tasked with identifying the writer of a particular document. Examples of handwriting evidence include ransom notes, forged documents and signatures, and threatening letters. At present, examiners rely on visual inspection of similarities and differences between the questioned document and reference writing samples. Here, we propose a principled modeling approach to compute the posterior predictive probability of writership when the author of the questioned document is part of a closed set of writers. Given a handwritten document, we extract measurements, including rotation angles that are related to the slant of writing, which are the response variables in a multilevel model. We fit the model and test its posterior predictive performance using writing samples from the United States and from Europe. We find that, as long as the questioned document is longer than a sentence or two, it is possible to correctly associate a writer with a document that he or she wrote with high probability. Earlier versions of this work have been well received by the community of forensic document examiners.
Get a Variable Grip: A Comparison of Three Gripping Techniques for Controller-Based Virtual Reality

This work-in-progress paper details a user study comparing three techniques for grasping virtual objects that differ in terms of how the firmness of users' grip of the controller affects the behavior of the virtual object: firm grip uses a direct mapping between the movement of the controller and object, firm+loose grip allows gravity to affect the object when the grip is less firm, and variable force grip varies the extent to which the object responds to gravity depending on how forceful the user's grip is. The study did not reveal any differences between the conditions in terms of perceived task load and usability. However, our findings suggest that variable force grip may be perceived as the most realistic, and we found some indication that variable force grip may impair task completion time.
Towards Discovering Meaningful Historical Relationships in Virtual Reality

Traditional digital tools for exploring historical data mostly rely on conventional 2D visualizations, which often cannot reveal all relevant interrelationships between historical fragments. We are working on a novel interactive exploration tool for historical data in virtual reality, which arranges fragments in a 3D environment based on their temporal, spatial and categorical proximity to a reference fragment. In this poster, we report on an initial expert review of our approach, giving us valuable insights into the use cases and requirements that inform our further developments.
A Commonsense Knowledge-based Object Retrieval Approach for Virtual Reality

Out-of-reach object retrieval is an important task in many applications in virtual reality. Hand gestures have been widely studied for object retrieval. However, the one-to-one mapping metaphor of the gesture would cause ambiguity problems and memory burdens for the retrieval of plenty of objects. Therefore, we proposed a grasping gesture-based object retrieval approach for out-of-reach objects based on a graphical model called And-Or graphs (AOG), leveraging the scene-object occurrence, object co-occurrence, and human grasp commonsense knowledge. This approach enables users to acquire objects by using natural grasping gestures according to their experience of grasping physical objects. Importantly, users could perform the same grasping gesture for different virtual objects and perform different grasping gestures for one virtual object in the virtual environment.
Fundamentos de la Educación Hibrida para una contextualización Curricular Universitaria emergente

The predisposition and educational trends of change towards academic and social modernity, It requires having a modern vision on curricular knowledge articulated and factual with the research processes, social responsibility, academic training, with prevailing knowledge of higher education, social action and the university-industry relationship. From this perspective, Hybrid Education requires the acquisition of technological skills, humanists to face the challenges of Education 4.0 and Industrial Trends, as the axis of university training programs. Based on this, an alternative is proposed for curriculum design from the Hybrid Education approach, in order to guarantee Comprehensive training and the development of society.
Analysis of the Effectiveness of Korean Language Education Programs for Foreign University Students using the Metaverse Platform

This study aims to analyze the learning effects and satisfaction changes in learners using metaverse-based educational programs and derive improvement plans. A survey was conducted on foreign university students participating in a metaverse-based Korean language education program. The results were compared and analyzed to measure the program's effectiveness and satisfaction differences. The study confirmed that the metaverse-based program effectively increased learner engagement and immersion. Blended metaverse classes showed higher learning outcomes and satisfaction than online metaverse and traditional classroom classes. The study suggests that educational institutions and educators consider implementing blended metaverse classes when developing and operating metaverse-based programs, which can provide more effective support for foreign university students learning Korean.