Reviewerly/exHarmony huggingface dataset:
Shedding light on social learning

Culture involves the origination and transmission of ideas, but the conditions in which culture can emerge and evolve are unclear. We constructed and studied a highly simplified neural-network model of these processes. In this model ideas originate by individual learning from the environment and are transmitted by communication between individuals. Individuals (or "agents") comprise a single neuron which receives structured data from the environment via plastic synaptic connections. The data are generated in the simplest possible way: linear mixing of independently fluctuating sources and the goal of learning is to unmix the data. To make this problem tractable we assume that at least one of the sources fluctuates in a nonGaussian manner. Linear mixing creates structure in the data, and agents attempt to learn (from the data and possibly from other individuals) synaptic weights that will unmix, i.e., to "understand" the agent's world. For a variety of reasons even this goal can be difficult for a single agent to achieve; we studied one particular type of difficulty (created by imperfection in synaptic plasticity), though our conclusions should carry over to many other types of difficulty. We previously studied whether a small population of communicating agents, learning from each other, could more easily learn unmixing coefficients than isolated individuals, learning only from their environment. We found, unsurprisingly, that if agents learn indiscriminately from any other agent (whether or not they have learned good solutions), communication does not enhance understanding. Here we extend the model slightly, by allowing successful learners to be more effective teachers, and find that now a population of agents can learn more effectively than isolated individuals. We suggest that a key factor in the onset of culture might be the development of selective learning.
Graph Condensation via Eigenbasis Matching

The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications. Recently, the emerging graph condensation (GC) sheds light on reducing the computational cost of GNNs from a data perspective. It aims to replace the real large graph with a significantly smaller synthetic graph so that GNNs trained on both graphs exhibit comparable performance. However, our empirical investigation reveals that existing GC methods suffer from poor generalization, i.e., different GNNs trained on the same synthetic graph have obvious performance gaps. What factors hinder the generalization of GC and how can we mitigate it? To answer this question, we commence with a detailed analysis and observe that GNNs will inject spectrum bias into the synthetic graph, resulting in a distribution shift. To tackle this issue, we propose eigenbasis matching for spectrum-free graph condensation, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs. Subsequently, GCEM leverages the spectrum of the real graph and the synthetic eigenbasis to construct the synthetic graph, thereby preserving the essential structural information. We theoretically demonstrate that the synthetic graph generated by GCEM maintains the spectral similarity, i.e., total variation, of the real graph. Extensive experiments conducted on five graph datasets verify that GCEM not only achieves state-of-the-art performance over baselines but also significantly narrows the performance gaps between different GNNs.
Univalent Double Categories

Category theory is a branch of mathematics that provides a formal framework for understanding the relationship between mathematical structures. To this end, a category not only incorporates the data of the desired objects, but also "morphisms", which capture how different objects interact with each other. Category theory has found many applications in mathematics and in computer science, for example in functional programming. Double categories are a natural generalization of categories which incorporate the data of two separate classes of morphisms, allowing a more nuanced representation of relationships and interactions between objects. Similar to category theory, double categories have been successfully applied to various situations in mathematics and computer science, in which objects naturally exhibit two types of morphisms. Examples include categories themselves, but also lenses, petri nets, and spans. While categories have already been formalized in a variety of proof assistants, double categories have received far less attention. In this paper we remedy this situation by presenting a formalization of double categories via the proof assistant Coq, relying on the Coq UniMath library. As part of this work we present two equivalent formalizations of the definition of a double category, an unfolded explicit definition and a second definition which exhibits excellent formal properties via 2-sided displayed categories. As an application of the formal approach we establish a notion of univalent double category along with a univalence principle: equivalences of univalent double categories coincide with their identities
Political claim identification and categorization in a multilingual setting: First experiments

The identification and classification of political claims is an important step in the analysis of political newspaper reports; however, resources for this task are few and far between. This paper explores different strategies for the cross-lingual projection of political claims analysis. We conduct experiments on a German dataset, DebateNet2.0, covering the policy debate sparked by the 2015 refugee crisis. Our evaluation involves two tasks (claim identification and categorization), three languages (German, English, and French) and two methods (machine translation -- the best method in our experiments -- and multilingual embeddings).
Question Answering for Electronic Health Records: A Scoping Review of datasets and models (Preprint)

<sec> <title>BACKGROUND</title> Question Answering (QA) systems on patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Significant amounts of patient data are stored in Electronic Health Records (EHRs), making EHR QA an important research area. In EHR QA, the answer is obtained from the patient’s medical record. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that employ medical websites or scientific papers to retrieve answers, making it critical to research EHR question answering. </sec> <sec> <title>OBJECTIVE</title> This study aimed to provide a methodological review of existing works on QA over EHRs. The objectives of this study were to (i) identify the existing EHR QA datasets and analyze them, (ii) study the state-of-the-art methodologies used in this task, (iii) compare the different evaluation metrics used by these state-of-the-art models, and finally (iv) elicit the various challenges and the ongoing issues in EHR QA. </sec> <sec> <title>METHODS</title> We searched for articles from January 1st, 2005 to September 30th, 2023 in four digital sources including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA guidelines. 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained a total of 47 papers for further study. The selected studies were then classified into two non-mutually exclusive categories depending on their scope: ‘EHR QA datasets’ and ‘EHR QA Models’. </sec> <sec> <title>RESULTS</title> A systematic screening process obtained a total of 47 papers on EHR QA for final review. Out of the 47 papers, 25 papers were about EHR QA datasets, and 37 papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. Also, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that MIMIC-III and the n2c2 datasets are the most popular EHR database/corpus used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models. </sec> <sec> <title>CONCLUSIONS</title> EHR QA research faces multiple challenges such as the limited availability of clinical annotations, concept normalization in EHR QA, as well as challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions. </sec>
Techniques to Detect and Mitigate Adversarial Attacks through Augmentation of Training Data: A Systematic Review

&lt;p&gt;This paper aims to cover and summarize research on the topic of data augmentation in cybersecurity. By exploring its use in various areas of the field, we hope to highlight the benefits of using data augmentation. To analyze a wide range of data, we opted for a systematic review. This paper covers data augmentation in image, text, audio, and hybrid data sets as well as the different augmentation techniques.&lt;/p&gt;
Pengaruh Video Pembelajaran Berbasis Edpuzzle pada Materi Fusion Pastry Bakery terhadap Level Kognitif dan Kinerja Siswa

Edpuzzle is one of the interactive video-based e-learning platforms that is often used in the world of education. Research objectives are Knowing the effect of using Edpuzzle videos on students' cognitive levels, knowing the influence of using Edpuzzle videos on student practicum performance and knowing the influence of Edpuzzle videos on cognitive levels and practicum performance simultaneously. The type of research used is a type of quantitative research with a quasi-experimental design research model (Pseudo-Experiment). The variables used were three variables with one independent variable (X: Edpuzzle Learning Video) and two dependent variables (Y1: Cognitive Level and Y2: Practicum Performance). Data collection is carried out using test methods and performance observation. The data was processed with Simple Regression T Test and Manova Test. After that, discussion and conclusion are drawn from the problem formulation. From this writing, it is concluded that the Sig value in each hypothesis shows a &gt; number of 0.05 probability, it can show that there is a significant influence between cognitive level and practicum performance and there is an influence of both on fusion pastry bakery learning materials using the Edpuzzle video-based learning method. In line with the conclusions above, it is hoped that the use of Edpuzzle learning media can provide something new to the world of education. And educators can apply innovative learning media to students.
Review of Machine Translation

Machine translation is the use of computers to automatically achieve translation between different languages. Machine translation technology reduces the difficulty of communication between speakers of different languages, alleviates the pressure of translators, and is conducive to the inheritance of languages with scarce resources. Firstly, the origin, implementation strategy and basic classification of machine translation are introduced. Then, different categories of machine translation are described, and the basic ideas, research progress, advantages and existing problems are summarized. Finally, the development trend of machine translation technology is forecasted.
Pembinaan Sistem Akuntansi Serta Pelaporan Keuangan di SMK Kolese Tiara Bangsa

Effective accounting and financial reporting systems are crucial for ensuring transparency, accuracy, and the sustainability of finances in vocational schools like SMK Kolese Tiara Bangsa. This research aims to examine the efforts in fostering and enhancing the accounting and financial reporting systems at SMK. The study involves data collection from various sources, including interviews with administrative staff, document analysis, and monitoring of accounting procedures. The research findings indicate that SMK Kolese Tiara Bangsa has undertaken significant efforts to improve their financial governance. These efforts include training for administrative staff, the utilization of state-of-the-art accounting software, and enhancing understanding of applicable accounting standards. Furthermore, the school has also revamped their financial reporting processes, encompassing the regular preparation of financial statements and strict budget monitoring. The improvement in the accounting and financial reporting system has aided SMK Kolese Tiara Bangsa in more effectively managing their finances, enhancing the accuracy of financial information, and increasing transparency and accountability. This study provides valuable insights into the importance of fostering and developing accounting systems in educational institutions, with the hope of setting an example for similar institutions aiming to improve their financial management.
Reconceptualizing Time to Understand How to Lead Digitalization in Education

This study investigates how preschool principals learn how to understand and lead the digitalisation process in their preschool organisations collaboratively within an action research initiative. It specifically investigates how perspectives on time affect their understanding of leading digitalisation by exploring time as a discursive arrangement in a principal’s professional learning. The question is what happens when principals and researchers theorise time as a way to understand how to lead digitalisation in education. The study is based on data collected during the first year of action research. It takes its point of departure from the field of practice theory and uses the theory of practice architectures to analyse what happens in conversations when the participating princpals were challenged to shift from an objective time perspective to viewing time in terms of practice processes. The findings describe how a reconceptualisation of time affected the principals’ understandings of digitalisation in educational practice. Furthermore, it affected how the principals related to the teachers and how the principals organised educational change at their local schools.
TCF-Trans: Temporal Context Fusion Transformer for Anomaly Detection in Time Series

Anomaly detection tasks involving time-series signal processing have been important research topics for decades. In many real-world anomaly detection applications, no specific distributions fit the data, and the characteristics of anomalies are different. Under these circumstances, the detection algorithm requires excellent learning ability of the data features. Transformers, which apply the self-attention mechanism, have shown outstanding performances in modelling long-range dependencies. Although Transformer based models have good prediction performance, they may be influenced by noise and ignore some unusual details, which are significant for anomaly detection. In this paper, a novel temporal context fusion framework: Temporal Context Fusion Transformer (TCF-Trans), is proposed for anomaly detection tasks with applications to time series. The original feature transmitting structure in the decoder of Informer is replaced with the proposed feature fusion decoder to fully utilise the features extracted from shallow and deep decoder layers. This strategy prevents the decoder from missing unusual anomaly details while maintaining robustness from noises inside the data. Besides, we propose the temporal context fusion module to adaptively fuse the generated auxiliary predictions. Extensive experiments on public and collected transportation datasets validate that the proposed framework is effective for anomaly detection in time series. Additionally, the ablation study and a series of parameter sensitivity experiments show that the proposed method maintains high performance under various experimental settings.
Performance and Security Considerations for Differential Privacy Model with Adaptive Metrics

Over time, the differential privacy model has undergone numerous extensions and implementations by various researchers. Recently, a notable advancement introduced two adaptive metrics aimed at achieving improved calibration of security and accuracy by considering the risk associated with statistical queries.
Number Express: a Digital Game to Improve Early Numeracy

Early numeracy (including basic skills like counting, order processing, etc.) has repeatedly been observed to predict later scholastic but also vocational prospects. As such, it seems sensible to foster it early on during development. In this article, we describe the development and initial evaluation of Number Express, a digital game designed to facilitate numerical skills in pre-and primary school considering the latest evidence from research on order processing. The game consists of a train with six carriages for the player to fill in with numbers in the correct order. The game progresses in difficulty across several levels, moving from smaller to larger numbers and sequences in steps of 2, 5, and 10. Informative feedback helps players in case they respond wrongly. Associating numbers to carriages in the correct order earns the player points, with which they can buy items in a virtual shop, providing additional experience with calculating with money. During the design phase, we sought feedback from researchers, teachers, and an education consultant specialising in the learning and teaching of primary mathematics. Piloting will involve testing the game with a small group of preschool children to evaluate its playability and ease of use and to resolve any remaining technical issues. Additionally, we will gather feedback from children to identify any areas of the game that might be improved or made more engaging. In the next step, the game will be implemented in an intervention study to determine its effectiveness in improving children's early numeracy skills. In case the game proves to be effective in improving children's early numeracy skills, it has the potential to be a valuable tool for educators and parents in supporting their children's numerical development.
The need for game-based learning methods to address cyber threats

Cyber security threats are increasingly a serious concern to organisations, with an annual worldwide cost of a trillion dollars in 2021. Potentially the most significant contributor to cyber security threats is the human element, yet this has typically been insufficiently addressed in proposed solutions. Significant resources have been allocated to software, training and other solutions designed to tackle this threat, yet existing methods to improve cyber security have failed to deliver the desired results. Commonly cited issues include the lack of engagement in training, leading to disinterest and a ‘one size fits all’ approach, meaning some groups benefit from training more than others. This study will examine the need for game-based training methods in addressing cyber security threats caused by human error. Game-based training methods have previously been proposed to improve engagement in training and this study will discuss other potential benefits of game-based training. The aim of this work is to justify the use of game-based training methods in cyber security and begin to determine which aspects of games may be most effective at causing long-term positive behaviour change. Following an extensive literature review, a pilot study was run in which a survey was presented to 37 individuals who have taken cyber security training in the past, to query opinions and perceptions regarding cyber security training participants had previously taken, and how they feel they would behave when faced with certain cyber security threats. Upon analysis in SPSS, the results of this work indicate that factors such as training frequency and exposure to cyber security attacks have a significant impact on cyber security behaviour. A correlation between engaging training and impact of training on behaviour also serves to justify the development of such training methods. When combined with previous results on cyber security training this highlights the need for training to be engaging, regular, and relevant, and shows that the realistic simulation of cyber security threats (such as in game-based training) is of significant benefit. These results will help inform future development of effective game-based training methods and encourage their use more widely.
Vocabulary Acquisition in New and Learned Contexts Using Immersive Virtual Reality

Vocabulary acquisition is important for learning a language. Language learners need multiple encounters with a word to add unknown word aspects to their word knowledge or to strengthen their knowledge of previously learned word aspects. Combining vocabulary acquisition with immersive virtual reality (IVR) makes it possible to have such multiple encounters. In IVR, objects representing target words can be shown in the same virtual environment as they were first encountered (learned context), but also in a new virtual environment (new context). To investigate learning words in either a new or learned context in IVR we built Wics (words in context system). We used Wics to carry out an experiment in which participants could learn 32 Japanese words, represented by 3D objects, in three sessions. In the second and third learning session all words were shown in either a new context, with a changed environment and a changed visual representation of the word objects, or a learned context, where only the placement of the objects was changed. Performance scores for an immediate and a delayed post-test (one week later) revealed no significant differences in performance between conditions. However, there was a tendency towards participants spending more time on learning the words in the learned context condition. We believe Wics to be the first IVR system to provide learners with multiple learning sessions to provide multiple encounters with the same words in different contexts, where learners have control over their own explorative learning, and where the post-tests are inside IVR.
Simulasi Setting WLC berdasarkan kebijakan VLAN pada Client yang terhubung ke AP di Gedung Gamma (Administrasi) Sekolah Vokasi IPB

IPB University Vocational School has several buildings including the Gamma Building as an administrative center. In the Gamma Building several access points were installed to support administrative activities. The gamma building consists of 4 floors where each floor has different needs. The 1st floor is for student administration activities, the 2nd floor is for financial administration, data and leadership rooms, the 3rd floor is lecturer meeting rooms and leadership rooms and the 4th floor is lecturer meeting rooms and lecturer rooms. Management is required to set the AP point configuration based on the SSID, so WLC is used to set the SSID based on the VLAN created. The methods used in making this simulation are observation, design and testing. Observations were carried out to see the need for access points in the gamma building and what SSIDs would be created, including VLAN 10 SSID for leadership, VLAN 20 SSID for lecturers, VLAN 30 SSID for students, VLAN 40 SSID for staff and VLAN 50 SSID for guests. Design is the process of making a network topology in a Cisco packet tracer simulation based on observations. Tests are carried out whether the SSID is obtained by each client based on a certain VLAN. Obtained from the results of SSID testing on each floor based on the provisions where on the 1st floor all SSID, on the 2nd floor Leaders, Lecturers and staff, on the 3rd floor Leaders, lecturers and guests and on the 4th floor leaders and lecturers. Each client gets its SSID based on the VLAN that is set.