Reviewerly/exHarmony huggingface dataset:
Meningkatkan Penguasaan Kosakata Baku dan Tidak Baku Melalui Model Kooperatif Tipe Index Card Match Menggunakan Aplikasi Zoom Kelas VI

In the learning process, the teacher is expected to provide teaching which is easily understood by students. However, there are some teachers who have not implemented the learning model in every learning process. The solution which can be taken by teachers is to use the index card match learning model. The index card match learning model or matching pair cards is one type of cooperative learning. The index card match learning model is varied by utilizing card media in order to improve student learning outcomes in mastering standard and non-standard vocabulary. This study aims to determine teacher and student activities, student learning outcomes, and student responses. The subjects in this study were sixth grade students of SDN Bahagia 06, totaling 20 students. The data collection instrument was carried out using teacher and student activity observation sheets, tests, and student questionnaires/response sheets. Data analysis used descriptive statistics of percentages. The result of this study indicates that the result of observing teacher activities in the first cycle obtained an average value of 3.46, which is a moderate category. The second cycle obtained an average score of 4.83, which is an excellent category, and the third cycle obtained an average score of 4.94, which is an excellent category. On the other hand, the student activities in the first cycle obtained an average score of 3.40, which is a moderate category, the second cycle obtained an average value of 4.75, which is an excellent category, and in the third cycle the average score was 4.91, which is an excellent category. Moreover, student learning outcomes using the index card match model in the first cycle obtained 70%, which is a good category, then it increased in the second cycle by acquiring 90%, which is an excellent and a perfect category, and in the third cycle acquired 100% of the student completion in learning process, which is an excellent category.
Knowledge-aware Response Selection with Semantics underlying Multi-Turn Open-Domain Conversations

Abstract Response selection is a critical issue in the AI community with important applications on the Web. The accuracy of the selected responses, however, tends to be insufficient due to the lack of contextual awareness, especially in open-domain conversations where words tend to have several meanings in different contexts. Our solution, SemSol, is a knowledge-aware response selection model that tackles this problem by utilizing the context-specific semantics behind words that are implicitly shared among users throughout the dialogue. SemSol simultaneously learns word sense disambiguations (WSD) for the words in the dialogue on the basis of an open-domain knowledge graph, i.e. WordNet, while learning the match between the context and the response candidates. Then, SemSol improves the accuracy of the response by exploiting the semantic information in knowledge graph in accordance with the dialogue context. Our model learns the topics of utterances in the context of the whole training dataset. This topic-level knowledge can provide topic-specific information in the dialogue context. This improves the WSDs and the response selection accuracy. Experimens with two open-domain conversational datasets, Douban (Chinese) and Reddit (English), demonstrated that the proposed SemSol model outperformed state-of-the-art baselins. SemSol is ranked #1 in the Douban leaderboard.
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation

While often assumed a gold standard, effective human evaluation of text generation remains an important, open area for research. We revisit this problem with a focus on producing consistent evaluations that are reproducible -- over time and across different populations. We study this goal in different stages of the human evaluation pipeline. In particular, we consider design choices for the annotation interface used to elicit human judgments and their impact on reproducibility. Furthermore, we develop an automated mechanism for maintaining annotator quality via a probabilistic model that detects and excludes noisy annotators. Putting these lessons together, we introduce GENIE: a system for running standardized human evaluations across different generation tasks. We instantiate GENIE with datasets representing four core challenges in text generation: machine translation, summarization, commonsense reasoning, and machine comprehension. For each task, GENIE offers a leaderboard that automatically crowdsources annotations for submissions, evaluating them along axes such as correctness, conciseness, and fluency. We have made the GENIE leaderboards publicly available, and have already ranked 50 submissions from 10 different research groups. We hope GENIE encourages further progress toward effective, standardized evaluations for text generation.
Evaluation of simplified 3D CAD data for conveying industrial assembly instructions via Augmented reality

Augmented Reality (AR) based training is gaining momentum in industrial sectors, particularly in assembly and maintenance. Generally, the media contents used to create AR assembly instructions include audio, video, images, text, signs, 3D data, and animations. Literature suggests that 3D CAD-based AR instructions spatially registered with the real-world environment are more effective and produce better training results. However, storing, processing, and rendering 3D data can be challenging even for state-of-the-art AR devices like HoloLens2, particularly in industrial usage. To overcome these concerns, heavy 3D models can be simplified to a certain extent with a minimal impact on the user experience, that is, the quality of visualization in AR. In the present paper, we evaluate the usability of a set of simplified 3D CAD models used to convey manual assembly information to novice operators. The experiment included 14 participants, six assembly operations, and two sets of 3D CAD models (i.e., originals and simplified) and was conducted in a laboratory setting. To simulate as much as possible a real-world assembly scenario, the components, and the original corresponding 3D CAD models were obtained from a real-world industrial setup. The present paper confirms that simplified 3D CAD models can replace the original 3D CAD models within AR applications without affecting the user experience with the help of subjective evaluations.
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detection

Reconstruction-based methods are widely explored in industrial visual anomaly detection. Such methods commonly require the model to well reconstruct the normal patterns but fail in the anomalies, and thus the anomalies can be detected by evaluating the reconstruction errors. However, in practice, it's usually difficult to control the generalization boundary of the model. The model with an overly strong generalization capability can even well reconstruct the abnormal regions, making them less distinguishable, while the model with a poor generalization capability can not reconstruct those changeable high-frequency components in the normal regions, which ultimately leads to false positives. To tackle the above issue, we propose a new reconstruction network where we reconstruct the original RGB image from its gray value edges (EdgRec). Specifically, this is achieved by an UNet-type denoising autoencoder with skip connections. The input edge and skip connections can well preserve the high-frequency information in the original image. Meanwhile, the proposed restoration task can force the network to memorize the normal low-frequency and color information. Besides, the denoising design can prevent the model from directly copying the original high-frequent components. To evaluate the anomalies, we further propose a new interpretable hand-crafted evaluation function that considers both the color and gradient differences. Our method achieves competitive results on the challenging benchmark MVTec AD (97.8\% for detection and 97.7\% for localization, AUROC). In addition, we conduct experiments on the MVTec 3D-AD dataset and show convincing results using RGB images only. Our code will be available at https://github.com/liutongkun/EdgRec.
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning

Instruction tuning is an emergent paradigm in NLP wherein natural language instructions are leveraged with language models to induce zero-shot performance on unseen tasks. Instructions have been shown to enable good performance on unseen tasks and datasets in both large and small language models. Dialogue is an especially interesting area to explore instruction tuning because dialogue systems perform multiple kinds of tasks related to language (e.g., natural language understanding and generation, domain-specific interaction), yet instruction tuning has not been systematically explored for dialogue-related tasks. We introduce InstructDial, an instruction tuning framework for dialogue, which consists of a repository of 48 diverse dialogue tasks in a unified text-to-text format created from 59 openly available dialogue datasets. Next, we explore cross-task generalization ability on models tuned on InstructDial across diverse dialogue tasks. Our analysis reveals that InstructDial enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection, and even better performance in a few-shot setting. To ensure that models adhere to instructions, we introduce novel meta-tasks. We establish benchmark zero-shot and few-shot performance of models trained using the proposed framework on multiple dialogue tasks.
Forget-SVGD: Particle-Based Bayesian Federated Unlearning

Variational particle-based Bayesian learning methods have the advantage of not being limited by the bias affecting more conventional parametric techniques. This paper proposes to leverage the flexibility of non-parametric Bayesian approximate inference to develop a novel Bayesian federated unlearning method, referred to as Forget-Stein Variational Gradient Descent (Forget-SVGD). Forget-SVGD builds on SVGD - a particle-based approximate Bayesian inference scheme using gradient-based deterministic updates - and on its distributed (federated) extension known as Distributed SVGD (DSVGD). Upon the completion of federated learning, as one or more participating agents request for their data to be "forgotten", Forget-SVGD carries out local SVGD updates at the agents whose data need to be "unlearned", which are interleaved with communication rounds with a parameter server. The proposed method is validated via performance comparisons with non-parametric schemes that train from scratch by excluding data to be forgotten, as well as with existing parametric Bayesian unlearning methods.
Common data model for COVID-19 datasets

A global medical crisis like the coronavirus disease 2019 (COVID-19) pandemic requires interdisciplinary and highly collaborative research from all over the world. One of the key challenges for collaborative research is a lack of interoperability among various heterogeneous data sources. Interoperability, standardization and mapping of datasets are necessary for data analysis and applications in advanced algorithms such as developing personalized risk prediction modeling.To ensure the interoperability and compatibility among COVID-19 datasets, we present here a common data model (CDM) which has been built from 11 different COVID-19 datasets from various geographical locations. The current version of the CDM holds 4639 data variables related to COVID-19 such as basic patient information (age, biological sex and diagnosis) as well as disease-specific data variables, for example, Anosmia and Dyspnea. Each of the data variables in the data model is associated with specific data types, variable mappings, value ranges, data units and data encodings that could be used for standardizing any dataset. Moreover, the compatibility with established data standards like OMOP and FHIR makes the CDM a well-designed CDM for COVID-19 data interoperability.The CDM is available in a public repo here: https://github.com/Fraunhofer-SCAI-Applied-Semantics/COVID-19-Global-Model.Supplementary data are available at Bioinformatics online.
Cybersecurity Challenges in Healthcare

Cyber security attacks evidence has shown that many sectors and industries are still at an insufficient level of readiness to counter these threats, including healthcare organizations and the entire healthcare industry. The COVID-19 pandemic has additionally launched the issue of cyber protection of healthcare systems and connected medical and other devices as well as modern IT components, which are often the entry point for attackers against healthcare organizations. With the aim of a systematic approach to cyber security in healthcare organizations, this article comprehensively presents cyber risks and possible consequences of attacks in the context of healthcare organization services, as well as identifies the five most important cyber security challenges and provides recommendations for establishing protection mechanisms in line with best practices.
Intellectual Debt

In this chapter, law and technology scholar Jonathan Zittrain warns of the danger of relying on answers for which we have no explanations. There are benefits to utilising solutions discovered through trial and error rather than rigorous proof: though aspirin was discovered in the late 19th century, it was not until the late 20th century that scientists were able to explain how it worked. But doing so accrues ‘intellectual debt’. This intellectual debt is compounding quickly in the realm of AI, especially in the subfield of machine learning. Whereas we know that ML models can create efficient, effective answers, we don’t always know why the models come to the conclusions they do. This makes it difficult to detect when they are malfunctioning, being manipulated, or producing unreliable results. When several systems interact, the ledger moves further to the red. Society’s movement from basic science towards applied technology that bypasses rigorous investigative research inches us closer to a world in which we are reliant on an oracle AI, one in which we trust regardless of our ability to audit its trustworthiness. Zittrain concludes that we must create an intellectual debt ‘balance sheet’ by allowing academics to scrutinise the systems.
Fostering the Common Good

In this chapter, the ethics and international law scholar Silja Voeneky and the mathematician Thorsten Schmidt propose a new adaptive regulation scheme for AI-driven products and services. To this end, the authors examine different regulatory regimes, including the European Medical Devices Regulation (MDR), and the proposed AI Act by the European Commission and analyse the advantages and drawbacks. They conclude that regulatory approaches in general and with regard to AI driven high risk products and services have structural and specific deficits. Hence, a new regulatory approach is suggested by the authors, which avoids these shortcomings. At its core, the proposed adaptive regulation requires that private actors, as companies developing and selling high risk AI driven products and services, pay a proportionate amount of money as a financial guarantee into a fund before the product or service enters the market. The authors lay down what amount of regulatory capital can be seen as proportionate and the accompanying rules and norms to implement adaptive regulation.
An Investigation into the Roles of Guessing and Partial Knowledge in the Vocabulary Size Test

Although an accurate measure of vocabulary size is integral to understanding the proficiency of language learners, the validity of multiple-choice (M/C) vocabulary tests to determine this has been questioned due to users guessing correct answers which inflates scores. In this paper the nature of guessing and partial knowledge used when taking the Vocabulary Size Test (VST) is examined. The analysis evaluates the thought processes of test takers through think-aloud protocols and self-reports. This provides a taxonomy of seven types of guesses used by learners while taking the VST and measures the frequency of their occurrence. Based on qualitative coding, guesses are investigated to determine if they exhibit partial word knowledge which is relevant to the test construct. The findings suggest that both guesses resulting from the use of partial knowledge and random guesses are included in estimates and this has a detrimental effect on test accuracy. The paper concludes that the VST does not provide an accurate measure of the vocabulary necessary for reading due to guessing and the meaning-recognition format. It also suggests that the role of partial knowledge should be considered when producing more sophisticated vocabulary tests in the future.
Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation

Research on Korean grammatical error correction (GEC) is limited, compared to other major languages such as English. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean GEC. In this work, we collect three datasets from different sources (Kor-Lang8, Kor-Native, and Kor-Learner) that covers a wide range of Korean grammatical errors. Considering the nature of Korean grammar, We then define 14 error types for Korean and provide KAGAS (Korean Automatic Grammatical error Annotation System), which can automatically annotate error types from parallel corpora. We use KAGAS on our datasets to make an evaluation benchmark for Korean, and present baseline models trained from our datasets. We show that the model trained with our datasets significantly outperforms the currently used statistical Korean GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets. The implementations and datasets are open-sourced.
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation

Data augmentation is a technique to generate new training data based on existing data. We evaluate the simple and cost-effective method of concatenating the original data examples to build new training instances. Continued training with such augmented data is able to improve off-the-shelf Transformer and Conformer models that were optimized on the original data only. We demonstrate considerable improvements on the LibriSpeech-960h test sets (WER 2.83 and 6.87 for test-clean and test-other), which carry over to models combined with shallow fusion (WER 2.55 and 6.27). Our method of continued training also leads to improvements of up to 0.9 WER on the ASR part of CoVoST-2 for four non English languages, and we observe that the gains are highly dependent on the size of the original training data. We compare different concatenation strategies and found that our method does not need speaker information to achieve its improvements. Finally, we demonstrate on two datasets that our methods also works for speech translation tasks.
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation

As language models become increasingly integrated into our digital lives, Personalized Text Generation (PTG) has emerged as a pivotal component with a wide range of applications. However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attributes. The model can inherit the bias and perpetuate inequality in generating text w.r.t. users' protected attributes, leading to unfair treatment when serving users. In this work, we investigate fairness of PTG in the context of personalized explanation generation for recommendations. We first discuss the biases in generated explanations and their fairness implications. To promote fairness, we introduce a general framework to achieve measure-specific counterfactual fairness in explanation generation. Extensive experiments and human evaluations demonstrate the effectiveness of our method.
A probabilistic perspective on nearest neighbor for implicit recommendation

Abstract Over the past years, the recommender systems community invented several novel approaches that reached better and better prediction accuracy. Sequential recommendation, such as music recommendation, has seen large improvements from neural network-based models such as recurrent neural networks or transformers. When no sequential information is available or not relevant, such as for book, movie, or product recommendation, however, the classic k -nearest neighbor algorithm appears to remain competitive, even when compared to much more sophisticated methods. In this paper, we attempt to explain the inner workings of the nearest neighbor using probabilistic tools, treating similarity as conditional probability and presenting a novel model for explaining and removing popularity bias. First, we provide a probabilistic formulation of similarity and the classic prediction formula. Second, by modeling user behavior as a combination of personal preference and global influence, we are able to explain the presence of popularity bias in the predictions. Finally, we utilize Bayesian inference to construct a theoretically grounded variant of the widely used inverse frequency scaling, which we use to mitigate the effect of popularity bias in the predictions. By replacing the formerly ad hoc choices of nearest neighbor with probabilistically founded counterparts, we are able to improve prediction accuracy over a variety of data sets and gain an increased understanding of the theory behind the method.
Precise Recommendation Algorithm for Online Sports Video Teaching Resources

INTRODUCTION: With the development of the epidemic, online teaching has gradually become a hot topic. However, unlike traditional teaching programs, there are many types of physical education resources, and the recommendation of related content has always been a difficulty in online teaching. OBJECTIVES: Therefore, this paper designs an accurate recommendation algorithm for online video teaching resources of sports to meet the personalized needs of online learning of sports majors. The data layer of the entire recommendation algorithm stores the video in the database and transmits it to the service processing layer after receiving the data. METHODS: This study was conducted using techniques from social network analysis. After receiving the data, the data layer of the recommendation algorithm stores the video in the database and transmits it to the business processing layer at the same time. The business processing layer uses the designed collaborative filtering resource recommendation algorithm to formulate recommendation results for different users, and push the recommended results to the user display interface of the user layer. RESULTS: The test results of the algorithm show that the designed system has a high recommendation success rate, and the system can still maintain stable running performance when the concurrent users are 500. The average precision of resource recommendation of this method is 98.21%, the average recall rate is 98.35%, and the average F1 value is 95.37%. CONCLUSION: The proposed resource recommendation algorithm realizes accurate recommendation of sports online video teaching resources through efficient recommendation algorithms.