Reviewerly/exHarmony huggingface dataset:
Comparison of A Judgment of Laypersons, Teachers, and Speech-Language Pathologists on Children’s Personal Narrative Quality

본 연구의 목적은 개인적 경험 이야기에 대한 일반인, 교사, 언어재활사의 질 판단을 비교하는 것이다. 총 60명의 일반인, 교사, 언어재활사를 대상으로 일반 초등학교 2학년 아동이 산출한 개인적 경험 이야기의 질을 4점 척도로 평가하게 하고 이야기의 질 판단에 영향을 미치는 평가 기준을 주관적으로 작성하게 하였다. 또한 주관적인 평가와 객관적인 이야기 평가 체계의 상관을 살펴보았고, 어휘, 이야기 구조, 구문, 생동감, 유창성을 포함하는 5개의 범주에 대해 7점 척도로 평가하게 하였다. 연구 결과, 일반인, 교사, 언어재활사 집단에서 ‘좋은 이야기’와 ‘부족한 이야기’에 대한 인식이 존재하는 것으로 나타났고, 주관적 평가와 객관적 이야기 평가체계에서 정적 상관을 보였다. 주관적으로 기술은 이야기 구조와 일관성에 대한 언급이 많은 것으로 나타났다. 즉, 이야기 질을 판단하는 데에 있어 어느 정도의 합의가 있고, 이야기를 판단하는 중요한 기준은 이야기 구조와 일관성인 것으로 보인다. 이는 개인적 경험 이야기의 평가 체계개발의 기초가 될 수 있을 것이다. This study was to explore the quality judgment of personal narrative of laypersons, teachers, and Speech and Language Pathologists(SLPs), and to examine the characteristics of narratives quality. A total of 60 laypersons, teachers and SLPs rated the quality of personal narratives produced by typically developing 2nd graders by 4 point scale and they were asked to write their judgment criteria which contribute to the quality of narratives subjectively. Also they rated the narratives using 7 point scale about five categories which included vocabulary, narrative structure, syntax, sparkle, fluency. Agreement among laypersons, teachers and SLPs was found for good narratives and poor narratives. A discourse analysis of subjective judgment writings on narratives showed that comments on the narrative structure and coherence were more than other categories. Also the results of 7 point ratings showed that narrative structure also were highly estimated. These results suggest that there is a consensus of narrative quality. The participants considered the narrative structure and coherence as an important criteria in judging the quality of narratives.
K-Means and K-Medoids Algorithm Comparison for Clustering Forest Fire Location in Indonesia

Forest fires are the most common cause of deforestation in Indonesia. This condition has a negative impact on the survival of living things. Of course, this has received special attention from various parties. One effort that can be made for prevention is to group these points into areas with the potential for fire using the clustering method. In this research, a comparative study of the clustering algorithm between K-Means and K-Medoids was conducted on hotspot location data obtained from Global Forest Watch (GFW). Besides that, important variables that affect the clustering process are also analyzed in terms of feature importance. There are nine important variables used in the clustering process, of which the Acq_time variable is the most important. The cluster quality of both algorithms is evaluated using the silhouette coefficient (SC). Both algorithms are capable of producing strong clusters. The best number of clusters is six clusters. The K-medoids algorithm is better at grouping data than K-means.
Teaching of Web Design and Programming as a Role-Playing Team Building Game

The paper examines a form of training whose aim is to teach technologies for designing and styling responsive web page views and implementing web site events by students in an academic environment. In this form of training a role-play, taken by the practice of a software company is simulated. Data collection and evaluation of the results was carried out through qualitative methods of participant observation, survey, interview, and quantitative comparative analysis. The article analyzes in details the role-playing games, training content, stages of implementation and other aspects of the organization of experimental form of training in Web Design and Web Programming. Teaching, as a simulated role-playing game, is performed during six school hours, in two consecutive weeks and consists of four stages. In the role play, students apply knowledge and teamwork skills in HTML, CSS, JavaScript, jQuery etc. Each team is made up of five students with roles of manager, web designer, graphics designer, web stylist and web developer. The teams’ organization is based on work products adaptation of the Rational Unified Process Methodology. The end product of the role-playing game is the development of a website by the team, while its final result for the learning process is the knowledge and skills acquired during formal training by the teacher, and the informal training that took place between the students during their teamwork.
Using a fingerprint scanner to protect data in medical information systems

Since the beginning of hostilities, medical institutions have suffered from cyber attacks, the number of which is not decreasing. The load on the servers even before the start of the war was already quite significant. Since Ukraine suffered severe restrictions due to Covid-19, in part, most institutions were transferred to global medical databases, but with a serious breakthrough, digital capacities did not have time to grow. Even small cyber attacks in the medical field can be critical, so it is necessary to identify accurately medical personnel for access and implement a special application, which will not allow other people to get it, this makes it possible to block immediately all other requests without processing, and this is one of the ways to optimize and increasing security.&#x0D; The task can be solved by standard means of authorization, but they are quite easy to bypass, ordinary software can also be reprogrammed, identification by phone number can be too expensive, so a combined method is proposed: special equipment for reading fingerprints and the corresponding software application. This method is sufficiently reliable and simple to implement (medical personnel will only need to put their finger to the sensor), as well as economical and not demanding.&#x0D; The developed prototype for authorization on the Arduino hardware platform provides the necessary functionality and meets the task. The sensor recognizes more than 100 people, and the convenient mini USB allows you to connect conveniently the device to any computer port. A special algorithm will block the device in case of intervention by a person who does not have access to the corresponding resource. Can be easily reprogrammed and configured to the required equipment, and the case can be printed according to the necessary requirements.
Blackboard System and Students’ Academic Performance: An Experimental Study in The Philippines

Purpose: The main purpose of this study is to determine the Blackboard System's effectiveness on students' academic performance in Araling Panlipunan. Research methodology: The study employed a quasi-experimental pre-test and post-test non-equivalent group design and was entirely quantitative. Seventy (70) Ligaya High School students in Grade 7 who were divided into the control and experimental groups made up the study's subjects. Both groups received instruction on related subjects throughout the first quarter of the Araling Panlipunan grading period. The t-test for dependent and independent samples was the statistical tool employed to evaluate the hypothesis. Results: Results indicate that using the Blackboard System to teach Araling Panlipunan is a more effective approach than using the traditional lecture technique. Further, it has a significant impact on students and learning processes. Teachers also gained great benefits in using this system since it provided them an easy way of tracking student progress reducing a lot of paperwork load. Limitations: This study was limited to only Grade 7 Araling Panlipunan learners in the school year 2019-2020. The duration of the experiment was only focused on the First Quarter grading period. Contribution: One of the key goals of the study is to raise the standard of education by using new technological trends, which will assist them to advance their skills and competencies in technology. Teachers will also benefit from the study to help them improve their craft with the use of effective pedagogy with ICT to cope with the changing world.
Probabilistic Reasoning and Learning for Trustworthy AI

As automated decision-making systems are increasingly deployed in areas with personal and societal impacts, there is a growing demand for artificial intelligence and machine learning systems that are fair, robust, interpretable, and generally trustworthy. Ideally we would wish to answer questions regarding these properties and provide guarantees about any automated system to be deployed in the real world. This raises the need for a unified language and framework under which we can reason about and develop trustworthy AI systems. This talk will discuss how tractable probabilistic reasoning and learning provides such framework. It is important to note that guarantees regarding fairness, robustness, etc., hold with respect to the distribution of the world in which the decision-making system operates. For example, to see whether automated loan decisions are biased against certain gender, one may compare the average decision for each gender; this requires knowledge of how the features used in the decision are distributed for each gender. Moreover, there are inherent uncertainties in modeling this distribution, in addition to the uncertainties when deploying a system in the real world, such as missing or noisy information. We can handle such uncertainties in a principled way through probabilistic reasoning. Taking fairness-aware learning as an example, we can deal with biased labels in the training data by explicitly modeling the observed labels as being generated from some probabilistic process that injects bias/noise to hidden, fair labels, particularly in a way that best explains the observed data. A key challenge that still needs to be addressed is that: we need models that can closely fit complex real-world distributions—i.e. expressive—while also being amenable to exact and efficient inference of probabilistic queries—i.e. tractable. I will show that probabilistic circuits, a family of tractable probabilistic models, offer both such benefits. In order to ultimately develop a common framework to study various areas of trustworthy AI (e.g., privacy, fairness, explanations, etc.), we need models that can flexibly answer different questions, even the ones it did not foresee. This talk will thus survey the efforts to expand the horizon of complex reasoning capabilities of probabilistic circuits, especially highlighted by a modular approach that answers various queries via a pipeline of a handful of simple tractable operations.
Graph of Graphs: A New Knowledge Representation Mechanism for Graph Learning (Student Abstract)

Supervised graph classification is one of the most actively developing areas in machine learning (ML), with a broad range of domain applications, from social media to bioinformatics. Given a collection of graphs with categorical labels, the goal is to predict correct classes for unlabelled graphs. However, currently available ML tools view each such graph as a standalone entity and, as such, do not account for complex interdependencies among graphs. We propose a novel knowledge representation for graph learning called a {\it Graph of Graphs} (GoG). The key idea is to construct a new abstraction where each graph in the collection is represented by a node, while an edge then reflects similarity among the graphs. Such similarity can be assessed via a suitable graph distance. As a result, the graph classification problem can be then reformulated as a node classification problem. We show that the proposed new knowledge representation approach not only improves classification performance but substantially enhances robustness against label perturbation attacks.
HAPI Explorer: Comprehension, Discovery, and Explanation on History of ML APIs

Machine learning prediction APIs offered by Google, Microsoft, Amazon, and many other providers have been continuously adopted in a plethora of applications, such as visual object detection, natural language comprehension, and speech recognition. Despite the importance of a systematic study and comparison of different APIs over time, this topic is currently under-explored because of the lack of data and user-friendly exploration tools. To address this issue, we present HAPI Explorer (History of API Explorer), an interactive system that offers easy access to millions of instances of commercial API applications collected in three years, prioritize attention on user-defined instance regimes, and explain interesting patterns across different APIs, subpopulations, and time periods via visual and natural languages. HAPI Explorer can facilitate further comprehension and exploitation of ML prediction APIs.
Generative Pipeline for Data Augmentation of Unconstrained Document Images with Structural and Textural Degradation (Student Abstract)

Computer vision applications for document image understanding (DIU) such as optical character recognition, word spotting, enhancement etc. suffer from structural deformations like strike-outs and unconstrained strokes, to name a few. They also suffer from texture degradation due to blurring, aging, or blotting-spots etc. The DIU applications with deep networks are limited to constrained environment and lack diverse data with text-level and pixel-level annotation simultaneously. In this work, we propose a generative framework to produce realistic synthetic handwritten document images with simultaneous annotation of text and corresponding pixel-level spatial foreground information. The proposed approach generates realistic backgrounds with artificial handwritten texts which supplements data-augmentation in multiple unconstrained DIU systems. The proposed framework is an early work to facilitate DIU system-evaluation in both image quality and recognition performance at a go.
T2G-FORMER: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction

Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogeneity nature of tabular features makes such features relatively independent, and developing effective methods to promote tabular feature interaction still remains an open problem. In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Such relation graphs organize independent tabular features into a kind of graph data such that interaction of nodes (tabular features) can be conducted in an orderly fashion. Based on our proposed Graph Estimator, we present a bespoke Transformer network tailored for tabular learning, called T2G-Former, which processes tabular data by performing tabular feature interaction guided by the relation graphs. A specific Cross-level Readout collects salient features predicted by the layers in T2G-Former across different levels, and attains global semantics for final prediction. Comprehensive experiments show that our T2G-Former achieves superior performance among DNNs and is competitive with non-deep Gradient Boosted Decision Tree models. The code and detailed results are available at https://github.com/jyansir/t2g-former.
Learn from Yesterday: A Semi-supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.
Ordinal Programmatic Weak Supervision and Crowdsourcing for Estimating Cognitive States (Student Abstract)

Crowdsourcing and weak supervision offer methods to efficiently label large datasets. Our work builds on existing weak supervision models to accommodate ordinal target classes, in an effort to recover ground truth from weak, external labels. We define a parameterized factor function and show that our approach improves over other baselines.
Learning Better Representations Using Auxiliary Knowledge

Representation Learning is the core of Machine Learning and Artificial Intelligence as it summarizes input data points into low dimensional vectors. This low dimensional vectors should be accurate portrayals of the input data, thus it is crucial to find the most effective and robust representation possible for given input as the performance of the ML task is dependent on the resulting representations. In this summary, we discuss an approach to augment representation learning which relies on external knowledge. We briefly describe the shortcoming of the existing techniques and describe how an auxiliary knowledge source could result in obtaining improved representations.
Lifelong Variational Autoencoder via Online Adversarial Expansion Strategy

The Variational Autoencoder (VAE) suffers from a significant loss of information when trained on a non-stationary data distribution. This loss in VAE models, called catastrophic forgetting, has not been studied theoretically before. We analyse the forgetting behaviour of a VAE in continual generative modelling by developing a new lower bound on the data likelihood, which interprets the forgetting process as an increase in the probability distance between the generator's distribution and the evolved data distribution. The proposed bound shows that a VAE-based dynamic expansion model can achieve better performance if its capacity increases appropriately considering the shift in the data distribution. Based on this analysis, we propose a novel expansion criterion that aims to preserve the information diversity among the VAE components, while ensuring that it acquires more knowledge with fewer parameters. Specifically, we implement this expansion criterion from the perspective of a multi-player game and propose the Online Adversarial Expansion Strategy (OAES), which considers all previously learned components as well as the currently updated component as multiple players in a game, while an adversary model evaluates their performance. The proposed OAES can dynamically estimate the discrepancy between each player and the adversary without accessing task information. This leads to the gradual addition of new components while ensuring the knowledge diversity among all of them. We show theoretically and empirically that the proposed extension strategy can enable a VAE model to achieve the best performance given an appropriate model size.
Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models

Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory from data. The basis for structure learning is mining repeating patterns in the data, known as structural motifs. Finding these patterns reduces the exponential search space and therefore guides the learning of formulas. Despite the importance of motif learning, it is still not well understood. We present the first principled approach for mining structural motifs in lifted graphical models, languages that blend first-order logic with probabilistic models, which uses a stochastic process to measure the similarity of entities in the data. Our first contribution is an algorithm, which depends on two intuitive hyperparameters: one controlling the uncertainty in the entity similarity measure, and one controlling the softness of the resulting rules. Our second contribution is a preprocessing step where we perform hierarchical clustering on the data to reduce the search space to the most relevant data. Our third contribution is to introduce an O(n ln(n)) (in the size of the entities in the data) algorithm for clustering structurally-related data. We evaluate our approach using standard benchmarks and show that we outperform state-of-the-art structure learning approaches by up to 6% in terms of accuracy and up to 80% in terms of runtime.
AutoNF: Automated Architecture Optimization of Normalizing Flows with Unconstrained Continuous Relaxation Admitting Optimal Discrete Solution

Normalizing flows (NF) build upon invertible neural networks and have wide applications in probabilistic modeling. Currently, building a powerful yet computationally efficient flow model relies on empirical fine-tuning over a large design space. While introducing neural architecture search (NAS) to NF is desirable, the invertibility constraint of NF brings new challenges to existing NAS methods whose application is limited to unstructured neural networks. Developing efficient NAS methods specifically for NF remains an open problem. We present AutoNF, the first automated NF architectural optimization framework. First, we present a new mixture distribution formulation that allows efficient differentiable architecture search of flow models without violating the invertibility constraint. Second, under the new formulation, we convert the original NP-hard combinatorial NF architectural optimization problem to an unconstrained continuous relaxation admitting the discrete optimal architectural solution, circumventing the loss of optimality due to binarization in architectural optimization. We evaluate AutoNF with various density estimation datasets and show its superior performance-cost trade-offs over a set of existing hand-crafted baselines.
Learning from Small-sized Multi-class Positive Data and Unlabeled Data

To model a high performance classifier, it is necessary to have a sufficient amount of data belonging to each class. However, it takes a lot of time and effort to label all collected data with class labels. When labeled multi-class positive data and a lot of unlabeled data are given, it is common to assume that the unlabeled data contains negative data samples that do not belong to any positive class. In this paper, we propose a multi-class positive and unlabeled learning (MPUL) method in high dimensional data to learn a classifier that predicts positive and negative classes from small-sized multi-class positive data and large amounts of unlabeled data. The proposed method performs the expansion of the labeled data by selecting reliable positive and negative data from the unlabeled data and it improves the classification performance alleviating the problem of imbalance between classes. Experimental results using text data showed that the proposed method has obtained high performance improvement compared to the other methods.