Reviewerly/exHarmony huggingface dataset:
An orthogonal partition selection strategy for the sampling of graph signals with successive local aggregations

We study the sampling of graph signals with successive local aggregations, which computes measurements as the result of iterated applications of the aggregation operator observed at one or few nodes. It has been proved in the literature that using the first few observations at a single node, a perfect reconstruction can be achieved for bandlimited graph signals. Yet, it requires some restrictions to make the sampling matrix have a Vandermonde structure. Unfortunately, Vandermonde matrices are notoriously ill-conditioned resulting in severe instability even for moderately-sized graphs. Moreover, such a sampling strategy is not applicable for the sampling scheme at multiple nodes. In view of these, we re-establish a theory for the perfect reconstruction in this paper, starting with the single-node sampling scheme. To handle large-sized graphs well, we extend it to the multi-node sampling scheme. To do this, an orthogonal partition selection strategy (OPSS) is proposed to find a node set I with a minimum size such that the observations at I guarantee a perfect reconstruction for bandlimited graph signals, and two reconstruction methods are provided. When considering a noisy regime, a performance analysis of OPSS is provided, and thus yields an improved OPSS (IOPSS) which guarantees a stable reconstruction by presetting a threshold σts. Finally, several experiments are conducted to evaluate the performance of these techniques.
PEMBELAJARAN DARING PADA MATA PELAJARAN MATEMATIKA SEKOLAH DASAR

Online learning in math subjects is very important to discuss so this study aims to evaluate online learning in math subjects. This study uses qualitative exploration case study method while sample technique is purposive sampling with a sample number of 45 students, 3 class teachers and 10 parents /guardians of grade V elementary school students in Pekanbaru. Data from research samples are collected through interviews and online questionnaires, so the results of this study explain the role of teachers and parents in the implementation of online learning in mathematics subjects, namely: 1) the role of teachers by creating a meaningful learning process, fun and adapted to the materials and characteristics of learners. 2) The role of parents is to guide, accompany, solve students' difficulties during home study, fostering students' learning interests. While the results of online learning evaluation in mathematics subjects show that students have difficulty focusing on understanding math materials and there are various obstacles such as on the network and the material taught is not discussed again by the teacher. As well alternative solutions for the implementation of learning in math subjects can be done by providing contextual teaching materials, creating a communicative learning environment, using varied methods, teachers and parents / guardians of students must work together and coordinate online by providing information related to their child's development during online learning at home and give creative time to students doing useful activities at home as well as the role of teachers and parents / guardians of students by providing reinforcement, motivation to students as a form of increasing interest, spirit in conducting online learning activities in math subjects.
Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network

Abstract The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminary processing. The simple structure of the algorithm, with appropriate training, can be adapted for wide practical application, for example, for creating mobile biosensors for early diagnosis of adverse events in medicine. The study results are important for the implementation of artificial intelligence on peripheral constrained IoT devices and for edge computing.
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead

Argument mining targets structures in natural language related to interpretation and persuasion.Most scholarly discourse involves interpreting experimental evidence and attempting to persuade other scientists to adopt the same conclusions, which could benefit from argument mining techniques.However, While various argument mining studies have addressed student essays and news articles, those that target scientific discourse are still scarce.This paper surveys existing work in argument mining of scholarly discourse, and provides an overview of current models, data, tasks, and applications.We identify a number of key challenges confronting argument mining in the scientific domain, and suggest some possible solutions and future directions.
How Do BERT Embeddings Organize Linguistic Knowledge?

Several studies investigated the linguistic information implicitly encoded in Neural Language Models.Most of these works focused on quantifying the amount and type of information available within their internal representations and across their layers.In line with this scenario, we proposed a different study, based on Lasso regression, aimed at understanding how the information encoded by BERT sentence-level representations is arranged within its hidden units.Using a suite of several probing tasks, we showed the existence of a relationship between the implicit knowledge learned by the model and the number of individual units involved in the encodings of this competence.Moreover, we found that it is possible to identify groups of hidden units more relevant for specific linguistic properties.
Region-Based Static Video Stitching for Reduction of Parallax Distortion

In this paper, we propose a semantic segmentation-based static video stitching method to reduce parallax and misalignment distortion for sports stadium scenes with dynamic foreground objects. First, video frame pairs for stitching are divided into segments of different classes through semantic segmentation. Region-based stitching is performed on matched segment pairs, assuming that segments of the same semantic class are on the same plane. Second, to prevent degradation of the stitching quality of plain or noisy videos, the homography for each matched segment pair is estimated using the temporally consistent feature points. Finally, the stitched video frame is synthesized by stacking the stitched matched segment pairs and the foreground segments to the reference frame plane by descending order of the area. The performance of the proposed method is evaluated by comparing the subjective quality, geometric distortion, and pixel distortion of video sequences stitched using the proposed and conventional methods. The proposed method is shown to reduce parallax and misalignment distortion in segments with plain texture or large parallax, and significantly improve geometric distortion and pixel distortion compared to conventional methods.
Evaluating the Values of Sources in Transfer Learning

Transfer learning that adapts a model trained on data-rich sources to low-resource targets has been widely applied in natural language processing (NLP).However, when training a transfer model over multiple sources, not every source is equally useful for the target.To better transfer a model, it is essential to understand the values of the sources.In this paper, we develop SEAL-Shap, an efficient source valuation framework for quantifying the usefulness of the sources (e.g., domains/languages) in transfer learning based on the Shapley value method.Experiments and comprehensive analyses on both cross-domain and cross-lingual transfers demonstrate that our framework is not only effective in choosing useful transfer sources but also the source values match the intuitive source-target similarity.
A MULTIRESOLUTION ADAPTIVE WAVELET METHOD FOR NONLINEAR PARTIAL DIFFERENTIAL EQUATIONS

The multiscale complexity of modern problems in computational science and engineering can prohibit the use of traditional numerical methods in multi-dimensional simulations. Therefore, novel algorithms are required in these situations to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. To meet these challenges, we present a multiresolution wavelet algorithm to solve PDEs with significant data compression and explicit error control. We discretize in space by projecting fields and spatial derivative operators onto wavelet basis functions. We provide error estimates for the wavelet representation of fields and their derivatives. Then, our estimates are used to construct a sparse multiresolution discretization which guarantees the prescribed accuracy. Additionally, we embed a predictor-corrector procedure within the temporal integration to dynamically adapt the computational grid and maintain the accuracy of the solution of the PDE as it evolves. We present examples to highlight the accuracy and adaptivity of our approach.
Verifiable and Compositional Reinforcement Learning Systems

We propose a framework for verifiable and compositional reinforcement learning (RL) in which a collection of RL subsystems, each of which learns to accomplish a separate subtask, are composed to achieve an overall task. The framework consists of a high-level model, represented as a parametric Markov decision process (pMDP) which is used to plan and to analyze compositions of subsystems, and of the collection of low-level subsystems themselves. By defining interfaces between the subsystems, the framework enables automatic decompositions of task specifications, e.g., reach a target set of states with a probability of at least 0.95, into individual subtask specifications, i.e. achieve the subsystem's exit conditions with at least some minimum probability, given that its entry conditions are met. This in turn allows for the independent training and testing of the subsystems; if they each learn a policy satisfying the appropriate subtask specification, then their composition is guaranteed to satisfy the overall task specification. Conversely, if the subtask specifications cannot all be satisfied by the learned policies, we present a method, formulated as the problem of finding an optimal set of parameters in the pMDP, to automatically update the subtask specifications to account for the observed shortcomings. The result is an iterative procedure for defining subtask specifications, and for training the subsystems to meet them. As an additional benefit, this procedure allows for particularly challenging or important components of an overall task to be identified automatically, and focused on, during training. Experimental results demonstrate the presented framework's novel capabilities in both discrete and continuous RL settings. A collection of RL subsystems are trained, using proximal policy optimization algorithms, to navigate different portions of a labyrinth environment. A cross-labyrinth task specification is then decomposed into subtask specifications. Challenging portions of the labyrinth are automatically avoided if their corresponding subsystems cannot learn satisfactory policies within allowed training budgets. Unnecessary subsystems are not trained at all. The result is a compositional RL system that efficiently learns to satisfy task specifications.
Efficient and expressive bytecode-level instrumentation for Java programs

We present an efficient and expressive tool for the instrumentation of Java programs at the bytecode-level. BISM (Bytecode-Level Instrumentation for Software Monitoring) is a light-weight Java bytecode instrumentation tool that features an expressive high-level control-flow-aware instrumentation language. The language is inspired by the aspect-oriented programming paradigm in modularizing instrumentation into separate transformers, that encapsulate joinpoint selection and advice inlining. BISM allows capturing joinpoints ranging from bytecode instructions to methods execution and provides comprehensive static and dynamic context information. It runs in two instrumentation modes: build-time and load-time. BISM also provides a mechanism to compose transformers and automatically detect their collision in the base program. Transformers in a composition can control the visibility of their advice and other instructions from the base program. We show several example applications for BISM and demonstrate its effectiveness using three experiments: a security scenario, a financial transaction system, and a general runtime verification case. The results show that BISM instrumentation incurs low runtime and memory overheads.
Human-Computer Interaction Perspective on Mobile Learning: Gender and Social Implications

&lt;p&gt;The state of Kuwait has implemented a national e-learning initiative that included the distribution of mobile devices (Tablets) to be used by students and educators. Understanding the obstacles which currently prevent a successful roll-out, implementation, and adoption of mobile learning methods in Kuwait is vital. The opportunities for learners are particularly essential in this respect, and an absence of research remains an issue as far as Kuwait is concerned. This research was carried out within Kuwait Higher Education (HE) facilities to assess students' opinions and motivations for adopting this form of learning, aiming to analyse its efficacy and explore the social factors and gender issues that might impact extending mobile learning (m-learning) potential throughout Kuwait. A questionnaire has been central to this research, sent out to 620 students in total. Two statistical methods, an analysis of variance (ANOVA), and an independent-sample t-test, have been applied. The latter is essential for assessing if any statistical significance exists between two unrelated groups (male and female being the two independent variables). The results reveal disparities of age and gender while highlighting certain social and cultural factors that might prove to be obstacles that m-learning will need to overcome. A firm analysis of such issues will enable improved approaches to help learners' access and make the most robust m-learning methods.&lt;/p&gt;
Unsupervised Domain Adaptation Based on Pseudo-Label Confidence

Unsupervised domain adaptation aims to align the distributions of data in source and target domains, as well as assign the labels to data in the target domain.In this paper, we propose a new method named Unsupervised Domain Adaptation based on Pseudo-Label Confidence (UDA-PLC).Concretely, UDA-PLC first learns a new feature representation by projecting data of source and target domains into a latent subspace.In this subspace, the distribution of data in two domains are aligned and the discriminability of features in both domains is improved.Then, UDA-PLC applies Structured Prediction (SP) and Nearest Class Prototype (NCP) to predicting pseudo-labels of data in the target domain, and it takes a fraction of samples with high confidence rather than all the pseudo-labeled target samples into next iterative learning.Finally, experimental results validate that the proposed method outperforms several state-of-the-art methods on three benchmark data sets.
INVESTIGATING STUDENTS’ WRITING QUALITY WITH DIFFERENT PROFICIENCY LEVELS IN INTERACTION-BASED PAIR WORKS

Pair work, an activity involving learners working together in pairs, has been widely used in language learning, especially in writing classes. Many studies have reported the effectiveness of this technique in improving students’ writing. However, there is scarce research on how the factors like students’ proficiency and the level of interaction may affect the effectiveness of pair work. This study, therefore, aims at investigating whether or not there is a significant difference in students’ writing (1) between the high proficient students working in the dominant-dominant pairs (H-DDP) and those working in the dominant-passive pairs (H-DPP), and (2) between the low proficient students working in the dominant-dominant pairs (L-DDP) and those working in the dominant-passive pairs (L-DPP). The experimental research design was taken on. There were forty-eight students involved in this study. The students were the English Department of private University in Madiun, East Java, Indonesia. Independent t-test by using SPSS 18.0 version was chosen to answer the research questions. The results show that the high proficient students working in the dominant-dominant pairs (H-DDP) perform better writing on argumentative essay than the high proficient students in the dominant-passive pairs (H-DPP). The students can manage their writing on argumentative essay effectively. The students give feedback to help each other by giving assistance in argumentative essay writing. Then, there is no significant difference of the low proficient students working in the dominant-dominant pairs (L-DDP) and those working in the dominant-passive pairs (L-DPP). Moreover, high proficiency influences the students working in pair work to write argumentative essay. This implies that teachers should consider students’ proficiency and level of social interaction before assigning students into pairs in writing classes.
TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration

The Shared Task on Multi-Hop Inference for Explanation Regeneration asks participants to compose large multi-hop explanations to questions by assembling large chains of facts from a supporting knowledge base.While previous editions of this shared task aimed to evaluate explanatory completeness -finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.To this end, this edition of the shared task makes use of a large set of approximately 250k manual explanatory relevancy ratings that augment the 2020 shared task data.In this summary paper, we describe the details of the explanation regeneration task, the evaluation data, and the participating systems.Additionally, we perform a detailed analysis of participating systems, evaluating various aspects involved in the multi-hop inference process.The best performing system achieved an NDCG of 0.82 on this challenging task, substantially increasing performance over baseline methods by 32%, while also leaving significant room for future improvement.
A fast and efficient Modal EM algorithm for Gaussian mixtures

Abstract In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either nonparametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corresponds to a bump of the density. The Modal Expectation‐Maximization (MEM) algorithm is an iterative procedure that can identify the local maxima of any density function. In this contribution, we propose a fast and efficient MEM algorithm to be used when the density function is estimated through a finite mixture of Gaussian distributions with parsimonious component‐covariance structures. After describing the procedure, we apply the proposed MEM algorithm on both simulated and real data examples, showing its high flexibility in several contexts.
Machine Translated Text Detection Through Text Similarity with Round-Trip Translation

Translated texts have been used for malicious purposes, i.e., plagiarism or fake reviews.Existing detectors have been built around a specific translator (e.g., Google) but fail to detect a translated text from a strange translator.If we use the same translator, the translated text is similar to its round-trip translation, which is when text is translated into another language and translated back into the original language.However, a round-trip translated text is significantly different from the original text or a translated text using a strange translator.Hence, we propose a detector using text similarity with round-trip translation (TSRT).TSRT achieves 86.9% accuracy in detecting a translated text from a strange translator.It outperforms existing detectors (77.9%) and human recognition (53.3%).
BERT based Transformers lead the way in Extraction of Health Information from Social Media

Sidharth Ramesh, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma. Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task. 2021.
News Headline Grouping as a Challenging NLU Task

Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks.These impressive results have led the community to introspect on dataset limitations, and iterate on more nuanced challenges.In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group.On HLGD, human annotators achieve high performance of around 0.9 F-1, while current state-of-the art Transformer models only reach 0.75 F-1, opening the path for further improvements.We further propose a novel unsupervised Headline Generator Swap model for the task of HeadLine Grouping that achieves within 3 F-1 of the best supervised model.Finally, we analyze highperforming models with consistency tests, and find that models are not consistent in their predictions, revealing modeling limits of current architectures.
Leveraging Wikipedia Navigational Templates for Curating Domain-Specific Fuzzy Conceptual Bases

Domain-specific conceptual bases use key concepts to capture domain scope and relevant information.Conceptual bases serve as a foundation for various downstream tasks, including ontology construction, information mapping, and analysis.However, building conceptual bases necessitates domain awareness and takes time.Wikipedia navigational templates offer multiple articles on the same/similar domain.It is possible to use the templates to recognize fundamental concepts that shape the domain.Earlier work in this domain used Wikipedia's structured and unstructured data to construct open-domain ontologies, domain terminologies, and knowledge bases.We present a novel method for leveraging navigational templates to create domain-specific fuzzy conceptual bases in this work.Our system generates knowledge graphs from the articles mentioned in the template, which we then process using Wikidata and machine learning algorithms.We filter important concepts using fuzzy logic on network metrics to create a crude conceptual base.Finally, the expert helps by refining the conceptual base.We demonstrate our system using an example of RNA virus antiviral drugs.