Reviewerly/exHarmony huggingface dataset:
The Impact of Teachersâ€™ Verbal and Non-Verbal Communication on Studentsâ€™ Motivation in Learning English

The objectives of this study are to explain the use of teachersâ€™ verbal communication, teachersâ€™ non-verbal communication, and the impact of teachersâ€™ verbal communication and non-verbal communication on students' motivation in learning English.This study used the framework of teachersâ€™ talk proposed by Sinclair and Brazil (1985) and Wang and Loewen (2015) to analyze the teachersâ€™ verbal communication and non-verbal communication in the English classroom. Data were taken from lesson transcripts and teachersâ€™ behaviors from the video recordings. Using the teachersâ€™ utterances and teachersâ€™ behaviors as the unit of analysis, procedures of analyzing data include: organizing and preparing the data, coding, describing, and interpreting. To validate the findings, initial results of the analysis had been examined by data source triangulation and using focused group discussion. Findings showed that the most-used teachersâ€™ verbal communication is questioning while the most-used teachersâ€™ non-verbal communication is hand movements and facial expressions. The most-motivating teachersâ€™ verbal communication is questions and the most-motivating teachersâ€™ non-verbal communication comes from facial expressions.
Compact hardware accelerator for field multipliers suitable for use in ultra-low power IoT edge devices

Adoption of IoT technology without considering its security implications may expose network systems to a variety of security breaches. In network systems, IoT edge devices are a major source of security risks. Implementing cryptographic algorithms on most IoT edge devices can be difficult due to their limited resources. As a result, compact implementations of these algorithms on these devices are required. Because the field multiplication operation is at the heart of most cryptographic algorithms, its implementation will have a significant impact on the entire cryptographic algorithm implementation. As a result, in this paper, we propose a small hardware accelerator for performing field multiplication on edge devices. The hardware accelerator is primarily composed of a processor array with a regular structure and local interconnection among its processing elements. The main advantage of the proposed hardware structure is the ability to manage its area, delay, and consumed energy by choosing the appropriate word size l. We implemented the proposed structure using ASIC technology and the obtained results attain average savings in the area of 95.9%. Also, we obtained significant average savings in energy of 63.2%. The acquired results reveal that the offered hardware accelerator is appropriate for usage in resource-constrained IoT edge devices.
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach

Recent works explore learning graph representations in a self-supervised manner. In graph contrastive learning, benchmark methods apply various graph augmentation approaches. However, most of the augmentation methods are non-learnable, which causes the issue of generating unbeneficial augmented graphs. Such augmentation may degenerate the representation ability of graph contrastive learning methods. Therefore, we motivate our method to generate augmented graph with a learnable graph augmenter, called MEta Graph Augmentation (MEGA). We then clarify that a "good" graph augmentation must have uniformity at the instance-level and informativeness at the feature-level. To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness. The objective of the graph augmenter is to promote our feature extraction network to learn a more discriminative feature representation, which motivates us to propose a meta-learning paradigm. Empirically, the experiments across multiple benchmark datasets demonstrate that MEGA outperforms the state-of-the-art methods in graph self-supervised learning tasks. Further experimental studies prove the effectiveness of different terms of MEGA. Our codes are available at https://github.com/hang53/MEGA.
Reinforcement Learning for Cross-Domain Hyper-Heuristics

In this paper, we propose a new hyper-heuristic approach that uses reinforcement learning to automatically learn the selection of low-level heuristics across a wide range of problem domains. We provide a detailed analysis and evaluation of the algorithm components, including different ways to represent the hyper-heuristic state space and reset strategies to avoid unpromising areas of the solution space. Our methods have been evaluated using HyFlex, a well-known benchmarking framework for cross-domain hyper-heuristics, and compared with state-of-the-art approaches. The experimental evaluation shows that our reinforcement-learning based approach produces results that are competitive with the state-of-the-art, including the top participants of the Cross Domain Hyper-heuristic Search Competition 2011.
Style Fader Generative Adversarial Networks for Style Degree Controllable Artistic Style Transfer

Artistic style transfer is the task of synthesizing content images with learned artistic styles. Recent studies have shown the potential of Generative Adversarial Networks (GANs) for producing artistically rich stylizations. Despite the promising results, they usually fail to control the generated images' style degree, which is inflexible and limits their applicability for practical use. To address the issue, in this paper, we propose a novel method that for the first time allows adjusting the style degree for existing GAN-based artistic style transfer frameworks in real time after training. Our method introduces two novel modules into existing GAN-based artistic style transfer frameworks: a Style Scaling Injection (SSI) module and a Style Degree Interpretation (SDI) module. The SSI module accepts the value of Style Degree Factor (SDF) as the input and outputs parameters that scale the feature activations in existing models, offering control signals to alter the style degrees of the stylizations. And the SDI module interprets the output probabilities of a multi-scale content-style binary classifier as the style degrees, providing a mechanism to parameterize the style degree of the stylizations. Moreover, we show that after training our method can enable existing GAN-based frameworks to produce over-stylizations. The proposed method can facilitate many existing GAN-based artistic style transfer frameworks with marginal extra training overheads and modifications. Extensive qualitative evaluations on two typical GAN-based style transfer models demonstrate the effectiveness of the proposed method for gaining style degree control for them.
Rethinking InfoNCE: How Many Negative Samples Do You Need?

InfoNCE is a widely used contrastive training loss. It aims to estimate the mutual information between a pair of variables by discriminating between each positive pair and its associated K negative pairs. It is proved that when the sample labels are clean, the lower bound of mutual information estimation is tighter when more negative samples are incorporated, which usually yields better model performance. However, in practice the labels often contain noise, and incorporating too many noisy negative samples into model training may be suboptimal. In this paper, we study how many negative samples are optimal for InfoNCE in different scenarios via a semi-quantitative theoretical framework. More specifically, we first propose a probabilistic model to analyze the influence of the negative sampling ratio K on training sample informativeness. Then, we design a training effectiveness function to measure the overall influence of training samples based on their informativeness. We estimate the optimal negative sampling ratio using the K value that maximizes the training effectiveness function. Based on our framework, we further propose an adaptive negative sampling method that can dynamically adjust the negative sampling ratio to improve InfoNCE-based model training. Extensive experiments in three different tasks show our framework can accurately predict the optimal negative sampling ratio, and various models can benefit from our adaptive negative sampling method.
Weakly-supervised Text Classification with Wasserstein Barycenters Regularization

Weakly-supervised text classification aims to train predictive models with unlabeled texts and a few representative words of classes, referred to as category words, rather than labeled texts. These weak supervisions are much more cheaper and easy to collect in real-world scenarios. To resolve this task, we propose a novel deep classification model, namely Weakly-supervised Text Classification with Wasserstein Barycenter Regularization (WTC-WBR). Specifically, we initialize the pseudo-labels of texts by using the category word occurrences, and formulate a weakly self-training framework to iteratively update the weakly-supervised targets by combining the pseudo-labels with the sharpened predictions. Most importantly, we suggest a Wasserstein barycenter regularization with the weakly-supervised targets on the deep feature space. The intuition is that the texts tend to be close to the corresponding Wasserstein barycenter indicated by weakly-supervised targets. Another benefit is that the regularization can capture the geometric information of deep feature space to boost the discriminative power of deep features. Experimental results demonstrate that WTC-WBR outperforms the existing weakly-supervised baselines, and achieves comparable performance to semi-supervised and supervised baselines.
Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies

We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite. For the dialect DL-LiteH, we show that ELIQs have a frontier (set of least general generalizations) that is of polynomial size and can be computed in polynomial time. In the dialect DL-LiteF, in contrast, frontiers may be infinite. We identify a natural syntactic restriction that enables the same positive results as for DL-LiteH. We use our results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact learning with only membership queries.
Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph

Conversational semantic role labeling (CSRL) is a newly proposed task that uncovers the shallow semantic structures in a dialogue text. Unfortunately several important characteristics of the CSRL task have been overlooked by the existing works, such as the structural information integration, near-neighbor influence. In this work, we investigate the integration of a latent graph for CSRL. We propose to automatically induce a predicate-oriented latent graph (POLar) with a predicate-centered gaussian mechanism, by which the nearer and informative words to the predicate will be allocated with more attention. The POLar structure is then dynamically pruned and refined so as to best fit the task need. We additionally introduce an effective dialogue-level pre-trained language model, CoDiaBERT, for better supporting multiple utterance sentences and handling the speaker coreference issue in CSRL. Our system outperforms best-performing baselines on three benchmark CSRL datasets with big margins, especially achieving over 4% F1 score improvements on the cross-utterance argument detection. Further analyses are presented to better understand the effectiveness of our proposed methods.
Fast and Fine-grained Autoscaler for Streaming Jobs with Reinforcement Learning

On computing clusters, the autoscaler is responsible for allocating resources for jobs or fine-grained tasks to ensure their Quality of Service. Due to a more precise resource management, fine-grained autoscaling can generally achieve better performance. However, the fine-grained autoscaling for streaming jobs needs intensive computation to model the complicated running states of tasks, and has not been adequately studied previously. In this paper, we propose a novel fine-grained autoscaler for streaming jobs based on reinforcement learning. We first organize the running states of streaming jobs as spatio-temporal graphs. To efficiently make autoscaling decisions, we propose a Neural Variational Subgraph Sampler to sample spatio-temporal subgraphs. Furthermore, we propose a mutual-information-based objective function to explicitly guide the sampler to extract more representative subgraphs. After that, the autoscaler makes decisions based on the learned subgraph representations. Experiments conducted on real-world datasets demonstrate the superiority of our method over six competitive baselines.
Long-Short Term Cross-Transformer in Compressed Domain for Few-Shot Video Classification

Compared with image few-shot learning, most of the existing few-shot video classification methods perform worse on feature matching, because they fail to sufficiently exploit the temporal information and relation. Specifically, frames are usually evenly sampled, which may miss important frames. On the other hand, the heuristic model simply encodes the equally treated frames in sequence, which results in the lack of both long-term and short-term temporal modeling and interaction. To alleviate these limitations, we take advantage of the compressed domain knowledge and propose a long-short term Cross-Transformer (LSTC) for few-shot video classification. For short terms, the motion vector (MV) contains temporal cues and reflects the importance of each frame. For long terms, a video can be natively divided into a sequence of GOPs (Group Of Picture). Using this compressed domain knowledge helps to obtain a more accurate spatial-temporal feature space. Consequently, we design the long-short term selection module, short-term module, and long-term module to comprise the LSTC. Long-short term selection is performed to select informative compressed domain data. Long/short-term modules are utilized to sufficiently exploit the temporal information so that the query and support can be well-matched by cross-attention. Experimental results show the superiority of our method on various datasets.
A Unified Meta-Learning Framework for Dynamic Transfer Learning

Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a stationary task distribution, thereby leading to the sub-optimal performance for dynamic tasks drawn from a non-stationary task distribution in real scenarios. To bridge this gap, in this paper, we study a more realistic and challenging transfer learning setting with dynamic tasks, i.e., source and target tasks are continuously evolving over time. We theoretically show that the expected error on the dynamic target task can be tightly bounded in terms of source knowledge and consecutive distribution discrepancy across tasks. This result motivates us to propose a generic meta-learning framework L2E for modeling the knowledge transferability on dynamic tasks. It is centered around a task-guided meta-learning problem with a group of meta-pairs of tasks, based on which we are able to learn the prior model initialization for fast adaptation on the newest target task. L2E enjoys the following properties: (1) effective knowledge transferability across dynamic tasks; (2) fast adaptation to the new target task; (3) mitigation of catastrophic forgetting on historical target tasks; and (4) flexibility in incorporating any existing static transfer learning algorithms. Extensive experiments on various image data sets demonstrate the effectiveness of the proposed L2E framework.
Lexicographic Entailment, Syntax Splitting and the Drowning Problem

Lexicographic inference is a well-known and popular approach to reasoning with non-monotonic conditionals. It is a logic of very high-quality, as it extends rational closure and avoids the so-called drowning problem. It seems, however, this high quality comes at a cost, as reasoning on the basis of lexicographic inference is of high computational complexity. In this paper, we show that lexicographic inference satisfies syntax splitting, which means that we can restrict our attention to parts of the belief base that share atoms with a given query, thus seriously restricting the computational costs for many concrete queries. Furthermore, we make some observations on the relationship between c-representations and lexicographic inference, and reflect on the relation between syntax splitting and the drowning problem.
MMT: Multi-way Multi-modal Transformer for Multimodal Learning

The heart of multimodal learning research lies the challenge of effectively exploiting fusion representations among multiple modalities.However, existing two-way cross-modality unidirectional attention could only exploit the intermodal interactions from one source to one target modality. This indeed fails to unleash the complete expressive power of multimodal fusion with restricted number of modalities and fixed interactive direction.In this work, the multiway multimodal transformer (MMT) is proposed to simultaneously explore multiway multimodal intercorrelations for each modality via single block rather than multiple stacked cross-modality blocks. The core idea of MMT is the multiway multimodal attention, where the multiple modalities are leveraged to compute the multiway attention tensor. This naturally benefits us to exploit comprehensive many-to-many multimodal interactive paths. Specifically, the multiway tensor is comprised of multiple interconnected modality-aware core tensors that consist of the intramodal interactions. Additionally, the tensor contraction operation is utilized to investigate intermodal dependencies between distinct core tensors.Essentially, our tensor-based multiway structure allows for easily extending MMT to the case associated with an arbitrary number of modalities. Taking MMT as the basis, the hierarchical network is further established to recursively transmit the low-level multiway multimodal interactions to high-level ones. The experiments demonstrate that MMT can achieve state-of-the-art or comparable performance.
Document-level Relation Extraction via Subgraph Reasoning

Document-level relation extraction aims to extract relations between entities in a document. In contrast to sentence-level relation extraction, it deals with longer texts and more complex entity interactions, which requires reasoning over multiple sentences with rich reasoning skills. Most current researches construct a document-level graph first, and then focus on the overall graph structure or the paths between the target entity pair in the graph. In this paper, we propose a novel subgraph reasoning (SGR) framework for document-level relation extraction. SGR combines the advantages of both graph-based models and path-based models, integrating various paths between the target entity pair into a much simpler subgraph structure to perform relational reasoning. Moreover, the paths generated by our designed heuristic strategy explicitly model the requisite reasoning skills and roughly cover the supporting sentences for each relation instance. Experimental results on DocRED show that SGR outperforms existing models, and further analyses demonstrate that our method is both effective and explainable. Our code is available at https://github.com/Crysta1ovo/SGR.
Imagine, Integrate, and Incorporate: English Language and its Pedagogical Implications in EFL Classrooms

As the English language is accepted globally, it becomes a challenge for the teachers to learn the new theories and practices and get updated as per the requirements and needs of the learners. It is very necessary to know the needs of the learners in order to help them or push them towards learning. Knowing your learners’ needs means half of the work has been done, the only thing the teacher has to do is to select the methods and apply the appropriate strategies in the classrooms. The existing practice of teaching English in the classroom does not offer much to the students to practice receptive and productive skills in real-life situations. The present study focus on developing language skills by using different strategies and activities in real-life classroom situation to enhance the language skills of the learners. The study uses a quantitative method to collect data from the respondents through a questionnaire developed by the researcher. The questionnaire was based on a five-point Likert scale and the reliability and validity were tested through SPSS version 20. The Cronbach alpha was tested and the results were satisfactory. The findings of the study clearly show that if the strategies and method of pedagogy in the classroom will be changed to more learners-centred and activity-based, the learning outcomes will be better and language skills were enhanced.
Predicting Age Group and Gender of Smartphone Users Using Keystroke Biometrics

This paper investigated the impact of various keystroke dynamics features on a predictive biometric system. In this paper, keystroke dynamics data of 50 individuals have been acquired using an open-source data software application on an Android smartphone. A total number of 21 commonly used keystroke dynamics features were extracted from the raw data. The collected data was used in training a Random Forest algorithm using four different training sample sizes while the remaining portion of the data was used for classification. The algorithm was then used to determine the importance of 21 different keystroke dynamics features. The results showed that each features offers varying degree of importance in age-group and gender predictions. While such efforts have been made in the area of predictive keystroke dynamics using computer keyboards, literature on the same topic using touchscreen smartphone virtual keyboards have been limited.
STUDENTS� PERCEPTION ON READING COMPREHENSION PROBLEMS IN NARRATIVE TEXT

This survey study aimed to know the students perception on reading comprehension problems of narrative text. The sample participants were randomly selected from the tenth grade students of SMAN 2 Jember in academic year of 2019/2020. Questionnaire adopted from Xiubo Zhang (2006) was used to collect the students perception and it was translated into Indonesian. The result showed that the students still experienced some problems in reading narrative text. Based on the result of the questionnaire, the most problematic aspect that hinder the students reading comprehension was Motivation (65.6%), followed by Strategies (63.7%), Background Knowledge (63.3%), Reading Process (60%) and Language Knowledge (55.4%). The result implied that the teacher should focus on fostering students motivation by giving intensive course and reading assignment based on the students interests. In order to see further into the problem faced by the students, future reseacher could use direct assesment by using diagnostic test or other measurement.
Crossover for cardinality constrained optimization

In order to understand better how and why crossover can benefit optimization, we consider pseudo-Boolean functions with an upper bound B on the number of 1s allowed in the bit string (cardinality constraint). We consider the natural translation of the OneMax test function, a linear function where B bits have a weight of 1 + ε and the remaining bits have a weight of 1. The literature gives a bound of Θ(n2) for the (1+1) EA on this function.