Reviewerly/exHarmony huggingface dataset:
Understanding Persuasion Mechanisms for Effective Communication in Online Educational Environments

This chapter aims at explaining the mechanisms of persuasion for effective communication in online educational environments. It starts by bringing awareness on the particularities of online educational environments and the psychological prerequisites of online studying. It tackles issues of digital skills as well as emotional intelligence abilities for online teaching, specific mindset and digital readiness, result expectations and assessment. Moreover, principles of microlearning and hybrid learning are explained as major approaches in online education. It then addresses issues related to technology-mediated communication with young ages vs. adults. It makes a brief analysis of mediated communication vs. direct communication in terms of adopted strategies according to age, adaptation of message, and feedback according to the online channel of factors affecting communication in online environments vs. face-to-face communication. Particular attention will be given to digital stress.
Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding

Attentional mechanisms are order-invariant. Positional encoding is a crucial component to allow attention-based deep model architectures such as Transformer to address sequences or images where the position of information matters. In this paper, we propose a novel positional encoding method based on learnable Fourier features. Instead of hard-coding each position as a token or a vector, we represent each position, which can be multi-dimensional, as a trainable encoding based on learnable Fourier feature mapping, modulated with a multi-layer perceptron. The representation is particularly advantageous for a spatial multi-dimensional position, e.g., pixel positions on an image, where $L_2$ distances or more complex positional relationships need to be captured. Our experiments based on several public benchmark tasks show that our learnable Fourier feature representation for multi-dimensional positional encoding outperforms existing methods by both improving the accuracy and allowing faster convergence.
Fuzzy Clustering with Similarity Queries

The fuzzy or soft $k$-means objective is a popular generalization of the well-known $k$-means problem, extending the clustering capability of the $k$-means to datasets that are uncertain, vague, and otherwise hard to cluster. In this paper, we propose a semi-supervised active clustering framework, where the learner is allowed to interact with an oracle (domain expert), asking for the similarity between a certain set of chosen items. We study the query and computational complexities of clustering in this framework. We prove that having a few of such similarity queries enables one to get a polynomial-time approximation algorithm to an otherwise conjecturally NP-hard problem. In particular, we provide probabilistic algorithms for fuzzy clustering in this setting that asks $O(\mathsf{poly}(k)\log n)$ similarity queries and run with polynomial-time-complexity, where $n$ is the number of items. The fuzzy $k$-means objective is nonconvex, with $k$-means as a special case, and is equivalent to some other generic nonconvex problem such as non-negative matrix factorization. The ubiquitous Lloyd-type algorithms (or, expectation-maximization algorithm) can get stuck at a local minima. Our results show that by making few similarity queries, the problem becomes easier to solve. Finally, we test our algorithms over real-world datasets, showing their effectiveness in real-world applications.
How Does Fine-tuning Affect the Geometry of Embedding Space: A Case Study on Isotropy

It is widely accepted that fine-tuning pre-trained language models usually brings about performance improvements in downstream tasks. However, there are limited studies on the reasons behind this effectiveness, particularly from the viewpoint of structural changes in the embedding space. Trying to fill this gap, in this paper, we analyze the extent to which the isotropy of the embedding space changes after fine-tuning. We demonstrate that, even though isotropy is a desirable geometrical property, fine-tuning does not necessarily result in isotropy enhancements. Moreover, local structures in pre-trained contextual word representations (CWRs), such as those encoding token types or frequency, undergo a massive change during fine-tuning. Our experiments show dramatic growth in the number of elongated directions in the embedding space, which, in contrast to pre-trained CWRs, carry the essential linguistic knowledge in the fine-tuned embedding space, making existing isotropy enhancement methods ineffective.
Storm Crowds: Evidence from Zooniverse on Crowd Contribution Design

Crowdsourcing - a collaborative form of content production based on the contributions of large groups of individuals - has proliferated in the past decade. Due to this growth, recent research has focused on understanding the factors that affect its sustainability. Prior studies have highlighted the importance of volunteers’ prosocial motivations, the sense of belong- ing to a community, and symbolic rewards within crowdsourcing websites. One factor that has received limited attention in the existing literature is how the design of crowdsourcing platforms affects their sustainability. We study whether the design element - particularly, the divisibility of contributions (i.e. whether contributing tasks are bundled together or can be carried out separately) - is a factor that affects the level and quality of crowdsourcing contributions. We investigate this in the context of Zooniverse, the world’s largest crowd- sourced science site, in which volunteers contribute to scientific research by performing data processing tasks. Our choice of empirical setting is motivated by the fact that one of the Zooniverse projects, Cyclone Center, underwent a format change that decreased the divisibil- ity of contributions, by bundling together two tasks that were previously separate. We refer to contributions for which both tasks were done as complete, and contributions for which only one task was done as incomplete. In this context, we develop a theoretical model that predicts (i) a positive relationship between contribution divisibility and the total number of contributions (i.e. complete and incomplete) per volunteer, as well as (ii) an ambiguous relationship between contribution divisibility and the number of complete contributions per volunteer. We test these predictions empirically by exploiting the format change in Cyclone Center. We find that after the format change, which decreased contribution divisibility, (i) the total number of contributions per volunteer decreased, and (ii) the number of complete contributions made by anonymous volunteers increased, while that made by registered volunteers remained unchanged. We also find that these results have positive implications for the quality of complete contributions. Our results have strategic implications for crowdsourcing platforms because they suggest that the design of crowdsourcing platforms, specifically the divisibility of contributions, is a factor that matters for their sustainability.
Generating Datasets with Pretrained Language Models

To obtain high-quality sentence embeddings from pretrained language models (PLMs), they must either be augmented with additional pretraining objectives or finetuned on a large set of labeled text pairs. While the latter approach typically outperforms the former, it requires great human effort to generate suitable datasets of sufficient size. In this paper, we show how PLMs can be leveraged to obtain high-quality sentence embeddings without the need for labeled data, finetuning or modifications to the pretraining objective: We utilize the generative abilities of large and high-performing PLMs to generate entire datasets of labeled text pairs from scratch, which we then use for finetuning much smaller and more efficient models. Our fully unsupervised approach outperforms strong baselines on several semantic textual similarity datasets.
FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER) than original ASR outputs. Previous works usually use a sequence-to-sequence model to correct an ASR output sentence autoregressively, which causes large latency and cannot be deployed in online ASR services. A straightforward solution to reduce latency, inspired by non-autoregressive (NAR) neural machine translation, is to use an NAR sequence generation model for ASR error correction, which, however, comes at the cost of significantly increased ASR error rate. In this paper, observing distinctive error patterns and correction operations (i.e., insertion, deletion, and substitution) in ASR, we propose FastCorrect, a novel NAR error correction model based on edit alignment. In training, FastCorrect aligns each source token from an ASR output sentence to the target tokens from the corresponding ground-truth sentence based on the edit distance between the source and target sentences, and extracts the number of target tokens corresponding to each source token during edition/correction, which is then used to train a length predictor and to adjust the source tokens to match the length of the target sentence for parallel generation. In inference, the token number predicted by the length predictor is used to adjust the source tokens for target sequence generation. Experiments on the public AISHELL-1 dataset and an internal industrial-scale ASR dataset show the effectiveness of FastCorrect for ASR error correction: 1) it speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER reduction) compared with the autoregressive correction model; and 2) it outperforms the popular NAR models adopted in neural machine translation and text edition by a large margin.
Effect of differences on image contrast value to the stitched image

Image stitching is a promising method for developing longer digital x-ray radiographs without sacrificing image resolution. This paper presents our effort on developing the image stitching method based on the SIFT (Scale Invariant Feature Transform) algorithm and the template matching method. Digital radiographic images generally have different contrast and brightness values that need to be adjusted in the image stitching procedure. In this paper, research was carried out related to the effect of differences in contrast values on image stitching results. The reference image, which is a thorax image, is cut into two parts and one of its part is changed the contrast values, to then be merged and stitched into one single complete image. The process of stitching images that has been carried out uses the Scale Invariant Feature Transform (SIFT) method and key point matching. The results of the stitched image are then analyzed using digital image correlation (DIC) values. The results of digital correlation coefficient value obtained from the DIC analysis is close to 1 for all variations in contrast. This shows that the image stitching result is similar and almost identical to the reference image. Therefore, with the method used, it can be obtained a longer image stitching result even though it has differences in contrast.
Unified Semantic Space for a Novel Multimodal Approach to Document Similarity

The paper explores a new method to represent digital documents - containing several sources of information (text, images, videos, multimedia galleries) - in a semantic space able to elevate the importance of each informative channel in giving a deeper meaning to the document. The proposed approach finds several applications such as Web Content Mining, semantic classification and document topic extraction.
ReLU Regression with Massart Noise

We study the fundamental problem of ReLU regression, where the goal is to fit Rectified Linear Units (ReLUs) to data. This supervised learning task is efficiently solvable in the realizable setting, but is known to be computationally hard with adversarial label noise. In this work, we focus on ReLU regression in the Massart noise model, a natural and well-studied semi-random noise model. In this model, the label of every point is generated according to a function in the class, but an adversary is allowed to change this value arbitrarily with some probability, which is {\em at most} $\eta < 1/2$. We develop an efficient algorithm that achieves exact parameter recovery in this model under mild anti-concentration assumptions on the underlying distribution. Such assumptions are necessary for exact recovery to be information-theoretically possible. We demonstrate that our algorithm significantly outperforms naive applications of $\ell_1$ and $\ell_2$ regression on both synthetic and real data.
Extend, don't rebuild: Phrasing conditional graph modification as autoregressive sequence labelling.

Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction. A recent work used this technique for modifying scene graphs (He et al. 2020), by first encoding the original graph and then generating the modified one based on this encoding. In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation. We propose the first model for the resulting graph extension problem based on autoregressive sequence labelling. On three scene graph modification data sets, this formulation leads to improvements in accuracy over the state-of-the-art between 13 and 24 percentage points. Furthermore, we introduce a novel data set from the biomedical domain which has much larger linguistic variability and more complex graphs than the scene graph modification data sets. For this data set, the state-of-the art fails to generalize, while our model can produce meaningful predictions.
cANT WUM : WEB USERS CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM

Web Usage Mining (WUM) is the use of data mining methods to extract knowledge from web usage data. One function of WUM is to support Business Intelligence (BI) purpose in which one of the important information needed is the classification of web users that can be used for acquisition, penetration, and user retention activity. There are two main problems encountered in conducting the classification of web users. The first is the determination of antecedent attributes as a term of classification rules, which is a major problem in data mining classification function in general. The second problem is the preprocessing activity which involves preparing the supporting data for the web users’ classification need which is the most difficult stage in WUM.For the web user classification method, we propose a classification method based on ant colony optimization method (ACO) as a distributed intelligent system using heuristic function which is in line with the problem areas. We proposed a heuristic functions for web user classification based on web usage data that uses entropy of antecedent candidate, information gain from attribute of total number of web user access  and average of access duration of web user.  For preprocessing purpose, a method of data preparation that can support the needs of web users’ classification is proposed. The data used consists of web access log data, web user profile data and web transaction data. The preprocessing activity consists of parsing, data cleansing, and extraction of the web user sessions using heuristic method concerning web page access timeout and differences in web browser agent.Testing is done by comparing the performance of the proposed algorithm with Ant-Miner algorithm, cAnt-Miner algorithm, and the Continuous Ant-Miner algorithm. The results of testing of four web data shows that the performance of the proposed algorithm is better in terms of accuracy of rules and simplification of rules.
Multiwavelet-based Operator Learning for Differential Equations

The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space. Towards this end, we introduce a \textit{multiwavelet-based neural operator learning scheme} that compresses the associated operator's kernel using fine-grained wavelets. By explicitly embedding the inverse multiwavelet filters, we learn the projection of the kernel onto fixed multiwavelet polynomial bases. The projected kernel is trained at multiple scales derived from using repeated computation of multiwavelet transform. This allows learning the complex dependencies at various scales and results in a resolution-independent scheme. Compare to the prior works, we exploit the fundamental properties of the operator's kernel which enable numerically efficient representation. We perform experiments on the Korteweg-de Vries (KdV) equation, Burgers' equation, Darcy Flow, and Navier-Stokes equation. Compared with the existing neural operator approaches, our model shows significantly higher accuracy and achieves state-of-the-art in a range of datasets. For the time-varying equations, the proposed method exhibits a ($2X-10X$) improvement ($0.0018$ ($0.0033$) relative $L2$ error for Burgers' (KdV) equation). By learning the mappings between function spaces, the proposed method has the ability to find the solution of a high-resolution input after learning from lower-resolution data.
Privacy-Preserving Distributed Estimation Over Multitask Networks

Distributed estimation over multitask networks, where the target parameter vectors (tasks) for different nodes can be different, has received much attention recently. In this article, we consider some practical application scenarios, where there are some similarities between tasks, and thus, intertask cooperation is beneficial to improve the estimation performance of nodes. In most of the existing multitask learning studies, local estimates are directly transmitted between neighboring nodes, and then, the adaptive combination strategy is adopted to achieve intertask cooperation. However, when the target parameter vectors contain sensitive information, direct transmission of local estimates may result in serious privacy breaches. To tackle this problem, we propose a privacy-preserving distributed multitask learning algorithm for collaborative estimation over networks. The proposed algorithm is implemented by a secure multiparty computation protocol designed on an organic combination of multiplicative/additive mask and additively homomorphic encryption technique. While allowing each node to adaptively cooperate with its neighbors, this protocol also preserves the privacy of the local estimates. Besides, we present a thorough privacy analysis of the proposed algorithm. Simulation results show that the proposed algorithm can effectively protect each node’s task against leakage without sacrificing the performance of estimation.
DEVELOPMENT OF BUSY BOOK LEARNING MEDIA BASED ON THE SYLLABLE METHOD TO IMPROVE READING ABILITY OF CHILDREN AGED 5-6 YEARS

The reading development of children aged 5-6 years on average has not yet developed, marked by children still needing teacher guidance in pronouncing letters and not distinguishing between vowels and consonants. For this reason, it is necessary to develop media that can attract attention and help children learn to read. The objectives of this study are: 1) Knowing the level of feasibility of the use of busy book learning media based on the syllable method to improve the reading ability of children aged 5-6 years. 2) Knowing the effectiveness of developing busy book learning media based on the syllable method to improve the reading ability of children aged 5-6 years. The method used in this research is developing a 4-D model (four D model) consisting of 4 main stages, namely: Define, Design, Develop, and Disseminate. The subjects of this study were students of group B RA Al-Hidayah Air Pinang Village in the even semester of the 2020/2021 academic year, totalling 11 people consisting of 4 girls and 7 boys. The object of this research is the development of busy book learning media based on the syllable method to improve the reading ability of children aged 5-6 years at RA Al-Hidayah, Air Pinang Village. The data collection techniques in this study were 1) expert team validation questionnaire sheets, 2) student and teacher response questionnaire sheets, 3) reading ability test observation sheets for children aged 5-6 years. The results of this study indicate that the busy book media based on the syllable method is very feasible to use, and the effectiveness of reading learning increases after using the busy book media based on the syllable method, which has implications for the average score of the child's initial test, which is 34.54 and on the final test it increases to 77. .72 with an increase in value of 43.18. 2) questionnaire responses from students and teachers, 3) observation sheets for reading proficiency tests for children aged 5-6 years. The results of this study indicate that the busy book media based on the syllable method is very feasible to use, and the effectiveness of reading learning increases after using the busy book media based on the syllable method, which has implications for the average score of the child's initial test, which is 34.54 and on the final test it increases to 77. .72 with an increase in value of 43.18. 2) questionnaire responses from students and teachers, 3) observation sheets for reading proficiency tests for children aged 5-6 years. The results of this study indicate that the busy book media based on the syllable method is very feasible to use, and the effectiveness of reading learning increases after using the busy book media based on the syllable method, which has implications for the average score of the child's initial test, which is 34.54 and on the final test it increases to 77.72 with an increase in value of 43.18.
Object Detection, Tracking and Trajectory Prediction for Autonomous Driving

This chapter discusses unified vehicle detection, tracking, and trajectory prediction of objects around the ego vehicle and includes sections that explain individual tasks in detail. State-of-the-art methods for object detection based on deep learning can be broadly classified into two types: two-stage detectors and single-stage detectors. Although object detection has gained huge attention in autonomous driving applications, state of-the-art algorithms still have some limitations in realistic scenarios. Deep IDNet proposed a deformable part-based CNN for object detection by designing a deformation-pooling layer to learn the geometric deformations of all instances of a part. However, multiple-object tracking provides adequate support for autonomous driving applications. In the future, unified algorithms can be built using CNNs by sharing an encoder among all three tasks of autonomous driving. They demonstrated the importance of three crucial tasks—namely, object detection, tracking, and trajectory prediction.