Reviewerly/exHarmony huggingface dataset:
Fast Adaptive Local Subspace Learning With Regressive Regularization

<i>L,ineaT</i> Discriminant Analysis (LDA) has been widely used in supervised dimensionality reduction fields. How- ever, LDA is usually weak in tackling data with Non-Gaussian distribution due to its incapability of extracting the intrinsic structure of data. In order to learn the intrinsic information more effectively, some dimensionality reduction methods incorporate the adaptive full-connected graph into the algorithm frame, but the defect is that the calculation of each pairwise distance is very time-consuming. In this paper, we propose a novel fast adaptive local subspace learning with regressive regularization model to solve the supervised dimensional reduction problem. Firstly, the adaptive anchor point graph is used to capture local structure information, which can greatly reduce computation complexity. Secondly, by using regressive regularization, the samples from different classes can be better separated in the projected space and the workload of selecting the optimal reduced dimension is easier. Moreover, entropy regularization is used to derive more appropriate weights. Finally, extensive experiments are conducted on real world data sets to verify the superiority of our model.
Toward Accessible Intelligent Tutoring Systems: Integrating Cognitive Tutors and Conversational Agents

The literature is rife with investigations into the use of ITS in mathematics, but scant on how these systems impact students with disabilities. Since ITS continue to permeate the educational landscape, and students with disabilities are co-located with their non-disabled peers, such investigations are overdue. To that end, we provide a theoretically grounded framework for authoring accessible ITS by drawing parallels between our work and relevant studies in the literature. Our framework enables the authoring of accessible ITS by integrating a cognitive tutor with a conversational agent. Our focus in this study is on an ITS created using Cognitive Tutor Authoring Tools (CTAT) and is augmented with adaptive capabilities which make it accessible to students who are blind or have motor-function impairments. We describe an ITS piloted with 115 students in two introductory college statistics courses, and share insights gained during the implementation of our framework. We highlight several contributions, including changes to the tutor interface to make it speech-interactive which is not currently available using CTAT, adaptations to the Bazaar infrastructure to enable solution step supports by the conversational agent, and how we used over 75,000 solutions steps and explanations by 415 students on 146 questions outside of an ITS to create supports within an accessible ITS. We conclude by proposing directions for future work on authoring accessible ITS.
Selecting Context Clozes for Lightweight Reading Compliance

We explore a novel approach to reading compliance, leveraging large language models to select inline challenges that discourage skipping during reading.This lightweight 'testing' is accomplished through automatically identified context clozes where the reader must supply a missing word that would be hard to guess if earlier material was skipped.Clozes are selected by scoring each word by the contrast between its likelihood with and without prior sentences as context, preferring to leave gaps where this contrast is high.We report results of an initial human-participant test that indicates this method can find clozes that have this property.
Monitoring Tutor Practices to Support Self-regulated Learning in Online One-To-One Tutoring Sessions with Process Mining

This paper reports on research that aims to examine what tutoring practices in an online environment can promote students' self-regulated learning (SRL). First, we propose a theoretically grounded framework of signifiers that can be used to track tutor-student interactions with respect to SRL. Second, we operationalize the framework using log data from a virtual learning environment and process mining approaches. Our results demonstrate that there are structural differences in tutor-learner interactions between the high performing versus low performing tutors. High performing tutors show complex patterns of engagement, which emphasize open-ended questioning and reasoning. Whilst the low performing tutors use a more restricted range of teaching practices that focus on instruction and are more strictly led by the learning platform in which they tutor. We conclude the paper with a discussion of these findings.
An Intelligent Named Entity Recognition Method Based on IoT Professional Knowledge

In order to improve the recognition ability and accuracy of professional entities in the field of Internet of things, an intelligent named entity recognition method based on Internet of things professional knowledge is proposed. This method combines the relevant professional knowledge involved in the actual teaching process of the Internet of things, constructs the professional knowledge base of the Internet of things in the form of question-and-answer pairs, proposes a new node triplet model, and labels the corpus. Based on Bidirectional Encoder Representation from Transformers, bi-directional long short-term memory, Conditional Random Field (BERT-BiLSTM-CRF) model, Multi-Head Self-Attention module is added to establish Bidirectional Encoder Representation from Transformers, bi-directional long short-term memory, Multi-Head Self-Attention, Conditional Random Field (BERT-BiLSTM-MHSA-CRF) model. The <tex>$F_{1}$</tex> value of the entity in the Internet of things professional knowledge base identified by the model reaches 95.23 &#x0025; BERT-BiLSTM-MHSA-CRF can more accurately identify the professional knowledge named entity involved in the Internet of things teaching.
How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets

A central question in natural language understanding (NLU) research is whether high performance demonstrates the models' strong reasoning capabilities.We present an extensive series of controlled experiments where pre-trained language models are exposed to data that have undergone specific corruption transformations.These involve removing instances of specific word classes and often lead to non-sensical sentences.Our results show that performance remains high on most GLUE tasks when the models are fine-tuned or tested on corrupted data, suggesting that they leverage other cues for prediction even in non-sensical contexts.Our proposed data transformations can be used to assess the extent to which a specific dataset constitutes a proper testbed for evaluating models' language understanding capabilities.
DC Health: Node-Level Online Anomaly Detection in Data Center Performance Data Monitoring

Data centers are critical environments for the availability of technology-based services. Aiming at the high availability of these services, performance metrics of nodes such as Virtual Machines (VM) or VMs clusters are widely monitored. These metrics, such as CPU and memory utilization, can show anomalous patterns associated with failures and performance degradation, culminating in resource exhaustion and total node failure. Thus, early detection of anomalies can enable remediation measures, such as VM migration and resource reallocation, before losses occur. However, traditional monitoring tools often use fixed thresholds for detecting problems on nodes and lack automatic ways to detect anomalies at runtime. In this sense, machine learning techniques have been reported to detect anomalies in computer systems with online and offline approaches. Thus, this work aims to propose and evaluate the DC Health application, pursues to anticipate the detection of anomalies in data center nodes. For this, this research was conducted from i) Systematic Literature Review previously performed, ii) problem modeling from real VM data and iii) DCH evaluation using the prequential method in 6 real-world datasets. Preliminary results showed that DCH excelled in constant memory usage and detection accuracy above 75% in the worst of the 5 cases and accuracy of 90% at best when applied in the real node datasets and an accuracy of 85% on the shuttle dataset. As a continuation of this research, it is expected to develop a case study with data center operators and the evaluation of the tool in a large volume of nodes.
MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network

While there have been advances in Natural Language Processing (NLP), their success is mainly gained by applying a self-attention mechanism into single or multi-modalities.While this approach has brought significant improvements in multiple downstream tasks, it fails to capture the interaction between different entities.Therefore, we propose MM-GATBT, a multimodal graph representation learning model that captures not only the relational semantics within one modality but also the interactions between different modalities.Specifically, the proposed method constructs image-based node embedding which contains relational semantics of entities.Our empirical results show that MM-GATBT achieves stateof-the-art results among all published papers on the MM-IMDb dataset.
Recommendation Algorithm Research Based on MPA

In the era of big data, recommendation system has been widely used in many applications, and the research on accurate and fast recommendation algorithm has become the focus of society. At present, the main applications are based on content-based recommendation algorithm and collaborative filtering recommendation algorithm. This paper integrates the two recommendation methods above to construct a &#x201C;host-router&#x201D; model, adopting MPA (multi path routing algorithm) to select multiple routes with strong connectivity for the host, so as to realize the recommendation process for users. Experimental results on the MovieLens dataset show that compared with the traditional open-source collaborative filtering algorithm, the proposed algorithm increases the recall rate by about 26&#x0025; and the F1 value by about 30&#x0025; at the same time. More accurate, wide scenario and comprehensively stronger recommendation can be achieved while innovating the recommendation architecture.
Leveraging Wearables for Assisting the Elderly With Dementia in Handwashing

Proper handwashing, having a crucial effect on reducing bacteria, serves as the cornerstone of hand hygiene. For elders with dementia, they suffer from a gradual loss of memory and difficulty coordinating handwashing steps. Proper assistance should be provided to them to ensure their hand hygiene adherence. Toward this end, we propose AWash, leveraging inertial measurement unit (IMU) readily available in most wrist-worn devices (e.g., smartwatches) to characterize handwashing actions and provide assistance. To monitor handwashing scenarios round-the-clock while achieving energy efficiency, we design methods that distinguish handwashing from other daily activities and dynamically adjust the sampling duty cycle. Upon detecting handwashing actions, we design several novel techniques to segment different handwashing actions and extract sensor-body inclination angles that handle particular interference of senile dementia patients. Moreover, a user-independent network model is built to recognize the handwashing actions of senile dementia patients without requiring their training data. Furthermore, we propose a transfer learning method that improves system performance. To meet users&#x2019; diverse needs, we use a state machine to make prompt decisions, supporting customized assistance. Extensive experiments on a prototype with eight older participants demonstrate that AWash can increase the user&#x2019;s independence in the execution of handwashing.
When the Going Gets Tough: Students’ Perceptions on Affect-Aware Support in an Exploratory Learning Environment for Fractions

It is well understood that affect interacts with and influences the learning process [2, 7, 9].
Exploring Fairness in Automated Grading and Feedback Generation of Open-Response Math Problems

The rapid growth and development of NLP techniques have resulted in Computer-Based Learning Platforms (CBLPs) leveraging innovative approaches toward automated grading and feedback generation of open-ended problems. Researchers have explored these techniques in driving a varying range of interventions that range from assessing the quality of the work and recommending changes to the answers that can enhance the quality of the responses for students to automated grading and feedback generation of responses for teachers. A crucial aspect of the automated assessment of student response is identifying and addressing fairness and equity issues in an educational context, as academic performance can impact the types of opportunities available to the students. While prior works have conducted posthoc analysis exploring aspects of algorithmic fairness of various models, the assessment of open-ended answers is often subjective. Teachers leverage contextual knowledge such as the perception of the student effort or students' prior knowledge. While such factors exist, it is not obvious how data from the teacher can introduce biases or introduce measurable risks to the fairness and equity of the NLP models. In this paper, we build on our prior analysis of the grading behavior of teachers on open-ended math problems for middle school students and explore possible next steps we can take to expand on our work. First, we propose a simulation study to explore the various risks associated with Human-AI interaction in the automated grading of open-ended problems. Second, we propose an extensive study expanding on our work to generate grades for open responses when a student is anonymized vs. not anonymized.
SeqL at SemEval-2022 Task 11: An Ensemble of Transformer Based Models for Complex Named Entity Recognition Task

In this paper, we present a system for detecting complex named entities in multilingual and code-mix settings.We discuss the results obtained in task 11 (MultiCoNER) of the Se-mEval 2022 competition.The model is an ensemble of various transformer-based language models combined with a Conditional Random Field (CRF) layer.Our model ranks fourth in track 12 (multilingual track) and fifth in track 13 (code-mixed track).We describe the details of our model implementation and discuss the effect of different aggregation methods.Finally, we conduct additional analyses to understand the performance differences between languages.
Probe-Less Probing of BERT’s Layer-Wise Linguistic Knowledge with Masked Word Prediction

The current study quantitatively (and qualitatively for an illustrative purpose) analyzes BERT's layer-wise masked word prediction on an English corpus, and finds that (1) the layerwise localization of linguistic knowledge primarily shown in probing studies is replicated in a behavior-based design and (2) that syntactic and semantic information is encoded at different layers for words of different syntactic categories.Hypothesizing that the above results are correlated with the number of likely potential candidates of the masked word prediction, we also investigate how the results differ for tokens within multiword expressions.
Counter-TWIT: An Italian Corpus for Online Counterspeech in Ecological Contexts

This work describes the process of creating a corpus of Twitter conversations annotated for the presence of counterspeech in response to toxic speech related to axes of discrimination linked to sexism, racism and homophobia.The main novelty is an annotated dataset comprising relevant tweets in their context of occurrence.The corpus is made up of tweets and responses captured by different profiles replying to discriminatory content or objectionably couched news.An annotation scheme was created to illustrate the relevant dimensions of toxic speech and counterspeech.An analysis of the collected and annotated data and of the Inter-Annotator Agreement (IAA) that emerged during the annotation process is included.Moreover, we report about preliminary experiments on automatic counterspeech detection, based on supervised automatic learning models trained on the new dataset.The results highlight the fundamental role played by the context in this detection task, confirming our intuitions about the importance to collect tweets in their context of occurrence.
Obj2Sub: Unsupervised Conversion of Objective to Subjective Questions

Exams are conducted to test the learner's understanding of the subject. To prevent the learners from guessing or exchanging solutions, the mode of tests administered must have sufficient subjective questions that can gauge whether the learner has understood the concept by mandating a detailed answer. Hence, in this paper, we propose a novel hybrid unsupervised approach leveraging rule based methods and pre-trained dense retrievers for the novel task of automatically converting the objective questions to subjective questions. We observe that our approach outperforms the existing data-driven approaches by 36.45% as measured by Recall@k and Precision@k.
Bilingual Tabular Inference: A Case Study on Indic Languages

Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language.However, due to the uneven distribution of text resources on the web across languages, it is common to have the tabular premise in a high resource language and the hypothesis in a low resource language.As a result, we present the challenging task of bilingual Tabular Natural Language Inference (bTNLI), in which the tabular premise and a hypothesis over it are in two separate languages.We construct EI-INFOTABS: an English-Indic bTNLI dataset by translating the textual hypotheses of the English TNLI dataset INFOTABS into eleven major Indian languages.We thoroughly investigate how pretrained multilingual models learn and perform on EI-INFOTABS.Our study shows that the performance on bTNLI can be close to its monolingual counterpart, with translate-train, translate-test and unified-train being strongly competitive baselines.
CMNEROne at SemEval-2022 Task 11: Code-Mixed Named Entity Recognition by leveraging multilingual data

Identifying named entities is, in general, a practical and challenging task in the field of Natural Language Processing.Named Entity Recognition on the code-mixed text is further challenging due to the linguistic complexity resulting from the nature of the mixing.This paper addresses the submission of team CM-NEROne to the SEMEVAL 2022 shared task 11 MultiCoNER.The Code-mixed NER task aimed to identify named entities on the codemixed dataset.Our work consists of Named Entity Recognition (NER) on the code-mixed dataset by leveraging the multilingual data.We achieved a weighted average F1 score of 0.7044, i.e., 6% greater than the baseline.
Minimally-Supervised Relation Induction from Pre-trained Language Model

Relation Induction is a very practical task in Natural Language Processing (NLP) area.In practical application scenarios, people want to induce more entity pairs having the same relation from only a few seed entity pairs.Thus, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting where only a couple of seed entity pairs per relation are provided.Although the conventional relation induction methods have made some success, their performance depends heavily on the quality of word embeddings.The great success of Pre-trained Language Models, such as BERT, changes the NLP area a lot, and they are proven to be able to better capture relation knowledge.In this paper, we propose a novel method to induce relation with BERT under the minimally-supervised setting.Specifically, we firstly extract proper templates from the corpus by using the mask-prediction task in BERT to build pseudo-sentences as the context of entity pairs.Then we use BERT attention weights to better represent the pseudosentences.In addition, We also use the Integrated Gradient of entity pairs to iteratively select better templates further.Finally, with the high-quality pseudo-sentences, we can train a better classifier for relation induction.Experiments on Google Analogy Test Sets (GATS), Bigger Analogy Test Set (BATS) and DiffVec demonstrate that our proposed method achieves state-of-the-art performance.
Improved Induction of Narrative Chains via Cross-Document Relations

The standard approach for inducing narrative chains considers statistics gathered per individual document.We consider whether statistics gathered using cross-document relations can lead to improved chain induction.Our study is motivated by legal narratives, where cases typically cite thematically similar cases.We consider four novel variations on pointwise mutual information (PMI), each accounting for crossdocument relations in a different way.One proposed PMI variation performs 58% better relative to standard PMI on recall@50 and induces qualitatively better narrative chains.
Multi-label Online Streaming Feature Selection Algorithms via Extending Alpha-Investing Strategy

Multi-label learning is a special supervised pattern classification issue, in which an instance is possibly associated with multiple class labels simultaneously. As various real world applications emerge continuously in the big data field, more attention has been paid to streaming data forms recently, i.e., instance, feature and label streams. In this paper, we focus on multi-label online streaming feature selection (OSFS) problem, whose features arrive sequentially over time, and instances and labels are given, to choose an optimal subset of features dynamically. Alpha-investing method is one of the most cited embedded-type single-label OSFS techniques, which mainly involves the linear regression model as its classifier. In this paper, we generalize such a technique to build two new multi-label OSFS algorithms (simply ML-AIBR and ML-AIMOR), which are based on binary relevance (BR) decomposition way and multi-output regression (MOR), respectively. To the best of our knowledge, such two algorithms are the first proposed embedded-type OSFS technique for multi-label streaming features so far. Our extensive experiments conducted on six benchmark data sets demonstrate that our two proposed methods performs better than three existing algorithms. Specially, our ML-AIMOR could filter out more irrelevant and redundant features effectively.
A Novel Composed Method of Cleaning Anomy Data for Improving State Prediction of Wind Turbine

Improving the state forecasting efficiency of wind turbine is an important goal for of wind energy utilization. But lots of abnormal data existing in supervisory control and data acquisition (SCADA) of wind turbines seriously affects the health state prediction. In this paper, a new composed method is proposed to clean SACAD data according to abnormal data types of wind turbine. In proposed composed method, a preprocessing method is first presented to get rid of outliers of power curve based on operational mechanism, and a new data cleaning method called TTLOF (Thompson tau-local outlier factor) is proposed to quantify particularly data points and eliminate outliers by setting correlation parameter thresholds. In TTLOF cleaning data, Empirical copula-based mutual information (ECMI) is used to select correlation parameters for anomaly characteristic assessments, and each parameter interval is divided for performing segmentation fine cleaning which can reduce the model complexity of identifying anomaly characteristics. Finally, a deep learning network which is long short-term memory (LSTM) is used to verify the effectiveness of the proposed data cleaning method. By analyzing the state monitoring results, it is shown the proposed composed method is more effective for cleaning anomy data than other methods.