Reviewerly/exHarmony huggingface dataset:
A Study on the Attitude of Chinese Generation Z Towards Influencers

With the development of China's Internet, a new career was born--influencer.They are difined as people who have become famous on the Internet and have some influence on the Internet.More and more people are choosing to put themselves in this industry, especially young people in China's Z era.This study focuses on the Chinese Z generation's attitude towards influencers in order to better understand the young people's thinking changes and help the development of influencers.This report sent questionnaire survey to the Chinese Z generation, then it is found that TikTok has become the main platform for the Chinese Z generation to know the influencers and the influencers are not only about themselves but also connecting with the capitals and the pushers.Also, the attitude of the Chinese Z generation towards influencers is at a good stage that they barely follow the influencers blindly and their opinions will help influencers get better and better.
GPU Accelerated Modelling and Forecasting for Large Time Series

Abstract Modelling of large scale data series is of significant importance in fields such as astrophysics and finance. The continuous increase in available data requires new computational approaches such as the use of multicore processors and accelerators. Recently, a novel time series modelling and forecasting method was proposed, based on a recursively updated pseudoinverse matrix which enhances parsimony by enabling assessment of basis functions, before inclusion into the final model. Herewith, a novel GPU (Graphics Processing Unit) accelerated matrix based auto-regressive variant is presented, which utilizes lagged versions of a time series and interactions between them to form a model. The original approach is reviewed and a matrix multiplication based variant is proposed. The GPU accelerated and hybrid parallel versions are introduced, utilizing single and mixed precision arithmetic to increase GPU performance. Discussions around performance improvement and high order interactions are given. A block processing approach is also introduced to reduce memory requirements for the accelerator. Furthermore, the inclusion of constraints in the computation of weights, corresponding to the basis functions, with respect to the parallel implementation are discussed. The approach is assessed in a series of model problems and discussions are provided.
Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions

Current QA systems can generate reasonablesounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot readily check the model's answers.This presents a challenge for building trust in machine learning systems.We take inspiration from real-world situations where diffcult questions are answered by considering opposing sides (see Irving et al., 2018).For multiplechoice QA examples, we build a dataset of single arguments for both a correct and incorrect answer option in a debate-style set-up as an initial step in training models to produce explanations for two candidate answers.We use long contexts-humans familiar with the context write convincing explanations for preselected correct and incorrect answers, and we test if those explanations allow humans who have not read the full context to more accurately determine the correct answer.We do not fnd that explanations in our set-up improve human accuracy, but a baseline condition shows that providing human-selected text snippets does improve accuracy.We use these fndings to suggest ways of improving the debate set up for future data collection efforts.
Teacher’s Directive Speech Acts on the Online Teaching and Learning Process of Kampus Mengajar Program

This research aims to identify directive speech acts by teacher during the online teaching and learning process.The data source of this research is in the form of teacher speeches during the instructional process at SD Negeri 6 Jimbung, Kalikotes Subdistrict, Klaten Regency.This research belongs to qualitative research for it aims to produce data in the form of words, expressions, speeches, or sentences related to the teacher's directive speech acts during the online class in SD Negeri 6 Jimbung.The data collection process employs Simak Bebas Libat Cakap (SBLC) which means uninvolved conversation observation, voice-recording, and note-taking techniques.The data from this study is analyzed using pragmatic methods.The results of this study show the forms of directive speech acts by the teacher during the teaching and learning process including: requesting (25%), pleading (7%), persuading (17%), encouraging (11%), questioning (16%), commanding (11%), prohibiting (5%), allowing (4%), and reminding (4%).
Distributed Interactive Proofs for the Recognition of Some Geometric Intersection Graph Classes

A graph $$G=(V,E)$$ is a geometric intersection graph if every node $$v \in V$$ is identified with a geometric object of some particular type, and two nodes are adjacent if the corresponding objects intersect. Geometric intersection graph classes have been studied from both the theoretical and practical point of view. On the one hand, many hard problems can be efficiently solved or approximated when the input graph is restricted to a geometric intersection class of graphs. On the other hand, these graphs appear naturally in many applications such as sensor networks, scheduling problems, and others. Recently, in the context of distributed certification and distributed interactive proofs, the recognition of graph classes has started to be intensively studied. Different results related to the recognition of trees, bipartite graphs, bounded diameter graphs, triangle-free graphs, planar graphs, bounded genus graphs, H-minor free graphs, etc., have been obtained. The goal of the present work is to design efficient distributed protocols for the recognition of relevant geometric intersection graph classes, namely permutation graphs, trapezoid graphs, circle graphs and polygon-circle graphs. More precisely, for the two first classes we give proof labeling schemes recognizing them with logarithmic-sized certificates. For the other two classes, we give three-round distributed interactive protocols that use messages and certificates of size $$\mathcal {O}(\log n)$$ . Finally, we provide logarithmic lower-bounds on the size of the certificates on the proof labeling schemes for the recognition of any of the aforementioned geometric intersection graph classes.
Analysing Semi-Supervised Convnet Model Performance with Computation Processes

The emergence of new Semi-Supervised Machine Learning (SSML) algorithms is becoming increasingly versatile leveraging different components in their structure. This versatility of design in SSML algorithms offered an improved overall performance, in terms of accuracy, training loss, and training time. However, identifying the key contributing factors for those SSML algorithms is still unknown. In the previous studies, researchers have utilized Deep Neural Networks (DNNs) to develop the primary learning models employing SSML algorithms with better classification accuracy. However, these research works lack a systematic study to grasp the impacts of primary contributing factors. In this article, we will describe some of those performance factors of the SSML models, their expected impact, and potential biases using Post-hoc Explainable artificial intelligence (XAI) methods. The audiences for this work are experienced AI researchers that have insight into SSML models. We start by generalizing the common components of SSML algorithms found previously (Π-Model, Temporal Ensembling, and Mean Teacher) compared to the new emerging contemporary SSML algorithms (MixMatch and RemixMatch). We then reform the Semi-Supervised Computation Processes (SSCPs) of both preprocessing focused and classifier focused designs. Subsequently, we introduce various types of DNNs in the network classifier module during the computational process. This integration demonstrates two main aspects to explore the performance effects: (1) parameter updates throughout the training process across both labelled and unlabelled data; (2) the labelled/unlabelled data ratio. Furthermore, we demonstrate our in-depth analysis of the 45 experiments with five different SSML algorithms, three DNNs, and three different labelled/unlabelled data ratios. Our experimental results show that one of the dominating factors to the learning precision and training loss is the parameter updates. Moreover, the experimental results also exhibit that training loss has been reduced by 8% and learning precision is improved by 6.75% using the network classifier, Shake-Shake26, in the RemixMatch SSML algorithm, compared to various other combinations. Similarly, the R score value of 0.701 and the p -value of 0.003597 clearly emphasize a positive correlation between the training time and the labelled/unlabelled data ratio. Finally, we conclude that the introduction of more labelled data precedes to prolonged training time that prompts additional parameter updates during the forward- and backward propagations.
Hierarchical Multi-task learning framework for Isometric-Speech Language Translation

This paper presents our submission for the shared task on isometric neural machine translation at International Conference on Spoken Language Translation (IWSLT).There are numerous state-of-art models for translation problems.However, these models lack any length constraint to produce short or long outputs from the source text.This paper proposes a hierarchical approach to generate isometric translation on the MUST-C dataset.We achieve a BERTscore of 0.85, a length ratio of 1.087, a BLEU score of 42.3, and a length range of 51.03%.On the blind dataset provided by the task organizers, we obtained a BERTscore of 0.80, a length ratio of 1.10, and a length range of 47.5%.We have made our code public hee https://github.com/aakash0017/Machine-Translation-ISWLT.
Utilization of e-ShodhSindhu consortium by faculty members of private autonomous engineering colleges in Andhra Pradesh: A study

Availability of resources is now measured more appropriate than collection building.The consortium facilitated the libraries to get the benefit of wider access to electronic resources at an affordable cost and the best term of licenses.The collective strength of resources of various institutions available to it is in a better position to address and resolve the problem of managing, organizing, and archiving the electronic resources.Increase in the habit of reading, thrust for knowledge and rise in the information through sharing, with economical and cheaper with print resources.To face the challenge of global changes in technical education and to reach by the Autonomous engineering colleges, India is not far behind in providing its academic community and private engineering colleges also.To cater the needs e ShodhSindhu a national consortium by providing e-journals and e-databases to all Indian community and using the same by Private Autonomous colleges is being focused.
Better Quality Estimation for Low Resource Corpus Mining

Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models.However, these models still lack the robustness to achieve general adoption.We show that Stateof-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly bad due to a lack of robustness to out-of-domain examples.We propose a combination of multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance.We show that our method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup.We increase the accuracy in PCM by more than 0.80, making it on par with state-of-the-art PCM methods that use millions of sentence pairs to train their models.In comparison, we use thousand times less data, 7K parallel sentences in total, and propose a novel low resource PCM method.
QAlayout: Question Answering Layout Based on Multimodal Attention for Visual Question Answering on Corporate Document

The extraction of information from corporate documents is increasing in the research field both for its economic aspect and a scientific challenge. To extract this information the use of textual and visual content becomes unavoidable to understand the inherent information of the image. The information to be extracted is most often fixed beforehand (i.e. classification of words by date, total amount, etc.). The information to be extracted is evolving, so we would not like to be restricted to predefine word classes. We would like to question a document such as “which is the address of invoicing?” as we can have several addresses in an invoice. We formulate our request as a question and our model will try to answer. Our model got the result 77.65% on the Docvqa dataset while drastically reducing the number of model parameters to allow us to use it in an industrial context and we use an attention model using several modalities that help us in the interpertation of the results obtained. Our other contribution in this paper is a new dataset for Visual Question answering on corporate document of invoices from RVL-CDIP [8]. The public data on corporate documents are less present in the state-of-the-art, this contribution allow us to test our models to the invoice data with the VQA methods.
Sentiment Analysis Approaches: A Systematic Review

The number of consumers who leave online comments has risen dramatically as the number of global Internet presentations continues to rise.Ample data, if correctly explored, should yield important insights.More and more individuals are sharing their ideas and opinions online as a result of the fast rise of social media on the web, forum debates such as reviews, news, comments, and blogs.As a result, this intriguing issue is becoming increasingly significant in business and society.Sentiment analysis is a subcategory of natural language processing and is used to determine the polarity of the user's opinion, which may be positive, negative, or neutral, against the entities or their aspects.SA offers a plethora of current applications in a variety of industries.In the commercial world, it enables organizations to automatically collect client feedback on their products or services.It may be used in politics to infer popular sentiment and reaction to political events, which aids decision-making.This paper explores the various lexicon-based, machine learning (ML), and hybrid techniques used in the field of SA.Hybrid approaches have proved more efficient than any other individual approach.
Detecting Annotation Errors in Morphological Data with the Transformer

Annotation errors that stem from various sources are usually unavoidable when performing large-scale annotation of linguistic data.In this paper, we evaluate the feasibility of using the Transformer model to detect various types of annotator errors in type-based morphological datasets that contain inflected word forms.We evaluate our error detection model on four languages by injecting three different types of artificial errors into the data: (1) typographic errors, where single characters in the data are inserted, replaced, or deleted; (2) linguistic confusion errors where two inflected forms are systematically swapped; and (3) self-adversarial errors where the Transformer model itself is used to generate plausible-looking, but erroneous forms by retrieving high-scoring predictions from a Transformer search beam.Results show that the model can with perfect, or nearperfect recall detect errors in all three scenarios, even when significant amounts of the annotated data (5%-30%) are corrupted on all languages tested.Precision varies across the languages and types of errors, but is high enough that the model can reliably be used to flag suspicious entries in large datasets for further scrutiny by human annotators.
Detoxifying Language Models with a Toxic Corpus

Existing studies have investigated the tendency of autoregressive language models to generate contexts that exhibit undesired biases and toxicity.Various debiasing approaches have been proposed, which are primarily categorized into data-based and decoding-based.In our study, we investigate the ensemble of the two debiasing paradigms, proposing to use toxic corpus as an additional resource to reduce the toxicity.Our result shows that toxic corpus can indeed help to reduce the toxicity of the language generation process substantially, complementing the existing debiasing methods.
Capturing Interaction Quality in Long Duration (Simulated) Space Missions With Wearables

Space exploration is evolving with the recent increase in interest and investment. For the success of planned long-duration crewed missions, good interpersonal interactions between crew members are crucial. In this study, we evaluate the use of wearables for detection and estimation of the quality of each social interaction participants have throughout a long mission rather than aggregate measures of interactions. Our proposed method utilizes Temporal Convolutional Networks(TCNs) for extracting individual representations from acceleration and audio streams and learnable pooling layers(NetVLAD) to aggregate these representations into fixed-size representations. Use of NetVLAD layers provides an intelligent alternative to simple aggregation for handling variable-sized interactions and interactions with missing data. We evaluate our method on a 4-month simulated space mission where 5 participants wore Sociometric Badges and provided reports on their interactions in terms of effectiveness, frustration, and satisfaction. Our method provides an average ROC-AUC score of 0.64. Since we are not aware of any comparable baselines, we compare our method to hand-crafted features formerly utilized for cohesion estimation in similar scenarios and show it significantly outperforms them. We also present ablation studies where we replace the components in our approach with well-known alternatives and show that they provide better performance than their respective counterparts.
Shallow Parsing for Nepal Bhasa Complement Clauses

Accelerating the process of data collection, annotation, and analysis is an urgent need for linguistic fieldwork and documentation of endangered languages (Bird, 2009). Our experiments describe how we maximize the quality for the Nepal Bhasa syntactic complement structure chunking model. Native speaker language consultants were trained to annotate a minimally selected raw data set (Suárez et al.,2019). The embedded clauses, matrix verbs, and embedded verbs are annotated. We apply both statistical training algorithms and transfer learning in our training, including Naive Bayes, MaxEnt, and fine-tuning the pre-trained mBERT model (Devlin et al., 2018). We show that with limited annotated data, the model is already sufficient for the task. The modeling resources we used are largely available for many other endangered languages. The practice is easy to duplicate for training a shallow parser for other endangered languages in general.
Smart education cloud resource platform

Smart education is an important symbol of educational informatization.Accelerating the construction of smart education is conducive to the popularization and sharing of high-quality resources and the balance and fairness of education; It is helpful to improve the level of teachers' application of information technology and promote quality education; It is conducive to students' autonomous learning and active learning by means of information technology; It is conducive to the close relationship between home and school and form the joint force of home and school education.
The Analysis of Government Strategy in Integrating Technology in Education

This study aims to analyze the Indonesian government's strategy in integrating technology in education.COVID-19 pandemic encourages schools to implement distance learning, and technology becomes a necessity.Already launching the subject named Information and Communication Technology (ICT), integrating ICT skills in all courses, and applying a new course called Informatics, teachers still feel unprepared in a pandemic situation.The researchers use qualitative method to study documents and interview seven respondents from six schools located in Jakarta to know (1) their perception of government policy for technology integration in education, (2) the government programs if answer the need of students, (3) and the suggestion for better implementation.As a result, the government still didn't have a good policy and master plan for integrating technology into education.The government tries to implement some policies and strategies, but they are only on paper and weak in implementation.The government only develops data of teachers and students, but the data didn't answer the need for schools and teachers about the need for technology in teaching-learning activities.The respondents suggest some solutions, including a solid and clear technology integration policy in education, the development of teachers' skills, and infrastructure.
Creation of a Mobile App Prototype for Internationalization of Rūta’s Products

The following paper accompanies the process of creation of a mobile app prototype with the goal of internationalizing Rūta’s products and services strengthening the ties of the company and its costumers as well as allowing the company to expand their business and giving their international buyers access to English content. This report describes the process of information gathering as well as app designing explaining all the decisions made.
A design of emergency services for pre-hospital cardiac arrest

The frequency of sudden cardiac death is increasing year by year. Timely rescue of cardiac arrest patients using aed devices can greatly improve patient survival rates. However, in most cases, patients do not receive timely assistance. The purpose of this project is to provide more timely and safer rescue for cardiac arrest patients. Therefore, this design integrates relevant resources through a set of service design to improve the response rate of cardiac arrest events and allow patients to receive timely assistance. First, we use literature reading method to understand the current development of technology and services. Then, we conducted structured interviews with stakeholders about the EMS process. Based on this, we used functional analysis and scenario simulation methods to explore the needs and summarize the service touchpoints. Finally, service design tools, such as service blueprints, user journey maps,system map etc., are used to describe the service design. The results of this project will help to improve the survival rate of cardiac arrest patients, and also have some reference value for the improvement of urban EMS system.