Reviewerly/exHarmony huggingface dataset:
The Influence of information quality, risk perception, and promotion on buying decision at Tokopedia on Mikroskil University students

The purpose of this study was to determine and analyze the influence information quality, risk perception, and promotion on buying decision at Tokopedia on Mikroskil University students. The type of this research used in this study was quantitative research with descriptive approach. The population of this research were students of Mikroskil university, faculty business amounted 162 respondents. The sampling technique was using a saturated sample and the number of samples in this study amounted 162 respondents. The method of data analysis used multiple linier regression analysis. The results showed that the information quality, risk perception, and promotion variables partially influence the buying decision at Tokopedia on Mikroskil University Students. However, simultaneously variables, information quality, risk perception, and promotion affect buying decision at Tokopedia on Mikroskil University Students. The results of the coefficient of determination is 82,3%, this shows that the variables consisting of information quality, risk perception, and promotion affect the buying decision variables by 82,3%. While the remaining 17,7% is explained by other variables not explained in this study.
Bayesian Federated Neural Matching that Completes Full Information

Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model. Due to the intrinsic permutation invariance of neural networks, Probabilistic Federated Neural Matching (PFNM) employs a Bayesian nonparametric framework in the generation process of local neurons, and then creates a linear sum assignment formulation in each alternative optimization iteration. But according to our theoretical analysis, the optimization iteration in PFNM omits global information from existing. In this study, we propose a novel approach that overcomes this flaw by introducing a Kullback-Leibler divergence penalty at each iteration. The effectiveness of our approach is demonstrated by experiments on both image classification and semantic segmentation tasks.
Language Function As A Support To Teachers’ Professionalism

ABSTRACT: This study seeks to determine the functions of language for the professionalism of teachers who must communicate and convey information or material to students. This study employed a qualitative methodology. This research was conducted in schools located in Denpasar, Bali, with teachers as its subjects. The language functions utilized by the teacher can be mapped as follows: 44 phatic functions (23%), 34 metalingual functions (18%), 34 referential functions (18%), 34 conative functions (18%), 25 emotive functions (13%), and 19 poetic functions (10%). From the results of the study, it can be concluded that language functions in their professionalism as teachers, namely a phatic function that facilitates socialization and interaction between teachers and students; a metalingual function that teaches students the vocabulary they have not yet mastered; a referential function plays a role in conveying topics, information, or material to students; a conative function allows teachers to instruct something to students; an emotive function serves to motivate or develop relationships between teachers and students; and a poetic function plays a role for teachers who need poetic or aesthetic language when providing examples of poetry or other literary works.&#x0D; Keywords: Language Function, Professionalism, Teachers
Provably Reliable Large-Scale Sampling from Gaussian Processes

When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate them. Na\"{i}vely doing so would cost \(\mathcal{O}(n^3)\) flops and \(\mathcal{O}(n^2)\) memory to generate a size \(n\) sample. We demonstrate how to scale such data generation to large \(n\) whilst still providing guarantees that, with high probability, the sample is indistinguishable from a sample from the desired GP.
Persian Emotion Detection using ParsBERT and Imbalanced Data Handling Approaches

Emotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces. Detecting emotion can help us in different fields, including opinion mining. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms is informal, making the emotion detection task difficult. EmoPars and ArmanEmo are two new human-labeled emotion datasets for the Persian language. These datasets, especially EmoPars, are suffering from inequality between several samples between two classes. In this paper, we evaluate EmoPars and compare them with ArmanEmo. Throughout this analysis, we use data augmentation techniques, data re-sampling, and class-weights with Transformer-based Pretrained Language Models(PLMs) to handle the imbalance problem of these datasets. Moreover, feature selection is used to enhance the models' performance by emphasizing the text's specific features. In addition, we provide a new policy for selecting data from EmoPars, which selects the high-confidence samples; as a result, the model does not see samples that do not have specific emotion during training. Our model reaches a Macro-averaged F1-score of 0.81 and 0.76 on ArmanEmo and EmoPars, respectively, which are new state-of-the-art results in these benchmarks.
Multilingual and Multimodal Topic Modelling with Pretrained Embeddings

This paper presents M3L-Contrast -- a novel multimodal multilingual (M3L) neural topic model for comparable data that maps texts from multiple languages and images into a shared topic space. Our model is trained jointly on texts and images and takes advantage of pretrained document and image embeddings to abstract the complexities between different languages and modalities. As a multilingual topic model, it produces aligned language-specific topics and as multimodal model, it infers textual representations of semantic concepts in images. We demonstrate that our model is competitive with a zero-shot topic model in predicting topic distributions for comparable multilingual data and significantly outperforms a zero-shot model in predicting topic distributions for comparable texts and images. We also show that our model performs almost as well on unaligned embeddings as it does on aligned embeddings.
Predicting Eye Gaze Location on Websites

World-wide-web, with the website and webpage as the main interface, facilitates the dissemination of important information. Hence it is crucial to optimize them for better user interaction, which is primarily done by analyzing users' behavior, especially users' eye-gaze locations. However, gathering these data is still considered to be labor and time intensive. In this work, we enable the development of automatic eye-gaze estimations given a website screenshots as the input. This is done by the curation of a unified dataset that consists of website screenshots, eye-gaze heatmap and website's layout information in the form of image and text masks. Our pre-processed dataset allows us to propose an effective deep learning-based model that leverages both image and text spatial location, which is combined through attention mechanism for effective eye-gaze prediction. In our experiment, we show the benefit of careful fine-tuning using our unified dataset to improve the accuracy of eye-gaze predictions. We further observe the capability of our model to focus on the targeted areas (images and text) to achieve high accuracy. Finally, the comparison with other alternatives shows the state-of-the-art result of our model establishing the benchmark for the eye-gaze prediction task.
EDEN : An Event DEtection Network for the annotation of Breast Cancer recurrences in administrative claims data

While the emergence of large administrative claims data provides opportunities for research, their use remains limited by the lack of clinical annotations relevant to disease outcomes, such as recurrence in breast cancer (BC). Several challenges arise from the annotation of such endpoints in administrative claims, including the need to infer both the occurrence and the date of the recurrence, the right-censoring of data, or the importance of time intervals between medical visits. Deep learning approaches have been successfully used to label temporal medical sequences, but no method is currently able to handle simultaneously right-censoring and visit temporality to detect survival events in medical sequences. We propose EDEN (Event DEtection Network), a time-aware Long-Short-Term-Memory network for survival analyses, and its custom loss function. Our method outperforms several state-of-the-art approaches on real-world BC datasets. EDEN constitutes a powerful tool to annotate disease recurrence from administrative claims, thus paving the way for the massive use of such data in BC research.
A Universal Discriminator for Zero-Shot Generalization

Generative modeling has been the dominant approach for large-scale pretraining and zero-shot generalization. In this work, we challenge this convention by showing that discriminative approaches perform substantially better than generative ones on a large number of NLP tasks. Technically, we train a single discriminator to predict whether a text sample comes from the true data distribution, similar to GANs. Since many NLP tasks can be formulated as selecting from a few options, we use this discriminator to predict the concatenation of input and which option has the highest probability of coming from the true data distribution. This simple formulation achieves state-of-the-art zero-shot results on the T0 benchmark, outperforming T0 by 16.0\%, 7.8\%, and 11.5\% respectively on different scales. In the finetuning setting, our approach also achieves new state-of-the-art results on a wide range of NLP tasks, with only 1/4 parameters of previous methods. Meanwhile, our approach requires minimal prompting efforts, which largely improves robustness and is essential for real-world applications. Furthermore, we also jointly train a generalized UD in combination with generative tasks, which maintains its advantage on discriminative tasks and simultaneously works on generative tasks.
DualNER: A Dual-Teaching framework for Zero-shot Cross-lingual Named Entity Recognition

We present DualNER, a simple and effective framework to make full use of both annotated source language corpus and unlabeled target language text for zero-shot cross-lingual named entity recognition (NER). In particular, we combine two complementary learning paradigms of NER, i.e., sequence labeling and span prediction, into a unified multi-task framework. After obtaining a sufficient NER model trained on the source data, we further train it on the target data in a {\it dual-teaching} manner, in which the pseudo-labels for one task are constructed from the prediction of the other task. Moreover, based on the span prediction, an entity-aware regularization is proposed to enhance the intrinsic cross-lingual alignment between the same entities in different languages. Experiments and analysis demonstrate the effectiveness of our DualNER. Code is available at https://github.com/lemon0830/dualNER.
Intelligent Mobile Models and Their Application in the Educational Process

Using ICTs in education allows the effective integration of new technologies into the educational process. Developing information and communication technologies also require timely changes in knowledge evaluation and use. As a result, methods, techniques, and technologies used in learning are being updated. In this study, a method for developing an automated educational and methodical complex based on a semantic network is described. Requirements for such educational and methodological complex have been formulated. Structural and formal models of tools based on a semantic network have been presented. The purpose of developing the model is to make it easier for students to perceive the various educational and methodological complexes through its compilation on the basis of a holistic view of the subject area, as well as to optimize the work of the teacher.
Affecting Factors for the Adoption of Cloud-Based ERP System in Iraqi SMEs: An Empirical Study

Cloud-based enterprise resource planning (ERP) and cloud computing are critical requirements for all SMEs since they can be used to facilitate the SMEs’ growth by creating competitive and personalized innovations considering their required business scope. To date, the growth of cloud technologies has led to the development of new systems and applications in many fields and areas including businesses. Our previous study proposed an adoption model to investigate the main determinants and logistical factors that influence decision-makers of SMEs to adopt cloud-based ERP systems. The aim of this research is to enhance the previous work by evaluating and validating the new model in real life to determine whether it has achieved what it was developed for and determine the reliability of the research results. The methodology and results of the evaluation and validation process of the proposed model are presented in this research. Considering there is little documentation in the literature specifically relevant to how proposed models have been evaluated and validated, hence providing this insight will assist both the academic researchers and decision-makers. The evaluation and validation methodology and the model itself contribute toward a better understanding of adoption processes. Furthermore, the evaluation and validation procedure in future work can be used to measure, enhance and determine whether the proposed models can be used in real life.
A Study on the Messages of Apartment Advertisements with a Text Mining Analysis

This study conducted an analysis with the semantic network analysis technique to figure out house values sought after in the messages of local apartment sales advertisements. The study collected analysis data from the messages of 54 apartment sales advertisements in total published in B, which is one of the most representative newspapers in Busan and has the most subscribers, for two years of June 2020~June 2022. The analysis focus was put on the headlines, subheadlines, body copies, and captions of newspaper ads. R was used in a text mining analysis including the analysis of word clouds and semantic networks as well as frequency analysis to identify the housing value factors conveyed to consumers exposed to these advertising messages. The main findings were summarized as follows: first, such keywords as "transportation network," "center," and "environment" were the core concept of the apartment sales advertising messages. The keywords of "premium," "station area," and "transportation network" were closely connected to one another; secondly, there were differences in the core concepts of housing values among different zones. The most essential concepts were "transportation network," "design," and "Centum" in the eastern zone of Busan, "premium," "Sasang," and "transportation network" in the northwestern zone of Busan, "North Port," "transportation network," "ocean views" in the southern zone of Busan, and "environment," "center," and "design" in the central zone of Busan; and finally, there were also differences in major keywords among the different zones. These findings are significant in that they analyzed objectively the ways that messages were accumulated in apartment sales advertisements in Busan through Big Data analyses. Based on these findings, the study is expected to provide useful practical implications for advertising message strategies sought after in apartment sales advertisements in Busan.
Analysis of Differences by Period and Type of Newspaper Articles Related to COVID-19 Using Topic Modeling

Newspaper articles are text data that record major issues or events that occur in the society we live in. Analyzing newspaper article text can derive interesting patterns of social phenomena. In this paper, topic modeling analysis was conducted by dividing the text of COVID-19-related newspaper articles by article writing period and newspaper type, and its meaning and implications were derived from a social and technical perspective. Texts of COVID-19-related articles were collected from Bigkinds, which provides news articles from various newspapers published in Korea, basic text analysis and major topics were extracted to examine major issues and countermeasures of COVID-19. The extracted topics were divided into the article writing period (2021 and 2022) and the newspaper type (national and local newspapers), and some implications were derived accordingly. First, the main topics of COVID-19-related news articles were "political support", "social response", "corona quarantine", and "overseas situation". Second, the "policy support" topic was widely covered in national newspapers in 2022 due to the influence of the 2022 presidential election. Third, it can be seen that local governments are more interested in support and consideration for the socially disadvantaged than the central government, and that they were also more active ahead of the presidential election. Fourth, the "COVID-19 Quarantine" topic was more covered in local newspapers than in national newspapers. Fifth, from a technical point of view, it was expected that performing T-verification as a method of measuring the accuracy of the extracted topics could be a good technical method for optimizing topic modeling.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications

Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks in an effort to help users choose the most suitable OpenIE systems for their applications. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance, making it important to choose the most appropriate model for one's applications. We demonstrate the applicability of our recommendations on a downstream Complex QA application.
Identifying Spurious Correlations and Correcting them with an Explanation-based Learning

Identifying spurious correlations learned by a trained model is at the core of refining a trained model and building a trustworthy model. We present a simple method to identify spurious correlations that have been learned by a model trained for image classification problems. We apply image-level perturbations and monitor changes in certainties of predictions made using the trained model. We demonstrate this approach using an image classification dataset that contains images with synthetically generated spurious regions and show that the trained model was overdependent on spurious regions. Moreover, we remove the learned spurious correlations with an explanation based learning approach.
Augmented Reality Pins facilitated through Geolocation

The current systems available for virtual interactions lack the ability to create direct contact between reality and virtuality. To fill this gap, we need the blend of a real and virtual world within our currently available device, and this blend is possible through augmented reality. In this project we used AR for the very same purposes giving control to the end user to manage their customized pins along with the 3D models they want to show at a given geographical location.
Cross-Stitched Multi-task Dual Recursive Networks for Unified Single Image Deraining and Desnowing

We present the Cross-stitched Multi-task Unified Dual Recursive Network (CMUDRN) model targeting the task of unified deraining and desnowing in a multi-task learning setting. This unified model borrows from the basic Dual Recursive Network (DRN) architecture developed by Cai et al. The proposed model makes use of cross-stitch units that enable multi-task learning across two separate DRN models, each tasked for single image deraining and desnowing, respectively. By fixing cross-stitch units at several layers of basic task-specific DRN networks, we perform multi-task learning over the two separate DRN models. To enable blind image restoration, on top of these structures we employ a simple neural fusion scheme which merges the output of each DRN. The separate task-specific DRN models and the fusion scheme are simultaneously trained by enforcing local and global supervision. Local supervision is applied on the two DRN submodules, and global supervision is applied on the data fusion submodule of the proposed model. Consequently, we both enable feature sharing across task-specific DRN models and control the image restoration behavior of the DRN submodules. An ablation study shows the strength of the hypothesized CMUDRN model, and experiments indicate that its performance is comparable or better than baseline DRN models on the single image deraining and desnowing tasks. Moreover, CMUDRN enables blind image restoration for the two underlying image restoration tasks, by unifying task-specific image restoration pipelines via a naive parametric fusion scheme. The CMUDRN implementation is available at https://github.com/VCL3D/CMUDRN.
Homomorphic Self-Supervised Learning

In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations. Specifically, we introduce a general framework we call Homomorphic Self-Supervised Learning, and theoretically show how it may subsume the use of input-augmentations provided an augmentation-homomorphic feature extractor. We validate this theory experimentally for simple augmentations, demonstrate how the framework fails when representational structure is removed, and further empirically explore how the parameters of this framework relate to those of traditional augmentation-based self-supervised learning. We conclude with a discussion of the potential benefits afforded by this new perspective on self-supervised learning.