Reviewerly/exHarmony huggingface dataset:
Summarizing Online Customer Review using Topic Modeling and Sentiment Analysis

With the massive implementation of social media in various forms in various business domains, business or product owners have the opportunity to be able to take advantage of user review data that is available free of charge to evaluate the products they issue. User reviews on social media platforms, marketplaces, and e-commerce are User Generated Content (UGC) which is very useful for product owners to find out the extent of user preferences for their products. However, to be able to comprehensively read the data, the right technology is needed considering that the data is in the form of text in very large quantities. Reading one by one and then drawing conclusions is certainly not the right approach because it will take quite a lot of time. So, in this study, the researcher will use a text analysis-based approach, especially topic modeling and sentiment analysis to summarize user reviews in the comments or reviews column on the e-commerce platform. The case study used in this study is user reviews in the comments column on the Amazon site for the Lenovo K8 Note smartphone product. From the experiments carried out, the approach used can summarize the reviews written by quite many users in one summary that can be easily understood.
THE CONVERGENCE OF LEGISLATION ON CYBERCRIME AND DATA PROTECTION IN SOUTH AFRICA: A Practical Approach to the Cybercrimes Act 19 of 2020 and the Protection of Personal Information Act 4 of 2013

This article seeks to give a historical background to the development of cybercrime laws in South Africa. It commences with a discussion on the common-law position regarding cyber-criminality then the article goes on to discuss the Electronic Communications Transactions Act (ECT) and the new Cybercrimes Act. This is followed by a discussion on Protection of Personal Information Act (POPIA) and same converges with the Cybercrimes Act, as well as the POPIA.
Conversations in Live Streaming Selling: A Case Study in China

As a new emergence of commercial activities, live steaming selling, which shares the characteristics of both live streaming and online shopping, has been developing rapidly with the prosperity of the Internet and the pervasive use of mobile electronic devices in recent years in China. Given that the consumption in live streaming selling is stimulated by the real-time interaction between live streamers (online sellers) and customers to a great extent, how live streamers construct their conversations in order to attract the audience and hold on to potential customers arouses research interests from linguists. To answer the call for more research on live streaming selling and to enrich the body of studies on discourse other than English, the present study aims at examining how trouble sources in live streaming conversations were taken advantage of to attract the audience as well as how sequences of speech were taken to emphasize the essential purpose of live streaming. The case study is based on the conversations between Li Jiaqi, the top live streamer in China, and Jin Jing, his guest, during a session of live streaming selling. The analysis reveals that a) different forms of repair were initiated skillfully by the two live streamers to create humor to attract more audience, and b) strategies of turn-taking were used smartly to get the topic back on the track of selling.
Combining Unsupervised and Supervised Learning for Sample Efficient Continuous Language Grounding

Natural and efficient communication with humans requires artificial agents that are able to understand the meaning of natural language. However, understanding natural language is non-trivial and requires proper grounding mechanisms to create links between words and corresponding perceptual information. Since the introduction of the "Symbol Grounding Problem" in 1990, many different grounding approaches have been proposed that either employed supervised or unsupervised learning mechanisms. The latter have the advantage that no other agent is required to learn the correct groundings, while the former are often more sample-efficient and accurate but require the support of another agent, like a human or another artificial agent. Although combining both paradigms seems natural, it has not achieved much attention. Therefore, this paper proposes a hybrid grounding framework which combines both learning paradigms so that it is able to utilize support from a tutor, if available, while it can still learn when no support is provided. Additionally, the framework has been designed to learn in a continuous and open-ended manner so that no explicit training phase is required. The proposed framework is evaluated through two different grounding scenarios and its unsupervised grounding component is compared to a state-of-the-art unsupervised Bayesian grounding framework, while the benefit of combining both paradigms is evaluated through the analysis of different feedback rates. The obtained results show that the employed unsupervised grounding mechanism outperforms the baseline in terms of accuracy, transparency, and deployability and that combining both paradigms increases both the sample-efficiency as well as the accuracy of purely unsupervised grounding, while it ensures that the framework is still able to learn the correct mappings, when no supervision is available.
Students’ Perceptions of Social Networking Sites for English Language Learning: A Study in an Indonesian Higher Education Context

This study explores the use of Social Networking Sites (SNS) as a performance-enhancing method of teaching and learning of English in the classroom via cell phones and mobile devices, as well as its effect on students' motivation to learn. A survey was disseminated to 176 students. The analysis showcases that Instagram is the most useful learning tool that students enjoy, and its use on mobile devices has the potential to become a significant teaching tool in the classroom. Because the most popular social networking sites, such as Instagram, are predominantly accessible via mobile devices, this is the case. Given that students currently utilize social networking sites to boost their learning informally, their acknowledgment of social networking sites as learning tools demonstrates their understanding of the learning potential given by technology.
Sensor Data Protection in Cyber-Physical Systems

Cyber-Physical Systems (CPS) have a physical part that can interact with sensors and actuators.The data that is read from sensors and the one generated to drive actuators is crucial for the correct operation of this class of devices.Most implementations trust the data being read from sensors and the outputted data to actuators.Real-time validation of the input and output of data for any system is crucial for the safety of its operation.This paper proposes an architecture for handling this issue through smart data guards detached from sensors and controllers and acting solely on the data.This mitigates potential issues of malfunctioning sensors and intentional sensor and controller attacks.The data guards understand the expected data, can detect anomalies and can correct them in real-time.This approach adds more guarantees for fault-tolerant behavior in the presence of attacks and sensor failures.
Denotational Model and Implementation of Scalable Virtual Machine in CPDev

Denotational semantic model and its implementation in C/C++ are presented for a virtual machine executing programs written in the CPDev development environment according to IEC 61131 standard.Programs written in IEC ST language are compiled to control-oriented intermediate language designed specifically for the machine.Architecture of the machine and its operation are represented by formal semantic model which assigns abstract algebraic objects to denote machine behaviour.Execution of intermediate language instructions is described in details by denotational semantic equations followed strictly by C/C++ implementations to assure reliability of the machine.
Encoder-Decoder Neural Network with Attention Mechanism for Types Detection in Linked Data

With the emergence of use of Linked Data in different application domains, several problems have arisen, such as data incompleteness.Type detection for entities in RDF data is one of the most important tasks in dealing with the incompleteness of Linked Data.In this paper, we propose an approach based on Deep Learning techniques, using an encoderdecoder model with attention mechanism, embedding layer to extract the features of each subject from the RDF triples and the GRU cells to address the problem of vanishing.We use the DBpedia dataset for the training and test phases.Initial test results showed the effectiveness of our model.
Rule-based approximation of black-box classifiers for tabular data to generate global and local explanations

The need to understand the decision bases of artificial intelligence methods is becoming widespread.One method to obtain explanations of machine learning models and their decisions is the approximation of a complex model treated as a black box by an interpretable rule-based model.Such an approach allows detailed and understandable explanations to be generated from the elementary conditions contained in the rule premises.However, there is a lack of research on the evaluation of such an approximation and the influence of the parameters of the rule-based approximator.In this work, a rulebased approximation of complex classifier for tabular data is evaluated.Moreover, it was investigated how selected measures of rule quality affect the approximation.The obtained results show what quality of approximation can be expected and indicate which measure of rule quality is worth using in such application.
Modeling and Simulation of Hourly Irradiance for Solar Applications in Chad: Case of the City of Abeche

In this paper, the general objective is to model and simulate the hourly irradiance followed by an assessment of the solar potential in the city of Abeche located in the climatic region of the Republic of Chad. Unlike previous work carried out in the study area on solar irradiance, two hourly solar irradiance estimation models (Capderou and Liu&amp;Jordan) were simulated at varying tilt angles. An analysis of the quality of their estimation is carried out through statistical methods (MBD, RMSD, MAD) of error calculation. The results show that the first model is well adapted on a horizontal surface to estimate the solar radiation and also during the dry season when the surface is inclined. On the other hand, the second model is preferred during the rainy season when the surface is inclined. The solar energy over the entire area of Abeche is estimated at 427 GWh on a horizontal surface in January and 504 GWh for the month of August on a tilted surface. This can be exploited by all solar technologies or applications.
AREL – Augmented Reality–based enriched learning experience

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;In the present era, teaching can occur either on a chalkboard or on a projected powerpoint presentation on the wall. Traditional teaching methods such as blackboards and powerpoint presentations are being phased out in favor of enriched learning experiences provided by emerging edtech. With the closure of schools due to COVID-19, the demand for online educational platforms has also increased. Furthermore, some of the recent trends in edtech include personalized learning, gamification and immersive learning with eXtended Reality (XR) technologies. Due to its immersive experience, XR is a pioneering technology in education, with multiple benefits including greater motivation, a positive attitude toward learning, concrete learning of abstract concepts, and so on. Existing Augmented Reality (AR) based education applications often rely on unimodal input such as marker-based trigger to launch the educational content. Hence, this work proposes a multi-modal interface to enable the content delivery through marker and speech recognition-based content delivery. Additionally, the proposed work is designed as mobile based AR platform with the regional language support to increase the ubiquitous accessibility of the AR content. Thus, the proposed mobile AR based enriched learning (AREL) platform provides a multi-modal mobile based educational AR platform for primary students. Based on the feedback received after the usage, it is observed that AREL improves the learning experience of the students.&lt;/span&gt;&lt;/p&gt;
Scientific Writing Guidance for Elementary School Teacher Candidate through Synchronous and Asynchronous

The purpose of this study is to understand the process of guidance on writing scientific articles through synchronous and asynchronous models for students as the outcome of field studies on children with special needs. The research was conducted through a pre-experimental design with one-shot case studies. One hundred fourteen elementary school teacher candidates at a state campus in Purwakarta Regency were involved in this study by filling out a questionnaire. Data dianalisis melalui analisis data survei. Guidance with a synchronous model utilizes the Zoom application, while asynchronous uses tutorials via YouTube and Google Classroom. Synchronous guidance allows students to have virtual discussions and receive direct explanations from lecturers, while asynchronous guidance makes it easier for students to review topics that have not been understood flexibly, cost-effectively, and comfortably. Guidance on writing scientific articles through synchronous and asynchronous models is considered effective in increasing the ability and confidence of elementary school teachers candidates in writing scientific articles. Combining synchronous and asynchronous models can create varied scientific article writing guidance and minimize the shortcomings of both. Experts are advised to explore various methods and digital platforms to provide guidance and access various sources to learn good scientific writing
A Survey on Machine Reading Comprehension Systems

Machine reading comprehension is a challenging task and hot topic in natural language processing. Its goal is to develop systems to answer the questions regarding a given context. In this paper, we present a comprehensive survey on different aspects of machine reading comprehension systems, including their approaches, structures, input/outputs, and research novelties. We illustrate the recent trends in this field based on 241 reviewed papers from 2016 to 2020. Our investigations demonstrate that the focus of research has changed in recent years from answer extraction to answer generation, from single to multi-document reading comprehension, and from learning from scratch to using pre-trained embeddings. We also discuss the popular datasets and the evaluation metrics in this field. The paper ends with investigating the most cited papers and their contributions.
With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual Representations (NNCLR), samples the nearest neighbors from the dataset in the latent space, and treats them as positives. This provides more semantic variations than pre-defined transformations. We find that using the nearest-neighbor as positive in contrastive losses improves performance significantly on ImageNet classification, from 71.7% to 75.6%, outperforming previous state-of-the-art methods. On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our method outperforms state-of-the-art methods (including supervised learning with ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate empirically that our method is less reliant on complex data augmentations. We see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train using only random crops.
Training β-VAE by Aggregating a Learned Gaussian Posterior with a Decoupled Decoder

The reconstruction loss and the Kullback-Leibler divergence (KLD) loss in a variational autoencoder (VAE) often play antagonistic roles, and tuning the weight of the KLD loss in $\beta$-VAE to achieve a balance between the two losses is a tricky and dataset-specific task. As a result, current practices in VAE training often result in a trade-off between the reconstruction fidelity and the continuity$/$disentanglement of the latent space, if the weight $\beta$ is not carefully tuned. In this paper, we present intuitions and a careful analysis of the antagonistic mechanism of the two losses, and propose, based on the insights, a simple yet effective two-stage method for training a VAE. Specifically, the method aggregates a learned Gaussian posterior $z \sim q_{\theta} (z|x)$ with a decoder decoupled from the KLD loss, which is trained to learn a new conditional distribution $p_{\phi} (x|z)$ of the input data $x$. Experimentally, we show that the aggregated VAE maximally satisfies the Gaussian assumption about the latent space, while still achieves a reconstruction error comparable to when the latent space is only loosely regularized by $\mathcal{N}(\mathbf{0},I)$. The proposed approach does not require hyperparameter (i.e., the KLD weight $\beta$) tuning given a specific dataset as required in common VAE training practices. We evaluate the method using a medical dataset intended for 3D skull reconstruction and shape completion, and the results indicate promising generative capabilities of the VAE trained using the proposed method. Besides, through guided manipulation of the latent variables, we establish a connection between existing autoencoder (AE)-based approaches and generative approaches, such as VAE, for the shape completion problem. Codes and pre-trained weights are available at https://github.com/Jianningli/skullVAE
YouTube: Spam Comments Filtration Using Hybrid Ensemble Machine Learning Models

In today’s era most of the YouTuber’s are facing the major problem with electronic spam as troublesome Internet phenomenon. This work proposes a methodology for the detection of spam comments on the video-sharing website - YouTube. YouTube is running its own spam blocking system but continues to fail to block them properly. In this work, we examined several top- performance classification techniques for spam comment screening and proposed a novel methodology. In this work, we have analyzed such comments by applying conventional machine learning algorithms such as Naive Bayes, Random Forest, Support Vector Machine, Logistic regression, Decision Tree and will construct another model utilizing ensemble and hybrid approach. This paper proposed the YouTube spam comments detection framework, examined, and validated by using data collected from the YouTube using Naïve Bayes multinomial, Gradient Boosting, Random Forest and tested in Weka and Python data mining tools.
On a Benefit of Mask Language Modeling: Robustness to Simplicity Bias

Despite the success of pretrained masked language models (MLM), why MLM pretraining is useful is still a qeustion not fully answered. In this work we theoretically and empirically show that MLM pretraining makes models robust to lexicon-level spurious features, partly answer the question. We theoretically show that, when we can model the distribution of a spurious feature $\Pi$ conditioned on the context, then (1) $\Pi$ is at least as informative as the spurious feature, and (2) learning from $\Pi$ is at least as simple as learning from the spurious feature. Therefore, MLM pretraining rescues the model from the simplicity bias caused by the spurious feature. We also explore the efficacy of MLM pretraing in causal settings. Finally we close the gap between our theories and the real world practices by conducting experiments on the hate speech detection and the name entity recognition tasks.
Novel Deep Learning Approach to Derive Cytokeratin Expression and Epithelium Segmentation from DAPI

Generative Adversarial Networks (GANs) are state of the art for image synthesis. Here, we present dapi2ck, a novel GAN-based approach to synthesize cytokeratin (CK) staining from immunofluorescent (IF) DAPI staining of nuclei in non-small cell lung cancer (NSCLC) images. We use the synthetic CK to segment epithelial regions, which, compared to expert annotations, yield equally good results as segmentation on stained CK. Considering the limited number of markers in a multiplexed IF (mIF) panel, our approach allows to replace CK by another marker addressing the complexity of the tumor micro-environment (TME) to facilitate patient selection for immunotherapies. In contrast to stained CK, dapi2ck does not suffer from issues like unspecific CK staining or loss of tumoral CK expression.
Application of Flashcard Media to improve Children's Speaking Ability

The problem in this study is that there are still some children who are not able to speak fluently.This study aims to determine whether Flashcard media can improve speaking skills of early childhood.This study uses a quantitative approach with a preexperimental research design type one group pretest-posttest design.In this study, the population was early childhood in TK Air Putih Sitakuak Sungai Tarab, Tanah Datar Regency with 10 children.The research sample was child b, totaling 7 people.The average pretest result was 11.86, after the pretest was carried out then treatment was given in the form of using Flashcard media, during the treatment there was an increase which was seen from the posttest results where the average was 16.57.If it is seen in the table, the value of tt at 5% level, the critical value is 2.44, so it can be seen that t0 is greater than tt, i.e. 3.867 > 2.44 because t0 is greater than tt, so the proposed null hypothesis (h0) is rejected and the proposed hypothesis is rejected.alternative is accepted (ha) This means that the application of Flashcard media can improve children's speaking skills at a significant level of 5%.So, it can be concluded that the application of Flashcard media can improve the speaking ability of early childhood.
The Turking Test: Can Language Models Understand Instructions?

Supervised machine learning provides the learner with a set of input-output examples of the target task. Humans, however, can also learn to perform new tasks from instructions in natural language. Can machines learn to understand instructions as well? We present the Turking Test, which examines a model's ability to follow natural language instructions of varying complexity. These range from simple tasks, like retrieving the nth word of a sentence, to ones that require creativity, such as generating examples for SNLI and SQuAD in place of human intelligence workers ("turkers"). Despite our lenient evaluation methodology, we observe that a large pretrained language model performs poorly across all tasks. Analyzing the model's error patterns reveals that the model tends to ignore explicit instructions and often generates outputs that cannot be construed as an attempt to solve the task. While it is not yet clear whether instruction understanding can be captured by traditional language models, the sheer expressivity of instruction understanding makes it an appealing alternative to the rising few-shot inference paradigm.