Reviewerly/exHarmony huggingface dataset:
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification

In a leading e-commerce business, we receive hundreds of millions of customer feedback from different text communication channels such as product reviews. The feedback can contain rich information regarding customers’ dissatisfaction in the quality of goods and services. To harness such information to better serve customers, in this paper, we created a machine learning approach to automatically identify product issues and uncover root causes from the customer feedback text. We identify issues at two levels: coarse grained (L-Coarse) and fine grained (L-Granular). We formulate this multi-level product issue identification problem as a seq2seq language generation problem. Specifically, we utilize transformer-based seq2seq models due to their versatility and strong transfer-learning capability. We demonstrate that our approach is label efficient and outperforms the traditional approach such as multi-class multi-label classification formulation. Based on human evaluation, our fine-tuned model achieves 82.1% and 95.4% human-level performance for L-Coarse and L-Granular issue identification, respectively. Furthermore, our experiments illustrate that the model can generalize to identify unseen L-Granular issues.
Teacher’s Digital Competency Evaluation Toolkit Study

The digital transformation of education is impossible without teachers mastering a wide range of new digital competencies. The first step in this direction is to evaluate the state of the teacher&amp;apos;s digital competencies, for which appropriate tools are needed. The analysis of the structure and content of the teacher&amp;apos;s digital competencies in various publications showed the lack of a unified approach. The authors have chosen an approach that consistently combines the requirements for digital competencies of a modern teacher, the position of Yandex researchers, and the EU digital competencies model. An analysis of the existing experience in assessing the teacher&amp;apos;s digital competencies made it possible to classify it into three groups according to the level of evidence of the degree of formation. As a result, the variant of case-meters was chosen. The authors propose a toolkit in the form of a test (case-measuring instrument), which includes tasks for assessing four competency areas: digital tools and their use, digital communication and methods of its organization, digital educational environment (for the implementation of a holistic educational process), digital ethics and security. Each of the competence areas is specified through a set of skills. Tasks are practical cases describing real educational and professional situations. The actions of the teacher in situations can be more or less effective in terms of possession of digital competencies, but each allows the teacher to resolve the situation.
Distributed Consensus for Permissionless Environment

Day-by-day, both data and network size are growing at a rapid rate. It is essential to keep private data secure and also prevent malicious activities. In a permissionless blockchain, nodes do not take permission for participation. One can directly mine a block by performing an open task. Security can be a significant issue here. Also, there is no third-party involvement in blockchain, so keeping trust among peers is an essential feature. The distributed public ledger stores history of old transactions to maintain trust between peers. To prevent malicious activities, consensus algorithms are used, which are defined as a complex task that a miner must perform to mine new blocks into the blockchain. In this chapter, various consensus mechanisms are mentioned with merits and demerits. With high computation power and digital currencies, nodes can quickly get into the blockchain and perform malicious activities. For that, various consensus algorithms are used like Proof of Work (PoW), Proof of Stake (PoS), Proof of Burn (PoB), Proof of Capacity (PoC), etc. Every consensus is developed to solve issues of previously developed consensus and provide more efficiency concerning resource allocation, scalability, security against attacks, power consumption, etc. Bitcoin is one of the use cases of blockchain, which is developed upon the PoW consensus method. Various companies have developed cryptocurrencies that are based on consensus algorithms. Consensus can be implemented on smart contracts to govern specific rules in the blockchain. While working with extensive transactions and a large chain of blocks, scalability, efficiency, and malicious attacks are significant issues. We have done a comparative analysis of all the consensus algorithms based on such issues.
Evaluating Content Features and Classification Methods for Helpfulness Prediction of Online Reviews: Establishing a Benchmark for Portuguese

Over the years, the review helpfulness prediction task has been the subject of several works, but remains being a challenging issue in Natural Language Processing, as results vary a lot depending on the domain, on the adopted features and on the chosen classification strategy.This paper attempts to evaluate the impact of content features and classification methods for two different domains.In particular, we run our experiments for a low resource language -Portuguese -, trying to establish a benchmark for this language.We show that simple features and classical classification methods are powerful for the task of helpfulness prediction, but are largely outperformed by a convolutional neural network-based solution.
Data Classification and Incremental Clustering Using Unsupervised Learning

Data modelling, which is based on mathematics, statistics, and numerical analysis, is used to look at clustering. Clusters in machine learning allude to hidden patterns; unsupervised learning is used to find clusters, and the resulting system is a data concept. As a result, clustering is the unsupervised discovery of a hidden data concept. The computing needs of clustering analysis are increased because data mining deals with massive databases. As a result of these challenges, data mining clustering algorithms that are both powerful and widely applicable have emerged. Clustering is also known as data segmentation in some applications because it splits large datasets into categories based on their similarities. Outliers (values that are “far away” from any cluster) can be more interesting than typical examples; hence outlier detection can be done using clustering. Outlier detection applications include the identification of credit card fraud and monitoring unlawful activities in Internet commerce. As a result, multiple runs with alternative initial cluster center placements must be scheduled to identify near-optimal solutions using the K-means method. A global K-means algorithm is used to solve this problem, which is a deterministic global optimization approach that uses the K-means algorithm as a local search strategy and does not require any initial parameter values. Instead of selecting initial values for all cluster centers at random, as most global clustering algorithms do, the proposed technique operates in stages, preferably adding one new cluster center at a time.
Neighborhood Network for Aspect-Based Sentiment Analysis

Different aspects of a sentence may contain different sentiments, and sentiment descriptors for a given aspect exist in different places in the sentence, making it difficult to determine its sentiment polarity. Aiming at the above problems, a neighborhood network (Nenet) for aspect-based sentiment analysis is proposed. Firstly, the context information of the text is encoded, and the neighborhood information of the target aspect at the grammar level is extracted by using a graph convolutional neural network. At the same time, the convolutional neural network is used to extract the neighborhood information at the physical level, and the two extracted features are combined to improve the text expression ability. Finally, an attention mechanism is used to express the key information in sentences for judging sentiment polarity, and the final text representation is input to the sentiment analysis layer to predict sentiment polarity. Experiments are carried out on three standard datasets, and the experimental results show that the neighborhood network outperforms other baseline models in both index accuracy and F1 value.
A Case Study of Marketing at Tesla Based on the 4V Theory

With the improvement of people's living standards and the emphasis on environmental protection, more and more consumers are choosing to buy new energy electric vehicles as a means of transportation.Tesla, as a typical representative company of the new era of electric vehicles, has attracted much attention in the field of electric vehicles for its every move.As a leader in the electric vehicle industry, it is worthwhile to explore how Tesla can achieve successful marketing and make other small and medium-sized enterprises compete to imitate it.Based on the 4V marketing theory of Tesla case study, this paper analyzes the effectiveness of Tesla's existing marketing strategy from the consumers' point of view by studying consumers' evaluation of Tesla's use and willingness to buy through questionnaire surveys, and at the same time, the success of Tesla's marketing strategy is thoroughly discussed and studied from four perspectives of the 4V theory of differentiation, functionality, added value and social resonance.Tesla is ahead of other domestic EV companies in terms of product diversification, functionality and added value, and although it can resonate with society in terms of environmental protection, there is still room for improvement in terms of aftersales service and pricing strategy.Finally, this paper puts forward its optimization measures in terms of maintaining the brand image and stabilizing the price, which has important practical significance for the development of the new energy vehicle industry.
The Use of Block Chain in the Informal Distributed Manufacturing Industry in Kenya

In the last decade, research and development around distributed ledger technology (DLT) has grown exponentially.The financial services industry has been revolutionized by the explosion of cryptocurrencies like Bitcoin and Ethereum.Researchers have taken the principles used in these cryptocurrencies and are using them to develop other DLTs in various fields.This study explores how blockchain can be used to provide traceability, visibility, and transparency in the Kenyan informal distributed manufacturing industry.SOKO, an aggregator of artisans spread all over Nairobi, was the case study used.Purposive and convenience-based were the sampling methods used.48 SOKO supply chain employees and active artisans were the sample population.Interviews and observations were data collection methods used.Content analysis, a qualitative data analysis method, was used to capture emerging and predetermined themes.Google sheets and Dovetail were the tools used for this study.This paper finds that the use of the immutability and proof of origin features of blockchain greatly enhances traceability within a supply chain.It is imperative that the granular information collected should be intuitive and accessible to all parties to enhance visibility.Amplified traceability and visibility greatly improved transparency and accountability within the SOKO ecosystem.A pivotal recommendation for future research is the usage of unit-based tagging technologies e.g.barcodes, QR codes, or RFID.The combined use of such technologies and blockchain would achieve the highest level of traceability, especially when working with diverse producers who produce similar products.
Grad$$_{2}$$VAE: An Explainable Variational Autoencoder Model Based on Online Attentions Preserving Curvatures of Representations

Unsupervised learning (UL) is a class of machine learning (ML) that learns data, reduces dimensionality, and visualizes decisions without labels. Among UL models, a variational autoencoder (VAE) is considered a UL model that is regulated by variational inference to approximate the posterior distribution of large datasets. In this paper, we propose a novel explainable artificial intelligence (XAI) method to visually explain the VAE behavior based on the second-order derivative of the latent space concerning the encoding layers, which reflects the amount of acceleration required from encoding to decoding space. Our model is termed as Grad $$_{2}$$ VAE and it is able to capture the local curvatures of the representations to build online attention that visually explains the model's behavior. Besides the VAE explanation, we employ our method for anomaly detection, where our model outperforms the recent UL deep models when generalizing it for large-scale anomaly data.
Measurement and Visualization of Drowsiness for Nonverbal Communication using Avatars

Several educational institutions have opted to implement distance learning such as online classes because of COVID-19. However, students turn off their camera for privacy reasons when they are online. Non-verbal communication is difficult when the camera is turned off; however, it can be conducted if avatars are used substitutes. It is crucial to combine multiple nonverbal languages for nonverbal communication. In this study, we attempted to communicate drowsiness using just the eye closure rate, and we asked the subjects to observe its expression on the avatars to verify whether it was communicated. The results indicated that drowsiness could be transmitted using single nonverbal information. Multiple nonverbal information should be combined for more accurate transmission. Furthermore, it must be verified in an online situation.
The Evaluation of Child Capacity by Machine Learning According to the Region the Evaluation of Child Capacity by Machine Learning According to the Region

Mongolian territory is 1.5 million square kilometers, there are 1.439 kindergartens that include 263.333 children.Mongolia has 5 regional zones.The research was made on selected provinces such as Sukhbaatar district from Ulaanbaatar region, Huvsgul aimag from Khangai region, Umnu-gobi from Central region, Dornod aimag from Eastern region and Bayan-Ulgii from Western region.Total 450 preschool children at age of 3 -5 years old (30 children at every 3, 4 and 5 years old) were selected randomly, they performed 5 tasks of Math according to the curriculum.The classification of age, sex and region was made under cluster analyses of children's mathematical ability using research method.The purpose of the research is classification of province zone, determination of inequality and difference between rural and urban areas.It is made support for developer's policy and decision makers of education under the base of financing and sharing kindergarten budget, specialization, retraining of teachers and children, developing, elaboration and planning curriculum.
Distilling Salient Reviews with Zero Labels

Many people read online reviews to learn about real-world entities of their interest.However, majority of reviews only describes general experiences and opinions of the customers, and may not reveal facts that are specific to the entity being reviewed.In this work, we focus on a novel task of mining from a review corpus sentences that are unique for each entity.We refer to this task as Salient Fact Extraction.Salient facts are extremely scarce due to their very nature.Consequently, collecting labeled examples for training supervised models is tedious and cost-prohibitive.To alleviate this scarcity problem, we develop an unsupervised method ZL-Distiller, which leverages contextual language representations of the reviews and their distributional patterns to identify salient sentences about entities.Our experiments on multiple domains (hotels, products, and restaurants) show that ZL-Distiller achieves state-of-theart performance and further boosts the performance of other supervised/unsupervised algorithms for the task.Furthermore, we show that salient sentences mined by ZL-Distiller provide unique and detailed information about entities, which benefit downstream NLP applications including question answering and summarization.
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification

Many recent deep learning-based solutions have adopted the attention mechanism in various tasks in the field of NLP.However, the inherent characteristics of deep learning models and the flexibility of the attention mechanism increase the models' complexity, thus leading to challenges in model explainability.To address this challenge, we propose a novel practical framework by utilizing a two-tier attention architecture to decouple the complexity of explanation and the decision-making process.We apply it in the context of a news article classification task.The experiments on two largescaled news corpora demonstrate that the proposed model can achieve competitive performance with many state-of-the-art alternatives and illustrate its appropriateness from an explainability perspective.We release the source code here 1 .
Narration as a Technique to Improve Process Model Comprehension: Tell Me What I Cannot See

Conceptual models play a vital role in the engineering of information systems. A variety of stakeholders rely on their use, but they often find it challenging to make sense of such models. This is particularly known to be the case for process models, which capture complex temporal behavior. In practice, professionals often extend process models with textual descriptions to make them easier to understand, but it is not known whether this creates an even higher cognitive burden. In this study, we adopt the dual coding theory and the cognitive theory of multimedia learning, which suggest that people experience a better learning process when materials are presented via two different sensory channels (i.e., auditory and visual). We used this theory to set up and conduct an experiment with 42 participants that involve models of two real-life processes. We also implemented an online environment, which presents additional information on process model elements through the auditory channel in the form of narration. Our findings support that the use of narration may have a positive impact on process model comprehension, although it seems to depend on the kind of model elements that are explained. We discuss the implications of these findings and suggest further directions for research into process model comprehension.
Multimodal correlations-based data clustering

&lt;p style='text-indent:20px;'&gt;This work proposes a novel technique for clustering multimodal data according to their information content. Statistical correlations present in data that contain similar information are exploited to perform the clustering task. Specifically, multiset canonical correlation analysis is equipped with norm-one regularization mechanisms to identify clusters within different types of data that share the same information content. A pertinent minimization formulation is put forth, while block coordinate descent is employed to derive a batch clustering algorithm which achieves better clustering performance than existing alternatives. Relying on subgradient descent, an online clustering approach is derived which substantially lowers computational complexity compared to the batch approach, while not compromising significantly the clustering performance. It is established that for an increasing number of data the novel regularized multiset framework is able to correctly cluster the multimodal data entries. Further, it is proved that the online clustering scheme converges with probability one to a stationary point of the ensemble regularized multiset correlations cost having the potential to recover the correct clusters. Extensive numerical tests demonstrate that the novel clustering scheme outperforms existing alternatives, while the online scheme achieves substantial computational savings.&lt;/p&gt;
An Improved Crowd Counting Method Based on YOLOv3

This paper proposes a method of crowd counting. We use ResNeSt-50 as the backbone network of YOLOv3. After the backbone network, we add SPP (Spatial Pyramid Potential) and PANet (Path Aggregation Network) to enhance the receptive field of convolutional neural network and improve the accuracy of stream of people or crowd counting in real application scenarios. In the application scenario of high-density crowd counting, an improved VGG network is used to design a deep network to capture high-level semantic information. At the same time, a shallow network is constructed to detect the head blob of people far away from the camera. The deep network and the shallow network are combined to detect high-density crowd. Finally, through the effective fusion of the above two network models, the accuracy and applicability of the algorithm are further improved. It can improve the detection accuracy in the case of small number of people and occlusion, and effectively reduce the estimation error in the scene with high density crowd.
Frequent occurring forms of internet frauds

The increase in the volume of electronic transactions has led to the need to strengthen data protection against criminal activities in the online environment. The author emphasizes that the perpetrators' goal is of a lucrative nature, might be gotten by disrupting the information system too, as well as offenders can be both individuals and legal persons. The profile of a cybercrime criminal, in general, implies excellent knowledge of digital proceedings, experience of acting in the online environment and active performing of information technology skills. The Internet has become an integral part of today's way of life, which is why the scope of criminal activities in this worldwide network for communication and data exchange is constantly expanding. The author deems this as the reason for the growing need to strengthen information security, which he determines as the practical implementing of measures to protect data from external influences in the online environment. This paper presents a statistical analysis of the Internet frauds' frequency from 2016 to 2020, in order to show the real intensity of this modern kind of criminal threat.
ACCirO: A System for Analyzing and Digitizing Images of Charts with Circular Objects

Automated interpretation of digital images of charts in documents and the internet helps to improve the accessibility of visual representation of data. One of the approaches for automation involves extraction of graphical objects in the charts, e.g., pie segments, scatter points, etc., along with its semantics encoded in the textual content of the chart. The scatter plots and pie charts are amongst the widely used infographics for data analysis, and commonly have circle objects. Here, we propose a chart interpretation system, ACCirO (Analyzer of Charts with Circular Objects), that exploits the color and geometry of circular objects in scatter plots, its variants, and pie charts to extract the data from its images. ACCirO uses deep learning-based chart-type classification and OCR for text recognition to add semantics, and templatized sentence generation from the extracted data table for chart summarization. We show that image processing and deep learning approaches in ACCirO have improved the accuracy compared to the state-of-the-art.