Reviewerly/exHarmony huggingface dataset:
Correctness-preserving Compression of Datasets and Neural Network Models

Neural networks deployed on edge devices must be efficient both in terms of their model size and the amount of data movement they cause when classifying inputs. These efficiencies are typically achieved through model compression: pruning a fully trained network model by zeroing out the weights. Given the overall challenge of neural network correctness, we argue that focusing on correctness preservation may allow the community to make measurable progress. We present a state-of-the-art model compression framework called Condensa around which we have launched correctness preservation studies. After presenting Condensa, we describe our initial efforts at understanding the effect of model compression in semantic terms, going beyond the top n% accuracy that Condensa is currently based on. We also take up the relatively unexplored direction of data compression that may help reduce data movement. We report preliminary results of learning from decompressed data to understand the effects of compression artifacts. Learning without decompressing input data also holds promise in terms of boosting efficiency, and we also report preliminary results in this regard. Our experiments centered around a state-of-the-art model compression framework called Condensa and two data compression algorithms, namely JPEG and ZFP, demonstrate the potential for employing model-and dataset compression without adversely affecting correctness.
Bootstrapping Chatbot Interfaces to Databases

In this paper, we present an automated technique for creating a chatbot interface to databases. To the best of our knowledge, our technique is the first one for bootstrapping chatbots for question answering on relational databases. Our method leverages the natural language classifiers present in industrial chatbot platforms for natural language to structure query translation. We use our framework to create usable chatbots starting from the databases in a short time. We instantiated several chatbots in different domains and demonstrate the usefulness of it.
A Classification Method based on Multi-decision Framework Using Regression Model

Most classification approaches utilize a single classifier and result in the classification performance depends on the classifier. In this paper, we propose a classification method based on a multi-decision framework instead of a single classification, which performs like consensus decision-making. The multi-decision framework consists of several decision operators in two or more stages. The core of the operator is a regression model, such as Support Vector Regression, Gaussian Process Regression, and random forest. Combining threshold discriminators and a voting system, the final operator fuses the decision results generated by the previous ones. The proposed scheme compares to the existing approaches, and the experiment results demonstrate our method achieves better accuracies than the single classifier does.
Long-Text Sentiment Analysis Based on Semantic Graph

The neural network of bilateral attention, especially Bidirectional Encoder Representations from Transformers (BERT), has achieved good results since its appearance, and has obtained high scores in various tasks of Natural Language Processing (NLP). However, Transformer completely adopts Attention mechanism and discards CNN and RNN, which also brings some problems. When analyzing long-text corpus, Transformer is usually limited by text length and cannot achieve good results. Due to the shortcoming, this paper proposed a long-text analyzing method called Semantic Graph Bidirectional Encoder Representations from Transformers (SG-BERT). SG-BERT obtains word features firstly, and then obtains sentence features, in corpus. After that, a semantic graph containing whole effective emotional will be generated, which can also be regarded as emotional labeling of long-text. Finally, semantic graph will be put into BERT to analysis sentiment. We experiment SG-BERT on Bag of Words Meets Bags of Popcorn (BoWMBoP) and online shopping 10 cats for sentiment analysis. In BoWMBoP and online shopping 10 cats 0~1000 length corpus, its ACC reached 96.3 and 95.9, respectively.
Multi-shuffled Convolutional Blocks for Low-complex Modulation Recognition

This paper proposes a convolutional neural network architecture for automatic modulation recognition (AMR) in resource-constrained devices, namely MBNet. The architecture comprises a series of connected blocks called shuffled blocks with residual connections, where each block contains grouped convolutional layers and one shuffling module. In the shuffled blocks, grouped convolutions parallelize the convolution process for the low-complex modulation recognition. Additionally, to overcome problems that arise from inefficient group interactions in grouped convolutional layers, a channel shuffling module is deployed to improve the communication among filter groups. Furthermore, MBNet deploys a residual connection from an input of each shuffled block to its corresponding output through an element-wise additional layer to learn spatiotemporal features from in-phase and quadrature signals repetitively to enhance the recognition accuracy. Moreover, to verify the performance of MBNet, experiments were conducted on the RadioML2018.01A dataset. From the experimental results, MBNet achieved high recognition accuracy and trainable parameter utilization efficiency over the state-of-the-art AMR approaches.
Catching a Robot Intruder with Limited Information

This paper provides solutions and insight into a new set of problems of catching a mobile robot intruder using limited information about the intruder's location. The information about the intruder's location, which is termed a snapshot, is only available upon request. We formulate the problem of tracking and catching an intruder with limited number of snapshots, which we termed the Moving Target Search with Snapshots (MTSWS). In the MTSWS problem a mobile guard A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> is chasing a mobile intruder At in R2. Here, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> knows the location of At either from a requested snapshot or if A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> is sufficiently close to A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> . The objective is to reduce the number of required snapshots and/or to reduce the distance travelled by A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> . We compute the number of snapshots that are necessary and sufficient to catch an intruder in the worst case. We also provide algorithmic solutions under a number of assumptions on the intruder's behaviour (e.g., intruder's random motion where At is oblivious to the actions taken by A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> . Last we provide solution to the problem when A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> is allowed to use k snapshots and determine the locations and the time that A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</sub> should take the snapshot.
Data and visual displays in the <i>Journal of Ecology</i> 1996–2016

Scholars in scientific disciplines face unique challenges in the creation of visualizations, especially in publications that require insights derived from analyses to be visually displayed. The literature on visualizations describes different techniques and best practices for the creation of graphs. However, these techniques have not been used to evaluate the impact of visualizations in academic publications. In the field of ecology, as in other scientific fields, graphs are an essential part of journal articles. Little is known about the connections between the kind of data presented and domain in which the researchers conducted their study that together produces the visual graphics. This study focused on articles published in the Journal of Ecology between 1996 and 2016 to explore possible connections between data type, domain, and visualization type. Specifically, this study asked three questions: How many of the graphics published between 1996 and 2016 follow a particular set of recommendations for best practices? What can Pearson correlations reveal about the relationships between type of data, domain of study, and visual displays? Can the findings be examined through an inter-reliability test lens? Out of the 20,080 visualizations assessed, 54% included unnecessary graphical elements in the early part of the study (1996–2010). The most common type of data was univariate (35%) and it was often displayed using line graphs. Twenty-one percent of the articles in the period studied could be categorized under the domain type “single species.” Pearson correlation analysis showed that data type and domain type was positively correlated ( r = 0.08; p ≤ 0.05). Cohen’s kappa for the reliability test was 0.86, suggesting good agreement between the two categories. This study provides evidence that data type and domain types are equally important in determining the type of visualizations found in scientific journals.
Swarm-Based Machine Learning Algorithm for Building Interpretable Classifiers

This paper aims to produce classifiers that are not only accurate but also interpretable to decision makers. The classifiers are represented in the form of risk scores, i.e. simple linear classifiers where coefficient vectors are sparse and bounded integer vectors which are then optimised by a novel and scalable discrete particle swarm optimisation algorithm. In contrast to past studies which usually use particle swarm optimisation as a pre-processing step, the proposed algorithm incorporates particle swarm optimisation into the classification process. A penalty-based fitness function and a local search heuristic based on symmetric uncertainty are developed to efficiently identify classifiers with high classification performance and a preferred model size or complexity. Experiments with 10 benchmark datasets show that the proposed swarm-based algorithm is a strong candidate to develop effective linear classifiers. Comparisons with other interpretable machine learning algorithms that produce rule-based and tree-based classifiers also demonstrate the competitiveness of the proposed algorithm. Further analyses also confirm the interpretability of the produced classifiers. Finally, the proposed algorithm shows excellent speed-up via parallelisation, which gives it a great advantage when coping with large scale problems.
2D Reprojection of Plenoptic 3D Voxel Data Using Gaussian Intensity Spreading

This paper presents an efficient way of 2D reprojection method of 3D voxel data which is generated from multiple 2D image sources. In order to minimize the image quality degradation generated by postprocessing such as hole filling, A 2D reprojection method using gaussian aggregation of voxel attribute intensity is proposed. Proposed method efficiently removes generation of holes while maintaining graceful intensity of projected 2D image planes, which can finally reconstruct original 2D source images with less quality degradation.
Communication Challenges in Agile Teams from The Communication Theory Prospective

People are the only constant component of any software project. Diversity of cultures, behavioral patterns, and life scripts leads to different understanding and meaning of interactions and information exchange among team members. As processes that follow Agile methodology approaches consider individuals and interaction as the highest priority, it was decided to analyze communication issues in teams who work by such processes. The work presents an overview of obstacles for effective communication based on literature review. The survey among 53 respondents was conducted to evaluate the most common communication challenges which Agile teams face in their work. These challenges are further mapped with communication theories. On the basis of the mapping, we developed recommendations for overcoming problems described in each of the theories. The results of the work could be used by people with managerial roles in Agile teams to learn more about the root causes of ineffective communication among team members. The solutions and recommendations could be used to overcome these issues.
Vietnamese Document Classification Using Graph Convolutional Network

Document classification is a classical and fundamental text mining problem for many applications. In such classifiers, text representation is an intermediate step, but it plays an important role in building the models. Recently, graph neural networks have been shown to be a potential method for text presentation since they not only have a rich relational structure but also preserve global word co-occurrence correlation. However, most of them have been proposed for English documents. In this paper, we present a model based on a graph convolutional network for Vietnamese document classification. We first present detailed steps in building the graph from Vietnamese documents and a two-layer GCN architecture for graph embedding. We then propose a method, named PMI filter, to improve the classification accuracy of the model. Furthermore, aspects of the proposed model are also investigated to provide a better understanding of the model behavior. The proposed work is evaluated on two large Vietnamese datasets. In experiments, the proposed model archives better results than its baseline and competitive performance compared to existing feature-selection based methods.
Risk Assessment of Sharing Cyber Threat Intelligence

Sharing Cyber Threat Intelligence (CTI) is advocated to get better defence against new sophisticated cyber-attacks. CTI may contain critical information about the victim infrastructure, existing vulnerabilities and business processes so sharing CTI may carry a risk. However, evaluating the risk of sharing CTI datasets is challenging due to the nature of the CTI context which is associated with the evolution of the threat landscape and new cyber attacks that are difficult to evaluate. In this paper, we present a quantitative risk model to assess the risk of sharing CTI datasets enabled by sharing with different entities in various situations. The model enables the identification of the threats and evaluation of the impacts of disclosing this information. We present two use cases that help to determine the risk level of sharing a CTI dataset and consequently the mitigation techniques to enable responsible sharing. Risk identification and evaluation have been validated using experts’ opinions.
Pharmaceutical Supply Chain Management Blockchain

This paper gives a glimpse and a precise summary of this emerging technology that is blockchain and how we are going to utilize it to form a tamper-proof system to remove fake products/drugs in pharmaceutical industries. Study of various reports shows that fake products entering the pharma supply chain are a serious concern. By the use of blockchain across the whole supply chain, many problems of the distributors and manufacturer and consumer will be solved that includes—(i) robust system, (ii) improved transparency, (iii) immutable, (iv) authentic, (v) reliable, and (vi) secure. Overall, here, we are showing a raw outlet on how a blockchain transaction over a supply chain management system will be far better as compared to the conventional one.
The Reverse Approach for Generating Maze With Unique Characteristics

Mazes that have unique features are considered. First, they have two or more passageways. Second, they have some special properties. These properties are based on psychological aspects of human, because of not a computer usually tries to pass maze, but a man. We offer our own list of properties. This leads to the fact that none of the existing algorithms can create mazes which will satisfy all requirements. So, the new solution was developed. Commonly algorithms generate mazes and then edit them to make new passes or delete unnecessary. The feature of our solution is that it is reversed: we build passes first, and then generate dead ends on the rest of the maze field. In order to generate passes the probabilistic approach is used. Besides, to improve uniqueness of the passageways we use various quality criteria.
Automatic Numerical Question Answering on Table using BERT-GNN

The table base numerical question-answering task requires a mechanism to understand the relation between table content and numbers (present in table and question). It also needs an efficient method to address complex reasoning on table context. Most of the existing approaches in the natural language processing technology address the context-based questions on the table but fail to address the numerical reasoning part. They are also built on a large search database, which makes it challenging to use them in multiple domains. These approaches use pre-trained models like BERT to perform context encoding of a complete table. Hence these models fail when a large table is provided as input, as full table encoding is a very resource and time-consuming task. In this paper, a framework is proposed to answer questions on the table with numerical reasoning. This framework uses a context-snapshot mechanism to filter irrelevant table rows before tokenizing the table content. The filtered context and tokenized question are converted into vector representation using a pre-trained BERT model. This proposed model finds the correlation between the tokenized context-snapshot and numbers in question using graph neural networks. Further, it uses a feed-forward neural network to perform the numerical operation to compute the answer. The model is trained and evaluated on WikiTableQuestions datasets, shows a promising result.
Detection and Analysis of Credit Card Application Fraud Using Machine Learning Algorithms

Abstract Fraud is a widespread problem in the financial industry with devastating effects. It is essential to prevent and reduce fraud effectively. Traditional approaches, such as expert system, suffers from the incapability to handle complex problems and tremendous amount of data, while the recent development of various machine learning techniques brings new solutions. With many research works focusing on tackle frauds of credit card transaction or insurance, only few mentioned the identity fraud of credit card application. This article presents a few machine learning models to detect such fraud. We firstly explore and clean up the data. Then 331 expert variables are created with professional consult and selected to 30 to reduce dimensionality of our data. Multiple models, such as logistic regression and decision trees, are built and fit on the training set. Finally, we found that the random forest model performs the best in terms of fraud detection rate, achieving 54% in out-of-time test. The obtained model can be applied in anti-fraud monitoring systems, or a similar model development process can be performed in related business areas to detect fraud and reduce the occurrence of such behaviors.
MR-UI: A Mobile Application Recommendation Based on User Interaction

With the rapid growth of mobile applications in major app stores, it is hard for users to choose their desired mobile applications. Therefore, it is necessary to provide a high-quality mobile application recommendation mechanism to meet the user's expectation. However, the existing recommendation methods are still not accurate enough in the embedding representations of users and mobile applications. Based on the neural graph collaborative filtering technique, we propose a mobile application recommendation method based on user interaction to solve this problem. First of all, by introducing the high-order connectivity between users and mobile applications, it exploits the embedding propagation to capture the collaborative filtering signals along the graph structure to further refine the embedding representations between mobile applications and users. Then, the user preferences for different mobile applications are predicted through inner product, and the recommendation task is completed. The real dataset of Kaggle is used to evaluate our approach and the experimental results show that our recommendation method can achieve the best results in different evaluation metrics. It can effectively improve the recommendation accuracy for mobile applications.
Realistic Style-Transfer Generative Adversarial Network With a Weight-Sharing Strategy

Style transfer aims to generate images by combining the style of one image and the content of another. Though valuable efforts have been made in generating high-quality style transferred images, the resulting images are far from the distribution of real images. This greatly limits the application of style transfer such as improving the diversity of training set in computer vision task. We find that the reason of style transfer failing to generate realistic images is lack of reference targets and neglect of preserving data distribution. To solve the problem, we propose a Style-transfer Generative Adversarial Network with a weight-sharing strategy to make the stylized images be resemblance to the real images. The experimental results demonstrate that the proposed method can generate images with satisfying style transfers and high visual quality. Moreover, we apply our stylized images to augment the training set of object detection task, and improve the average precision faithfully. We believe that our method can enhance the performance of style transfer on computer vision tasks.
Sustainable development of higher education in modern economy

The actions of each person underlie the principles of sustainable development of society. That is why education plays an essential role in the implementation of the ideas of sustainable development. In the minds and behaviour of people, their attitude to the environment, education acts as an instrument of positive transformations. Positive changes are taking place in society from the quality of education to the quality of life. Education acts as a regulator of various social processes: from the growth of culture and the rise of human needs to the development of production and the activation of global processes. The goal of education for sustainable development is the formation of the intellectual potential of society, which serves as the basis for ensuring a high quality of life and social equality. In the digital economy, the basis of the educational process should be innovation and the upbringing of a generation capable of generating innovation and learning throughout life. All this variety of competencies will form a personality that is resistant to modern digital reality. The transformation of higher education in the context of widespread digitalization requires an appropriate level of infrastructure, the level of digital competencies of teachers, and their readiness to change. After all, digital technologies are fundamentally changing the content and form of courses delivery. Higher education should work eproactively, shaping the digital competence of graduates, exceeding the existing range of knowledge, skills and abilities.