Reviewerly/exHarmony huggingface dataset:
Social Media Opinion Leader Identification Based on Sentiment Analysis

Growing number of enterprises nowadays are pursuing online marketing strategies, with their eyes focusing on the effectiveness of opinion leader value-creation on social media platform. Therefore, how to accurately identify opinion leaders on social media platforms is of great significance. The emotional value generated by communication between opinion leaders and fans will have a significant impact on the potential consumption behavior of fans. Most of the existing research on opinion leader identification is to establish models based on the existing indicator data of the platform, without taking the value of emotional communication into account. This paper proposes a social media opinion leader identification model based on online comment sentiment analysis. We first crawl online comments, then analyze the text data characteristics, establish emotional indicators of different attributes, calculate the sentiment value of the text data, and finally use artificial neural network technology to train to form an opinion leader recognition model. The experimental results show that emotional communication is a very important factor in opinion leader identification, and the proposed model can identify opinion leaders more accurately.
Word Embeddings Evaluation on Indonesian Translation of AI-Quran and Hadiths

Abstract Word vectors are an important part of machine learning. Word vectors are a numerical representation of text data. One of the methods that can be used to convert text into numerics is word embeddings. The word embeddings algorithm that researchers often use is Continuous Bag of Word, Skip-Gram, and FastText. This paper will discuss the transformation of textual data from Islamic knowledge domain documents into numerical forms using these three algorithms, then evaluate the word vector results using intrinsic and extrinsic evaluation techniques. We conduct intrinsic evaluations by determining the words to be evaluated, then checking for the existence of synonyms, antonyms, related words, and derived words from the nearest set of words based on vector values. We also tried to use vector words to solve word analogy problems. The best word vector in extrinsic evaluation is the result of the CBOW algorithm which is integrated with Binary Relevance and Multilayer Perceptron, with an accuracy value of 77.56% and a hamming loss value of 8.14%.
On Equivalence Relations Between Interpreted Languages, with an Application to Modal and First-Order Language

Abstract I examine notions of equivalence between logics (understood as languages interpreted model-theoretically) and develop two new ones that invoke not only the algebraic but also the string-theoretic structure of the underlying language. As an application, I show how to construe modal operator languages as what might be called typographical notational variants of bona fide first-order languages.
Healthcare and Usability Professionals’ Performance in Reflecting on Visualized Patient-Reported Outcomes

Usability evaluations of healthcare technologies are of utmost importance to ensure efficient and safe usage, suitable workload and acceptance. However, number and availability of healthcare professionals that can be involved is limited in many cases. For this reason, Human-Computer Interaction (HCI) professionals are often considered as substitutes of real users by applying usability inspection methods such as heuristic evaluations and cognitive walkthroughs. This study compares the performance of 8 healthcare professionals and 13 HCI professionals in the assessment of the health-related status in follow-up care of patients with head and neck cancers (HNC). For this, a cognitive walkthrough task with an interactive system designed for immediate feedback on therapy progress using patient-reported outcomes was chosen. Results indicate that HCI professionals might misclassify relevance of usability issues due to limited knowledge of the healthcare domain.
Degrading Lists

We discuss the relationship between monads and their known generalisation, graded monads, which are especially useful for modelling computational effects equipped with a form of sequential composition. Specifically, we ask if a graded monad can be extended to a monad, and when such a degrading is in some sense canonical. Our particular examples are the graded monads of lists and non-empty lists indexed by their lengths, which gives us a pretext to study the space of all (non-graded) monad structures on the list and non-empty list endofunctors. We show that, in both cases, there exist infinitely many monad structures. However, while there are at least two ways to complete the graded monad structure on length-indexed lists to a monad structure on the list endofunctor, such a completion for non-empty lists is unique.
On the Influence of Grammars on Crossover in Grammatical Evolution

Standard grammatical evolution (GE) uses a one-point crossover (“ripple crossover”) that exchanges codons between two genotypes. The two resulting genotypes are then mapped to their respective phenotypes using a Backus-Naur form grammar. This article studies how different types of grammars affect the resulting individuals of a ripple crossover. We distinguish different grammars based on the expected number of non-terminals chosen when mapping genotype codons to phenotypes, $$B_{avg}$$ . The grammars only differ in $$B_{avg}$$ but can express the same phenotypes. We perform crossover operations on the genotypes and find that grammars with $$B_{avg} > 1$$ lead to high numbers of either very small trees or invalid individuals. Due to the re-sampling of the invalid individuals, the algorithmic runtime is higher compared to grammars with a small $$B_{avg}$$ , despite being able to express the same phenotypes. In grammars with $$B_{avg} \le 1$$ , the bias towards small trees is reduced and instead, the frequency of valid large trees is increased. Our results give insights on favorable grammar designs and underline the central role of grammar design in GE.
Comparative Analysis of Various Recommendation Systems

In recent years, there is a rise in platforms like YouTube, Amazon, Netflix and many other Web services. Thus, there is a need of recommender systems in our lives. Recommender systems have become unavoidable in online journeys. Also, people are largely depending on online shopping platforms. When people buy from online shopping platforms they search and browse various products for their purchase. And even if they only browse, they can see the products everywhere on the Internet, i.e., on their social media platforms, blogs, etc. So, online shopping platforms like Flipkart and Amazon use recommendation systems to recommend products related to their customers. These platforms collect information about their customer’s pattern of shopping and purchasing behavior to give accurate and needful recommendation to the customers. In this paper, various recommendation systems are discussed. Comparative analysis is also performed for these recommendation systems. It also gives the idea of how these recommendation systems help to improve and manage audience for online shopping platforms. It also discusses that how the similarity can be measured among the items for content-based filtering and similarity among users for collaborative filtering. It also describes that hybrid-based filtering can be used to overcome the disadvantages of content-based and collaborative filtering to give item of interest and other items recommendation to users.
TSS-Net: Time-based Semantic Segmentation Neural Network for Road Scene Understanding

In this research, a multitask convolutional neural network that can do end-to-end road scene classification and semantic segmentation, which are the two crucial tasks for advanced driver assistance systems (ADAS), is proposed. We name the network TSS which means time-based semantic segmentation. The network contains three main modules: an image encoder, a scene classifier, and two time-based segmentation decoders. For each road scene image, the encoder extracts image features which will be used for classifier and decoders. Next, the image features are fed to the classifier to predict the scene type (in this case a day or a night scene). Then, based on the predicted scene type, the same extracted features are fed to a corresponding segmentation decoder to produce the final semantic segmentation result. By using this classification-driven decoder approach, we can improve the accuracy of the segmentation model, even when the model has been trained excessively earlier. Through the experiment, the validity of our proposed method has been proven. Our approach can be considered as stacking multiple segmentation modules on top of the classification module with all of them share the same image encoder. With this approach, we can utilize the result from classification to gain more accuracy in segmentation in one feed forward only.
Evolutionary Algorithms-Based Machine Learning Models

Machine learning models have found immense applications in various sectors such as energy, stock market, demand–supply chain, logistic management, health and many more, but their efficiency and accuracy depend on a very important factor, i.e., data. Data plays a major role in affecting the performance of these models. Mostly dataset with several features is fed as input to the machine learning models. A dataset containing crisp and relevant features can highly improve the accuracy whereas a dataset containing redundant and irrelevant features can detoriate the same. While performing data collection, basically one tries to collect as much information as it can about a specific domain, but when we need to draw any viable inference from that data, definitely, the inference is drawn, basing on a particular perspective and we may not need all the features of the collected data for this purpose. So, choosing some appropriate features of the dataset, which need to be fed to the models, has always been a crucial task. It is an obvious fact that complexity of feature selection increases with the increase in number of features and choosing some features out of human intuition is, of course, not appreciable. Evolutionary algorithms have been proved highly beneficial for solving such issues, for their stochastic nature. This chapter discusses some recent application of evolutionary algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, etc. to optimize the parameters of machine learning algorithms, e.g., support vector regression, artificial neural networks, random forest, etc. and their uses in various sectors like engineering, applied sciences, disaster management, finance and economy and health sector.
Causality Model for Text Data with a Hierarchical Topic Structure

This study describes a method for constructing a causality model from text data, such as review data. Topic modeling is useful to find these evaluation factors from text data. The method based on hierarchical latent Dirichlet allocation is useful because it automatically constructs relationships among topics. However, the depth of each topic in a hierarchical structure is the same even if the contents differ for each topic. Accordingly, the method can generate less important topics that are not worth analyzing. To solve this problem, we construct a hierarchical topic structure with different depths and more important topics by using Bayesian rose trees. In the experiment, the values of the hyperparameters for constructing a hierarchical topic structure are estimated by using evaluation indexes for causal analysis. In addition, the experiment compares the proposed method with related approaches to demonstrate the usefulness of this model.
Locking the door: tackling credential abuse

Credential abuse is a major issue facing organisations of all sizes. It affects not only simple login processes but also solutions and technologies such as application programming interfaces (APIs), the deployment of Internet of Things (IoT) devices and machine-to-machine authentication. Cyber criminals and nation-state attackers alike have automated the process of credential abuse while also refining highly targeted attacks. And the problem is getting worse. Credential abuse is a major issue facing organisations of all sizes. Cyber criminals and nation-state attackers alike have automated the process while also refining highly targeted attacks. The problem is getting worse and credential abuse presents itself in many forms. And there are equally as many solutions touted by vendors, all of which have their own challenges and weaknesses. However, there are successful solutions available and tackling the issue just takes a proper awareness of the problem and applying the appropriate technology and training, writes Steve Mansfield-Devine.
Topic-to-Essay Generation with Corpus-Based Background Information

Abstract This study aims to generate more topic-related and coherence essays based on user-defined topic words. Existing research generates essays without considering the semantic information from the corpus. However, the corpus contains statistical relationships of words which can be used to guide the model to generate more coherent and fluent essays. To fill this gap, we propose a corpus-based topic-to-essay generation model (C-TEG). We elaborately devise a background network based on the co-occurrence relationships of words from the corpus. The empirical results demonstrate that our approach has achieved 4.14 average score in subjective evaluation and a better BLEU-2 score, which shows that our model is able to generate more topic-related and coherent text than existing models.
Tree-Based Hybrid Genetic Algorithm for Density-Based Data Clustering

Data clustering algorithms partition a given set of data points into groups containing very similar data points. Representative-based and density-based algorithms are generally used for data clustering. These algorithms are heuristic algorithms and may stuck at a sub-optimal clustering. Crisp clustering problem is a combinatorial optimization problem. Genetic Algorithms generally perform better than heuristic algorithms for combinatorial optimization. In this work, we propose a hybrid Genetic Algorithm for density-based clustering. For this purpose, we represent a cluster using a forest of trees, where the nodes of the trees are the data points. We use a tree-based fitness function. Beside 1-point crossover, we use a deterministic improvement of offspring. We implement the proposed algorithm using C language and run on a personal computer. We experiment with five datasets from UCI Machine Learning Repository. The proposed algorithm outperforms for both low and high-dimensional datasets over existing algorithms, except for one high-dimensional dataset.
A new Approach for surveying the axis of cylindrical pipes using a reflectorless total station

Abstract To survey the pipe’s axis used in the installation of the cable-stayed bridge, a reflectorless total station and a proposed surveying observation technique were used. The approach includes a new method for observation and a graphical solution for the pipe’s axis determination. The technique shown in this paper can be applied for vertical and semi-vertical cylindrical pipes. A field test with real data and results is also presented.
Memory Efficient Graph Convolutional Network based Distributed Link Prediction

Graph Convolutional Networks (GCN) have found multiple applications of graph-based machine learning. However, training GCNs on large graphs of billions of nodes and edges with rich node attributes consume significant amount of time and memory resources. This makes it impossible to train such GCNs on general purpose commodity hardware. Such use cases demand high-end servers with accelerators and ample amounts of memory. In this paper we implement a memory efficient GCN based link prediction on top of a distributed graph database server called JasmineGraph <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our approach is based on federated training on partitioned graphs with multiple parallel workers. We conduct experiments with three real world graph datasets called DBLP-V11, Reddit, and Twitter. We demonstrate that our approach produces optimal performance for a given hardware setting. JasmineGraph was able to train a GCN on the largest dataset DBLP-V11(>10GB) in 20 hours and 24 minutes for 5 training rounds and 3 epochs by partitioning it into 16 partitions with 2 workers on a single server while the conventional training method could not process it at all due to lack of memory. The second largest dataset Reddit took 9 hours 8 minutes to train with conventional training while JasmineGraph took only 3 hours and 11 minutes with 8 partitions-4 workers in the same hardware giving 3 times improved performance. In case of Twitter dataset JasmineGraph was able to give 5 times improved performance. (10 hours 31 minutes vs 2 hours 6 minutes;16 partitions-16 workers).
Learning continuous temporal embedding of videos using pattern theory

Visual Question Answering (VQA) is a challenging task in artificial intelligence and has received increasing attention from both the computer vision and the natural language processing communities. Joint embedding learning for it suffers from the background noise of images and text. Learning continuous temporal embedding is a potential solution to extract both visual elements and textual elements with a proper temporal structure. In this paper, we address a continuous temporal embedding model based on pattern theory (CTE-PT) and fully express the atomism and combinatory based on Grenander's pattern theory. First, we generate atomic actions from videos and note them as generators, which reflects the atomism in pattern theory. Second, we design a CTE-PT model to discover the discriminative configuration of videos, which reflects the combinatory of atomic actions in pattern theory. In the CTE-PT model, we design the configuration proposal module to remove some background information initially, and the configuration interpretation module to minimize the interpretive energy of continuous temporal embedding. We estimate the energy by each pair in this embedding sequence and optimize it based on the Viterbi algorithm. The experimental results show that our CTE-PT model outperforms the baseline C3D+LSTM model on Olympic Sports, UCF 101, and HMDB51 datasets, which proves the effectiveness of mining the common continuous temporal embedding as the class-specific configuration for activity discriminator.
User Acceptance of QR Code based Mobile Application in Interactive E-Commerce

Advertisement on products or services can be found in many forms; video, banners, pamphlets, etc. However, these advertisements only function as a one-way communication as consumers are only presented with the product information and the seller. Further action on purchasing the product is absent or is to be made separately. Such an approach may not guarantee a sale for the seller as the customer may only retrieve the information of the product but purchase it elsewhere. Hence, this study demonstrates the use of QR code technology not only in marketing but also in e-commerce. The technology is realized in "On the GoShop" mobile application that has two types of users: seller and buyer. Sellers are people who wanted to promote their product and/or services while buyers are the ones who have a desire to purchase. An evaluation study on the acceptance of the mobile application was then performed on a total of 50 respondents and it is learned that the proposed e-commerce application is well accepted. Ninety percent of the respondents agree with the usefulness of the application and are looking forward to using the application in daily routines. The use of QR code technology not only facilitates consumer in purchasing their desired product but also contribute to cost-effective marketing.
COVID-19 Case Number Prediction Utilizing Dynamic Clustering With Polynomial Regression

The novel coronavirus disease 2019 (COVID-19) has spread rapidly throughout the world since its first reported case in 2019. As of December 2020, over 70 million cases and 1.5 million deaths have been reported [3]. As a result, the pandemic has sparked a new, widespread need for knowledge discovery and prediction using the available data. In this project, I applied Dynamic Clustering with Polynomial Regression (DyCPR) to a dataset of daily increases in Coronavirus cases. The results show that for some US states, DyCPR performs better than Moving Average in predicting the number of new COVID-19 cases on the day following a limited subsequence of the time-series data.
Path-building and localization system for mobile robotic platforms based on Raspberry Pi

Abstract The paper demonstrates a system for detection of location of robotic platform FESTO Robotino and optimal route building. It processes data from the camera and transmits control signals to the control system of the robot. The whole system is based on Raspberry Pi. It detects robot’s current coordinates, current angular rotation, angular difference (difference between current and previous angular rotation) and displacement of the robot in its own coordinate system. It uses an ArUco marker, placed on the top of the mobile robot for that. System also builds an optimal path, when moving from one point of the surface to another, according to the permeability of the surface. The authors set the permeability of testing surfaces. Using that, a weighted graph is built through the centers of particular surfaces, which are detected via an algorithm on Raspberry Pi. The optimal path is constructed through the edges of the graph via modified Dijkstra algorithm.
Intelligent Drumming Robot for Human Interaction

The advances in information technology have witnessed great progress on robot technologies in various domains nowadays. These new technologies enable robots to be used in industries, agriculture, education, and all aspects of our lives. In the field of robotic musicians, the core challenges are to teach robots to play musical instruments. Previous studies rely on manual closed-loop adjustments or pre-designed feature functions that rely on the expertise. However, they ignore the mechanisms to learn from humans and lacks corresponding science education and art teaching functions. In addition, most of the playing robots are created by the artificial pre-set audio action sequence. When facing new data features, it is difficult to compute. In order to solve these problems, this paper presents an intelligent drumming robot, based on machine learning computing technologies. The system consists of a user dashboard module, a computing module, and a data-oriented execution module. The drumming robot can be used for drum teaching and provides a new way of learning for the teaching of drums. The results of this study show that the combination of machine learning physical devices can be used to enhance the performance of drumming robot systems, allowing humans to interact with robots and enjoy a variety of smart applications and services.