Reviewerly/exHarmony huggingface dataset:
Universal Self-Attention Network for Graph Classification

Existing graph neural network-based models have biasedly used a supervisedsetting for graph classification, and they often share the conventionallimitations in exploiting potential dependencies among nodes. To this end, wepresent U2GNN -- a novel embedding model leveraging the strength of thetransformer self-attention network -- to learn low-dimensional embeddings ofgraphs. In particular, given an input graph, U2GNN applies a self-attentionmechanism followed by a recurrent transition to update vector representation ofeach node from its neighbors. Thus, U2GNN can address the limitations in theexisting models to produce plausible node embeddings whose sum is the finalembedding of the whole graph. Experimental results in both supervised andunsupervised settings show that our U2GNN achieves new state-of-the-artperformances on a range of well-known benchmark datasets for the graphclassification task. To the best of our knowledge, this is the first work totrain a GNN-based model in the unsupervised setting to improve classificationperformance.
Machine Learning Integration of Herzberg’s Theory using C4.5 Algorithm

The emergence of the global economy, particularly to the ASEAN Community in 2015, marked a significant milestone on its path towards becoming a highly competitive region in terms of the labor force integrated into the global economy.Human Resource Management Society revealed that businesses would spend the equivalent of six to nine times the monthly wage of an employee to locate and train a replacement.The Philippines is recognized to have a high aptitude skilled workforce and top talent.The I.T. industry became diversified, where sophisticated skillset is part of job hiring requirements.Retention of employees became a critical success factor of an organization.In the 1950s, Fredrick Herzberg, a psychiatrist, carried out a study of employee satisfaction.Herzberg proved in his experimentation test that job satisfaction and dissatisfaction are related to workplace environment factors.Some situations make an employee engaged and discouraged, which affects the attitude towards the job.The Machine Learning, as part of Data Mining classification tasks towards a knowledge acquisition process.It is categorized and carried out through the analysis.This method is intending to use talent databases to conduct the process of acquiring talent expertise and retention pattern in Human Resource (H.R.).The decision tree family C4.5 classifier algorithm suggests an appropriate dataset classifier.The classification method performed is using the management of talent for the identification or estimation of retention of employees.The decision tree induction adopts a top-down approach, which begins with a tuple training set and related class labels.As the tree constructs, the training set is repetitively dividing into smaller subsets.The result of this study affirms Herzberg's theory of satisfaction through a mathematical technique C4.5 Algorithm.The employment retention model using the machine learning technique identifies the dataset properties that can avoid business disruptions in multiple ways.It is measuring the robustness of the datasets using WEKA, giving a 99.06% data performance.
Identity and access management systems

This chapter discusses concepts and various research-oriented IAM techniques for growing connected healthcare system. Identity and access management (IAM) in a computer system includes a security framework, tools, and technologies to control access to critical information resources to legitimate users in the right context. IAM is even more challenging for healthcare organization as they are responsible for the protection of users identities and valuable medical records. Health care organizations are more prone to attacks than financial institutions. Data breaches in e-healthcare can lead to identifying theft, billing, and insurance frauds. To prevent such breaches IAM for health care must be even more secure and scalable to meet current challenges. IAM cannot be explained without learning the concept of identification.
Logic-Enhanced Adaptive Network-Based Fuzzy Classifier for Fall Recognition in Rehabilitation

Currently, the most widely adopted fall prediction methods use kinematic sensors for data collection and set thresholds to identify a fall based on artificial experience or neural network learning algorithms.These methods require classification and calibration in advance.Striking a balance between sensitivity and specificity is difficult when setting thresholds.Backpropagation (BP), support vector machines (SVMs) and other neural network algorithm models require professional expertise and are too complicated, difficult to understand, and computationally expensive, which renders embedded development with these models difficult.In response to these problems, this paper builds a fuzzy logic-enhanced adaptive neural network classifier (FLEANN); uses the prior knowledge of experts to construct fuzzy rules; employs neural networks to drive the underlying data to confirm and optimize logical rules; and utilizes an adaptive fuzzy neural network.The parameters of the algorithm are modified and optimized using a large amount of data training to improve the heuristics, transparency, and robustness of the neural network.The enhanced adaptive fuzzy neural network is applied in rehabilitation training for the classification of imprecise, uncertain, and nonlinear falls; abnormal gait; and normal gait.The experimental results confirm that the accuracy of this classifier is better than that of conventional neural network classifiers and that its logical structure is clear, which enables its application to embedded development.The reduction in computational demand enables the elimination of high-configuration PCs and reliance on a single chip microcomputer.
Study on the Usefulness of Machine Translation in the Migratory Context: Analysis of Translators’ Perceptions

Abstract The use of machine translation in the field of migrations seems to be very limited and, in view of the latest developments, it is only natural to explore its usefulness in the migratory context. In an attempt to introduce this technology into this particular area, this article reports on a qualitative study on translators’ perceptions towards machine translation and post-editing tasks. The findings of the study indicate that both are not widely developed within the migratory context and further work is required. Based on our findings, we believe that this study can contribute to opening the way for machine translation and post-editing tasks to be included into the field of migrations.
Pulse on Pulse Deinterleaving Radar Algorithm

This paper intends to demonstrate interleaving radar pulses extracted from a deinterleaving algorithm and to discuss the problems encountered from deinterleaving radar pulses. The deinterleaving algorithm will be handling multiple pulse trains at a time. The deinterleaving algorithm will use dynamic threshold and finite state machine to distinguish interleaving pulse trains based on how the pulses are aligned and positioned. The deinterleaving algorithm will track the time of arrival (TOA), pulse width (PW), and amplitude of each pulse to find the pulse repetition interval (PRI) of the pulse train. The PRI will help identify the interleaving pulse trains.
A Novel Solution to JSPs Based on Long Short-Term Memory and Policy Gradient Algorithm

Based on long short-term memory (LSTM) and policy gradient algorithm, this paper proposes a novel solution to the job-shop scheduling problems (JSPs).Firstly, two LSTM networks with identical structures were established, serving as the encoding and decoding networks, respectively.Next, a pointer network was introduced to determine the job with the highest priority in the current state, creating a job sequence.Another neural network (NN) was constructed to evaluate the current job sequence.The evaluation results were taken as the baseline of the policy gradient algorithm for reinforcement learning.Then, the job sequence was optimized and updated by gradient descent.The effectiveness of our method was demonstrated through contrastive experiments on benchmark problems.
Digital Literacy Levels in ASEAN (Comparative Study on ASEAN Countries)

This paper would attempt at explaining the digital literacy levels among ASEAN (Association of Southeast Asian Nations) countries, how their awareness in this aspect emerged, and their causes.In their entrance into the 4.0 revolution, ASEAN countries experienced an increase in digital literacy levels, especially since.Countries with a high level of digital literacy maintained their policies and programs whereas countries with a low level of digital literacy expected to improve by implementing better programs and putting forward digital literacy aspects as their main policy.Digital literacy level among ASEAN countries is not different from one another.Nevertheless, the level of digital literacy can be ranked by mean rank from 1 to 8 that sequence is Singapore (24.6), Thailand (24.0), Indonesia (20,5), Vietnam (20.4), Myanmar (19, 90), Philippines (19.80),Malaysia (19.20) and Cambodia (15.6).The calculation of the Kruskal Wallis Test shows a significant number, that is 0.957.In conclusion, our H1 was rejected, which we interpreted as no difference from digital literacy levels among ASEAN countries.These were caused by information literacy, technology literacy, and media literacy of ASEAN countries which were almost at the same level.ASEAN countries had the capacity to produce digital content, to adopt new technology, to manage media information and to adopt technology in certain situations.
Boosted Metaheuristic Algorithms for QoE-Aware Server Selection in Multiplayer Cloud Gaming

Cloud Gaming (CG) provides a high performance and cost-effective solution where players with low-end devices can play high-end games without the need for advanced hardware. A cloud-based video game system offloads all the computational tasks to the cloud. Considering the dynamic nature of game workloads and resource capacity, resource management is still a significant challenge. Since CG is a real-time gaming service, graphics processing units (GPUs) are necessary to accelerate game scene rendering. GPUs are one of the most expensive resources in a CG platform. Therefore, service providers have a strong incentive to utilize GPUs efficiently to maximize their economic profit. In addition, players' quality of game experience (QoE) is a crucial parameter that can directly affect a service provider's profit and must be taken into account in any resource scheduling optimization. To satisfy both parties, in this paper we propose two efficient methods for GPU based server selection in CG. The proposed methods are an improved version of two well-known metaheuristic algorithms called Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which we refer to as Boosted-PSO and Boosted-GA, respectively. The proposed methods consider service providers' profits and players' experience simultaneously. Our objective is to maximize GPU utilization, which will not only lead to the service provider's economic benefit, but also increase the player's QoE. Our simulation results show that compared to the existing methods to solve such an NP-Hard optimization problem, our Boosted-PSO method, followed by Boosted-GA, achieves the highest efficiency in terms of GPU utilization, capacity wastage, and player's QoE.
Document Clustering Meets Topic Modeling with Word Embeddings

We propose a new statistical-learning approach to marrying topic modeling and document clustering. In particular, a Bayesian generative model of text collections is developed, in which the two foresaid tasks are incorporated as coupled latent factors, that govern document wording. The latter consists of word embeddings, so as to capture the semantic and syntactic regularities among words. Collapsed Gibbs sampling is derived mathematically and implemented algorithmically, along with parameter estimation, with the aim to jointly perform topic modeling and document clustering through Bayesian reasoning. Comparative tests on benchmark real-world corpora reveal the effectiveness of the devised approach in clustering collections of text documents and coherently recovering their semantics.MSC codesDocument ClusteringTopic ModelingWord EmbeddingsBayesian Text Analysis
Effects of the Online Interactive Learning Media on Student's Achievement and Interest in Physics

The use of interactive media integrating digital media and a structured digital environment make student interact with each other for learning purpose.This research developed interactive learning media for enhancing the learning interest and learning achievement of eleventh-grade students.The software used for developing media was Lectora Inspire.This research used the mix method approach.The development model was DDD-E.Subject experts, media experts, and teachers validated the product using a questionnaire containing relevant factors.This instrument used a likert scale from 1 (Absolutely Disagree) to 5 (Absolutely Agree).The teacher applied the valid learning media in the three classes consisting of 108 students to find out the impact on learning interest and learning achievement.Measurement of learning outcomes using multiple choice questions in the cognitive domain.Analysis of learning interest used a questionnaire with a likert scale and used to analyze was MANOVA.As a limitation in this research that interactive media is only accessible for students who have a personal password.Impacts measured have not been on various learning performance indicators, the sequence and variety of types of media.The value of this research is the integration of various kinds of media becomes an opportunity for the media.
Data Cube Clustering with Improved DBSCAN based on Fuzzy Logic and Genetic Algorithm

Multi-dimensional data, such as data cube, are constructed based on aggregating data in data warehouses and it requires to analyze with high flexibility. Also, clustering, which is an unsupervised pattern recognition analysis, has significant challenges to perform on data cube. In this paper, two new drafts of density-based clustering methods are designed to recognize unsupervised patterns of the data cube. In the first draft, DBSCAN clustering is hybridized by genetic algorithm and called the Improved DBSCAN (IDBSCAN). The motivation of designing the IDBSCAN optimizes the DBSCAN’s parameters by a meta-heuristic algorithm such as GA. The second draft, which is called the Soft Improved DBSCAN (SIDBSCAN), is designed based on fuzzy tuning parameters of the GA in the IDBSCAN. The fuzzy tuning parameters are performed with two fuzzy groups rules of Mamdani (SIDBSCAN-Mamdani) and Sugeno (SIDBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN’s parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the SIDBSCAN-Mamdani and the SIDBSCAN-Sugeno are compared with the IDBSCAN and the DBSCAN. The experimental results, consisted of 20 times running, indicate that the proposed ideas achieved to their targets.
An Enhanced Deep Neural Network-Based Architecture for Joint Extraction of Entity Mentions and Relations

Named entity recognition and relation extraction are two principal tasks in most natural language processing systems.The majority of methods used in the field implement these two issues independently, thus leading to possible problems such as error propagation from one component (entity detection) to another (relation extraction).To solve such problems, we propose a new architecture for joint identification of entity mentions and their relation by employing a deep neural network framework.The model not only overcomes the error propagation challenge but also improves the detection results of both tasks owing to the cooperation with each other.Experiments on publicly available sources demonstrate that our joint model surpasses competitors in terms of accuracy.The results highlight the improvement achieved by the proposed deep neural network framework for the entity mention and relation classification tasks.Furthermore, we tested the effect of increasing the sentence length and demonstrated its negative influence on the performance.
Investigating the influence of environmental factors on the incidence of renal disease with compositional data analysis using balances

This research uses an urban soil geochemistry database of elemental concentration to examine the potential relationship between Standardised Incidence Rates (SIRs) of Chronic Kidney Disease (CKD) of uncertain aetiology (CKDu), and cumulative low level geogenic and diffuse anthropogenic contamination of soils with PTEs. A compositional data analysis approach was applied to determine the elemental balance(s) of the geochemical data showing the greatest association with CKDu. The research concludes that both anthropogenic and geogenic factors may be contributing influences to explain high incidences of CKDu, up to 12 times greater in some Super Output Areas (SOAs) than would be expected for the average population. The role of As, Cr, Cu, Pb, Sb and Mo was highlighted, which may be linked to anthropogenic sources such as historical industrial sources, atmospheric pollution deposition and brake emissions. Geogenic factors were shown to be important in areas with elevated relative concentrations of naturally occurring potentially toxic elements (PTEs).
Age Estimation and Gender Prediction Using Convolutional Neural Network

A face of a person plays a very crucial part to identify the person uniquely. Two important traits namely age and gender can enhance the performance of face recognition. From the human perspective, it is very easy to estimate the person’s gender and age on the basis of visualization but if we see from the machine perspective they can’t. To estimate the age and gender from the facial image is a challenging task for a machine due to variations, lighting and other conditions in the face. This paper proposed the estimation of age and predict the gender of a person from the facial image using the convolutional neural network. In the proposed methodology before the training and testing, also apply the PCA to reduce the extracted features dimensions. This work is done on the publicly available IMDB-WIKI dataset as well as own dataset using the MATLAB platform for the implementation purpose.
Enhanced Numeral Recognition for Handwritten Multi-language Numerals Using Fuzzy Set-Based Decision Mechanism

Handwritten character and numeral recognition have gained interest in the research community as part of the big picture of Machine Learning.Writer independent recognition systems are still in the working and the research is geared towards an optimized technique that can achieve this.In this paper, we propose a numeral recognition system that forms fuzzy sets of the features extracted using modified structural features for English, Arabic, Persian, and Devanagari Numerals.The structural features extract the geometrical primitives that distinguish each image.After the feature extraction phase, the results are input into a classifier, we test two different classifiers namely Neural Network and Naïve Base.To further enhance the recognition process with low overhead the erroneously recognized numerals (confusion matrix) are processed through the fuzzy set-based decision mechanism to enhance the numeral recognition process.Results indicate that recognition is enhanced by applying the fuzzy set-based decision mechanism for both classifer.
A loyalty program based on Waves blockchain and mobile phone interactions

Abstract Loyalty cards programs have been used by retailers to increase customer retention. Loyality cards provide means to identify a particular customer and to collect customer-specific data, thus enabling individualized marketing; however, operating a loyalty program is complicated for retailers since they require to manage balances, collections, and transfers of customers. This is exactly the same problem the retailers were facing before credit cards were readily available. A new problem is that customers now have too many cards, customers may forget, or even deliberately decide to carry only a selection of their cards. This paper proposes a loyalty program based on a blockchain that does not require a physical card for identifying customers as it associates customers to their phone numbers, since nowadays people always carry their phone. In this perspective, companies can reduce overhead costs associated to managing the loyalty program. This paper reviews the technology required and describes the implementation of a loyalty program based on blockchains. Finally, it also enumerates the reasons for choosing the blockchain technology for this application.
Reliable Administration Framework of Drones and IoT Sensors in Agriculture Farmstead using Blockchain and Smart Contracts

IOT adoption is significantly increasing across different industries in the recent decade and the security is the biggest concern for the enterprise and industries to safe guard the data which is emanating out of IOT sensors, Drones and aggregators. In this paper we discuss how blockchain's smart contracts will help to manage the security challenges of IOT sensors and devices. We provide the literature review and approaches on how we build Agro's drone specific smart contracts with different ledger and platform in section II and III. We will elaborate the high-level approach with conceptual framework in section V. we will share the deployment considerations and issues in section VI. In section VII we will present our significance and in section VIII we will share our recommendations and conclusion
A Graph Attention Model for Dictionary-Guided Named Entity Recognition

The lack of human annotations has been one of the main obstacles for neural named entity recognition in low-resource domains. To address this problem, there have been many efforts on automatically generating silver annotations according to domain-specific dictionaries. However, the information of domain dictionaries is usually limited, and the generated annotations may be noisy which poses significant challenges on learning effective models. In this work, we try to alleviate these issues by introducing a dictionary-guided graph attention model. First, domain-specific dictionaries are utilized to extract entity mention candidates by a graph matching algorithm, which can capture word patterns of domain entities. Furthermore, a word-mention interactive graph is leveraged to integrate the semantic and boundary information of entities into their context. We evaluated our model on the biomedical-domain datasets of recognizing chemical and disease entities, namely BC5CDR and NCBI disease corpora. The results show that our model outperforms several state-of-the-art models with different methodologies, such as feature-based models (e.g., BANNER), ensemble models (e.g., CollaboNet), multi-task learning models (e.g., MTM-CW), dictionary-based models (e.g., AutoNER). Moreover, the performance of our model is also comparable with BioBERT that owns huge parameters and needs large-scale pre-training.