Reviewerly/exHarmony huggingface dataset:
Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks

At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such as multi-causal relations and nested causal relations difficult to extract. To solve these problems, a head-to-tail entity annotation method is proposed, which can express the complete semantics of complex causal relations and clearly describe the boundaries of entities. Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. The attention layer is combined with a dependency tree to enhance the model’s ability to perceive relational features, and a bi-directional graph convolutional network is constructed to further capture the deep interaction information between entities and relationships. Finally, the classifier iteratively predicts the relationship of each word pair in the sentence and analyzes all causal pairs in the sentence by a scoring function. Experiments on SemEval 2010 task 8 and the Altlex dataset show that our proposed method has significant advantages in solving complex causal extraction compared to state-of-the-art methods.
Workplace Deviant Behavior: A Theoretical Highlights

The article attempts to discuss the significance and functions of literature review including theories underlie in research especially as guide to young researchers.Basically, the review enables a researcher to have an up-to-date understanding of the research problem, particularly deviant behavior and to avoid him from attempting unnecessarily to understand what has been fully understood.The review will help the researcher to have relevant information of the context of the problem because a problem does not exist by itself, there must be certain relationship with other problems.This is essential when trying to find solution to the problem.The review also exposes the researcher to the related theories that will enable him to plan the research regarding deviant behavior in workplace appropriately.Literature review is performed by browsing the database using keywords; deviant, theory of deviant behavior and workplace deviant behavior.There are several theories that provide a clear picture of workplace deviant behavior such as Social Learning Theory, Social Exchange Theory, General Strain Theory, Mount, Judge and Illies's (2006) model of Workplace Deviant, Conservation of Resources Theory, and Social Control Theory.Understanding through the highlights of this theory can be the basis for a more detailed study of deviant behavior in the workplace, especially in the context of organizations in Malaysia.
USE AND QUALITY ASSESSMENT OF TEST TECHNOLOGIES IN THE EDUCATIONAL PROCESS

The publication describes various aspects of using tests in the educational process and assessing their quality through the LCMS Moodle test questions analysis system. The reliability of learning tests significantly depends on the complexity of their implementation, which in turn is determined by the ratio of correct and incorrect answers to test questions. The inclusion in the test of such tasks, to which all students answer correctly or incorrectly, sharply reduces the reliability of the test as a whole. The test improvement process should not end with approbation. It is necessary to constantly monitor the quality of test items, paying special attention to the items added recently. When building a training course, it can be filled with small training tests of various kinds (self-control test, training). The main characteristic of such tests should be the ability of the student to analyze and correct his mistakes.
Naïve Terminological Annotation of Legal Texts in Slovak

Correct automatic terminological annotation of texts in a corpus can be sometimes a challenging task, especially for moderately or heavily inflected languages with relatively free word order. We explore the possibility of simple annotation based on sequence matching of lemmatized texts to annotate Slovak language corpus with IATE terminological entries. The accuracy of annotating legal language is very good when annotating multiword terms, while accuracy of single-word terms can be increased by applying simple filters based on word lengths and blacklisting most frequent false positives.
Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things

Abstract This article proposes a software fusion processing method for multi-sensor monitoring the Internet of Things based on the fractional differential filtering algorithm. The purpose is to solve the difference between the detection data caused by the difference in each production information sampling node’s equipment performance and working environment. The article applies the above algorithm to the remote detection data fusion processing experiment of the discrete manufacturing system of the Internet of Things. The research found that the algorithm realizes the remote high-precision measurement of the Internet of Things sensor information. The fractional integral operator has high fusion accuracy in remote detection data fusion processing.
Using Online Podcast as a Speaking Activity in Online Classroom

In digital era, English educators are required to improve the quality of the learning instruction by integrating technology in learning process. Yet, challenges occur in integrating technology in speaking activity. The aim of the current study was to seek for students’ perspectives on using online podcast as a speaking activity in online classroom. The study used qualitative approach with the total of 47 English for Specific Purpose students coming from Tourism Management program participated in the study. The data were gathered through questionnaire distribution and interview and analyzed through three stages, including data reduction, data display, and conclusion drawing. The result showed that most of the students perceived the online podcast as a positive activity for their speaking skill. Majority of the students admitted the use of online podcast in online classroom motivates them to practice their speaking, enhance their creativity, and improve their public speaking skill.
Blockchain-Based Decentralized E-Voting System Using Smart Contract

In any evolved and growing world, elections should be obvious. This excessive degree of transparency promotes a way of life of accountability. Voting performs an essential position in constructing a democratic society in any country. The modern-day vote casting device entails voter residents who can solidify their votes at distinct vote casting booths, which calls for a massive quantity of time and budget. Open vote casting strengthens openness and integrity in organizational governance. An excessive degree of participation in any election regularly suggests a healthy party. E-vote casting or digital vote casting may be described as a voter who can report his or her vote electronically in a stable manner. In different words, the definition of e-Voting: Electronic vote casting (e-Voting) may be described as a web primarily based totally software program device wherein vote casting, voter effects evaluation of political elections is ruled through information and communication technology (ICT) generation at the least at some point of the vote casting and effects evaluation process. Blockchains used in cryptocurrency can be used to validate the vote transactions from their start to their destination.
Hand Gesture Recognition Using 3D CNN and Computer Interfacing

Since the 1970s, the field of gesture recognition and its applications has been at the centre of considerable research in human–computer interaction. Researchers have been able to construct strong models that can recognize gestures in real time thanks to recent advances in deep learning and computer vision, but they face hurdles when it comes to classifying gestures in variable lighting conditions. In this paper, we train a 3D convolutional neural network to recognize dynamic hand gestures in real time. Our focus is on ensuring that gesture recognition systems can perform well under varying light conditions. We use a huge training set consisting of numerous clips of people performing specific gestures in varying lighting conditions to train the model. We were able to attain an accuracy of 76.40% on the training set and 66.56% on the validation set with minimal pre-processing applied to the data set. The trained model was able to successfully recognize hand gestures recorded from a Webcam in real time. We were then able to use the model’s predictions to control video playback on the VLC media player such as increasing/decreasing volume and pausing video. These experimental results show the effectiveness and efficiency of the proposed framework to recognize gestures in both bright and dim lighting conditions.
Enhancing imputation techniques performance utilizing uncertainty aware predictors and adversarial learning

One crucial problem for applying machine learning algorithms to real-world datasets is missing data.The objective of data imputation is to fill the missing values in a dataset to resemble the completed dataset as accurately as possible.Many methods are proposed in the literature that mostly differs on the objective function and types of the variables considered.The performance of traditional machine learning methods is low when there is a nonlinear and complex relationship between features.Recently, deep learning methods are introduced to estimate data distribution and generate values for missing entries.However, these methods are originally developed for large datasets and custom data types such as image, video, and text.Thus, adopting these methods for small and structured datasets that are prevalent in real-world applications is not straightforward and often yields unsatisfactory results.Also, both types of methods do not consider uncertainty in the imputed data.We address these issues by developing a simple neural network-based architecture that works well with small and tabular datasets and utilizing a novel adversarial strategy to estimate the uncertainty of imputed data.The estimated uncertainty scores of features are then passed to the imputer module, and it fills the missing values by paying more attention to more reliable feature values.It results in an uncertainty-aware imputer with a promising performance.Extensive experiments conducted on some realworld datasets confirm that the proposed methods considerably outperform state-of-the-art imputers.Meanwhile, their execution time is not costly compared to peer state-of-the-art methods.
Natural Language Interface in Dogri to Database

Database is one of the most common terms used in today’s digital era in almost every domain. Digital applications use database for convenient storage and retrieval of data in an organized manner. Database management system is used to perform data operations while ensuring consistency, security, sharing, and privacy of data besides backup and recovery of the data. However, application of database requires sound technical skills since a particular language in a specific format is required by the system to obtain desired data. Dogri language is mainly spoken in Jammu and Kashmir and in some of its neighboring border district of states Himachal Pradesh and Punjab. It is added in the 8th schedule of Indian constitution and is one of the 22 official languages of India. In this paper, a novel attempt has been made to transform the Dogri language query to its equivalent SQL query format. Emphasis has been made on the fundamental and mostly used select queries that the proposed system accepts as input which is then transformed in SQL-formatted query. Since user is independent of following any predefined format for querying the database, some preprocessing operations on data are inevitable. Once data preprocessing like tokenization and stopword removal was done, Dogri language query was partitioned into clauses. These clauses were mapped to the most likely clause in SQL format. Clauses were finally sorted in a manner that a well-formatted SQL query is obtained. Natural languages are highly ambiguous, about five versions of each query were taken, and around 150 queries were tested. The proposed model obtained an accuracy of 88% in transforming appropriately Dogri language query to their equivalent SQL-formatted query.
A Pilot Study for Devanagari Script Character Recognition Using Deep Learning Models

Across the globe, over 300 million people understand the Devanagari Script (DS) Based languages i.e. Hindi. Despite the number of speakers, there is no accurate Optical Character Recognition (OCR) System available for the Hindi language/other DS-based languages. Various problems are faced while developing an efficient OCR for DS such as segmentation of characters (As there is no gap between characters in DS-based languages), touching characters (characters formed by the combination of pure consonants and consonants). Given the complexity of DS-OCR, it has emerged as an interesting area of research. In this paper, Deep Neural Models (VGG16, ResNet50, and MobileNet) are explored for Handwritten Hindi Characters Recognition. VGG16 model outperforms with an accuracy of 98.47%.
Improving blockchain security for the internet of things: challenges and solutions

&lt;span&gt;Due to its uniquely suited to the knowledge era, the blockchain technology has currently become highly appealing to the next generation. In addition, such technology has been recently extended to the internet of things (IoT). In essence, the blockchain concept necessitates the use of a decentralized data operation system to store as well as to distribute data and the transactions across the net. Therefore, this study examines the specific concept of the blockchain as a decentralized data management system in the face of probable protection threats. Furthermore, it discusses the present solutions that can be used to counteract those attacks. The blockchain security enhancement solutions are included in this study by summarizing the key points of these solutions. Several blockchain systems and safety devices that register security defenselessness can be developed using such key points. At last, this paper discusses the pending matters and the outlook research paths of blockchain-IoT systems.&lt;/span&gt;
Review on effectiveness of deep learning approach in digital forensics

&lt;p&gt;&lt;span&gt;Cyber forensics is use of scientific methods for definite description of cybercrime activities. It deals with collecting, processing and interpreting digital evidence for cybercrime analysis. Cyber forensic analysis plays very important role in criminal investigations. Although lot of research has been done in cyber forensics, it is still expected to face new challenges in near future. Analysis of digital media specifically photographic images, audio and video recordings are very crucial in forensics This paper specifically focus on digital forensics. There are several methods for digital forensic analysis. Currently deep learning (DL), mainly convolutional neural network (CNN) has proved very promising in classification of digital images and sound analysis techniques. This paper presents a compendious study of recent research and methods in forensic areas based on CNN, with a view to guide the researchers working in this area. We first, defined and explained preliminary models of DL. In the next section, out of several DL models we have focused on CNN and its usage in areas of digital forensic. Finally, conclusion and future work are discussed. The review shows that CNN has proved good in most of the forensic domains and still promise to be better.&lt;/span&gt;&lt;/p&gt;
Optimal k-means clustering using artificial bee colony algorithm with variable food sources length

&lt;span&gt;Clustering is a robust machine learning task that involves dividing data points into a set of groups with similar traits. One of the widely used methods in this regard is the k-means clustering algorithm due to its simplicity and effectiveness. However, this algorithm suffers from the problem of predicting the number and coordinates of the initial clustering centers. In this paper, a method based on the first artificial bee colony algorithm with variable-length individuals is proposed to overcome the limitations of the k-means algorithm. Therefore, the proposed technique will automatically predict the clusters number (the value of k) and determine the most suitable coordinates for the initial centers of clustering instead of manually presetting them. The results were encouraging compared with the traditional k-means algorithm on three real-life clustering datasets. The proposed algorithm outperforms the traditional k-means algorithm for all tested real-life datasets.&lt;/span&gt;
Bounds on adaptive quantum metrology under Markovian noise

We analyze the problem of estimating a scalar parameter $g$ that controls the Hamiltonian of a quantum system subject to Markovian noise. Specifically, we place bounds on the growth rate of the quantum Fisher information with respect to $g$, in terms of the Lindblad operators and the $g$ derivative of the Hamiltonian $H$. Our new bounds are not only more generally applicable than those in the literature---for example, they apply to systems with time-dependent Hamiltonians and/or Lindblad operators, and to infinite-dimensional systems such as oscillators---but are also tighter in the settings where previous bounds do apply. We derive our bounds directly from the master equation describing the system, without needing to discretize its time evolution. We also use our results to investigate how sensitive a single detection system can be to signals with different time dependences. We demonstrate that the sensitivity bandwidth is related to the quantum fluctuations of $\ensuremath{\partial}H/\ensuremath{\partial}g$, illustrating how ``nonclassical'' states can enhance the range of signals that a system is sensitive to, even when they cannot increase its peak sensitivity.
Exploring Google Translate-friendly strategies for optimizing the quality of Google Translate in academic writing contexts

The present study explored the types of errors found in Google Translate (GT) Chinese-to-English translations and, based on those error types, proposes strategies for optimizing the performance of GT. Seven abstracts written in both Chinese and English from seven articles published in English Teaching and Learning in 2017 were selected as the materials. The researchers compared the GT translations to the English abstracts written by the original author(s) and analyzed the problems in the translations. The problematic translations consisted of grammatical errors (35%) and lexical errors (65%). Relatedly, we propose nine specific strategies to employ when writing Chinese abstracts to be translated into English using GT. According to the strategies, we suggest that users (1) avoid native language-specific expressions, (2) maintain the use of original English terminologies in composing Chinese abstracts, and (3) enhance logical relations and expressions for the discipline-specific discourse community. Further analyses revealed that 99% of the 69 identified problems in the GT translations of the seven abstracts could be avoided by using the proposed strategies. A conceptual framework for the collaboration between GT and GT users is proposed and pedagogical implications are discussed.
Periodic Plane-Wave Electronic Structure Calculations on Quantum Computers

Abstract A procedure for defining virtual spaces, and the periodic one-electron and two-electron integrals, for plane-wave second quantized Hamiltonians has been developed and demonstrated using full configuration interaction (FCI) simulations and variational quantum eigensolver (VQE) circuits on Quantinuum's ion trap quantum computers accessed through Microsoft's Azure Quantum service. This work is an extension to periodic systems of a new class of algorithms in which the virtual spaces were generated by optimizing orbitals from small pairwise CI Hamiltonians, which we term as correlation optimized virtual orbitals with the abbreviation COVOs. In this extension, the integration of the first Brillouin zone is automatically incorporated into the two-electron integrals. With these procedures we have been able to derive virtual spaces, containing only a few orbitals, that were able to capture a significant amount of correlation. The focus in this manuscript is on comparing the simulations of small molecules calculated with plane-wave basis sets with large periodic unit cells at the Γ-point, including images, to results for plane-wave basis sets with aperiodic unit cells. The results for this approach were promising as we were able to obtain good agreement between periodic and aperiodic results for an LiH molecule. Simulations performed on the Quantinuum H1-1 quantum computer were able to produce surprisingly good energies, reproducing the FCI values for the 1 COVO Hamiltonian to within 11 milliHartree (6.9 kcal/mol), when corrected for noise.
Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security

—Face liveness detection is an important biometric authentication method for face recognition securitythat is used to determine a fake face from an authentic one. In this paper, a liveness detection method based on optimized LeNet5 is proposed. The LeNet-5 is optimized by increasing the convolution kerneland byintroducing a global average pooling. The simulation results show that the proposed model obtained the highest recognition rate of 99.95% as against the 96.67% and 98.23% accuracy from the Support Vector Machine (SVM) and LeNet-5 models, respectively.The results denote that the proposed model has a high recognition rate in face liveness detection
Virtual Reality Locomotion in Place for Virtual Museum Exhibitions of Culture Heritage: Comparing Joystick Controller, Point &amp; Teleport and Arm Swinging

Locomotion methods are user interaction approaches that assist users in navigating the virtual environment when using virtual reality display device of HMDs (head-mounted displays). Most previous studies have been focused on the locomotion techniques with joystick and teleport in general and gaming context. However, there are lack study of locomotion techniques in virtual museum for context of culture heritage. In this study, this paper presents a comparative evaluation with sixty participants of the three locomotions: Joystick Controller, Point &amp; Teleport and Arm Swinging to measure sickness, presence and flow state. In order to evaluate, this paper developed the virtual museum of Himmapan animals (https://angkaew.com/Hinmapan_vr/) which consisted of digital paint art, digitization objects, animation and digitization books. The results showed that Point &amp; teleport less sickness of nausea than other techniques and Point &amp; teleport also highest in perceived enjoyment than other techniques. However, we found no significant difference in presence. Finally, this paper suggested implementing Point &amp; Teleport locomotion techniques in the application of virtual museums
Research on 3D facial assessment for facial plastic surgery

In order to objectively and quantitatively evaluate the facial morphology of patients in plastic surgery, the key points of facial aesthetics are extracted by interaction and specified face database, and the geometric features are calculated to obtain accurate quantitative face data, and the aesthetic evaluation model is established. Face assessment, eyebrows and eyes overall assessment and nose assessment were carried out for the subjects. The results showed that the model could objectively give the facial score of the subjects, find out the deviation from the standard, and provide objective and effective guidance for further plastic surgery.