Reviewerly/exHarmony huggingface dataset:
Enhancing Biomedical ReQA With Adversarial Hard In-Batch Negative Samples

Question answering (QA) plays a vital role in biomedical natural language processing. Among question answering tasks, the retrieval question answering (ReQA) aims to directly retrieve the correct answer from candidates and has attracted much attention in the community for its efficiency. Recently, researchers have introduced ReQA into the biomedical domain as BioReQA. Typically BioReQA models rely on the dual-encoder to gain semantic representation and are trained following the settings of dense retrieval. However, they normally utilize easy in-batch negative samples in training process to avoid the extra forwarding cost and GPU memory required by encoding additional negative samples. However, hard negative samples have been proved more important with regard to the overall performance of BioReQA tasks. Therefore in this research, we focus on effectively constructing hard in-batch negative samples. Inspired by the classic linear assignment problem, we propose an Iterative Linear Assignment Grouping (ILAG) algorithm to construct hard in-batch negative samples. To further enhance performance for given hard batches in a low-resource scenario, we also employ adversarial training to augment the difficulty of batches. Extensive experiments have shown our proposed method's promising potential in the area of biomedical retrieval question answering.
Virtual Reality, Augmented Reality and Mixed Reality for Teaching and Learning in Higher Education

This study aimed to explore the impact of Virtual Reality, Augmented Reality, and Mixed Reality in higher education. It highlighted the advantages and disadvantages of virtual technologies and suggested how they can be implemented effectively to enhance higher education. The study adopted the systematic literature review method and included 19 articles in the analysis. The study found that several advantages and challenges are reported on virtual technologies in the extant literature. Some of the benefits include increasing the student’s performance, motivation, and positive attitude towards learning. However, there are also negative outcomes or risks to keep in mind such as the possibility of damaging the student’s relationships or hindering their communication skills, design challenges and integrating the technologies in teaching may be time-consuming.
KIASOntoRec: A Knowledge Infused Approach for Socially Aware Ontology Recommendation

The World Wide Web is progressively adopting web 3.0 standards, and there is a demand for semantically compatible recommendation techniques in today’s world. This paper presents a framework to facilitate Knowledge infused automatic Ontology recommendation. User queries and user clicks data are preprocessed and Structural Topic Modelling is employed to identify the latent sematic structure found in the corpus and generate topics. RDF triplets are generated based on the topics extracted using OntoCollab. RDF data along with the Google’s Knowledge Base API used to obtain the subgraphs and relevant topics. Bagging algorithm based on Random Forest Classification Technique with Decision Trees Algorithm is used to classify the terms. A knowledge base is constructed by merging the subgraphs. Semantic similarity among entities of the knowledge graph and the top 25% of the classified results is computed using SemantoSim and ANOVA. The topic set is extracted and subjected to axiomatization. Finally, the most relevant Ontology to the user query is suggested to the user.
Bi-CSem: A Semantically Inclined Bi-Classification Framework for Web Service Recommendation

Web services are products in the era of service-oriented computing and cloud computing. As the number of web services on the Internet grows, selecting and recommending them becomes more important. Consequently, in the realm of service computing, how to propose the finest Web services for researchers is now a popular research topic. To determine the proper recommendation, the BI-CSem model was proposed and tested with multiple baseline models using real-world Web Service datasets in this research. Aside from that, thesaurus is built using Web service keywords gathered from Web service repositories such as UDDI,WSDI, and from the World Wide Web Cloud. The extracted terms are then subjected to semantic similarity, which is determined using SemantoSim, Concept similarity, and KL divergence measure, and the terms from the user, such as query, user click, and previous historical data, are pre-processed the terms from the semantic alignment are then classified using XGBoost, while the Web service dataset is classified using XGBoost and GRU. Semantic similarity is determined using just SemantoSim, based on the classification intersection of the top 75 percent words from the extracted terms from the two classifiers and features from the created intermediate term tree generated using STM. Finally, terms are reranked and recommended to the user, and the precision, accuracy, recall, F-measure, and FDR for the Web service recommendation system are calculated, and the Bi-CSem model is found to have an excellent precision percentage of 94.37% and the lowest FDR of 0.06.
Imbalanced Data Classification Method Based on LSSASMOTE

Imbalanced data exist extensively in the real world, and the classification of imbalanced data is a hot topic in machine learning.In order to classify imbalanced data more effectively, an oversampling method named LSSASMOTE is proposed in this paper.First, the kernel function parameters and penalty parameters of the support vector machine (SVM) were optimized using levy sparrow search algorithm (LSSA), and a fitness function was correspondingly designed.Then, during the optimization process, SMOTE sampling rate was combined, and LSSA iteration was used to select the best combination of SVM parameters and SMOTE sampling rate.In addition, the oversampled samples were noise processed by Tomek Link.In this case, the LSSASMOTE+SVM classification model was constructed to classify the imbalanced data.Eight of the datasets used in the experiments were obtained on UCI and KEEL, and the other three datasets were created manually.The experimental results confirm that the model can effectively improve the classification accuracy of imbalanced data and can be used as a new imbalanced data classification method.
User-Context Collaboration and Tensor Factorization for GNN-Based Social Recommendation

One goal of social recommendation is to utilize social information to alleviate data sparsity and improve recommendation accuracy. User social relationships are inherently graph-structured, graph neural network (GNN) has recently attracted extensive attention in social recommendation because of its capability to integrate structural information and topology. However, current graph neural network (GNN)-based social recommendation models fail to consider context information during user interactions, which hinders more accurate modeling of user interest features. To address this problem, we propose a new social recommendation model based on context-aware graph neural network named CENTRIC (User- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ont <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xt Collaboratio <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> enso <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> Factorization for GNN-based Soc <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> al Re <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ommendation). Specifically, first a multi-channel GNN model with user-context collaboration module is designed, so that context can directly affect user features and participate in the calculation of user interaction probability with items. Then tensor factorization is adopted in output layer to effectively fuse the features extracted from different channels. Experiments on three public datasets show that CENTRIC significantly outperforms other state-of-the-art social recommendation models, further experiments also demonstrate that context information and tensor factorization help improve the accuracy of GNN-based social recommendation.
Adding Blockchain and Smart Contracts to a Low-Code Development Platform

Given the evolution of the Internet over the past few years, there has been an increasing number of innovative technologies. One example of this is blockchain technology, which fights some of the weaknesses of other technologies and provides performance, efficiency, thrust and non-repudiation through a distributed, decentralised, immutable and transparent network. One possible area of applicability is on e-voting ballot boxes to store the voting data. This work’s main goal was to develop a prototype capable of following the processes inherent to a typical e-voting system, namely all the logistical and procedures associated with a vote and its participants, as well as all the relevant actions, using: (i) blockchain to store, maintain the integrity and assist those processes and (ii) a low-code development platform to enable a fast proof-of-concept. The prototype is composed by two parts: a reactive web app, intended for voting managers and candidates; and a mobile app, exclusively for voters. The OutSystems low-code development platform was used to build the application that comprehends the functionalities associated with the e-voting process, while providing interaction with a blockchain through a smart contract. To analyse the application’s usability, a survey was developed consisting of five questions evaluated through a five-level scale. The focus of the results from this survey was on the two higher levels, so it is possible to state that the application meets the needs of end-users, being suitable for implementation in specific situations.
DRHTG: A Knowledge-Centric Approach for Document Retrieval Based on Heterogeneous Entity Tree Generation and RDF Mapping

There is an enormous amount of data stored on the World Wide Web throughout the years of internet practice. There is a necessity for sophisticated techniques of acquiring and categorizing information resources available on the internet. In the domains of information retrieval and natural language processing, document retrieval has been an important task. Despite the fact that document retrieval algorithms have evolved significantly over the years, there is always space for improvement. Tokenization, lemmatization, SWE, and NER are among the preparation methods applied to the dataset. The preprocessed dataset is then transferred to XML format, where the RDF mapping is performed using the Jellyfish method. The knowledge graph is built using blogs, social network APIs, crowdsource community ontology, wiki data and Freebase, and LODC. The formulated knowledge graph is converted to RDF format from OWL format. The Jellyfish method is used on the updated knowledge graph in OWL format and the RCVI dataset in OWL format to conduct RDF mapping in terms of (subject predicate object predicate). To rank the document in increasing order of semantic similarity, KL divergence is applied to the RDF mapping obtained in the previous phase. Based on the increasing order of semantic similarity the most relevant document is recommended. The proposed framework accomplished an accuracy of 92.78%.
New MDS Self-Dual Codes Over Finite Field F<sub> <i>r</i> </sub> <sup>2</sup>

MDS self-dual codes have nice algebraic structures and are uniquely determined by lengths. Recently, the construction of MDS self-dual codes of new lengths has become an important and hot issue in coding theory. In this paper, we construct six new classes of MDS self-dual codes by using generalized Reed-Solomon (GRS for short) codes and extended GRS codes. Together with our constructions, the proportion of all known MDS self-dual codes relative to possible MDS self-dual codes generally exceed 57%. As far as we know, this is the largest known ratio. Moreover, some new families of MDS self-orthogonal codes are also constructed.
WCMIVR: A Web 3.0 Compliant Machine Intelligence Driven Scheme for Video Recommendation

Video recommendation is the need of the hour in the era of Web 3.0 . And there is a need for semantically inclined Web 3.0 compliant framework for video recommendation. In this paper, a WCMIVR framework which is a Web 3.0 compliant web video recommendation framework. WCMIVR has been proposed, although it is a query driven approach, it is centred on staged query enrichment by auxiliary knowledge addition. The proposed WCMIVR model is a knowledge centric semantically inclined framework where the TF-IDF is applied on the documents crawled from the web repository in order to aggregate the enriched categories of videos and also informative words are added from the TF-IDF model. Wikidata and Google Knowledge graph API are standard knowledge stores which are used further used for enrichment of the video categories. Ontology alignment is applied for feature discovery in order to classify the dataset of videos using Machine learning based logistic regression feature controlled classifier. The semantic similarity is calculated using Horn’s index, Twitter semantic similarity and Jaccard similarity which improvisions a strong relevant computation mechanism in order to rank and recommend the videos to the person using it. All in all, the proposed WCMIVR model acquires largest average precision percentage of 97.88, largest average recall percentage of 99.04, largest average percentage of accuracy of 98.46, largest average F-Measure percentage of 98.45 and with the lowest overall FDR measure of 0.03 which makes WCMIVR model the best classifier for web video recommendation which is compliant to Web 3.0.
PGSim: Efficient and Privacy-Preserving Graph Similarity Query Over Encrypted Data in Cloud

The boom of cloud computing has stimulated the prevalence of outsourced query services, and privacy concerns further motivate extensive studies on privacy-preserving queries in the cloud. Graph similarity query is one critical query type, in which the similarity between two graphs is usually measured by graph edit distance (GED). Although many schemes have been proposed for GED computation/graph similarity query, they do not consider data privacy and are not applicable to the cloud computing scenario. To address this issue, in this paper, we propose the first efficient and privacy-preserving graph similarity query (PGSim) scheme in the filter and verification framework. Specifically, we first identify the pivot filter property of GED and use the property to design a pivot R-tree based filter algorithm, which can efficiently retrieve candidate graphs for graph similarity query. Then, we design a vertex mapping (VM) tree to index all vertex mappings between two graphs and develop a GED query verification algorithm to verify candidate graphs. After that, we design a suite of private algorithms based on a symmetric homomorphic encryption scheme and apply them to propose a pivot R-tree based filter predicate encryption (PRFilter) scheme and a private GED query verification (PGQVerify) algorithm. Based on the PRFilter scheme and the PGQVerify algorithm, we propose our PGSim scheme. Rigorous security analysis shows that our scheme is selectively secure. Performance evaluation also demonstrates the high efficiency of our scheme.
Comparative Analysis of Filter Impact on Brain Volume Computation

The computation of the volume of the brain is a challenging area for medical researchers. One of the most significant elements that may be taken into account while diagnosis and monitoring of several brain related diseases is brain volume. This study aims to analyze the performance of different image filters in order to enhance the images and hence in turn, contribute to more accurate computation of brain volume using MRI (Magnetic Resonance Imaging) sequences. In this paper, different types of binary, grayscale and ternary image filters are applied and then brain volumes are computed. The experiment was conducted on MRI volumetric data obtained from OASIS (Open Access Series of Imaging Studies) dataset which is publicly available for research purpose. The results indicate that Ternary Magnitude Squared Image filter outperforms all other filters in terms of maximum voxel detection and gives brain volume computation accuracy of 89.74%.
FraudAuditor: A Visual Analytics Approach for Collusive Fraud in Health Insurance

Collusive fraud, in which multiple fraudsters collude to defraud health insurance funds, threatens the operation of the healthcare system. However, existing statistical and machine learning-based methods have limited ability to detect fraud in the scenario of health insurance due to the high similarity of fraudulent behaviors to normal medical visits and the lack of labeled data. To ensure the accuracy of the detection results, expert knowledge needs to be integrated with the fraud detection process. By working closely with health insurance audit experts, we propose FraudAuditor, a three-stage visual analytics approach to collusive fraud detection in health insurance. Specifically, we first allow users to interactively construct a co-visit network to holistically model the visit relationships of different patients. Second, an improved community detection algorithm that considers the strength of fraud likelihood is designed to detect suspicious fraudulent groups. Finally, through our visual interface, users can compare, investigate, and verify suspicious patient behavior with tailored visualizations that support different time scales. We conducted case studies in a real-world healthcare scenario, i.e., to help locate the actual fraud group and exclude the false positive group. The results and expert feedback proved the effectiveness and usability of the approach.
Toward Robust Facial Action Units’ Detection

Facial action unit (AU) detection plays an important role in performing facial behavioral analysis of raw video inputs. Overall, there are three key factors that contribute toward the optimal performance of AU detectors: 1) being able to capture local AU-centered features; 2) exploiting the fact that some AUs co-occur with others; and 3) utilizing appearance changes across frames. We briefly review current techniques addressing each factor and discuss the challenges they meet. Given that very few works consider how to effectively and efficiently merge them all into a single framework that can be trained in an end-to-end manner, we propose facial AU detection with face alignment(AUNet), a simple yet strong baseline for landmark-based AU detection. AUNet implements the abovementioned key factors by: 1) using the intermediate layers of a pretrained face alignment model to act as our AU features’ space; 2) optimized to satisfy a correlation constraint, derived from the AU labels, and 3) temporal constraint, derived from variations in the contents of consecutive frames in the input videos. The proposed model, with its three key components, remains simple in nature and aligns with the primary AU detection task. Experiments on several benchmarks show that it substantially improves the AU detector’s accuracy and achieves new state-of-the-art AU detection results on popular benchmarks: BP4D and DISFA. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jingyang2017/AU-Net</uri> .
An Efficient Machine Learning Model for Bitcoin Price Prediction

Crypto currencies are one kind of digital currencies that resembles the stock market and operate on a block chain database. A bitcoin is an earliest form of crypto currency and due to its erratic price trends, the market is unstable. Similar to the stock market, bitcoin provides investment opportunities. But due to its volatility, investors find it difficult to invest. A user-friendly interface is used for predicting the price of bitcoin using several algorithms. Hence, to forecast the price of bitcoin, we apply a variety of machine learning (ML) algorithms during this research. The set of algorithms are SVM, Bayesian regression, Random Forest and boosting ensemble, ARIMA, Multilayer LSTM and GRU model. By comparing the resulting values of RMSE, we will establish the most successful method for the prediction of price of bitcoin.
A Recommender System to Close Skill Gaps and Drive Organisations’ Success

Fast-paced technological and social change is leading to widening skill gaps in the workforce, posing a significant risk for organisations. To bridge them, it is important to find and promote hidden champions. This paper presents a conceptual framework for designing a Talent Recommender System (TRS) that finds hidden talents and matches them to the right task. This is achieved, through the innovative approach of combining ontology and clustering techniques to build a comprehensive skill ontology. Another novelty resides in using the skill ontology together with Collaborative Filtering (CF), which can result in a more serendipitous task recommendations when compared to existing methods of skill matching.
Investigating the Performance of a Dual-Axis Solar Tracking System in a Tropical Climate

This paper presents the design and implementation of a dual-axis solar tracker that allows the PV panel on which it is mounted to capture maximum solar energy throughout the day. The device tracks the azimuth and elevation angles of the Sun as it moves across the sky to maintain the PV panel perpendicular to sunlight at all times. Four light sensors are used for this purpose and an Arduino microcontroller processes their signals to actuate driving mechanisms that maintain the orthogonal position of the PV panel with respect to sunlight. The mechanical structure of the solar tracker is designed in such a way as to minimize its inherent energy consumption so that the overall energy performance is optimized. The prototype is tested in the tropical island of Mauritius. Previous studies have shown that the energy performance of solar tracking systems is highly dependent on the climate, with negligible energy gain achieved in very hot regions. Results indicate that improvements of 30.5% and 28.5% in the total energy output of the PV panel are obtained compared to a fixed PV panel on a cloudy and sunny day respectively.
A Study on Data Security and Privacy Issues in Cloud Computing

Cloud computing is an emerging field due to its high performance, high availability, and low cost. The cloud offers numerous services. Cloud services major use case is data storage. Cloud provides to the customer massive amounts of storage capacity. However, many cloud providers are still hesitant to implement cloud computing technology due to an inadequate security policies and protection measures, which leads to numerous challenges in cloud computing. The goal of this survey is to identify the different cryptographic algorithms eventually ensure the cloud highly secure. The main objective of this survey is to prevent unauthorized data access and to provide data security in the cloud by proposing a homomorphic encryption algorithm.