Reviewerly/exHarmony huggingface dataset:
Cloud-assisted quantum primitive protocols and applications

Abstract Security and privacy have always been key concerns for individuals in various cloud-assisted services. In order to enhance security, we focus on quantum cryptography in novel cloud-based applications. First, we present two quantum primitive protocols to compute XOR of two private bits and AND of two private bits with the assistance of a quantum cloud, respectively. Then, we generalize two-party protocols to the corresponding multi-party cases. These quantum primitive protocols can ensure information-theoretical security and provide long-term security, which are guaranteed by the principles of quantum mechanics. Furthermore, we present a grouping strategy to improve the communication efficiency of multiparty quantum protocols. In addition, we investigate privacy-preserving applications of these quantum primitive protocols in cloud-based environments, e.g., cloud-based Anonymous Voting and Multiparty Private Set Intersection. Finally, simulated experiments show our proposed quantum protocols are correct and feasible with the present quantum technology.
Computational Design of a Split Cavity with Localized Independent Electric Field Tunability

Cavity-mediated strong coupling is an essential element for quantum photonic technologies, but there are multiple challenges to creating scalable device platforms in which multiple matter-based qubits can be strongly coupled to a single cavity. Among these challenges is the inherent inhomogeneity in ensembles of matter-based qubits, which necessitates independent tuning of each qubit into resonance with the mode of the cavity. We present a new design for a photonic crystal split cavity that enables independent electric field tuning of two distinct qubits based on indium arsenide quantum dots in a gallium arsenide heterostructure. Using finite difference time domain simulations, we alter the cavity geometry and the local arrangement of photonic crystal holes to optimize the cavity quality factor. We achieve a simulated quality factor of 20,000, which is comparable to values that have been used to demonstrate strong coupling. We further show that distinct electric fields can be applied to the two qubits with negligible cross talk. The result demonstrates the viability of a new approach to coupling multiple qubits to a single cavity and opens the door for both further numerical optimization and experimental realization of this new paradigm.
LETICIA: A toolbox for search task deployment, execution, and user interaction logging on interactive information retrieval studies

The analysis of search behaviors as proxies of users’ cognitive and affective states – in the context of information seeking – is an active research topic on interactive information retrieval (IIR). This type of research requires specialized tools either to run new studies or to replicate previous ones. While several IIR studies have been conducted over the years, the required tools are not always available, making the replicability of such studies more difficult and expensive. To address this problem, in this article, we introduce LETICIA (simuLated sEarch Task deploy and InteraCtIon cApture platform), an open-source solution designed to deploy study designs, conduct them, and collect various types of data. LETICIA offers ready-to-use functionalities that can be customized to fit researchers’ needs. These include a built-in search engine, a tool to build document collections, an application programming interface (API), a powerful data logger, and a session flow manager. LETICIA aims to be a contribution to the IIR community, offering a flexible yet simple tool to deploy environments to conduct user studies.
Predicting Cloud Resource Provisioning using Machine Learning Techniques

It can be difficult to provision assets in a cloud environment. Both inadequate and excessive provisioning are bad for the system's overall performance. Researchers are increasingly turning to proactive provisioning techniques that foresee resource needs in advance and set the system up to accommodate such real-time demands. Although proactive provisioning methods are more complicated than reactive provisioning strategies, they offer a generally faster reaction time since decisions about resource provisioning are made in advance of the actual requirement for those resources. The adoption of an analytical framework that anticipates resource requirements is necessary for these proactive provisioning strategies to function well. The provisioning of resources for computational operations in cloud computing is a significant concern. Unsurprisingly, previous research has identified a number of ways to deliver Cloud resources effectively. However, a forecast of the impending computational jobs' future resource consumption is necessary to implement a comprehensive resource provisioning model is required. However, the subject of cloud prediction the use of resources is still in its early stages
A heterogeneous graph neural recommendation model with hierarchical social trust

In traditional social recommendation algorithms, methods based on Graph Neural Networks (GNNs) representation of social networks are widely used, but the increasing sparsity of data and the tendency of users' social influence to be complex make it difficult for the algorithms to improve the recommendation quality efficiently. To address the issues, a heterogeneous graph neural recommendation model with hierarchical social trust (GraphNON) is proposed. The algorithm is improved by fully exploiting user-friend relationships and modeling different propagation channels of social influence in multiple ways respectively. Firstly, different types of potential friends are mined based on user-user social graph and user-item rating graph respectively. Different dynamic influences of direct friends and indirectly friends among users are modeled through hierarchical influence propagation. Secondly, associations between items are mined from user-item rating graph, and influence transmission among items is modeled through hierarchical influence propagation. Finally, updated user characteristics and item characteristics are used to predict whether a user is interested in an item or not. It is shown on both datasets that the model has significant improvement in both hit rate (HR) and normalized discounted cumulative gain (NDCG) metrics compared to the optimal benchmark model. For a potential feature dimension of 64, the HR of the Yelp dataset improved by 18.3% and the NDCG by 29.3%, while the HR of the Flickr dataset improved by 31.4% and the NDCG by 37.1%.
ReRender: Reality Rendering

&lt;p&gt;As technology and social media platforms consume traditional social spaces regularly found in the built world, a deficit of Third Space is developing. When communities become reliant on devices to communicate, those users lose the social security once provided by a built space. Architects may ameliorate this deficit through integrating digital systems within their designs. With personal devices becoming wayfinding, program defining, and space crafting tools, perceptions of space have expanded beyond physical barriers. This augmentation of digital spaces in the built world may provide opportunities for communities to generate a long-lasting sense of place. &lt;/p&gt;
A path planning method for large-aperture non-spherical surface detection profilometer

To enhance the accuracy, efficiency, and safety of large-aperture non-spherical optical components, we have proposed a novel strategy for path planning and detection, which uses a non-contact optical probe suitable for spindle-rotating contourimeters. Our strategy leverages the contourimeter's working principle and the structural characteristics of the surface, transforming the three-dimensional collision problem into a two-dimensional detection space. To accomplish this, we first decompose the cross-section of the workpiece into a quadtree space and establish oriented bounding boxes for each sub-node. We then use the separation axis principle to ensure motion safety between the positions of the test points. Next, we utilize the ant colony optimization algorithm to plan the detection path and generate an interference-free scanning route that ensures safety during the detection process. Overall, our path-planning strategy significantly improves detection efficiency, while ensuring that the detection process is conducted safely. By combining these methods, we created a robust and effective approach to detecting large-aperture non-spherical optical components.
Sentiment Classification of Cryptocurrency-Related Social Media Posts

Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency domain, resulting in CryptoBERT. Second, we generate the language-universal cryptocurrency emoji (LUKE) sentiment lexicon and prediction pipeline, utilizing the sentiment of emojis prevalent in social media. CryptoBERT is highly accurate, while LUKE is suitable for non-English posts, thus allowing for direct classification and noisy label generation in less popular languages. Our research can help cryptocurrency investors develop trading software supported by sentiments mined from social media.
Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study’s findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces.
Detecting Click Fraud Using an Improved Lenet-5 Convolution Neural Network

Online advertising has grown drastically over the last couple of decades by making billions worth of business markets all over the world. Click Fraud can be identified as one of the common malpractices when it comes to digital platforms. This leads to an increase in the revenue of the Ad publishers and huge losses for the advertisers. Hence the need of detecting click fraud has become a major concern in online marketing. Recent studies have proposed different kinds of machine learning based approaches to detect these fraud activities. In this study, we propose an improved Lenet-5 Convolution Neural Network to identify click fraud. This proposed novel deep learning algorithm was able to achieve an accuracy of 99.09% by using deep features of the proposed Lenet-5 based Convolution Neural Network.
A Sentiment Analysis of COVID-19 Tweets Data Using Different Word Embedding Techniques

The COVID-19 virus that invaded the world in 2019 caused many casualties while creating enormous mental turmoil among humans. During this pandemic period, humans were confined to prevent the virus from spreading. Due to the isolation, people used social media platforms like Twitter to express their ideas. Therefore, this study analyzed tweets related to COVID-19. Initially, text data processing techniques were employed, and sentiment labels were assigned. Then the data were trained using different machine learning (ML) models such as Multinomial Naïve Bayes (MNB), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbours (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and CatBoost (CB). During the training phase, word embedding techniques such as Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Global Vectors for Word Representation (Glove), Bidirectional Encoder Representations from Transformers (BERT), and Robustly Optimized BERT-Pretraining Approach (RoBERTa) were used, and evaluation metrics such as accuracy, macro average precision, macro average recall, and macro average f1-score were calculated to evaluate these models. According to the results, the CB model, which used the RoBERTa technique, achieved an accuracy of 97%. Therefore, it can be concluded that CB with RoBERTa provides better results when classifying tweet data.
Adoptability of Chaos Engineering with DevOps to Stimulate the Software Delivery Performance

The efficiency of the business processes has a major impact on improving the productivity of organisations. Many organisations use IT-related tools, primarily software, to enhance the efficiency of their business processes. Therefore, timely and reliable delivery of software products has become a top priority. As a result, advancing the concept of “Agility”, organisations implement DevOps practices. However, maintaining the quality of the software delivery service has become an issue due to several challenges related to the implementation of DevOps. Hence, this study was conducted with the aim of understanding the DevOps-related challenges and how “chaos engineering” can be applied along with DevOps to address those challenges. The practice of ”chaos engineering” contributes to the reduction of chaos. A systematic literature review was conducted to investigate the concept of “chaos engineering” and the challenges that DevOps-implemented organisations face. Later, a qualitative study was conducted to see how chaos engineering practices can be used to address the identified DevOps challenges. Based on the thoughts and views of the industry experts who participated in this study, it was revealed that implementing chaos engineering with DevOps helps organisations address most of the DevOps challenges both directly and indirectly. Also, the study suggests a methodology to implement chaos engineering with DevOps within organisations to successfully overcome DevOps-related challenges.
Impact of Feature Selection Towards Short Text Classification

feature selection technique is used in text classification pipeline to reduce the number of redundant or irrelevant features. Moreover, feature selection algorithms help to decrease the overfitting, reduce training time and improve the accuracy of the build models. Similarly, feature reduction techniques based on frequencies support eliminating unwanted features. Most of the existing work related to feature selection was based on general text and the behavior of feature selection was not evaluated properly with short text type dataset. Therefore this research was conducted to investigate how performance varied with selected features from feature selection algorithms with short text type datasets. Three publicly available datasets were selected for the experiment. Chi square, info gain and f measure were examined as those algorithms were identified as the best algorithms to select features for text classification. Moreover, we examined the impact of those algorithms when selecting different types of features such as 1gram and 2-gram. Finally, we look at the impact of frequency based feature reduction techniques with the selected dataset. Our results showed that info gain algorithm outperform other two algorithms. Moreover, selection of best 20% feature set with info gain algorithm provide the same performance level as with the entire feature set. Further we observed the higher number of dimensions was due to bigrams and the impact of n grams towards feature selection algorithms. Moreover, it is worth noting that removing the features which occur twice in a document would be ideal before moving to apply feature selection techniques with different algorithms.
Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.
Forecasting of Medium-Term Energy Output of On-Grid Rooftop Photovoltaic Arrays -Case Study for a Sri Lankan Solar Panel Installer

The world is shifting towards the higher utilization of renewable energy sources in the road to greener energy which conserves an environmentally friendly atmosphere. The generation of sustainable energy via adopting solar photovoltaic is common worldwide. The objectives of the research study are to identify the salient factors contributing to the energy generation of photovoltaic systems, to utilize a gamut of machine learning algorithms to build the predictive model and to identify the best machine learning algorithm to predict the energy generation based on accuracy and precision metrices. These objectives aid to achieve the aim of this study, which is to build a predictive model to determine the medium-term energy generated from on-grid rooftop solar systems. The study has unveiled a new piece of knowledge on how the photovoltaic system dynamics and location specific data has contributed to the prediction of the power output of the system. Further the findings are of paramount importance to the industry experts as well as the current and prospective solar panel users. The data of all solar panel sites of the installer was utilized and it was extracted from the source information systems. The necessary transformations and validations were applied and a detailed analysis was performed. The feature engineering, feature scaling, outlier-handling, multi-collinearity and feature selection was performed on data. The intended forecasting model based on fourteen supervised machine learning algorithms was built. The KNN Regression algorithm in the factor analysis of all features after principal component analysis has outperformed all other built models. Moreover, a strong positive co-relation was observed in the principal component analysis towards the solar panel energy output prediction. As part of future work, it’s imperative to build models utilizing a wider sample of on-grid roof top solar plants.
Preserving India’s Rich Dance Heritage: A Classification of Indian Dance Forms and Innovative Digital Management Solutions for Cultural Heritage Conservation

Deep connections exist between dance and cultural heritage. Dance is frequently passed down through generations as an essential component of a culture’s identity and as a means of maintaining and honoring that culture’s distinctive traditions and customs. A culture’s history, beliefs, and values can be powerfully expressed via dance, which can also be used for communication and storytelling. For the purpose of protecting and promoting India’s cultural legacy, it is crucial to comprehend how Indian dance styles are categorized. This paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) deep learning approach for the accurate classification of Indian dance style categories. The proposed model combines the strengths of both CNN and RNN to leverage spatial and temporal information, respectively, resulting in enhanced performance and improved accuracy. Extensive experiments were conducted to evaluate the performance of the proposed approach. The results demonstrate that the hybrid CNN-RNN model achieved an impressive accuracy of 97.74%, outperforming traditional methods and single-model architectures.
Smart System to Support Hearing Impaired Students in Tamil

This research paper introduces a groundbreaking smart system designed to assist hearing-impaired students in comprehending spoken Tamil, the second language in Sri Lanka. The system addresses the challenges faced by these students by incorporating cutting-edge deep learning techniques, including Convolutional Neural Networks, Hidden Markov Models, and Recurrent Neural Networks, for efficient feature extraction and sequence modeling within an Automatic Speech Recognition framework. Additionally, the paper proposes a novel method for automatically recognizing Tamil Sign Language gestures using advanced hand gesture recognition algorithms and a comprehensive dataset of Tamil Sign Language. The system encompasses four primary classification approaches, enabling the conversion of Tamil Sign Language to Text. Text to Tamil Sign Language, lip reading to Tamil Sign Language, Normal voice to Tamil Sign Language -Sign Language to Normal Voice and physical object identification to both text and Tamil Sign Language. Notably, the system achieves remarkable results, boasting an impressive accuracy rate of 0.99% surpassing existing Automatic Speech Recognition and Text-to-Speech systems. This significant breakthrough holds immense potential in enhancing the learning experience of hearing-impaired students in Tamil-speaking regions. Furthermore, the system’s adaptability allows for future expansion to support additional languages, making it highly versatile for diverse educational and communication settings.
Web-Based Data Hiding: A Hybrid Approach Using Steganography and Visual Cryptography

In today’s digital age, protecting sensitive data during transmission and storage is a critical concern. The rise of cyber threats has made it essential to develop secure communication channels to prevent unauthorized access and theft of confidential information. In this research, we propose a system that utilizes a combination of steganography and visual cryptography for secure data hiding. The main goal of this research is to address the issue of secure communication by concealing information in a digital image using steganography. After encoding the text in the image, the resulting steganographic image is divided into two shares using visual cryptography, ensuring that the data is protected from unauthorized access. This approach offers a practical and effective solution for secure data hiding, which can have potential applications in fields such as information security, privacy protection, and digital forensics. Overall, this research offers a viable solution to the problem of secure communication, which can help safeguard confidential information in today’s digital world.