Reviewerly/exHarmony huggingface dataset:
Pre-Evaluation with a Personalized Feedback Conversational Agent Integrated in Moodle

Pre-evaluation of the learner's level is a common learning strategy designed to determine the prior knowledge and skills of learners. A pre-evaluation is carried out at the beginning of the course and based on the results obtained, personalized resources will be provided that respond to individual learner needs. This paper presents a pre-evaluation for a C programming language course by providing, at the end of the quiz, a personalized formative feedback and recommendation to the learners. We have developed our conversational chatbot named QuizCbot, which allows learners to go directly to the parts where they need the most help through the personalized feedback provided to them, including their final scores, the questions they answered correctly and the questions they answered incorrectly with the correct answer and explanation. Hence, the chatbot makes a recommendation on the concepts in which the learner did not obtain the average, identifying the concepts not mastered where the learner needs more (or less) support. Determining what learners know and don't know can help to improve the learning experience. We have integrated our QuizCbot chatbot, which is based on Natural Language Understanding (NLU), into the Moodle learning environment.
The Impact of the COVID-19 Pandemic on the Volatility of Cryptocurrencies

This study aimed to investigate the interactions between Bitcoin to euro, gold, and STOXX50 during the period of COVID-19. First, a bibliometric analysis based on the R package was applied to highlight the research trends in the field during the period of the COVID-19 pandemic. While investigating the effects of the pandemic on Bitcoin, the number of cases of COVID-19 was used as a proxy. Using daily data for the period 1 March 2020 to 3 March 2020 and based on a vector autoregressive model, impulse response, and variance decomposition were utilized to analyze the dynamic relationships among the variables. The results revealed that the COVID-19 cases and gold hurt the exchange rate of Bitcoin to euro, while there was great volatility regarding the response of Bitcoin to a shock of STOXX50. The Granger causality test was constructed to investigate the relationships among the variables. The results show the presence of unidirectional causality running from new cases to STOXX50 and from STOXX50 to gold. This study contributes to the existing scholarly research into the dynamic relationships that appeared among Bitcoin, gold, and STOXX50 in a period of great uncertainty. Finally, the findings have significant implications for investors, who are interested in diversifying their portfolios.
Evaluation Techniques for Shale Oil Lithology and Mineral Composition Based on Principal Component Analysis Optimized Clustering Algorithm

Shale oil reservoirs are characterized by complex lithology, complex mineral composition and strong heterogeneity. This causes great difficulty in lithologic evaluation. In this paper, a method of lithology identification is proposed by means of intersection plot method and machine learning method, and lithology evaluation is carried out by combining the calculation of mineral content with a multi-mineral optimization model. The logging response characteristics of five lithologies are analyzed by using the logging curves selected by principal component analysis (PCA) discriminant analysis. In lithology identification, the system clustering algorithm is selected to identify shale oil reservoir lithology through layer-by-layer subdivision of sample lithology classification. Logging data has high vertical resolution and good continuity, and mineral prediction using logging data can ensure high accuracy. In this paper, the method of calculating mineral content by using multi-mineral optimization model has achieved good results in practice.
Accelerating network layouts using graph neural networks

Graph layout algorithms used in network visualization represent the first and the most widely used tool to unveil the inner structure and the behavior of complex networks. Current network visualization software relies on the force-directed layout (FDL) algorithm, whose high computational complexity makes the visualization of large real networks computationally prohibitive and traps large graphs into high energy configurations, resulting in hard-to-interpret "hairball" layouts. Here we use Graph Neural Networks (GNN) to accelerate FDL, showing that deep learning can address both limitations of FDL: it offers a 10 to 100 fold improvement in speed while also yielding layouts which are more informative. We analytically derive the speedup offered by GNN, relating it to the number of outliers in the eigenspectrum of the adjacency matrix, predicting that GNNs are particularly effective for networks with communities and local regularities. Finally, we use GNN to generate a three-dimensional layout of the Internet, and introduce additional measures to assess the layout quality and its interpretability, exploring the algorithm's ability to separate communities and the link-length distribution. The novel use of deep neural networks can help accelerate other network-based optimization problems as well, with applications from reaction-diffusion systems to epidemics.
Action Recognition Based Real-time Bangla Sign Language Detection and Sentence Formation

Sign language is a system of communication that uses visual motions and signs to communicate with persons who are deaf or mute due to a hearing or speech impairment. A real-time Bangla Sign Language (BdSL) detection system was proposed in this paper, which can generate Bangla sentences from a sequence of images or a video feed which can help those who are not familiar with sign language. Blazepose algorithm was used to identify the sign language body posture sequence. After detecting the body posture the data was gathered as a numpy file. A Long Short-Term Memory (LSTM) network was used to train the numpy files since this network can generate predictions based on sequential data. After 85 epochs of training, the model's training accuracy was 93.85%, and its validation accuracy was 87.14%, which indicates that the model's ability to recognize BdSL sentences in real-time is adequate.
Towards Efficient and Privacy-Preserving High-Dimensional Range Query in Cloud

The Internet of Things (IoT) boom has enabled Internet Service Providers (ISPs) to collect an enormous amount of high-dimensional data. Performing range queries on such data can effectively reuse them to help ISPs offer better services. Owing to the low cost and high resource utilization of cloud computing, an increasing number of ISPs are inclined to outsource data and services to it. However, as the cloud is not fully trusted, data need to be encrypted before being outsourced, which inevitably hinders many query services, e.g., range queries. Various schemes were proposed for privacy-preserving range queries, yet they struggled to extend to high-dimensional scenarios and did not support dimension selection. Aiming at this challenge, in this article, we propose an efficient and privacy-preserving high-dimensional range query scheme (PHRQ) based on an iMinMax tree while supporting dimension selection. Specifically, we first build an iMinMax tree for high-dimensional data and utilize a symmetric homomorphic encryption technique to design a suite of privacy-preserving protocols to achieve secure high-dimensional range queries. Then, we design a sub-dimensional range determination protocol to support dimension selection. Further, based on the iMinMax tree and our privacy-preserving protocols, we propose our PHRQ scheme. Finally, security analysis shows that our scheme is privacy-preserving, and performance evaluation demonstrates that our scheme is efficient in high-dimensional range query processing.
Cyber Resilience, a Survey of Case Studies

Considering the potential magnitude and impact of cyber-attacks, organizations must be able to understand their capabilities to prevent, respond and re-cover from these attacks as well to implement and refine adequate resilience plans. Due to the importance of cyber resilience, this survey aimed to review relevant case studies published in the scientific literature. The identified case studies followed different approaches since some of them were focused on risk assessment and risk management processes and the complexity of their implementation, while others were focused on the use of well-known frameworks to assess cyber resilience or on proposing new cyber resilience frameworks and tools.
Continuous User Authentication Using Machine Learning and Multi-finger Mobile Touch Dynamics with a Novel Dataset

As technology grows and evolves rapidly, it is increasingly clear that mobile devices are more commonly used for sensitive matters than ever before. A need to authenticate users continuously is sought after as a single-factor or multi-factor authentication may only initially validate a user, which doesn't help if an impostor can bypass this initial validation. The field of touch dynamics emerges as a clear way to non-intrusively collect data about a user and their behaviors in order to develop and make imperative security-related decisions in real time. In this paper we present a novel dataset consisting of tracking 25 users playing two mobile games - Snake.io and Minecraft - each for 10 minutes, along with their relevant gesture data. From this data, we ran machine learning binary classifiers - namely Random Forest and K-Nearest Neighbor - to attempt to authenticate whether a sample of a particular user's actions were genuine. Our strongest model returned an average accuracy of roughly 93% for both games, showing touch dynamics can differentiate users effectively and is a feasible consideration for authentication schemes. Our dataset can be observed at https://github.com/zderidder/MC-Snake-Results
Model-Driven Software Development Approach: Ensuring Safety of an IoT-Based Plant Health Monitoring System

Environmental monitoring is the process of collecting environmental data over time and monitoring for any changes that could be detrimental to the manufacturing or distribution of a product. Accurate monitoring of environmental conditions is crucial across all regulated industries, as overlooking even minor errors can result in significant financial losses and put the safety of humans at risk. Key to this process is enabling monitoring operations to be safe, reliable, and cost effective, i.e., the connected devices must be correctly configured and operate as intended. This paper presents safe monitoring development practice from software engineering perspectives and describes an associated framework, which ensures safety of an IoT-based plant health monitoring system. Furthermore, this study investigates some of the challenges that occur when building the system that operates with environmental sensing and processing devices through IoT, in particular, how such systems remain safe and how they can be built to be re-configurable. The study is based on postgraduate projects carried out at the University of Southern Denmark. The projects set out to exploit the challenges that engineering students faced from early on the design to the actual implementation of a plant monitoring system. This study also addresses how adapting continuous software engineering and technology practices could mitigate such challenges and provide identification of new research directions and continuous development.
Particle Filtering with Adaptive Diversifying Scheme for Abruptly Changing Hidden States Estimation

Particle filtering (PF) is a powerful sequential Monte Carlo (SMC) method used to estimate hidden states of parameters of interest from noisy measurements obtained from dynamic systems. The particles are randomly drawn samples of all possible hidden states according to previous knowledge or prior information, if any. Degeneracy occurs when only a few particles locate in high-probability regions (or have significant weights), while the others locate in low-probability regions. Resampling is a simple and straight forward way to tackle degeneracy but introduces another problem called particle impoverishment that destroys particle diversity. Conseqeuntly, the particles are not able to find the high-likelihood state values in the next step, that may suddenly vary from the current values. This paper proposes a scheme to ensure that the particles remain adequately diverse to cover high-likelihood values during the prediction stage. When noisy measurement becomes available, particles are herded to form a better approximation of the posterior probability density function (PDF) of the hidden state that can then be estimated or inferred with high accuracy.
The possibilities of changes in learning experiences with Metaverse

This review aims to define the Metaverse, present the roles of AR, MR and VR, and also the concepts of digital twins and lifelogging. The evolution of applications for Metaverse in various sectors, especially gaming, has created the possibility of using Metaverse for education. We present the vast field of these applications and educational projects. The challenges that educators face are discussed and the potential and limitations of its educational applications are explained. It's suggested to embrace the Metaverse in classes but not in a full-time learning environment, instead, it should be used as a complement, when justified. Some of its limitations may be weaker social connections there are concerns for privacy and security. The big potential offered by Metaverse technologies is the immersive experience of content and social interactions.
Less Is More: Unsupervised Mask-Guided Annotated CT Image Synthesis With Minimum Manual Segmentations

As a pragmatic data augmentation tool, data synthesis has generally returned dividends in performance for deep learning based medical image analysis. However, generating corresponding segmentation masks for synthetic medical images is laborious and subjective. To obtain paired synthetic medical images and segmentations, conditional generative models that use segmentation masks as synthesis conditions were proposed. However, these segmentation mask-conditioned generative models still relied on large, varied, and labeled training datasets, and they could only provide limited constraints on human anatomical structures, leading to unrealistic image features. Moreover, the invariant pixel-level conditions could reduce the variety of synthetic lesions and thus reduce the efficacy of data augmentation. To address these issues, in this work, we propose a novel strategy for medical image synthesis, namely Unsupervised Mask (UM)-guided synthesis, to obtain both synthetic images and segmentations using limited manual segmentation labels. We first develop a superpixel based algorithm to generate unsupervised structural guidance and then design a conditional generative model to synthesize images and annotations simultaneously from those unsupervised masks in a semi-supervised multi-task setting. In addition, we devise a multi-scale multi-task Fréchet Inception Distance (MM-FID) and multi-scale multi-task standard deviation (MM-STD) to harness both fidelity and variety evaluations of synthetic CT images. With multiple analyses on different scales, we could produce stable image quality measurements with high reproducibility. Compared with the segmentation mask guided synthesis, our UM-guided synthesis provided high-quality synthetic images with significantly higher fidelity, variety, and utility ( by Wilcoxon Signed Ranked test).
How Natural Language Processing Can Aid With Pulmonary Oncology Tumor Node Metastasis Staging From Free-Text Radiology Reports: Algorithm Development and Validation

Background Natural language processing (NLP) is thought to be a promising solution to extract and store concepts from free text in a structured manner for data mining purposes. This is also true for radiology reports, which still consist mostly of free text. Accurate and complete reports are very important for clinical decision support, for instance, in oncological staging. As such, NLP can be a tool to structure the content of the radiology report, thereby increasing the report’s value. Objective This study describes the implementation and validation of an N-stage classifier for pulmonary oncology. It is based on free-text radiological chest computed tomography reports according to the tumor, node, and metastasis (TNM) classification, which has been added to the already existing T-stage classifier to create a combined TN-stage classifier. Methods SpaCy, PyContextNLP, and regular expressions were used for proper information extraction, after additional rules were set to accurately extract N-stage. Results The overall TN-stage classifier accuracy scores were 0.84 and 0.85, respectively, for the training (N=95) and validation (N=97) sets. This is comparable to the outcomes of the T-stage classifier (0.87-0.92). Conclusions This study shows that NLP has potential in classifying pulmonary oncology from free-text radiological reports according to the TNM classification system as both the T- and N-stages can be extracted with high accuracy.
On the Performance of Consensus Mechanisms in Privacy-Enabled Decentralized Peer-to-Peer Renewable Energy Marketplace

This work defines a decentralized blockchain-based peer-to-peer (P2P) energy marketplace which addresses actors’ privacy and the performance of consensus mechanisms. The defined marketplace utilizes private permissioned Ethereum-based blockchain client Hyperledger Besu (HB) and its smart contracts to automate the P2P trade settlement process. Also, to make the marketplace compliant with energy trade regulations, it includes the regulator actor, which manages the issue and generation of guarantees of origin and certifies the renewable energy sources used to generate traded electricity. Finally, the proposed marketplace incorporates privacy-preserving features, allowing it to generate private transactions and store them within a designated group of actors. Performance evaluation results of HB-based marketplace with three main consensus mechanisms for private networks, i. e., Clique, IBFT 2.0, and QBFT, demonstrate a lower throughput than another popular private permissioned blockchain platform Hyperledger Fabric (HF). However, the lower throughput is a side effect of the Byzantine Fault Tolerant characteristics of HB’s consensus mechanisms, i. e., IBFT 2.0 and QBFT, which provide increased security compared to HF’s Crash Fault Tolerant consensus RAFT.
Low-light image enhancement by deep learning network for improved illumination map

In the case of insufficient illumination conditions, the quality of the captured image is poor. At this time, increasing the brightness of the dark region of the whole image will inevitably aggravate noise pollution. Therefore, the ideal state of low-light image enhancement should be to make the dark areas brighter while not allowing the noise part to be enhanced as well. In this paper, we combine Retinex theory and convolutional neural networks to build a simple and effective model with three sub-modules: the decomposition module, illumination module, and reflection module, in which the decomposition module is used to decompose the input low-light image into illumination and reflection maps, the illumination module improves the feature extraction part of the simple convolutional layer used previously and constitutes a new feature extraction structure by Depthwise separable convolution, which makes up for the shortcoming that some dark areas are not well brightened and better adjusts the brightness of the illumination map, and the reflection module adjusts the texture details more clearly because it adds the illumination map as a reference. In this way, the original space is decoupled into two smaller subspaces to the extent that better results can be achieved. We demonstrate through extensive experiments the effectiveness of the design and its superiority over state-of-the-art techniques, especially in terms of robustness to severe visual defects and flexibility in adjusting luminance.
WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets.
Exploring the mediating influence of job satisfaction on the relationship between job security and turnover intention: A case study of the hospitality industry of Jordan

This study aims to examine the mediation influence of job satisfaction on job security and turnover intention among employees working at the frontline of Jordanian hotels. A quantitative empirical investigation is based on a self-administered electronic questionnaire; the data were collected from 170 frontline employees of 20 five-star hotels in Amman. The paper used Smart-PLS 4 software and statistical techniques to test the research hypotheses and analyze the obtained results. The study confirms the mediating role of job satisfaction in the relationship between job security and employee turnover intention. The results also found a significant negative impact of job security on turnover intention and a positive impact of job security on achieving employee job satisfaction. The paper highlights the need for senior management and human resources departments working in the hotel sector of Jordan to ensure employees’ job security and provide requirements that achieve employees’ job satisfaction, as these factors are essential in retaining employees.
Exploring the Potential of Big Data Analytics in Improving Library Management in Indonesia: Challenges, Opportunities, and Best Practice

AbstractAbstractThis column aims to explore the potential of big data analytics in improving library management in Indonesia and identify the challenges, opportunities, and best practices for libraries when applying big data analytics. Using of literature review of published studies, this column examines the current state of big data analytics in library management in Indonesia and provides recommendations, including a SWOT analysis, for implementing big data analytics in operations. It also highlighted several challenges such as limited resources, limited access to technology and digital literacy among librarians, and limited data security measures which could put sensitive patron data at risk.Keywords: Big DatachallengesIndonesialibrary managementopportunities