Reviewerly/exHarmony huggingface dataset:
Multimorbidity Content-Based Medical Image Retrieval and Disease Recognition Using Multi-Label Proxy Metric Learning

Content-based medical image retrieval is an important diagnostic tool that improves the explainability of computer-aided diagnosis systems and provides decision-making support to healthcare professionals. A common approach to content-based image retrieval is learning a distance metric by transforming images into a feature space where the distance between samples is a similarity measure. Proxy metric learning methods are effective at learning this transformation due to the use of proxy feature vectors that enable efficient learning. Training with a distance-based classification loss enables a single proxy model to be suitable for both retrieval and classification. However, these methods are designed only for single-label data, making them unsuitable for multimorbidity medical images. Addressing this, we propose a novel multi-label proxy metric learning method for content-based image retrieval and classification. Unlike existing proxy-based methods, training samples assign to multiple proxies that span multiple class labels. This results in a feature space that encodes the complex relationships between diseases. We introduce negative proxies to better encode the relationships between samples without detected diseases. The efficacy of our approach is demonstrated experimentally on two multimorbidity radiology datasets. Results show that our method outperforms state-of-the-art image retrieval systems and baseline approaches. Our method is clinically significant as it improves on two key factors shown to affect medical professionals’ willingness to use computer-aided diagnosis systems: accuracy and interpretability.
Design and Development of the Graphology-based Career Analysis and Prediction System (G-CAPS) for Engineering Students

The decisions regarding a prospective career choice and the paths leading to it are life-changing actions for any student. Engineering students particularly have plenty of career opportunities to choose from after their graduation or even during campus placements. The wide gamut of opportunities may sometimes cause engineering students to end up in career paths that do not match their aptitudes, skills, and personality traits. Developing efficient career prediction and guidance systems exclusively for engineering students is a pressing priority. However, there is a scarcity of research studies on automated career prediction systems for engineering education settings. Against this backdrop, we propose a novel solution rooted in artificial intelligence titled Graphology-based Career Analysis and Prediction System (G-CAPS). Advanced graphology tools are employed to connect handwriting features with the personality traits of individual students. The Holland theory of vocational interests is adopted in G-CAPS to characterize and model individual career interests. Existing literature indicates that no such graphology-based prediction system was developed based on vocational personality traits. The G-CAPS model can be trained and tested using the handwriting samples collected from engineering students and working professionals with engineering degrees. Distinct handwriting features are captured and processed utilizing an array of Convolutional Neural networks (CNN). The system architecture development of the model and its working process is particularised in the paper. It is anticipated that the G-CAPS model can soundly address the career path selection issues of engineering students and graduates looking for a job. The innovative prediction system can be scaled to assist engineering students and graduates across the globe in selecting potential career paths most suitable for their specific character traits.
Impact of Remote Labs in Preparing Students for Work 4.0

Industry 4.0 will enable new types of interactions between man and machine with ramifications on the nature of work and the labor force. New technologies will require new skills and competencies, and engineers, entrepreneurs, programmers, and workers will need a broad skillset to address the technological advancement of the fourth industrial revolution. Remote labs stand as an efficient and reliable platform to train students, improve their skills and prepare them for the Work 4.0 paradigm. The story of Princess Sumaya University for Technology with remote labs and its efforts to provide students with a glimpse of Industry 4.0 is presented. In addition, assessment methods to em-power students with virtual collaboration, social intelligence, and communication skills are investigated, showing improvements in performance and achievements.
Automated Evaluation System for Microcontroller Assignments

As IoT systems have increased the number of deployed embedded devices drastically and most of these devices are used in safety or security critical environments, the education of embedded software engineers is more important than ever. A critical part of their education is the development of their intuition for secure and safe software. In this paper <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This research was funded by the city of Vienna (MA-23 call 21, project no. 9). we present an evaluation system used to generate fast and accurate feedback for student submission in, but not limited to, embedded software development courses. The system can be used as a first feedback loop to outline to the students where problems exist in their code and give them the opportunity to analyze and correct their errors. These extra steps ensure that the students can and will be notified early about their mistakes and can search for correct solutions, supporting the student's learning process. We present the implementation of the system and analyze its deployment in a microcontroller software development lecture. This analysis was done by means of surveys of the students and lecturers as well as a statistical analysis of the student submissions. The results show that the students made use of this extra features and even would prefer to have this feedback in other software development lectures as well.
A Gamified Approach to Concrete Structure's Interpretation using Virtual Reality: A Study of Performance and Simulation Sickness

Innovation in the methods and strategies of teaching has become of high importance to educational institutions. Shifting teaching strategies from static to immersive, interactive, and engaging environments through gamification approaches is paving its way into education with substantial benefits. The integration of disruptive technologies such as Virtual Reality (VR) as a tool to gamify teaching has shown improved performance and motivation of students. Though, the use of such technologies appears to adversely affect the student's wellbeing resulting in simulation sickness. Civil engineering undergraduate students were divided into three groups and asked to interpret and identify structural mistakes in a concrete building using three different approaches: The static traditional 2-Dimensional Plans (2D), a gamified 3-Dimensional model (3D) and an immersive Virtual Reality (VR) environment. The students' performance and simulation sickness development were measured using three main simulation sickness categories. Results revealed that students using VR performed 35% and 4% better than 2D and 3D respectively. In addition, the data collected shows no correlation between traditional and gamified methods with regard to simulation sickness.
Is Education Ready To Embrace Metaverse?

Metaverse, an immersive virtual world that is facilitated by the use of virtual reality (VR) and augmented reality (AR), is expected to be soon part of our lives. Indeed, in some areas, such as in the game industry, this revolutionary technology is already being used. In the context of education, however, metaverse is not yet being extensively utilized and its possible usage is currently being debated. To contribute to this debate, the current study explores students' and educators' perceptions of metaverse and its potential to be used in education. To study these questions a mixed research approach was employed combining both quantitative and qualitative data collection. Specifically, an online questionnaire was distributed to students and semi structured interviews have been conducted with lecturers. Results suggest that students and teachers have a relatively good understanding of metaverse and foresee its utilization in education in the next few years. They remain open about the extent to which this will happen and divided as how fundamental a change it will be.
Teaching Business Skills in the Cloud: A Process Model for Cloud-Based Enterprise Software Integration in Higher Education

The cloud-based enterprise software (ES) market is booming due to ease of access and scalability among other factors. Within ES, all data, resources, and processes of a company are managed. Thus, ES comes with complexity that requires skilled users. Therefore, it is important to train students in cloud-based ES so that they can make informed decisions in their future professional life. So far, research on the intersection of education and cloud-based software has mainly focused on virtual labs and education software in the cloud. In contrast to that, we focus on the integration of cloud-based ES in higher education as a basis for teaching students practical and interdisciplinary ES skills. Also, we depict the role of an educational service provider (ESP) as an intermediary in that setting. In our paper, we conducted a case study within the context of a large ES company's global academic alliance program and an ESP. The requirements and procedures derived for integrating cloud-based ES in higher education teaching are transferred to a process model. This process model gives insights into how the integration is handled so far and can serve as a guide for other cloud-based ES being introduced to higher education teaching. We contribute to both research and practice by emphasizing students' transdisciplinary education in the ES domain and the role of ESPs as a linking connection between higher education and the ES industry.
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques

The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. In response to the increasing diversity and complexity of data, many researchers have developed deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of these techniques, with a particular focus on the types of data, intending to assist healthcare researchers from various disciplines in dealing with missing data.We searched five databases (MEDLINE, Web of Science, Embase, CINAHL, and Scopus) for articles published prior to February 8, 2023 that described the use of DL-based models for imputation. We examined selected articles from four perspectives: data types, model backbones (i.e., main architectures), imputation strategies, and comparisons with non-DL-based methods. Based on data types, we created an evidence map to illustrate the adoption of DL models.Out of 1822 articles, a total of 111 were included, of which tabular static data (29%, 32/111) and temporal data (40%, 44/111) were the most frequently investigated. Our findings revealed a discernible pattern in the choice of model backbones and data types, for example, the dominance of autoencoder and recurrent neural networks for tabular temporal data. The discrepancy in imputation strategy usage among data types was also observed. The "integrated" imputation strategy, which solves the imputation task simultaneously with downstream tasks, was most popular for tabular temporal data (52%, 23/44) and multi-modal data (56%, 5/9). Moreover, DL-based imputation methods yielded a higher level of imputation accuracy than non-DL methods in most studies.The DL-based imputation models are a family of techniques, with diverse network structures. Their designation in healthcare is usually tailored to data types with different characteristics. Although DL-based imputation models may not be superior to conventional approaches across all datasets, it is highly possible for them to achieve satisfactory results for a particular data type or dataset. There are, however, still issues with regard to portability, interpretability, and fairness associated with current DL-based imputation models.
Detection and Prevention of Data Leakage in Transit Using LSTM Recurrent Neural Network with Encryption Algorithm

As the amount of data generated by organizations continues to multiply, there is a need to ensure the security and transmission of these data at any stage that they may be at. Data breaches have constituted a large loss when it comes to the integrity of an organization and their sensitive information being leaked or authorized by an unauthorised user. This research implements a solution to detect and prevent data leakage in transit using LSTM and the AES-256 encryption algorithm. Using a real-world news dataset, the study introduced a solution using LSTM to train the dataset and AES-256 encryption to encrypt the data based on the trained dataset. Test data was then passed to confirm the accuracy of the model and detect if there was a match on the news dataset. From the results of the study, the model was able to detect the sample or test data accurately as leaked data with an accuracy of 93.7%. The importance of developing a system to mitigate these risks is to help provide organizations with confidence in how their confidential information is transmitted.
Ensemble Learning for the Survivability Prediction of Breast Cancer Patients Using METABRIC and SEER Datasets

Breast cancer is the most life-threatening disease among women worldwide. Nowadays, clinical and genomic data in breast cancer datasets are analyzed to identify the incidence and impact of the disease. This paper proposes an analysis on the clinical breast cancer datasets, METABRIC and SEER for the prediction of survivability of breast cancer patients through ensemble classifiers. It attempts to identify informative independent features for those classifiers using filter methods, Mutual Information and Chi-Square and wrapper methods, Sequential Forward Selection, Sequential Backward Selection and Recursive Feature Elimination in the feature selection sequence. An analysis on the performance of ensemble techniques namely, bagging, boosting and stacking shows that the ensemble techniques produce higher accuracy when compared to the accuracies produced by the base classifiers. Also, the stacking ensemble model produces higher accuracies of 99.2% and 99.8% than other ensemble techniques on METABRIC and SEER datasets respectively. The SBS wrapper method aids higher accuracy to all classifiers than SFS and RFE.
Comparative Analysis of Recent Data-Level Methods for Imbalance Classification

In recent days Machine Learning Technique has become one of the ubiquitous techniques used for solving different real-life problems. The Data sets used in Machine Learning techniques play a vital role in enhancing the efficiency and performance of the algorithm. An accurate outcome is not possible most of the time because of multiple factors. One of them may also be the nature of the data set. Real-world data sets are more cluttered, complex, and unorganized. Because of this reason, real-world data sets are imbalanced in nature. Machine learning experts believe that imbalanced data is a significant issue and researchers are working to tackle this. Three approaches are employed to fix this issue, those are Data level, Algorithm level, and Hybrid level approaches. We have made an effort to conduct a comparative study on recent data-level approaches and also try to conduct a thorough analysis of the undersampling and oversampling approaches.
User Interface of Blockchain-Based Agri-Food Traceability Applications

Blockchain is a new digital technology that enables pervasive financial transactions amongst scattered untrustworthy stakeholders without relying on agents like banks. This article examines how blockchain technology has an impact on agriculture and the food supply chain, highlights ongoing initiatives, and assesses the broad implications, challenges, and potential while keeping a critical eye on project maturity. Our research demonstrates that block chain technology is a potential method for a reliable food supply chain, including several current activities involving various food items and nutrition difficulties, but it still has a number of limitations and impediments that prevent it from being widely adopted by farmers and systems. These difficulties include issues with technology, education, legislation, and regulatory systems.
A Blockchain-Assisted Authentication for SDN-IoT Network Using Smart Contract

The Internet of Things (IoT) is a rapidly growing technology due to the demand for automation in various industries, such as healthcare, manufacturing, and home automation etc. As IoT devices continue to proliferate, authentication and access control remain a major challenge. By verifying the identity of the device and the user, authentication helps prevent fraudulent activities and ensure that the IoT data is not tampered with. The existing works are dependent on a centralized server which can become overwhelmed by large users, making them difficult to scale. In this work, we greatly utilize the characteristics of Blockchain to authenticate the user in the Software-Defined Network based IoT (SDN-IoT) network. The SDN provides centralized management of growing IoT devices. Blockchain is a decentralised and tamper-proof method for storing and sharing authentication information making it suitable for the system. We generate a digital token for each device access request through Smart Contract. The token manager on the Smart Contract validates the token for each user before giving access to it. The Smart Contract can revoke the token on unsuccessful authentication from the user to avoid an attack on the system. We conduct an experiment on the potency of the proposed work by testing the Smart Contract based on authentication delay, transaction cost and ability to resist the attack. The experiment result shows that the proposed work is a promising solution for authentication in SDN-IoT networks.
NMT for a Low Resource Language Bodo: Preprocessing and Resource Modelling

Implementation of neural machine translation for low-resource language requires special attention. In this paper we have discussed about the resource modelling and preprocessing steps for a low resource Indic language Bodo for the purpose of neural machine translation from English. To capture the essence of the Bodo language, syntactic and morphological characteristics of the language are discussed in this paper. It includes the steps of data preparation, tokenization and subword tokenization which are considered as the pre steps for preparing the training and test data. The research highlights multiple subword tokenizers to capture the rare words, unknown words and generate the vocabulary. It discusses the SentencePiece, Byte-pair Encoding (BPE) and WordPiece tokenizer for the Bodo language. We have shown the outcomes of these subword tokenizers with respect to Bodo language which will serve as a priori for the future researcher working in NMT for Bodo and any other low resource language in general. Proper sub-tokenization can aid in raising the caliber of neural machine translation that is the reason three different tokenizers are experimented with while preparing the dataset. This paper also discussed about Seq2Seq LSTM and Transformer Machine Translation architectures.
Proposal Of User-Friendly Design Of NFT Marketplace

A non-fungible token (NFT) is a digital asset that does not have a fixed value and it cannot be replaced neither interchanged as it has unique properties. Overall, non-fungible tokens represent a significant advancement in the world of digital asset ownership and authentication. They provide a means of verifying the ownership and authenticity of unique digital assets, while also enabling a new level of interoperability and interoperability between different platforms and applications. As the use of NFTs continues to grow and evolve, they are likely to play an increasingly important role in the digital economy of the future. The NFT market has witnessed a remarkable upswing, reaching a trading volume of ${\$}$10 billion in 2021. However, the complexity of the consensus algorithm in blockchain technology enhances security but leads to elevated transaction fees, energy inefficiency, and restricted scalability. To address these challenges, various public blockchain platforms with different consensus protocols have emerged as alternatives for NFT marketplaces. Choosing the most suitable public blockchain platform is a critical decision in designing an NFT marketplace. In this study, we evaluated the cost and performance of three public blockchain platforms, namely, Fantom, Avalanche, and Polygon, for minting and transferring NFTs. Our approach employed machine learning models to predict the transaction cost and throughput for each platform, facilitating the selection of the optimal platform. Our experimental results revealed that the Polygon network is more efficient than the other two platforms in terms of transaction fees and throughput.
Deterministic Construction of QFAs Based on the Quantum Fingerprinting Technique

It is known that for some languages quantum finite automata are more efficient than classical counterparts. Particularly, a QFA recognizing the language $$MOD_{p}$$ has an exponential advantage over the classical finite automata. However, the construction of such QFA is probabilistic. In the current work, we propose a deterministic construction of the QFA for the language $$MOD_{p}$$ . We construct a QFA for a promise problem $$Palindrome_{s}$$ and implement this QFA on the IBMQ simulator using qiskit library tools.
A Composable Generative Framework Based on Prompt Learning for Various Information Extraction Tasks

Prompt learning is an effective paradigm that bridges gaps between the pre-training tasks and the corresponding downstream applications. Approaches based on this paradigm have achieved great transcendent results in various applications. However, it still needs to be answered how to design a general-purpose framework based on the prompt learning paradigm for various information extraction tasks. In this article, we propose a novel composable prompt-based generative framework, which could be applied to a wide range of tasks in the field of information extraction. Specifically, we reformulate information extraction tasks into the form of filling slots in pre-designed type-specific prompts, which consist of one or multiple sub-prompts. A strategy of constructing composable prompts is proposed to enhance the generalization ability in data-scarce scenarios. Furthermore, to fit this framework, we transform relation extraction into the task of determining semantic consistency in prompts. The experimental results demonstrate that our approach surpasses compared baselines on real-world datasets in data-abundant and data-scarce scenarios. Further analysis of the proposed framework is presented, as well as numerical experiments conducted to investigate impact factors of performance on various tasks.