Reviewerly/exHarmony huggingface dataset:
Introduction to artificial intelligence and machine learning algorithms

The machine learning paradigm and other data-driven approaches have seen a substantial rise in prominence in recent years. Fueled by advancements in data storage and a wider availability of hardware with the processing power needed to make use of such algorithms, machine learning has allowed researchers to tackle increasingly complex problems in a wide variety of domains. In this chapter, the authors will provide a survey of different forms of artificial intelligence, including natural language processing and various machine learning methodologies. This chapter begins with a brief introduction covering some of the history and computational advancements behind artificial intelligence (AI). The second section of this chapter provides further discussion of recent historical milestones in the surge of adoption seen just after the turn of the 21st century, followed by discussion on the difficulties still observed in developing standardized methods of applying AI techniques. The third section contains a survey of AI in its different forms with discussion on the variety of methodologies involved, including the different machine learning approaches such as supervised versus unsupervised, and predictive and generative algorithms. This will include examples showing the state-of-the-art capabilities currently in the industry. During the discussion of supervised machine learning methodology, the authors provide further examples showing the variety and scope of previous research have employed machine learning methodology within the context of diabetes research. The fourth section of this chapter provides a discussion of some of the ethical concerns related to AI and its applications. In this chapter's fifth section, the authors will then discuss the inclusion of the machine learning paradigm within a model of research that employs both data- and hypothesis-driven approaches. Following this model enables researchers to benefit from the respective advantages of each approach as they complement each other, thereby providing the researchers with a more complete model for their scientific endeavors.
Word2Vec Embeddings for Categorical Values in Synthetic Tabular Generation

Although more and more generative models for synthetic tabular data exist, not all of them handle numerical and categorical data equally well. Some of the approaches are solely limited to numerical data. To extend the application of these methods beyond numerical data, we propose a Word2Vec-inspired approach for converting categorical values into numerical values. We demonstrate on the Census Income dataset that the proposed embeddings are capable of learning semantic relationships for ordinal variables. In general, we observed that with larger embedding sizes the quality of the learned embeddings increases. We trained state-of-art CTGAN models on this data and compared it to CTGAN's built-in method for learning categorical data. Our proposed method achieved comparable results. We, therefore, suggest our proposed method as a versatile algorithm that can improve on the synthetic tabular data generation without the need to change existing architectures.
Arithmetic Optimization Algorithm Based Feature Selection Approach for Malicious URL Detection

Today, web applications are the most cost-effective manner of software delivery and they are used by a wide range of businesses and organizations to provide their services over the internet. However, due to the huge number of users who can access these websites; make them vulnerable and easily targeted by different forms of attacks. This paper uses Arithmetic Optimization Algorithm (AOA) as a Feature Selection (FS) based approach to select optimal and informative Feature subset. The AOA is validated on an open source dataset for the detection of malicious URLs. Overall results show that the AOA gained better performance outcomes and faster than other compared to Particle Swarm Optimization, Multi-Verse Optimizer and Salp Swarm Algorithm FS methods.
Code-Level Functional Equivalence Checking of Annotative Software Product Lines

Software functional equivalence checking is a technique for analyzing the impact of change of a portion of code on the rest of the system. The existing functional equivalence checking approaches are applicable only at the individual software product level. In this paper, we propose a lifted functional equivalence checking approach, CLEVER-V, that can efficiently handle annotative software product lines. Instead of checking functional equivalence of every product separately, CLEVER-V analyzes all products together to iteratively identify groups of non-equivalent products with common causes. We report on the implementation of the lifted functional equivalence checking approach and demonstrate its effectiveness and scalability on a suite of 288 realistic software updates from BusyBox.
The taste of virtual, augmented, and mixed reality

In this chapter, you discover the opportunities that virtual, augmented, and mixed reality (VR-AR-MR) applications can bring to medical and pharmaceutical care. The VR-AR-MR examples included here act as concrete treatments for diseases, reducing the need for medication or removing the fear of medical intervention. Visualization, in combination with computer-simulated environments, can enhance pharmaceutical care concepts and both improve understanding and literacy among those who use medication. Readers are also informed on VR-AR-MR technologies that support customer services and augmenting educational pharmacy programs and the efficacy of daily pharmaceutical care processes. The feasibility, affordability, and accessibility challenge the use of VR-AR-MR applications and can hinder global adoption; however, a bright future is still anticipated for VR-AR-MR applications.
How to Retire and Replace a Software Product Line

Software product-line engineering provides a framework for an organization to develop a family of similar, yet customized, software systems based on a common platform. This platform allows the organization to configure a system to changing customer requirements, while also achieving long-term benefits like reduced development and maintenance costs. Consequently, a product line is typically used for a long-living family of systems and is continuously evolved. However, at some point even a product line may be retired and potentially replaced by a successor, for instance, because of outdated technology that cannot be replaced easily and thus makes developing a new product line more feasible. Such a retiring of product lines is mentioned in previous work, but has not been investigated in detail. In this paper, we tackle this gap by describing a process for retiring and replacing a product line, which we defined based on a real-world action-research-like case study. Via this case study. we describe how our process can be executed in practice, what decisions must be considered, as well as the pros and cons we experienced with retiring a product line. We expect that these contributions will help practitioners retire product lines more systematically and with fewer problems. We also indicate open research directions that should be tackled in the future.
Lung Cancer Prediction System using CNN, ANN, And Naïve Bayes

For both men and women, lung cancer continues to be the primary cause of cancer-related mortality, and its prevalence is rising worldwide. we are creating a system that indicates the tumor location and predicts the type of the lung cancer by using three advanced and powerful models which are CNN, ANN and Naïve bayes. The core objective is to come into an accurate and an efficient result that helps doctors to diagnose and predict correctly. Therefore, CT scan is going to be the leading point of the process to use the three algorithms and to get images. The dataset from kaggle consists of CT scan images of three types of lung cancer. We use CNN as feature extraction and as classifier we use ANN and NB. An accuracy in ANN is 0.95% and in Naïve Bayes is 0.78% have been achieved.
Improvement of Courses Recommendation System using Divide and Conquer Algorithm

The recommendation of online courses is a crucial tool for learners who want to learn on their own. The issue of recommendations in the educational sector is unique and cannot be dissociated from irrational elements like learner preferences, performance expectations, etc. In order to increase learners’ excitement for studying, online course recommendations should aim to match learners’ needs with appropriate courses. In this paper, we provide a novel approach to course recommendation for students with multiple preferences. This method achieves the objective of recommendation by generating a list of recommended courses using the Divide and Conquer (D&C) algorithm which makes it possible to divide the database of available courses according to the category chosen by the learner and then use the Skyline Block-Nested Loops (BNL) algorithm to recommend courses that most closely match the preferences of the learner.
On the Reuse of Existing Configurations for Testing Evolving Feature Models

Software Product Lines (SPLs) are used for representing a variety of highly configurable systems or families of systems. They are commonly represented by feature models (FMs). Starting from FMs, configurations, used as test cases, can be generated to identify the products of interest for further activities. As the other types of software, SPLs and their FMs may evolve due to changing requirements or bug-fixing. However, no guidance is usually given on what to do with derived configurations when an FM evolves. The common approach is based on generating all configurations from scratch, which is not optimal since a greater effort is required for concretizing the new tests, and some of the old ones may be still applicable.
Graph Convolutional Network for Multilingual Sentiment Analysis

Sentiment Analysis, known also as opinion mining, discover and extract subjective information from source data allowing businesses to better understand the social sentiment around their products or services. The multilingual characteristics of these data require efficient multilingual sentiment analysis tools. In this work, we propose a graph-based approach for multilingual sentiment analysis. We construct a single heterogeneous text graph based on semantic, sequential, and statistical information to represent the entire multilingual corpus. Then, a graph convolution network learns predictive representation for nodes in a semi-supervised manner. Extensive experiments in real-world multilingual sentiment analysis dataset, demonstrate the effectiveness of the proposed approach. Also, it significantly outperforms the baseline models.
Enhanced Anomaly Detection in Manufacturing Processes Through Hybrid Deep Learning Techniques

Smart factory systems have been introduced to prevent the decline in overall equipment effectiveness caused by the presence of defects within factory manufacturing equipment. In this context, it is crucial to predict process downtime using manufacturing equipment data and take preemptive actions. However, anomaly detection models for preemptive actions have limitations in labeling anomaly alarms. Moreover, from real-time data collection to model deployment, it needs to consider the model-based service implementation for stakeholders. Our research develops a hybrid deep learning-based anomaly detection model that does not require data labeling. The advantage of this model stems from its ability to identify anomalous patterns via the reconstruction of the sequential progression inherent in the data. According to our experimental findings, the proposed hybrid model demonstrated superior efficacy compared to alternative anomaly detection algorithms. By preemptively predicting downtime within the manufacturing process, it contributes to enhanced production efficiency. Furthermore, we develop an anomaly detection system based on a real-time service framework from the data collection step to improve the activation of smart factories. This research contributes to the literature on the monitoring and management of anomaly detection in smart factories. It also has practical implications for the manufacturing industry by recommending efficiency measures for smart factories to reduce downtime in manufacturing processes and improve product quality.
Improving the Application of Blockchain Technology for Tracking Processes in the Supply Chain Integrated Business Intelligence

Trust, traceability, and especially real-time tracking and transparency emerge as critical factors in the design of circular blockchain platforms in the management of supply chains consisting of multiple parties, by adopting various tracking and tracing technologies. Therefore, it influences the perspective of the business intelligence process. In this article, we illustrate the needs and requirements for managing supply chain projects in companies by tracking and tracing their products. This type of tracking and tracing is particularly needed in distributed architectures engaged in project-based enterprises where multiple suppliers are involved in a single project. This tracking and tracing data can be widely used for the business intelligence process. In that case, it will affect the performance of the supply chain by using Ethereum smart contracts to help companies manage a crisis in a targeted manner, after the discovery of such a crisis, and provide distributed trust. In addition, tracking and tracing data allows consumers to ensure that this information has not been tampered with.
Enhancing SPARQL Query Performance With Recurrent Neural Networks

DBpedia is one of the most resourceful link databases today, and to access information in DBpedia databases, we need to use query syntax (e.g., SPARQL). However, not all users know SPARQL, so we must use a natural language query system to translate the user’s query into the corresponding query syntax. Generating query syntax through the query system is both time-consuming and expensive. To improve the efficiency of query syntax generation from user questions, the multi-label template approach, specifically Light-QAwizard, is utilized. Light-QAwizard transforms the problem into one or more single-label classifications using multi-label learning template approaches. By implementing Light-QAwizard, query costs can be reduced by 50%, but it introduces a new label during the transformation process leading to sample imbalance, compromised accuracy, and limited scalability. To overcome these limitations, this paper employs employ two multi-label learning methods, namely Binary Relevance (BR) and Classifier Chains (CC), for question transformation. By employing Recurrent Neural Networks (RNNs) as a multi-label classifier for generating RDF triples, we predict all the labels that align with the query intentions. To better account for the relationship between RDF triples, we integrate BR into an ensemble learning approach, resulting in the Ensemble BR. Experimental results demonstrate that our proposed method outperforms previous research in terms of improving query accuracy.
Focal Distillation From High-Resolution Data to Low-Resolution Data for 3D Object Detection

LiDAR-based 3D object detection plays an essential role in autonomous driving. Although the detector trained on high-resolution data has much better performance than the same detector trained on low-resolution data, the high-resolution LiDAR cannot be widely used due to its high price. In this work, we propose a new distillation method called Focal Distillation to bridge the gap between high-resolution detector (teacher model) and low-resolution detector (student model). It consists of three essential components: focal classification distillation (FCD), focal regression distillation (FRD) and focal feature distillation (FFD). Taking the low-resolution data as input, the student model can learn discriminative features and produce more accurate results with the assistance of the teacher model trained on high-resolution data. We conducted extensive experiments to validate the effectiveness of Focal Distillation. Evaluated on the KITTI validation set, a typical SECOND model trained with Focal Distillation outperformed its non-distilled counterpart by 3.37%, 7.52%, 11.35% mAP on the category Car, Pedestrian, and Cyclist of moderate level, respectively. Moreover, the remarkable improvements observed on different models and different datasets further demonstrate the generalization ability of our proposed method.
Moving forward: the future of blockchain technology in agriculture

This article examines the application of blockchain technology in agriculture, a promising approach to enhance efficiency and transparency. By reviewing existing literature, conducting a study, and analyzing findings, we aim to demonstrate the effectiveness of blockchain in agriculture. Our discussion offers a comprehensive understanding of how blockchain technology can revolutionize agricultural practices and supply chains, providing a robust system for tracking and verifying transactions, improving efficiency, and fostering trust among stakeholders.
HFENet: Hybrid Feature Enhancement Network for Detecting Texts in Scenes and Traffic Panels

Text detection in complex scene images is a challenging task for intelligent transportation. Existing scene text detection methods often adopt multi-scale feature learning strategies to extract informative feature representations for covering objects of various sizes. However, the sampling operation inherent in multi-scale feature generation can easily impair high-frequency details (e.g., textures and boundaries), which are critical for text detection. In this work, we propose an innovative Hybrid Feature Enhancement Network (dubbed HFENet) to explicitly improve the quality of high-frequency information for detecting texts in scenes and traffic panels. To be concrete, we propose a simple yet effective self-guided feature enhancement module (SFEM) for globally lifting feature representations to highly discriminative and high-frequency abundant ones. Notably, our SFEM is pluggable and will be removed after training without introducing extra computational costs. In addition, due to the challenge and importance of accurately predicting boundaries for text detection, we propose a novel boundary enhancement module (BEM) to explicitly strengthen local feature representations in the guidance of boundary annotation for accurate localization. Extensive experiments on multiple publicly available datasets (i.e., MSRA-TD500, CTW1500, Total-Text, Traffic Guide Panel Dataset, Chinese Road Plate Dataset, and ASAYAR_TXT) verify the state-of-the-art performance of our method.
A Comparative Study of Consensus Algorithms Used in Blockchain and Their Adaptation to the IoT Networks

IoT devices are typically designed to operate in low-power environments, which means they have limited processing power, memory, and energy resources. Despite these limitations, they are still capable of communicating with other devices without the need for human intervention. There are several challenges for IoT networks such as, heterogeneity of IoT systems, complexity of networks, and resource constraints of IoT devices. Blockchain technology brings opportunities to overcome these challenges. It relies on consensus algorithms; these replace centralized validation. Although blockchain technology has successes in the field of cryptocurrencies such as bitcoin, it can also be applied in other areas such as IoT networks. For this purpose, this paper presents a comparative study on blockchain consensus algorithms for validation and authentication, as well as their adaptations with resource-constrained IoT networks. The comparison between these algorithms is based on the following criteria: computational power, latency, and storage capacity.
STATUS QUO OF CHINESE CULTURAL INHERITANCE

Chinese culture has a long history and is one of the oldest ancient civilizations. In the course of thousands of years of development, the Chinese nation has created a rich and splendid Chinese culture. Chinese culture is the spiritual pillar for the long-term continuation and continuous development of the Chinese nation. The main spiritual core of Chinese culture is Confucian culture, which has been inherited for thousands of years. Confucian culture is the muscle and bone of Chinese culture. Meanwhile, Taoism, as a local religion, and Buddhism, which is integrated with society in the history of Chinese civilization for thousands of years. Chinese culture has been influenced by foreign cultures and embraces fine foreign cultures. The rapid development of national economy and the integration of global economy have accelerated the influence of foreign culture on China. Media and business hype, promote the "foreign festival" is heating up, behind which is mainly huge commercial profits; It is also inevitable that the Chinese people begin to lead a rich life and pursue a rich and colorful spiritual life. At the same time, traditional culture is regarded by a large number of young people as dross, feudal superstition, backward and unprogressive representative. Chinese culture has thrown off its shackles, been in line with The Times, actively innovated, and carried out new media communication. More and more people have spontaneously joined the camp of protecting cultural inheritance. The method of cultural inheritance also changes with The Times, but what remains unchanged is the sense of belonging to culture.
Distinguishing User Paths for Personas and Stakeholders Through Motives and Decision Making

Personas play a crucial role in the design process by aligning user needs with project functionality and identifying potential obstacles. However, if not evaluated properly during the assumption stage, personas may not accurately represent users. The objective of this research is to develop a framework that considers users’ past experiences and circumstances to guide researchers during the initial assumption phase. The study aims to examine persona development in greater detail during this stage, considering the motives and decision-making processes of external stakeholders in both public and private sectors. By studying discussions taken with users as they examine digital tools with the researcher the preliminary research explores the use of thinking more of the observations made by the persona. The findings suggest that additional information could be potentially considered in the early stages of persona creation for design purposes such as objective and subjective perspectives.
Interactive Visualization of Sport Climbing Data

The official website of the International Federation of Sport Climbing (IFSC) stores information about sport climbing competitions and athletes. While the website shows comprehensive data, it was mainly static and there was limited interaction or effective visualization, impeding the attempts to understand the performance of the athletes. To address this problem, we developed IFSC $$^{+}$$ , an interactive visualization system for sport climbing data from the IFSC official website. This paper details the design of the interactive visualizations, highlighting how they can be used to compare athlete performance and identify promising candidates in future competitions. Our work demonstrates the value of interactive visualizations in supporting effective meaning-making and informed decision-making in sports.