Reviewerly/exHarmony huggingface dataset:
Creative Companion

Having ascribed a variety of personas to generative AI, this chapter moves the idea forward that through various personas that you prompt, generative AI has the capacity to generate prototypes that will further augment your creative process. In saying that, this chapter further transforms generative AI into a creative companion that provides inspiration like a muse.
Building Blocks

Experienced creatives who prototype share a lot in common with improvisers. They are curious in their attempts to create something they haven’t experienced before. Some creatives are tasked to do this as part of their jobs, to find a new market for the product or service they are creating. To create rapidly they rely on a combination of skills, technique, and experience and are used to “ramping up” on a variety of different software applications that they can create their work with.
Use Cases

This chapter details some use cases to demonstrate pragmatic uses of generative AI and how its use is supporting the creative process for different types of guest creatives. The real-world use cases have been implemented with subscription-based generative AI sites, tools that are in beta, and open source APIs (Application Programming Interfaces). The chapter will also reveal that using multiple machine learning models to layer AI-generated content, in combination with your own original vision, concept, and artistry, can fuel accelerated prototypical workflows. The workflows presented point the way for you to take advantage of the strengths of multiple machine learning models and combine their generated content in new and innovative ways. The use cases affirm what has been repeated throughout the other chapters:
Automated Analysis of Connections in Model Diagrams

A model diagram is a crucial aspect of various scientific documents that are available as hard or soft copies. Often, the information captured in these diagrams is complex and needs deeper comprehension by human experts. Automating this process of reading and analyzing has become extremely important for various industrial applications. Hence, in this paper, we aim to extract the context from model diagrams by solving for connections and converting them into a graph using sophisticated image processing and pattern recognition algorithms.
The Art of the Prompt

This chapter explores the practice of text-based prompting in detail, discussing the different types of generative AI systems that can be prompted, such as language models, image-generating AI, and others. Included in this chapter are best practices for crafting effective text-based prompts, including recommendations for the type and structure of prompts, as well as the importance of iteratively creating prompts. Finally, audio, images, and video that accompany text prompts are important forthcoming features to highlight in generative AI prompting.
A computer vision monitoring for human fall using visible light camera and thermal imager

In order to solve the problems of image blur, uneven illumination and object occlusion in visual monitoring, a human fall detection algorithm based on visible light camera and thermal imager is proposed in this paper. Firstly, the visible light and thermal images are denoised to reduce the interference of noise. Secondly, the skeleton and joint coordinates of the human body are extracted through the lightweight human posture recognition model. Finally, three human posture parameters are designed as recognition features to achieve accurate fall recognition. The method is verified on self-built datasets and public datasets. The experimental results show that the accuracy of the method is 0.93 and 0.94 respectively. Compared with the most advanced algorithms, the proposed method has higher accuracy and better real-time performance.
Compressed Image Super-Resolution Using Pre-trained Model Assistance

Single Image Super-Resolution (SISR) has seen flourishing research in the past years witnessing many different solutions ranging from heuristics based to deep learning based models. However, little attention has been paid to compressed images that are mostly found in the commonplace. The challenge involved with compressed image super-resolution is that there are compression artefacts which makes it even more difficult to learn the high-resolution image from the low-resolution one. To this end, we evaluate a simple, convolution based model which uses a large scale pre-trained feature extractor network for learning during the training phase and then works independently during inference without the pre-trained model, making it a lightweight, deployable solution. The performance of our proposed system is assessed with various existing literature and experimental results show that our technique achieves competitive results in comparison.
Real-Time Prediction of In-Hospital Outcomes Using a Multilayer Perceptron Deployed in a Web-Based Application

In-hospital mortality prediction in real time can offer clinicians a convenient and easy indicator of patient acuity and hospital efficiency. The latter also significantly relies on adequately utilizing the hospital resources to deliver quality treatment and care. The provision of quality care merits the reduction of the average duration of patient stay, especially for intensive care units (ICU) where patients are in critical condition. Knowing the length of patient stay can thus serve as an indicator of hospital efficiency and help drastically improve hospital resource utilization. Also, extensive hospital stay usually denotes prolonged bed rest, the primary cause of pulmonary embolism (PE). We have also taken into cognizance that despite recent advancements in hospital infrastructures, cardiovascular disorders are a leading cause of mortality, among which heart failure (HF) and ST-elevation myocardial infarction (STEMI) have lately gained prominence. The advent of electronic health records (EHR) has allowed the usage of machine learning prediction algorithms to determine various health disorders from patient data. Therefore, we have developed sequential deep neural networks capable of predicting HF, STEMI, and PE from patients’ demographic data, prior medical history, and initial lab parameters, with an AUC of 0.867, 0.861, and 0.761, respectively. We have also developed neural networks for predicting Mortality with an AUC of 0.985 and Duration of ICU and Hospital Stay with Mean Absolute Error of 2.03 and 2.54, respectively, by adding doctors’ diagnoses of comorbidities to patient records. We have designed a web application that can be deployed in a hospital and used to test these models on real-time patient data.
GradeChain-$$\alpha $$: A Hyperledger Fabric Blockchain-Based Students’ Grading System for Educational Institute

The technological advance has an impact on education for many decades. Education is a core area where different peers need to share and modify shared information. Educational institutes are managing large amounts of student records pertaining to student data, marks, and teachers’ details to provide better grading solution. Marks are more sensitive as its disclosure might lead to revealing the identity of the students. There are several advantages to use blockchain in the education system where digitalized students’ record is one of them. In such a system, the organizations can generate marks, and can store marksheets and certificates of individual students which can be accessed and verified by the other institute/company if the students have applied to that company/organization for getting a job or for getting admission to higher studies. This paper has designed a Hyperledger Fabric blockchain-based marks generation marks storing system of an educational organization, named GradeChain- $$\alpha $$ . The generator of the marks can be tracked by the administrator of the system. The reliable, replicated, redundant, and fault-tolerant (RAFT) protocol has been used to automate the result publication of students. The implementation status shows that this architecture can be used in an automated students’ grading system.
Adoption of Technologies and Innovations for Sustainable Rice-Paddy Farming in Kelantan

Paddy production is an important sector for the country as it ensures food security. The National Agrofood Policy (NAP), 2011–2020, emphasises the importance of increasing domestic rice production to ensure an adequate rice supply. Meanwhile, NAP 2.0 (2021–2030), which is expected to be launched in 2021, will focus on addressing issues of productivity, production costs, and dependence on the labour force. This policy will include efforts to modernise the agricultural sector in line with the development of technology and the Industrial Revolution 4.0. Integrated agricultural technologies of a “high-tech” nature will be integrated into the sectors below NAP 2.0 as part of the emphasis on technology and applications. Rice production is a nation’s staple food source, so adopting advanced technology in agricultural production is crucial, especially in developing countries like Malaysia. In addition to ensuring the sufficiency of the national food supply and its sovereignty, consistent yields and returns can also improve the living standards of current rice entrepreneurs as well as attract new ones, especially from the younger generations. Therefore, this study was conducted to assess the level of acceptance of technology and innovation among rice farmers in Kelantan. Using SmartPLS analysis, the sample size is 45 farmers in Kelantan. As a result of the study, it was found that farmers are generally eager to adopt MARDI and KADA technology and innovations.
Aggregated Tests Based on Supremal Divergence Estimators for Non-regular Statistical Models

A methodology is proposed to build statistical test procedures pertaining to models with incomplete information; the lack of information corresponds to a nuisance parameter in the description of the model. The supremal approach based on the dual representation of CASM divergences (or $$f-$$ divergences) is fruitful; it leads to M-estimators with simple and standard limit distribution, and it is versatile with respect to the choice of the divergence. Duality approaches to divergence-based optimisation are widely considered in statistics, data analysis and machine learning: indeed, they avoid any smoothing or grouping technique which would be necessary for a more direct divergence minimisation approach for the same problem. We are interested in a widely considered but still open problem which consists in testing the number of components in a parametric mixture. Although common, this is still a challenging problem since the corresponding model is non-regular particularly because of the true parameter lying on the boundary of the parameter space. This range of problems has been considered by many authors who tried to derive the asymptotic distribution of some statistic under boundary conditions. The present approach based on supremal divergence M-estimators makes the true parameter an interior point of the parameter space, providing a simple solution for a difficult question. To build a composite test, we aggregate simple tests.
A General System for Dataset Generation from Smartwatch Sensors for Biomedical Research

This paper describes a novel Android Wear OS application, designed to enable researchers to conveniently collect sensor data from commonly available Wear OS smartwatches for dataset generation. The application was primarily developed to ensure the reliability of data collection during physical exercises. The application thus proposed implements a reliable dataset generation architecture, which researchers can extend and modify to adapt to their workflows. In the application, data is collected from sensors present on the smartwatch and then transferred to a paired smartphone. Thus, the application eliminates the need for a third device or service (such as cloud storage) for dataset generation. Data obtained from the smartwatch is stored in a predetermined folder/directory in the smartphone in CSV format making it convenient for performing data analysis. The application is tested by recording data from multiple subjects in the lab environment as well as outside the lab. The quality of data was confirmed by plotting it in normal working time, and during physical exercise and the changes are properly observed, which ensures that the data we are collecting is correct. Since the use of smartwatches has become prevalent for studying and diagnosing diseases, the application will be very useful for researchers across the globe for creating datasets for biomedical research.
Functional Properties of PDE-Based Group Equivariant Convolutional Neural Networks

We build on the recently introduced PDE-G-CNN framework, which proposed the concept of non-linear morphological convolutions that are motivated by solving HJB-PDEs on lifted homogeneous spaces such as the homogeneous space of 2D positions and orientations isomorphic to $$G=SE(2)$$ . PDE-G-CNNs generalize G-CNNs and are provably equivariant to actions of the roto-translation group SE(2). Moreover, PDE-G-CNNs automate geometric image processing via orientation scores and allow for a meaningful geometric interpretation. In this article, we show various functional properties of these networks: We verify the properties and show favorable results on various datasets.
Temporal Alignment of Human Motion Data: A Geometric Point of View

Temporal alignment is an inherent task in most applications dealing with videos: action recognition, motion transfer, virtual trainers, rehabilitation, etc. In this paper we dive into the understanding of this task from a geometric point of view: in particular, we show that the basic properties that are expected from a temporal alignment procedure imply that the set of aligned motions to a template form a slice to a principal fiber bundle for the group of temporal reparameterizations. A temporal alignment procedure provides a reparameterization invariant projection onto this particular slice. This geometric presentation allows us to elaborate a consistency check for testing the accuracy of any temporal alignment procedure. We apply this consistency check to some alignment procedures from the literature based on dynamic programming for the task of aligning motions of tennis players. The comparison of the obtained results leads us to propose a version of dynamic programming that incorporates keyframe correspondences. The temporal alignment procedures produced are not only more accurate, but also computationally more efficient.
Uniform Distribution Law as a Base of Statistical Decision Criteria

Nowadays, testing the hypothesis of the uniform distribution law, or in other words, identification, is a relevant topic of research. The main goal of the study is to determine the most powerful goodness-of-fit test. The uniform distribution law is a good basis for the statistical decision criteria. At the same time, the uniform distribution law can be easily implemented. The issue of determining the most powerful goodness-of-fit test has recently been attracting much attention. For example, the power of known tests was studied for moderate-sized samples with size $$n \ge 10$$ . Samples of the smaller size are called small samples. Currently, the theory of making statistical decisions using small samples lacks scientific justification and development. The complexity of the problem of determining the best estimates using small amount of statistical data depends on the type of distribution and the sample size. Moreover, such problem cannot be the object of general mathematical theory in the conditions of non-asymptotic formulation.
Attention-Based Random Forests and the Imprecise Pari-Mutual Model

A modification of the attention-based random forest (RF) called ABRF-PM is proposed. In contrast to the original attention-based RF, the ABRF-PM model uses the imprecise pari-mutual model in place of the Huber’s contamination model for representing the attention weights and trainable parameters. Moreover, a general approach to applying various imprecise models to the attention-based RF, which is based on considering extreme points of sets of probability distributions produced by the imprecise models as sets of attention weights, is proposed such that the pari-mutual model can be regarded as a special case of the sets. The general approach ensures the linear relationship between attention weights and the corresponding attention trainable parameters, which leads to the standard quadratic optimization problem for computing the trainable parameters. Numerical experiments with real datasets illustrate the proposed ABRF-PM model.
Prelims

Casebook of Indigenous Business Practices in AfricaThis unique collection of case studies across multiple sectors and regions in Africa presents a compelling resource for the advancement of indigenous knowledge and business practices on the continent.It provides an invaluable Africa context-specific resource to inform knowledge development about African businesses, ensure socio-economic advancement, preserve cultural heritage, achieve sustainability and enhance business decision-making.
A Smart Contract Privacy Protection Scheme Based on Smart Grid

The emergence of Ethereum Smart Contract based Grid (ESCG) not only solves the one-way transmission defect of traditional power grid, but also focuses on the protection of users’ private data. Aiming at the privacy problem of timing transaction in ESCG, this paper proposes a privacy protection scheme based on smart contract. In the proposed scheme, we deploy the agent contract in the user flow table, and use the contract execution to complete the data security transmission between the scheduling contract and the agent contract. Then, the malicious behavior reporting is used to deal with the attack behavior or security threat in the whole process. Finally, simulation results show that the proposed scheme has lower gas consumption and lower time overhead in ESCG.
Analysis of Crisis Leadership Research Trends Using Text Mining: Focusing on Business Administration

This study was to analyze research trends in the field of business administration by applying text mining techniques. In particular, the 2008 global financial crisis caused many management difficulties for companies. It was intended to consider how these events were reflected in research trends. This study performed 'frequency analysis', 'TF-IDF analysis', and 'topic analysis' among the text mining techniques. First, before and after the global crisis, “management” and “leadership” were ranked top tier. Also 'leadership' and 'management' belonged to the top tier in the total period. Second, the most noticeable word was 'safety' before 2008. And 'integration' is the top priority after 2008. Thus, it was found that the business administration field dealt with 'integration' and 'decision making' meaningfully after 2008. Third, we found a paper that corresponds to the gamma value by topics. Key words explain these papers well. In this way, the meaning of extracted words was analyzed by applying text mining techniques, and research trends in the field of business administration were examined through this. In addition, words that appear important and words with special meanings were analyzed by comparing around 2008.
A Policy Proposal on the Counter-terrorism Directive of the Ministry of National Defense

This study aims to examine the history, legal character, and limitations of the 'Counterterrorism Directive of the Ministry of National Defense' and propose directions for revising relevant laws to overcome these limitations. The 'Counterterrorism Directive of the Ministry of National Defense' is an administrative rule and internal regulation that cannot serve as a legal basis to infringe upon the basic rights of citizens or restrict their freedom. The 'Counterterrorism Directive of the Ministry of National Defense' is an administrative rule and internal regulation that cannot serve as a legal basis to infringe upon the basic rights of citizens or restrict their freedom. There is also a concern that the overlapping involvement of the Defense Counterintelligence Command and the Criminal Investigation Command of the Ministry of National Defense in counterterrorism investigations could lead to confusion during the initial stages of the investigation. Therefore, it is necessary to revise the relevant laws to ensure legal control over the military's counterterrorism investigations and the collection of information on terrorist suspects, and the reevaluation of the counterterrorism investigative agency for military facilities should be conducted.
Environmental Financial Reporting and Corporate Performance of Listed Pharmaceutical Companies in Nigeria

A nation's economic growth and development depend critically on the pharmaceutical business because of its inextricable link to people's health and, by extension, their ability to put in productive hours at work. This study analyzed the effect of environmental financial reporting on the profitability of publicly listed pharmaceutical companies in Nigeria. The study relied on the ex post facto research design and made use of historical financial data on the adopted study variables. At the 0.05 threshold of significance as decision criteria, the study tested two hypotheses. The research used secondary data, and the businesses studied were a representative sample of the pharmaceutical companies listed on the Nigerian Exchange Group. Ordinary least square regression was used in conjunction with E-views version 9 to analyze the collected data. Earnings per share of listed pharmaceutical businesses in Nigeria were shown to be favorably connected with environmental financial reporting proxy by employee's welfare policy and community development cost. Management was urged to invest more in their workers' well-being to boost morale and productivity in the pharmaceutical industry.
Comparative Analysis of Machine-Learning Techniques for Insult Detection in Social Commentary

Social media users' immediate and long-term well-being is seriously threatened by cyberbullying. With an eye towards creating early warning systems for the anticipation of harmful comments vulnerable to attacks, we investigate the detection of cyberbullying in social networks. Online harassment disturbs by cyberbullying has grave repercussions. The ability to automatically detect cyberbullying and recognize the roles that individuals assume in social interaction is required to address this issue in online contexts. The use of technology as a bullying tool is known as cyberbullying. Cyberbullying is a problem that has arisen along with technology development and poses a risk to adolescents' psychological welfare. A framework is suggested to provide two distinct descriptions of cyberbullying. Cyberbullying is a significant issue on the internet that affects both adults and teenagers. Mistakes like despair and suicide have resulted from it. There is an increasing demand for content on social media platforms to be regulated. The following study builds a model based on the identification of cyberbullying in text data using a Naive Bayes classifier, utilizing data from Twitter and comments based on personal assaults from Wikipedia forums. The model offers accuracy levels above 90% for data from Tweets and accuracy levels above 80% for data from Wikipedia.
Modal and Justification Logics for Multi-agent Systems (Invited Talk)

Epistemic modal logic is an important tool in the area of distributed and multi-agent systems.