Reviewerly/exHarmony huggingface dataset:
Challenges and future work directions in artificial intelligence with human-computer interaction

Artificial intelligence–based systems are developed and successfully used for applications like home appliances, defense systems, virtual assistance, robotics, self-driving vehicles, and many more. Their success lies in accurate and timely decision-making ability. But the other side of these systems is a lack of transparency that can be described as black box. Due to the opaque nature of existing artificial intelligence systems, researchers are not able to interpret the decisions that have been derived from given input situations. The lack of openness not only causes the end users to resist trusting the system but also tends to make it difficult for machine learning engineers to detect and mitigate the fault in case of failure in deriving desired output. The solution is to open the black box working nature of the system and provide required explanations as well interpretations to making the whole processes humanly understandable and meaningful. This chapter focuses on the need for explainable artificially intelligent systems, present paradigms that exist to achieve it, along with various forms of explanations expected by different stakeholders and challenges in the field of making transparent systems in the direction of trustworthy human–computer interaction.
Development of a predictive model for classifying colorectal cancer using principal component analysis

Colorectal cancer (CRC) is the leading second cause of cancer death cases in both genders in the United States. On the other hand, smoking, an unhealthy lifestyle, excessive alcohol use, physical inactivity, and excess body weight represent more than half of all cases and deaths, making them possibly avoidable. With effective screening and monitoring, CRC morbidity and death can be decreased. This study gives a review of colon-rectal cancer prediction techniques using machine learning classifiers. Machine learning (ML) approach is profound for cancer prediction and analysis, which is implemented through Juypter via a coding approach. An experimental analysis was performed on the dataset gotten from Kaggle using support vector machine, K-nearest neighbor, and random forest, it also made use of principal component analysis for dimensionality reduction. The dataset consists of 5000 instances and 785 attributes. The training set (80%) was used to determine the optimal combinations of variables that will generate a good predictive model and the testing set (20%) was used to provide an unbiased evaluation of a final model fit on the training dataset. The results of the classification technique showed that support vector machine, KNN, and random forest had accuracy of 77.75%, 81.00%, and 84.00%, respectively, without the application of PCA, while technique showed that SVM, KNN, and random forest had accuracy of 80.40%, 81.90%, and 96.00%, respectively, with the application of PCA. It is imperative to note that a good number of the classification techniques were able to predict CRC based on the Colon Cancer Gene Expression dataset. From the findings, support vector machine had the minimum accuracy 80.40%, sensitivity as well as specificity, while random forest had the highest accuracy 96.00%, sensitivity, and specificity and therefore was chosen as the best classifier for colon cancer prediction. Therefore, beyond accuracy, other performance evaluation metrics are critical for optimal algorithm selection for predictive analytics.
Introduction to human-computer interaction using artificial intelligence

Human–computer interaction (HCI) mainly focuses on the design and evaluation of systems with the goal to improve user experience. Currently, HCI is used in different fields of artificial intelligence (AI), so many research trends are emerging in various fields of healthcare, autonomous vehicles, etc. This chapter describes the two areas, i.e., AI and HCI. Additionally, it is discussed in detail how both fields are relevant to each other and then the latest research trends in a variety of contexts, such as e-commerce, banking, teaching, and also the interactions with smart technologies and eye-tracking.
The Paradox of Knowability

In this chapter the significance of the Paradox of Knowability is discussed with respect to the question of how to conceive truth within an anti-realist conceptual framework. In Sect. 9.1 the Paradox is introduced; Sect. 9.2 articulates the intuitionisticTruthintuitionistic equation of truth with knowledge, first by putting into evidence (Sects. 9.2.1–9.2.3) the conditions at which the equation is acceptable: transparency of knowledge and ‘disquotational property’ of truth; then by showing (Sects. 9.2.4 and 9.2.5) how the charge of inconsistency can be resisted. In Sect. 9.3 the neo-verificationist approaches to the Paradox are discussed, and it is shown how the Paradox hits the neo-verificationist idea of the necessity of a notion of truth irreducible to proof possession. In Sect. 9.4 the Dummettian problem is discussed of how a debate between alternative logics can be rationally shaped.
Two-phase privacy-preserving scheme for federated learning in edge networks

In this article, to protect sharing parameters from being leaked and reduce time cost or low federated learning accuracy, we propose a two-phase privacy-preserving scheme for federated learning (TPPP-FL). Our method works in two phases. Specifically, in the uploading phase, we present a many-to-one HE algorithm to protect model parameters. In the downloading phase, to reduce the size of the keys, the zero-knowledge signature scheme is improved, and then a one-to-many zero-knowledge digital signature scheme is proposed to ensure the integrity and irreversibility of model parameters. Two datasets (MNIST and ORL) and two training models (CNN and MLP) have been set in experiments. Theoretical analysis and experimental results show that the TPPP-FL can effectively reduce time costs without losing the accuracy of the models compared to the same type of other federated learning schemes.
Understanding of healthcare problems and solutions using deep learning

The nature of healthcare data is complex. It is not easy to interpret data by analyzing or manual process. Machine learning methodologies such as deep neural network models have grown more appealing in recent years in the healthcare sector. Machine learning (ML) algorithms are data analysis techniques that are efficient and effective at uncovering hidden patterns and other helpful information from large amounts of health data those conventional analytics cannot handle. On the other hand, deep learning (DL) approaches have been proved to be viable methodologies for pattern identification in healthcare systems. This chapter aims to consider DL methodologies for healthcare systems by reviewing recent trends, cutting-edge network topologies, applications, and industry developments. The initial goal is to provide a detailed understanding of how DL models are used in healthcare solutions to connect DL human healthcare interpretability and approaches. This chapter also focuses on the current and future open challenges and directions.
Human-centered artificial intelligence

The supersized potential of artificial intelligence (AI) in all spheres of technology, business, and day-to-day life has helped to change the ways we are doing things at a meteoric pace. Although our prime focus is on the development and optimization of learning algorithms to augment AI performance, the need for including humans in the AI loop cannot be underestimated. The paradigm of human-centered artificial intelligence (HAI) incorporates the AI-based hardware and software systems that are improving progressively because of inputs provided by humans. The gap between machines and humans can be bridged by introducing robotic intelligence having the goal of understanding human language, emotion, and behavior. For a successful HAI, we require solving a lot of aspects of human–robot interactions. There are giant subfields that are required to work together in the HAI such as the differentiation between the complex biologic brain and the electrical brain derived from neuroscience advancements, sociology, psychology, and the mathematical modeling of the behavior of the brain using game theory. The success of HAI also depends on extracting human behavior from ergonomics, from computer science using natural language processing, machine learning algorithms, and finally building the hardware using these things using the branch of robotics.
Deep learning for clinical decision-making and improved healthcare outcome

A subset of machine learning (ML) known as “deep learning” (DL) has gained popularity recently as a result of advancements in the use of artificial neural networks, huge data, and processing power. DL in healthcare is significantly influencing the healthcare system by enhancing diagnosis and raising patient outcome standards. DL helps clinicians analyze data and discover a variety of illnesses, such as heart difficulties and cancers, that can be found using picture analysis, cancer diagnosis using malignant cells found in the body, diabetes patients' blood sugar levels, and cancer that can be found in blood samples. Rapid disease detection made possible by the application of DL and ML has allowed doctors to save lives quickly, spend priceless time with their patients, and decrease hospital stays and healthcare costs. With the ability to handle “large complex data,” DL and ML have become quite popular over the past 10 years and are now finding application in the healthcare industry. Computational models built on neural networks can learn to describe data at different levels of abstraction, thanks to DL. When ML is used in clinical decision-making, it implies that the system will interpret a particular individual by collecting and analyzing data pertinent to that individual's health, and it will then use the data to explain about the best method that should be used to maintain or improve the individual's health. According to research, ML has the potential to aid in the identification of numerous mental diseases as well as to enhance patient outcomes. The most quickly expanding concept of the 21st century is the incorporation of AI into medical research. The great potential of AI in healthcare has been revealed by the dramatic rise in AI-related research and publications over the past 10 years. In order to manage the massive and multidimensional data necessary for healthcare research, ML is used. Other applications of AI algorithms in healthcare include diagnosis, prognostication, decision assistance, screening and triage, and treatment suggestion. When used in clinical trials, ML has the potential to improve the process' generalizability, patient-centeredness, accuracy, and success. From preclinical drug development to pretrial planning, including study implementation, to data management and analysis, ML works across the spectrum of clinical trials in healthcare. In actuality, DL machines would not take the job of advisors; instead, they will enhance our abilities to diagnose aberrant lesions in remote settings in a clear and understandable context. DLM may offer new hope and an affordable alternative for health service providers in low-income countries with diverse, low-density demographics who want to take advantage of technological advancements in developed countries and receive relatively comparable standard care at reasonable costs while investing in expanding their care provision and addressing the shortage of medical professionals needed to reach their goal of universal health coverage.
Applied picture fuzzy sets with its picture fuzzy database for identification of patients in a hospital

Recently, the identification information of criminal persons with their attributes such as name, hand, hair coverage, hair texture, face shape, and skin color has faced having insufficient information since it provides mixed information of human recognition in an uncertain environment. It is a difficult question for scientists to identify human features in a disaster or accident. This chapter presents picture fuzzy sets combined with a criminal database of personal attributes. In the proposed model, a picture fuzzy set is an extension of the fuzzy and intuitionistic fuzzy set combined with a criminal database to improve human recognition and features in an uncertain environment. To validate the proposed method, we have implemented an application to test human recognition with insufficient information in an uncertain environment. Experimental results show that the proposed method is effective by dealing with uncertain personal information for the identification of patients in a hospital.
Introduction to artificial intelligence and current trends

Artificial intelligence (AI) is the study of understanding and constructing intelligent machines that are human-like (or more intelligent than humans). Simulation of AI approaches should be classified as emulation of human brains. Methods and procedures of analysis should be used to recognize and utilize cognitive processes. We provide examples of both the history of AI and the most recent breakthroughs in AI so that readers can get a sense of both. This chapter also describes advances in AI applications. Neural networks, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, and pattern recognition are briefly introduced. Additionally, we have discussed how AI is being used in a variety of cutting-edge multidisciplinary applications, including industry 4.0 and the AI revolution, explainable AI, recommender systems, and their integration with AI. Additionally, the chapter looks at the advantages and disadvantages of AI. Because AI has so many diverse applications, there is a discussion over its benefits and risks. This chapter also discusses future scope and ethics-related projects.
Development of a no-regret deep learning framework for efficient clinical decision-making

In a healthcare system, sometimes, a patient may opt for a second opinion to know more about their situation and to decide whether they accept the current clinical decision or visit other avenues of treatment, etc. This increases the cost related to health care. Deep learning plays a major role in day-to-day life by including artificial intelligence. With the help of no-regret learning we propose to make effective clinical decisions, thus enhancing the potential of e-healthcare systems. In this chapter, the authors introduce the novel mechanism of no-regret learning in deep architecture to minimize errors while making clinical decisions. The no-regret algorithm provides sublinear growth with every input sequence in the deep architecture, thereby increasing the overall prediction rate of the learning model. They have observed optimal results by applying their approach with respect to throughput. Also, after comparing the results against the most popular methods of online machine learning techniques, the proposed hybrid algorithm provides an appreciable improvement in the high performance of medical support systems. This chapter is a sincere effort toward the design of robust deep learning techniques for big data e-healthcare ecosystems.
Symptom-based diagnosis of diseases for primary health check-ups using biomedical text mining

The medical community is a vast area, there are affluent data collected within the medical system. The biomedical text mining technique is one of the best analyzing tools to recognize hidden relationships and drift in the data. The disease is a particular quality or disposition regarded as adversely affecting a person. The number of suffering people per day due to diseases in the real world is beyond all decent contemplations. As such, the aim of this chapter is to give a model for the primary diagnosis of disease. Due to the various scenarios such as the rise of population and infinite queuing system, there is an essence of developing a model for automated dictation of diseases based on the patient's symptoms. Therefore, this chapter includes a natural language processing approach to classify certain types of diseases based on the symptoms reported. The same is a challenging task to conduct due to similar symptoms for different diseases. Hence, the count vectorization approach is adopted here for multilabel symptom classification. Based on the symptoms and frequency count, an algorithm is developed to classify a certain type of disease. The verified symptoms are updated in the dataset to improve the efficiency of the system. Further, it is demonstrated for both the test and the new dataset. Then, the obtained results are reported here, and found a good agreement with the actual results. Besides, the decision tree classifier is also considered to get the prediction confidence score.
Hand-Written Devanagri Character Recognition Using Convolutional Neural Network in Python with Tensorflow

One of the most difficult fields in computer sciences over the past few decades has been artificial intelligence, which gives robots human-like abilities. One of the main goals of AI is to enable machines to see, understand, and read text. Although much effort has been done in this area, the intricacy of the problem has not yet been fully resolved. A good text recognizer can be used for a wide range of practical and commercial purposes, such as automating the post office or looking for information in scanned books. The process of recognition also involves determining the distance between the feature vector of the input image and the one that is recorded in the database, then producing the symbol with the least amount of divergence. Neural networks give total independence of character set and recognition process in contrast to this. This neural network is initially trained using several sample photos for each letter of the alphabet. The input image is then directly fed into the neural network throughout the recognition process, and the output is the identified symbol. The benefit of neural networks is that the character set’s domain can be simply expanded; all one needs to do is train the network over the new character set. The fact that neural networks are particularly noise-resistant is another benefit. In order to test CNN’s performance, we employed the Modified National Institute of Standards and Technology (MNIST) Devanagari dataset.
Intention-Aware Lane Keeping Assist Using Driver Gaze Information

A lane keeping assist system uses sensor and environmental information to automatically steer the vehicle, whenever necessary, to keep it within the lanes. As the system overrides the driver, it is important that automatic interventions are only used when the driver is unaware of the traffic situation, i.e., in cases of unintentional lane departures. Hence, one of the major challenges for such systems is to distinguish between intentional and unintentional driving behaviors. In this work, we implement an intention-aware lane keeping assist system based on machine learning, where the goal is to activate interventions only when the lane departure is unintentional. The system performance is evaluated using a real-world data set, partly consisting of unintentional lane departure events, normal driving, and intentional lane departure events. The results show that driver state information, obtained from a camera-based gaze-tracking system, improves the lane keeping assist system’s performance, especially for intentional lane departure events. They also show that it is hard to predict the driver intention for prediction horizons longer than 1.5 s.
Elder-oriented Active Learning for Adaptation of Perception Intelligence in Home Service Robots

Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs. In robotics, active learning allows a robot to adapt its perception intelligence to a new environment with users’ help. This paper presents a new active learning method for elderly care robots to select data that is not only useful for learning but also easy for the elderly user to label. First, a series of image properties related to annotation difficulty are determined based on existing medical researches in human vision in elderly population. Based on that, a user study is conducted to determine the ground truth of annotation difficulty of images for the older adults. Second, a robust annotation difficulty predictor is developed using the results of the user study, and the difficulty prediction of an image is combined with three other active learning criteria to form an annotation difficulty-aware active learning metric, which facilitates the query data selection as the robot adapts its perception intelligence in a home environment. Third, we present an ablation study of the proposed active learning method through a simulation experiment. The experimental results validate the advantages of the proposed method.
Gender analysis of labour force outcomes: Evidence from Cameroon

The present study contributes to the extant literature by assessing how financial and human developments moderate the incidence of vulnerable female employment on female labour force participation in Cameroon for the period 1987 to 2020 using the generalised least squares (GLS) estimation approach. It is apparent from the findings that human development in the perspective of the human development index (HDI) and broad money supply are necessary and sufficient conditions to moderate vulnerable female employment for female labour force participation. Accordingly, HDI thresholds of between 0.591 and 0.634 are needed to reverse the negative incidence of female vulnerable employment on female labour force participation. Furthermore, a threshold of 30.294 (% of GDP) of broad money supply is also needed to reverse the negative incidence of vulnerable female employment on female labour force participation. Other implications for policy are discussed.
Three-dimensional Super-resolution of X-ray CT Data of Rock Samples by Sparse Representation Learning

In recent years, computed tomography (CT) has been widely used during scientific drilling, providing continuous data of various rock structures such as rock layers, sedimentary layers, fractures and pores. Low-resolution CT used in drilling is insufficient to reveal the fine structures of rocks. On the other hand, X-ray CT, such as that used in the laboratory, has high resolution but is limited by the size of the sample. If the different scale-resolutions between high-resolution and low-resolution CT data can be linked, important information for multiscale analysis can be extracted. We therefore propose three-dimensional sparse super-resolution for CT data of rock samples. We show that the proposed method can reconstruct particles, veins, and texture microstructures from low-resolution three-dimensional data with super-resolution. Using multiple evaluation indices, we also demonstrate the effectiveness of the proposed method by comparing the proposed method with conventional interpolation methods.
Buone pratiche per la didattica digitale dell’italiano L2

Best Practices for the Digital Teaching of Italian L2. The following research showcases the activities carried out at the University Language Centre (CLA) of the University of Basilicata for teaching Italian as L2. The experience gathered at the Language Centre contributed to developing an efficient language teaching methodology, specifically modulated on the students’ needs and profiles, which also included the online mode of delivery. Such a tool can be of help both to improve the teaching and learning of Italian L2 and consolidate key skills in academic and professional contexts.