Reviewerly/exHarmony huggingface dataset:
Entropic principal component analysis using Cauchy–Schwarz divergence

Modern pattern recognition applications are frequently associated with high-dimensional datasets. In the last decades, different approaches have been proposed to address the curse of dimensionality phenomena present in this kind of data. Principal component analysis is a classic method that even today is widely used for this purpose. However, its procedure is based in the covariance matrix, which is built by the feature vectors scalar product in a point-wise fashion. This makes it very sensible to noise and outliers. This work presents a patch-based mapping to an entropic space. Given a data sample neighborhood, each feature value set is mapped to an univariate Gaussian distribution described by its parameters. Then, each scalar coordinate of a data sample is replaced by the parameter tuple that describes each feature. The difference between two data sample vectors in the entropic space is defined as the vector where each scalar coordinate is given by the stochastic divergence between two probability distributions. The covariance matrix is still defined by the scalar product between the difference vector of a data sample and the average sample, so it can be used with transparency in the original PCA algorithm. This patch mapping in the entropic space aims to mitigate the effect of noise and outliers. Experiments adopting the Cauchy–Schwarz divergence show that the new framework can outperform several existing dimensionality reduction algorithms in cluster analysis tasks in multiple real datasets.
Reproducible clustering with non-Euclidean distances: a simulation and case study

Abstract Certain categorical sequence clustering applications require path connectivity, such as the clustering of DNA, click-paths through web-user sessions, or paths of care clustering with sequences of patient medical billing codes. $$K$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>K</mml:mi> </mml:math> -means and $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -medoids clustering with non-Euclidean distance metrics such as the Jaccard or edit distances maintains such path connectivity. Although $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -means and $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -medoids clustering with the Jaccard and edit distances have enjoyed success in these domains, the limits of accurate cluster recovery in these conditions have not yet been defined. As a first step in approaching this goal, we performed a simulated study using $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -means and $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>k</mml:mi> </mml:math> -medoids clustering with non-Euclidean distances and show the performance deteriorates at a certain level of noise and when the number of clusters increases. However, we identify initialization strategies that improve upon cluster recovery in the presence of noise. We employ the use of the Tibshirani and Guenther (J Comput Graph Stat 14(3):511–528, 2005) Prediction Strength method, which creates a hypothesis testing scenario that determines if there is clustering structure to the data (if the clusters are reproducible), with the null hypothesis being there is none. We then applied the framework to perinatal episodes of care and the clusters reproducibly and organically split between Cesarean and vaginal deliveries, which itself is not a clinical finding but sensibly validates the approach. Further visualizations of the clusters did bring insights into subclusters that split along groups of physicians, cost and risk scores, warranting the outlined future work into ways of improving this framework for better resolution.
Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.
A State-of-the Art Survey on Chatbots Technology Developments and Applications in Primary Healthcare Domain

Chatbots, defined as artificial intelligence program able to simulate processes of human conversation via auditory or textual methods, are deployed by firms to automate customer service. In recent years, chatbots have received tremendous attention from scholars in numerous fields including e-health, e-learning, and e-commerce over many sectors. However, the technology developments and applications specifically in the primary healthcare domain are still insufficiently explored. The principal purpose of the study is to provide a broad review of the current technology developments and applications in primary healthcare domain and future directions in the research. First, we describe features of chatbots considering the healthcare domain. Next, we provide a classification of technology developments and applications in primary healthcare with a focus on recent advances. Then, we present a density map of applications in the primary healthcare domain. Furthermore, we introduce future directions in the core research technology. We expect this study to serve as a comprehensive resource for researchers in healthcare domain.
ProQuest Academic Complete

ProQuest Academic Complete is an ebook collection that is designed to serve as a foundation for libraries wishing to grow their ebook collections and/or provide access to titles in areas that the institution may not want to purchase in print because demand might not be high enough or the program may be completely online. This collection contains content that academic researchers will want to use, and it is easy to search and navigate.
Remainder particles detection of closed electronic equipment equipped in spacecraft based on a coarse- to- fine framework

Abstract Remainder particles in closed electronic equipment equipped in spacecraft are a potential threat to spacecraft. The volume of remainder particles is tiny, and remainder particles are similar to the general components of equipment in shape and size. So the current methods used to detect remainder particles cause false detection and missing detection seriously. In order to resolve above problems, a coarse- to- fine framework is proposed to detect remainder particles in closed electronic equipment of spacecraft, which includes a backbone network, a coarse classifier, and a fine detector. At first, we use ResNet-18 as the backbone network to learn the robust feature of remainder particles. Then, we design a coarse classifier. The feature maps containing remainder particles are filtered from it. Finally, the extracted patches containing remainder particles are sent to the fine detector for further location of remainder particles. The experimental results indicate that our framework exhibits superior performance on various evaluation indicators, especially reaching 92.54% on AP. It realizes efficient and accurate detection of remainder particles in closed electronic equipment of spacecraft.
Minimum path deviation rapid online path planning of unmanned aerial vehicles with avoidance zones constraints

Abstract This paper investigates a minimum path deviation online path planning problem for UAV with constrainted avoidance zones. The nonlinear and nonconvex of this problem make the path planning is very time consuming and not acceptable for using onboard. Three new state variables and two control variables are redefined to convert the nonconvex problem into a second-order cone convex programming problem, which can be solved in polynomial time by primal-dual interior-point method, theoretical proof demonstrates the equivalence of the relaxed convex problem and the original problem. We propose a sequential second-order cone programming algorithm in order to get the optimal solution of the convexified problem. Numerical simulations show the effectiveness and less time-consuming of the proposed algorithm, no initial guess required and efficient solving process indicate that the algorithm has potential future for online path planning applications.
Blockchain-Enabled Supply Chain Management

The Fourth Industrial Revolution and digital transformation are reviewed in this chapter. Supply chain organizations must change. Most corporate functions will alter, especially R&amp;D, production, logistics management, and purchasing. Supply chain management is the second theoretical background. The authors discuss its importance, how companies compete globally, and how the Fourth Industrial Revolution affects supply chain management dynamics. The writers address supply chain issues like information technology, knowledge sharing, and blockchain-based collaboration. The current analysis highlights blockchain as a future IT-based solution that addresses complete supply chain stakeholder collaboration. Thus, the research begins with an analysis of blockchain technology, continues with an observation of how it works, and concludes with an assessment of supply chain management blockchain opportunities and barriers. The chapter finishes with an informative discussion and predictions about blockchain technology in supply chain management.
Judgment Work

This chapter explores the scholarship that addresses the relationship between artificial intelligence (AI) and strategic management. While technology offers organizations many unique benefits, scholars and practitioners question the limits of AI with regard to the adoption and dissemination of strategic managerial decisions. This chapter offers judgment work as an important competency during this Fourth Industrial Revolution (Industry 4.0) as it helps managers to differentiate between tasks that can be automated by AI and tasks that can be augmented by the strategic use of AI. Implications for business education are discussed as well as suggestions for future research.
Assessment of the Artificial Intelligence and Quantum Computing in the Smart Management Information Systems

Abstract— Studies in the literature argue that solving the memory problem of quantum computers (QC) will lead to groundbreaking developments in artificial intelligence algorithms affecting every process, strategy, business, and polic,es. Quantum adaptive algorithms have been widely applied, which improve existing methods by taking advantage of artificial intelligence and quantum computing. Although new developments are achieved with hybrid quantum systems, which aim to have sub-threads made by quantum computers, it cannot be predicted precisely where these studies will reach in the future and what kind of benefits and risks, they involve. In this study, based on the knowledge of contemporary and interdisciplinary literature, the current and potential uses of quantum computers, super artificial intelligence applications, and quantum computing methods on machine learning, whose applications have just begun in the laboratory environment, are examined, and assessed. It is claimed that the hardware disadvantages of quantum computers will disappear, and new quantum applications will be developed in the future. This is a requirement of adaptation to the prevalent usage of IoT in Industry 4.0 and Society 5.0 applications. Therefore, theoretically, and conceptual aspects of AI and quantum computing are evaluated to realize guidance of the miracles of the Prophets that can be discovered in the future.
CPLR-SFS: Contrastive Prompt Learning to Reduce Redundancy for Scientific Faceted Summarization

Abstract Scientific faceted summarization is a task to generate four summaries for a scientific article from different facets, including Purpose, Method, Findings, and Value. Existing works usually generate summary independently for each facet using pre-training or prompt-training paradigms. However, these works tend to produce duplicate content among different facets of the same scientific article, because they do not consider the relation among the aforementioned four facets. To solve the redundancy problem, we propose a novel Contrastive Prompt Learning method to Reduce redundancy for Scientific Faceted Summarization, named CPLR-SFS, to generate concise and less-overlapping faceted summaries. Specifically, CPLR-SFS receives the facet-specific prompt to guide the generation and utilizes the faceted contrastive loss for better distinguishing different faceted summaries in semantic space. Extensive experiments on the FacetSum dataset demonstrate that the proposed model can generate better faceted summaries than the state-of-the-art baselines with less redundancy.
Deep multi-similarity hashing technique based on multi-label semantics

Abstract A deep multi-similarity hashing technique based on multi-label semantics has been proposed in this paper as a means of a new controlled deep hashing system for multi-label image retrieval. Our proposed methodology comprises hash code learning and semantically-aware similarity matrix creation. To create a similarity matrix that considers semantic context, we integrate label-level and semantic-level similarity. Using the higher-order statistics of deep features as inputs, we meticulously craft the multi-similarity loss and quantization error loss for hash code learning, ensuring that the learned binary codes retain their high-ranking similarity. Our suggested strategy is successful, as shown by several testing on CIFAR-10 and NUS-WIDE.
Monte Carlo averaging for uncertainty estimation in neural networks

Abstract Although convolutional neural networks (CNNs) are widely used in modern classifiers, they are affected by overfitting and lack robustness leading to overconfident false predictions (FPs). By preventing FPs, certain consequences (such as accidents and financial losses) can be avoided and the use of CNNs in safety- and/or mission-critical applications would be effective. In this work, we aim to improve the separability of true predictions (TPs) and FPs by enforcing the confidence determining uncertainty to be high for TPs and low for FPs. To achieve this, we must devise a suitable method. We proposed the use of Monte Carlo averaging (MCA) and thus compare it with related methods, such as baseline (single CNN), Monte Carlo dropout (MCD), ensemble, and mixture of Monte Carlo dropout (MMCD). This comparison is performed using the results of experiments conducted on four datasets with three different architectures. The results show that MCA performs as well as or even better than MMCD, which in turn performs better than baseline, ensemble, and MCD. Consequently, MCA could be used instead of MMCD for uncertainty estimation, especially because it does not require a predefined distribution and it is less expensive than MMCD.
Broadcast Attention Learning for Real Telephone Speech Keyword Spotting

Abstract With the development of mobile smart devices, keyword spotting plays an important role in the interaction between machines and users. However, low storage and low energy consumption of mobile devices limit the accuracies of keyword spotting tasks. Therefore, how to achieve a balance between the high accuracy and low consumption is a research hotspot for a keyword spotting system. Convolutional neural networks have been widely adopted in recent keyword spotting systems due to their superior accuracies, and the success of the transformer architecture in many areas demonstrates the effectiveness of self-attention. In this paper, we combine self-attention and convolutional neural networks, and propose the broadcast attention learning network (BA-net), using a small number of parameters while achieving the accuracies of 97.18% and 78.44% respectively on the Google speech command dataset and a real telephone speech dataset.
SNER-CS: Self-training Named Entity Recognition in Computer Science

Abstract As the number of scientific publications grows, especially in computer science domain (CS), it is important to extract scientific entities from a large number of CS publications. Distantly supervised methods, generating distantly annotated training data by string match with external dictionary automatically, have been widely used in named entity recognition task. However, there are two challenges to use distantly supervised methods in computer science NER task. One is that more and more new tasks, methods and datasets in CS are proposed rapidly, which makes it difficult to build a computer science entity knowledge base with high coverage. The other is noisy annotation, because there is no uniform entity representation standard in computer science domain. To alleviate the two problems above, we propose a novel self-training method based pretraining language model with a distantly supervised label automatic construction system in CS (SNER-CS). Experimental results show that the proposed model SNER-CS performs previous state-of-the-art methods in computer science NER task.
Dynamically Scaled Fuzzy Control of Autonomous Intelligent Actor

The article presents an approach toward the implementation of an Autonomous Intelligent Actor's (AIA) [1] fuzzy control mechanism, when each step of it is based on dynamically defined scale.Such a scale is directed by fuzzy conditional inference rule.The approach, offered in the article, allows "soft landing" of AIA on a Target even in a case of "unfriendly" docking situation.
Past, Present and Tackling the Future of Artificial Intelligence (AI) in Education: Maintaining Agency and Establishing AI Laws

The urgency to establish laws for using generative artificial intelligence (GAI) is upon our society.At the end of the year, 2022 OpenAI made available to an international public its ground-breaking software, ChatGPT which is utilized by 1.8 billion users per month.Never before has a technology application been so successful so quickly.In this paper, the author outlines a history of artificial intelligence (AI), discusses ways in which generative artificial intelligence (GAI) technologies are used today, and delineates the future use of GAIs in education for all areas of study.A focus is on analyzing the advantages and disadvantages of GAIs with particular attention to the consideration of human agency versus machine agency.The author examines ways to avoid problems using GAIs currently.Also considered are ways in which human beings can use GAIs in the future while maintaining their own power, autonomy and control.To support this, Marshall McLuhan's laws for the electronic media are revised as "Laws of Generative Artificial Intelligence" to aid educators from kindergarten to higher education for teaching in the "GAI Era".
Varieties of Semantical Anti-realism

In the preceding chapter I argued that Chomskyan motivations for an internalist theory of meaningMeaningtheory of converge with certain motivations for semantical anti-realismAnti-realism. In this chapter, after a detailed characterization of semantical realismRealismsemantical and anti-realism, two varieties of semantical anti-realismAnti-realismsemantical are introduced—mathematical intuitionismIntuitionism and neo-verificationismNeo-verificationism—and the question is discussed of which variety is best suited to the motivations specified in Chap. 1 . More precisely, in Sect. 2.1 Dummett’s[aut] Dummett, M. semantic characterization of the the traditional debates between realists and anti-realists (realism/anti-realismAnti-realism debates, for short) is expounded, his proposal of bivalence as the criterion of realism in semantics is justified, and some necessary refinements and qualifications of this basic idea are introduced. The basic ideas of Brouwer[aut] Brouwer, L. E. J. and Heyting[aut] Heyting, A.—the pères fondateurs of intuitionismIntuitionism—about logic and the meaning of the logical constants are outlined in Sect. 2.2, while Sect. 2.3 presents the neo-verificationist program(s) for a theory of meaning elaborated by Dummett, Prawitz[aut] Prawitz, D. and Martin-Löf[aut] Martin-Löf, P. as the outgrowth of some basic results in proof-theoryProof-theory like normalization theoremsTheoremnormalization for natural deductionNatural deduction systems[aut] Curry, H. and Curry-Howard[aut] Howard, W. A. isomorphismCurry-Howard isomorphism. In Sect. 2.4 a comparison is made between intuitionist and neo-verificationist views about some basic issuesTonk (tonk’s problem, the canonical/non-canonical distinctioncanonical/non-canonical distinction, epistemic transparencyTransparency of proofs), from which the rationale is extracted for preferring intuitionism as a source of inspiration for an internalist semantic.
Avant-garde techniques in machine for detecting financial fraud in healthcare

Machine learning is a blooming technology in the current Internet world. Mostly in all the fields, the machine learning started exploring its techniques. One such area is the medical field. In this sector, the patients' records should be precise for the medication process. Machine learning techniques help the medical field in this regard. In this prominent medical sector, there is a possibility of collecting huge amounts of data. These data include the financial details like electronic payments, credit card details, pin numbers, etc. This will lead to a challenge to secure these details from the fraudulent utilizations. Whatever security methods followed, the hackers are updating themselves to get the electronic payment details. Thus, there should be a perpetual refinement for the design. Some scenarios like virus attack, whaling, smishing, etc., which have a great impact on financial losses. This process gets the important sensitive details about the card holders, and this makes the threat for the electronic card users. To overcome this issue, a proficient technology must be used to diagnose such sensitive issues which were conducted in the electronic payment cards such as credit cards. In this work, different types of algorithms like random forest, K-nearest neighbor (KNN), Naive Bayes, and logistic regression are used to detect the fraudulent usage of the credit cards. Here, different types of machine learning approaches are used. Following are some of the speculations which are considered in this work. In recent number of transactions, the total amount of transaction statistics, and statistics based on the regional area. With the help of these details the trusted owner and the fraud use of the card can be found out easily. The accuracy of the data can be calculated by the dataset, which is collected from the real time entries. The comparative analysis generates the better results. This is proved visually with the analysis done in this work.