Reviewerly/exHarmony huggingface dataset:
Selecting features by utilizing intuitionistic fuzzy Entropy method

Feature selection is the most significant pre-processing activity, which intends to reduce the data dimensionality for enhancing the machine learning process. The evaluation of feature selection must consider classification, performance, efficiency, stability, and many factors. Nowadays, uncertainty is commonly occurred in the feature selection process due to time limitations, imprecise information, and the subjectivity of human minds. Moreover, the theory of intuitionistic fuzzy set has been proven as an extremely valuable tool to tackle the uncertainty and ambiguity that arises in many practical situations. Thus, this study introduces a novel feature selection framework using intuitionistic fuzzy entropy. In this regard, new entropy for IFS is proposed first and then compared with some of the previously developed entropy measures. As entropy is a measure of uncertainty present in data (features), features with higher entropy values are filtered out, and the remaining features having lower entropy values have been used to classify the data. To verify the effectiveness of the proposed entropy-based feature selection, some experiments are done with ten standard benchmark datasets by employing a support vector machine, K-nearest neighbor, and Naïve Bias classifiers. The outcomes of the study validate that the proposed entropy-based filter feature selection is more feasible and impressive than existing filter-based feature selection methods.
Evaluating classifier performance with highly imbalanced Big Data

Abstract Using the wrong metrics to gauge classification of highly imbalanced Big Data may hide important information in experimental results. However, we find that analysis of metrics for performance evaluation and what they can hide or reveal is rarely covered in related works. Therefore, we address that gap by analyzing multiple popular performance metrics on three Big Data classification tasks. To the best of our knowledge, we are the first to utilize three new Medicare insurance claims datasets which became publicly available in 2021. These datasets are all highly imbalanced. Furthermore, the datasets are comprised of completely different data. We evaluate the performance of five ensemble learners in the Machine Learning task of Medicare fraud detection. Random Undersampling (RUS) is applied to induce five class ratios. The classifiers are evaluated with both the Area Under the Receiver Operating Characteristic Curve (AUC), and Area Under the Precision Recall Curve (AUPRC) metrics. We show that AUPRC provides a better insight into classification performance. Our findings reveal that the AUC metric hides the performance impact of RUS. However, classification results in terms of AUPRC show RUS has a detrimental effect. We show that, for highly imbalanced Big Data, the AUC metric fails to capture information about precision scores and false positive counts that the AUPRC metric reveals. Our contribution is to show AUPRC is a more effective metric for evaluating the performance of classifiers when working with highly imbalanced Big Data.
Blended learning: definition, concept, and relevance

This article discusses blended learning (BL) as an optimal way to organize the educational process during crises, such as a pandemic or war. The article aims to answer two research questions: 1) What is the appropriate Ukrainian scientific term for "blended learning" given the country's European integration processes? 2) What is the meaning of the term "blended learning"? The analysis leads to the conclusion that the appropriate Ukrainian scientific term for "blended learning" is "kombinovane navchannia". The article defines blended learning as a planned, pedagogically balanced, adaptive combination, integration, and interpenetration of technologies (face-to-face and distance learning, formal and informal learning, real and virtual, individual and collective learning) with the aim of optimally satisfying the educational needs of participants in the educational process through the use of intelligent technologies.
Bipolar complex fuzzy subalgebras and ideals of BCK/BCI-algebras

The conception of the bipolar complex fuzzy set (BCFS) is one of the fundamental and significant modifications of the fuzzy set (FS) to tackle the tricky and awkward information. BCFS has a rich and wider structure and has been utilized in various fields. In this article, we introduce the concept of bipolar complex fuzzy (BCF) subalgebras (BCFSAs), BCF ideals (BCFIs) of a BCK/BCI-algebra along with certain properties. Further, we investigate the relations between BCFSA and a BCFI and a necessary condition for BCFSA to be a BCFI. We also investigate characterizations of BCFI. Moreover, we introduce the notion of equivalence relations on the collection of all BCFIs of BCK/BCI algebra and the associated properties of equivalence relations.
Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model

In the era of information extension today, videos are easily captured and made viral in a short time, and video tampering has become more comfortable due to editing software.So, the authenticity of videos becomes more essential.Video inter-frame forgeries are the most common type of video forgery methods, which are difficult to detect by the naked eye.Until now, some algorithms have been suggested for detecting inter-frame forgeries based on handicraft features, but the accuracy and processing speed of those algorithms are still challenging.In this paper, we are going to put forward a video forgery detection method for detecting video interframe forgeries based on convolutional neural network (CNN) models by retraining the available CNN model trained on ImageNet dataset.The proposed method based on state-the-art CNN models, which are retrained to exploit spatial-temporal relationships in a video to detect inter-frame forgeries robustly and we have also proposed a confidence score instead of the raw output score based on these networks for increasing accuracy of the proposed method.Through the experiments, the detection accuracy of the proposed method is 99.17%.This result has shown that the proposed method has significantly higher efficiency and accuracy than other recent methods.
The Metaverse for Healthcare: Trends, Applications, and Future Directions of Digital Therapeutics for Urology

In recent years, the emergence of digital therapeutics as a novel approach to managing conditions has garnered significant attention. This approach involves using evidence-based therapeutic interventions that are facilitated by high-quality software programs to treat, manage, or prevent medical conditions. The incorporation of digital therapeutics into the Metaverse has increased the feasibility of their implementation and application in all areas of medical services. In urology, substantial digital therapeutics are being produced and researched, including mobile apps, bladder devices, pelvic floor muscle trainers, smart toilet systems, mixed reality-guided training and surgery, and training and telemedicine for urological consultations. The purpose of this review article is to provide a comprehensive overview of the current impact of the Metaverse on the field of digital therapeutics and identify its current trends, applications, and future perspectives in the field of urology.
Width and Complexity of Belief Tracking in Non-Deterministic Conformant and Contingent Planning

It has been shown recently that the complexity of belief tracking in deterministic conformant and contingent planning is exponential in a width parameter that is often bounded and small. In this work, we introduce a new width notion that applies to non-deterministic conformant and contingent problems as well. We also develop a belief tracking algorithm for non-deterministic problems that is exponential in the problem width, analyze the width of non-deterministic benchmarks, compare the new notion to the previous one over deterministic problems, and present experimental results.
Evaluation, Orientation, and Action in Interactive StoryTelling

Interactive storytelling encompasses a wide variety of applications and systems that aim to allow players in a virtual world to affect the narrative progression prescribed by the author. The story world is often modeled by a set of hand-crafted causal and temporal relationships that define the possible narrative sequences based on the user's actions. In our view, a more promising approach involves applying methods from statistical language processing to create data-driven stories. Here, we explore integrating a typology of narrative clauses from Labov &amp; Waletzky's theory of oral narrative(henceforth L&amp;W) into the Say Anything data-driven storytelling approach. We show that we can automatically distinguish evaluation clauses from orientation and action clauses with 89% accuracy in fables, suggesting that it will be possible to develop new types of data-driven stories using L&amp;W's typology.
Toward Automatic Role Identification in Unannotated Folk Tales

This paper presents an approach for automatically identifying high-level narrative structure information, particularly character roles, from unannotated folk tales. We introduce a new representation called em action matrices to encode Propp's narrative theory on character role and their sphere of action. We tested our approach in a fully automated system (Voz) using a corpus of 10 folk tales.Our experimental evaluation shows that action matrices capture useful information for role identification, provides insight into the error introduced by individual steps, and identifies the current bottlenecks.
Founded semantics and constraint semantics of logic rules

Abstract Logic rules and inference are fundamental in computer science and have been studied extensively. However, prior semantics of logic languages can have subtle implications and can disagree significantly, on even very simple programs, including in attempting to solve the well-known Russell’s paradox. These semantics are often non-intuitive and hard-to-understand when unrestricted negation is used in recursion. This paper describes a simple new semantics for logic rules, founded semantics, and its straightforward extension to another simple new semantics, constraint semantics, that unify the core of different prior semantics. The new semantics support unrestricted negation, as well as unrestricted existential and universal quantifications. They are uniquely expressive and intuitive by allowing assumptions about the predicates, rules and reasoning to be specified explicitly, as simple and precise binary choices. They are completely declarative and relate cleanly to prior semantics. In addition, founded semantics can be computed in linear time in the size of the ground program.
Quantum Tokens for Digital Signatures

The fisherman caught a quantum fish. "Fisherman, please let me go", begged the fish, "and I will grant you three wishes". The fisherman agreed. The fish gave the fisherman a quantum computer, three quantum signing tokens and his classical public key. The fish explained: "to sign your three wishes, use the tokenized signature scheme on this quantum computer, then show your valid signature to the king, who owes me a favor". The fisherman used one of the signing tokens to sign the document "give me a castle!" and rushed to the palace. The king executed the classical verification algorithm using the fish's public key, and since it was valid, the king complied. The fisherman's wife wanted to sign ten wishes using their two remaining signing tokens. The fisherman did not want to cheat, and secretly sailed to meet the fish. "Fish, my wife wants to sign ten more wishes". But the fish was not worried: "I have learned quantum cryptography following the previous story (The Fisherman and His Wife by the brothers Grimm). The quantum tokens are consumed during the signing. Your polynomial wife cannot even sign four wishes using the three signing tokens I gave you". "How does it work?" wondered the fisherman. "Have you heard of quantum money? These are quantum states which can be easily verified but are hard to copy. This tokenized quantum signature scheme extends Aaronson and Christiano's quantum money scheme, which is why the signing tokens cannot be copied". "Does your scheme have additional fancy properties?" the fisherman asked. "Yes, the scheme has other security guarantees: revocability, testability and everlasting security. Furthermore, if you're at sea and your quantum phone has only classical reception, you can use this scheme to transfer the value of the quantum money to shore", said the fish, and swam away.
Questions and Dependency in Intuitionistic Logic

In recent years, the logic of questions and dependencies has been investigated in the closely related frameworks of inquisitive logic and dependence logic. These investigations have assumed classical logic as the background logic of statements, and added formulas expressing questions and dependencies to this classical core. In this paper, we broaden the scope of these investigations by studying questions and dependency in the context of intuitionistic logic. We propose an intuitionistic team semantics, where teams are embedded within intuitionistic Kripke models. The associated logic is a conservative extension of intuitionistic logic with questions and dependence formulas. We establish a number of results about this logic, including a normal form result, a completeness result, and translations to classical inquisitive logic and modal dependence logic.
Automated Gameplay Generation from Declarative World Representations

An open area of research for AI in games is how to provide unique gameplay experiences that present specialized game content to users based on their preferences, in-game actions, or the system's goals. The area of procedural content generation (PCG) focuses on creating or modifying game worlds, assets, and mechanics to generate tailored or personalized game experiences. Similarly, the area of interactive narrative (IN) focuses on creating or modifying story worlds, events, and domains to generate tailored or personalized story experiences. In this paper we describe a game engine that utilizes a PCG pipeline to generate and control a range of gameplay experiences from an underlying IN experience management construct.
Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation

Traditional recommender systems suffer from the data sparsity problem. However, user knowledge acquired in one domain can be transferred and exploited in several other relevant domains. In this context, cross-domain recommender systems have been proposed to create a new and effective recommendation paradigm in which to exploit rich data from auxiliary domains to assist recommendations in a target domain. Before knowledge transfer takes place, building reliable and concrete domain correlation is the key ensuring that only relevant knowledge will be transferred. Social tags are used to explicitly link different domains, especially when neither users nor items overlap. However, existing models only exploit a subset of tags that are shared by heterogeneous domains. In this paper, we propose a complete tag-induced cross-domain recommendation (CTagCDR) model, which infers interdomain and intradomain correlations from tagging history and applies the learned structural constraints to regularize joint matrix factorization. Compared to similar models, CTagCDR is able to fully explore knowledge encoded in both shared and domain-specific tags. We demonstrate the performance of our proposed model on three public datasets and compare it with five state-of-the-art single and cross-domain recommendation approaches. The results show that CTagCDR works well in both rating prediction and item recommendation tasks, and can effectively improve recommendation performance.
Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

A stochastic gradient (SG)-based particle filter (SG-PF) algorithm is developed for an ARX model with nonlinear communication output in this paper. This ARX model consists of two submodels, one is a linear ARX model and the other is a nonlinear output model. The process outputs (outputs of the linear submodel) transmitted over a communication channel are unmeasurable, while the communication outputs (outputs of the nonlinear submodel) are available, and both of the two-type outputs are contaminated by white noises. Based on the rich input data and the available communication output data, a SG-PF algorithm is proposed to estimate the unknown process outputs and parameters of the ARX model. Furthermore, a direct weight optimization method and the Epanechnikov kernel method are extended to modify the particle filter when the measurement noise is a Gaussian noise with unknown variance and the measurement noise distribution is unknown. The simulation results demonstrate that the SG-PF algorithm is effective.
On the classification of linear complementary dual codes

We give a complete classification of binary linear complementary dual codes of lengths up to $13$ and ternary linear complementary dual codes of lengths up to $10$.
Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

One of the main causes of traffic congestion, especially at intersections, is because traffic lights have not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.
Ising-dropout: A Regularization Method for Training and Compression of Deep Neural Networks

Overfitting is a major problem in training machine learning models, specifically deep neural networks. This problem may be caused by imbalanced datasets and initialization of the model parameters, which conforms the model too closely to the training data and negatively affects the generalization performance of the model for unseen data. The original dropout is a regularization technique to drop hidden units randomly during training. In this paper, we propose an adaptive technique to wisely drop the visible and hidden units in a deep neural network using Ising energy of the network. The preliminary results show that the proposed approach can keep the classification performance competitive to the original network while eliminating optimization of unnecessary network parameters in each training cycle. The dropout state of units can also be applied to the trained (inference) model. This technique could compress the number of parameters up to 41.18% and 55.86% for the classification task on the MNIST and Fashion-MNIST datasets, respectively.
Comparative Study within Scrum, Kanban, XP Focused on Their Practices

There are quite a few up-to-date and every so habitually used Agile style but among those Serum, Kanban and XP (Extreme Programming) are generally the most mutual. On instance, organizations use typically tools and techniques from any but rather a lot they use roughly of the amalgamations of those approaches. Devouring in cognizance that those approaches are just a framework they allow firms to adapt it for their particular tasks as well as for other limits. Serum, Kanban, and XP frameworks manage and deal with the advancement of Software Development. These frameworks are utilized in various circumstances and work processes. Subsequently, they are compelling for various Agile collaborators and tasks in different circumstances. However, the utilization of a wrong approach or practice prompts Software System Development procedures that are unbendable and inefficient, affecting the association and instigating wasteful advancement. Incorporators of Serum, Kanban, XP trust that the erroneous performs of these frameworks remain hazardous and in this manner, Agile workmates ought to be aided in their basic leadership. The idea of this paper is to decide the fundamental variables to ruminate amid the determination of Serum, XP and Kanban frameworks. The recognizable proof of elements was directed over a top to bottom audit of the essential task, subsequently, the documents scrutiny was utilized in the investigation concerning information. By way of found, system recommendation, people, and tasks, adoption phase, team capacity, group stature, prioritizing of requirements, functionality scope, prime interval, operational observes, budget, and eminence were the primary aspects that lead Agile coworkers in preference Serum, XP or Kanban and in accumulation their practices. This paper portrays a strategy to perform inspections on Agile approaches, in view of an arrangement of important highlights and characteristics.
Machine learning accurately classifies age of toddlers based on eye tracking

How people extract visual information from complex scenes provides important information about cognitive processes. Eye tracking studies that have used naturalistic, rather than highly controlled experimental stimuli, reveal that variability in looking behavior is determined by bottom-up image properties such as intensity, color, and orientation, top-down factors such as task instructions and semantic information, and individual differences in genetics, cognitive function and social functioning. These differences are often revealed using areas of interest that are chosen by the experimenter or other human observers. In contrast, we adopted a data-driven approach by using machine learning (Support Vector Machine (SVM) and Deep Learning (DL)) to elucidate factors that contribute to age-related variability in gaze patterns. These models classified the infants by age with a high degree of accuracy, and identified meaningful features distinguishing the age groups. Our results demonstrate that machine learning is an effective tool for understanding how looking patterns vary according to age, providing insight into how toddlers allocate attention and how that changes with development. This sensitivity for detecting differences in exploratory gaze behavior in toddlers highlights the utility of machine learning for characterizing a variety of developmental capacities.
YouTube Usage in the University Classroom: An Argument for its Pedagogical Benefits

The demands of teaching at the tertiary level are increasing all the time. YouTube is one of the new e-resources that can be used in contemporary tertiary education pedagogy. This paper is an account of YouTube usage in three psychology classes (adolescent, developmental, and educational psychology) at the University of Trinidad and Tobago. This paper reports on the usage of YouTube videos as means of summarising course content, clarifying complex concepts, concretising abstract ideas, modelling appropriate interventions, reinforcing domain-specific jargon, exploring vicarious laboratory experimentation, and capturing first-hand musings and expositions of historical and contemporary theorists in the field of psychology. A rationale for each particular use was also given as well as the real and potential benefits to modern students.