Reviewerly/exHarmony huggingface dataset:
Predicting complications of myocardial infarction within several hours of hospitalization using data mining techniques

Myocardial Infarction, commonly known as heart attack, is an extremely dangerous condition caused by inadequate blood flow to the heart. It is a major cause of death worldwide and swift appropriate actions need to be taken after hospitalization to save a patient's life. Clinicians need to be extremely cautious in handling such situations. A decision-support system predicting the risk in MI patients can be quite beneficial to clinicians. In this study, we develop such a system to predict the complications in MI patients immediately after hospitalization. In that regard, we utilized a dataset containing the records of 1700 MI patients. Class imbalance is a major concern in most medical datasets as it biases the predictions of the ML algorithms towards the majority (negative) class. Appropriate measures need to be taken to address the imbalance scenario and develop a reliable prediction system. In that regard, we propose a new approach that combines sampling techniques with cost-sensitive learning. The advantage of this hybrid approach is that it reduces the number of minority class samples required to be generated or the number of majority class samples needed to be eliminated to obtain balance. Additionally, it reduces the weight to be assigned as the penalty for the cost-sensitive classifier. The complications associated with using sampling or cost-sensitive learning separately are reduced by using this kind of hybridization. We hypothesize that a suitable balance between the two approaches can optimize the prediction performance. The proposed approach performed significantly better than both traditional sampling techniques and cost-sensitive learning. The highest ROC-AUC score of 80.88% and MCC score of 66.53% were achieved using this approach. External validation was also performed on 36 imbalanced datasets and the proposed methodology outperformed other popular techniques used in imbalanced learning. Thus, the prediction framework presented in this study can ensure reliable risk prediction of MI patients at an early stage of hospitalization which can be quite beneficial to clinicians in the decision-making process.
Plan Selection Framework for Policy-Aware Autonomous Agents

This paper proposes a framework for representing and reasoning about the plan selection process of an autonomous agent that is expected to operate within the boundaries of a given policy. We assume that the agent takes into consideration both policy compliance and plan length, and may prioritize one of these aspects over the other, based on circumstances. We consider authorization and obligation policies specified in the language $$\mathcal{AOPL}$$ by Gelfond and Lobo. Our framework builds upon the AAA agent architecture and is implemented in ASP.
Concept Combination in Weighted DL

Building on previous work on Weighted Description Logic (WDL), we present and assess an algorithm for concept combination grounded in the experimental research in cognitive psychology. Starting from two WDL formulas representing concepts in a way similar to Prototype Theory and a knowledge base (KB) modelling background knowledge, the algorithm outputs a new WDL formula which represent the combination of the input concepts. First, we study the logical properties of the operator defined by our algorithm. Second, we collect data on the prototypical representation of concepts and their combinations and learn WDL formulas from them. Third, we evaluate our algorithm and the role of the KB by comparing the algorithm’s outputs with the learned WDL formulas.
Complexity and Scalability of Defeasible Reasoning with Typicality in Many-Valued Weighted Knowledge Bases

Weighted knowledge bases for description logics with typicality under a “concept-wise” multi-preferential semantics provide a logical interpretation of MultiLayer Perceptrons. In this context, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning in the finitely many-valued case, providing a $$\varPi ^p_2$$ upper bound on the complexity of the problem, nonetheless leaving unknown the exact complexity and only providing a proof-of-concept implementation. This paper fulfils the lack by providing a $${P^{NP[log]}}$$ -completeness result and new ASP encodings that deal with weighted knowledge bases with large search spaces.
Past-Present Temporal Programs over Finite Traces

Extensions of Answer Set Programming with language constructs from temporal logics, such as temporal equilibrium logic over finite traces ( $$\text {TEL}_{\!f}$$ ), provide an expressive computational framework for modeling dynamic applications. In this paper, we study the so-called past-present syntactic subclass, which consists of a set of logic programming rules whose body references to the past and head to the present. Such restriction ensures that the past remains independent of the future, which is the case in most dynamic domains. We extend the definitions of completion and loop formulas to the case of past-present formulas, which allows for capturing the temporal stable models of past-present temporal programs by means of an $$\text {LTL}_{\!f}$$ expression.
Belief Reconfiguration

We study a generalisation of iterated belief revision in a setting where we keep track not only of the received information (in the form of messages) but also of the source of each message. We suppose that we have a special source, the oracle, which never fails. That is, all of the information provided by the oracle is assumed to be correct. We then evaluate the reliability of each source by confronting its messages with the facts given by the oracle. In this case it is natural to give higher priority to messages coming from more reliable sources. We therefore re-order (reconfigurate) the messages with respect to the reliability of the sources before performing iterated belief revision. We study how to compute this reliability, and the properties of the corresponding reconfiguration operators.
Towards Systematic Treatment of Partial Functions in Knowledge Representation

Partial functions are ubiquitous in Knowledge Representation applications, ranging from practical, e.g., business applications, to more abstract, e.g., mathematical and programming applications. Expressing propositions about partial functions may lead to non/denoting terms resulting in undefinedness errors and ambiguity, causing subtle modeling and reasoning problems. In our approach, formulas are well-defined (true or false) and non/ambiguous in all structures. We develop a base extension of three/valued predicate logic, in which partial function terms are guarded by domain expressions ensuring the well/definedness property despite the three/valued nature of the underlying logic. To tackle the verbosity of this core language, we propose different ways to increase convenience by using disambiguating annotations and non/commutative connectives. We show a reduction of the logic to two/valued logic of total functions and prove that many different unnesting methods turning partial functions into graph predicates, which are not equivalence preserving in general, are equivalence preserving in the proposed language, showing that ambiguity is avoided.
Computing MUS-Based Inconsistency Measures

We detail two instantiations of a generic algorithm for the problematic and MUS-variable-based inconsistency measures, based on answer set programming and Boolean satisfiability (SAT). Empirically, the SAT-based approach allows for more efficiently computing the measures when compared to enumerating all minimal correction subsets of a knowledge base.
Rational Closure Extension in SPO-Representable Inductive Inference Operators

The class of inductive inference operators that extend rational closure, as introduced by Lehmann or via Pearl’s system Z, exhibits desirable inference characteristics. The property that formalizes this, known as (RC Extension), has recently been investigated for basic defeasible entailment relations. In this paper, we explore (RC Extension) for more general classes of inference relations. First, we semantically characterize (RC Extension) for preferential inference relations in general. Then we focus on operators that can be represented with strict partial orders (SPOs) on possible worlds and characterize SPO-representable inductive inference operators. Furthermore, we show that for SPO-representable inference operators, (RC Extension) is semantically characterized as a refinement of the Z-rank relation on possible worlds.
Categorical Approximation Fixpoint Theory

Approximation fixpoint theory (AFT) is a powerful framework that has been widely used for defining the semantics of non-monotonic formalisms in artificial intelligence and logic programming. In particular, AFT is used to derive the fixed points of (potentially non-monotonic) operators over complete lattices. However, in certain application domains, there arise operators defined over structures that are not necessarily complete lattices. Therefore, the quest for a more general version of AFT has been lingering as an interesting research direction. We develop an extension of AFT, namely Categorical AFT, that allows us to study the fixed points of (potentially non-functorial) operators defined over categories. Since categories are more general structures than complete lattices, we argue that our approach provides a more general and unified framework for the study of non-monotonicity. The versatility of category theory creates the potential of new insights and applications.
Hybrid Modal Operators for Definite Descriptions

In this paper, we study computational complexity and expressive power of modal operators for definite descriptions, which correspond to statements ‘the modal world which satisfies formula $$\varphi $$ ’. We show that adding such operators to the basic (propositional) modal language has a price of increasing complexity of the satisfiability problem from PSpace to ExpTime. However, if formulas corresponding to descriptions are Boolean only, there is no increase of complexity. Furthermore, we compare definite descriptions with the related operators from hybrid and counting logics. We prove that the operators for definite descriptions are strictly more expressive than hybrid operators, but strictly less expressive than counting operators. We show that over linear structures the same expressive power results hold as in the general case; in contrast, if the linear structures are isomorphic to integers, definite descriptions become as expressive as counting operators.
Reasoning in Assumption-Based Argumentation Using Tree-Decompositions

Abstract We address complex reasoning tasks in assumption-based argumentation (ABA) by developing dynamic programming algorithms based on tree-decompositions. As one of the prominent approaches in computational argumentation, our focus is on NP-hard reasoning in ABA. We utilize tree-width, a structural measure describing closeness to trees, for an approach to handle computationally complex tasks in ABA. We contribute to the state of the art by first showing that many reasoning tasks in ABA are fixed-parameter tractable w.r.t. tree-width using Courcelle’s theorem, informally signaling wide applicability of dynamic programming algorithms for ABA. Secondly, we develop such algorithms operating on tree-decompositions of given ABA frameworks. We instantiate the algorithms in the recent D-FLAT framework allowing for declarative and extensible specification of dynamic programming algorithms. In an experimental evaluation on a resulting prototype, we show promise of the approach in particular for complex counting tasks.
Optimal Planning with Expressive Action Languages as Constraint Optimization

We consider the problem of optimal planning in deterministic domains specified with expressive action languages. We show how it is possible to reduce such problem to finding an optimal solution of a constraint optimization problem incorporating a bound n on the maximum length of the plan. By solving the latter, we can conclude whether (i) the plan found is optimal even for bounds greater than n; or (ii) we need to increase n; or (iii) it is useless to increase n since the planning problem has no solution.
Deciding Subsumption in Defeasible $$\mathcal {ELI}_\bot $$ with Typicality Models

Some reasoning methods for Defeasible Description Logics (DDLs) suffer from quantification neglect (QN) as they omit un-defeated information for quantified objects. Reasoning in defeasible $$\mathcal{E}\mathcal{L}_\bot $$ based on so-called typicality models (TMs), which extend canonical models of classical $$\mathcal{E}\mathcal{L}_\bot $$ , can alleviate QN. The DDL $$\mathcal {ELI}_\bot $$ extends $$\mathcal{E}\mathcal{L}_\bot $$ by inverse roles, i.e., a limited form of value restriction. Extending TMs to inverse roles is challenging due to their interaction with existential restrictions. In this paper, we develop TMs for $$\mathcal {ELI}_\bot $$ for 4 different semantics reliant on rational and relevant closure. Our computation methods for those TMs are effective decision procedures for subsumption in defeasible $$\mathcal {ELI}_\bot $$ and the stronger forms of TMs can mitigate QN.
An Efficient Approach to Estimate Software Cost by Analogy Using ACO

The accurate software cost estimation can be failed because of the following reasons like environmental changes, lack of trained employees, and expectation of actual development cost. It is the process of predicting an effort required for a project development. Cost estimation is mainly depending on a basic input. Both an overestimate and underestimate of software cost may lead to an unsuccessful project. Software cost estimation process became a crucial factor in any software project. Artificial Intelligence is a technique which helps an object to think like a human being to solve the more complex problem in real time application. Analogy is a process of estimation based on actual data and past experience of similar projects. Ant colony optimization is part of computational intelligence. A method to evaluate optimal paths and give approximate solutions for optimization problems. Some of the applications of ACO are traveling salesperson, network routing etc. ACO is used to find the very best estimation of software cost.
The Role of Blockchain Technology and Student Coin Case in Saudi Arabia's Higher Education Sector. The Challenge of Achieving 2030 Vision

At present, researchers and practitioners are increasingly interested in blockchain technology. This is due to its unique characteristics, including safety, regionalisation, dependability, and data security. Even with the increased attention and engagement, few individuals are aware of the present state of knowledge and usage of Blockchain (BC) technology, more so in the context of higher education in the Kingdom of Saudi Arabis. This paper offers a comprehensive evaluation of prior research pertaining to blockchain-based educational applications. It focuses on three primary themes: (1) educational applications and its use created by using blockchain technology, (2) the advantages of blockchain technology in higher education and (3) the adoption area in higher education challenges and problems of blockchain technology. Detailed analysis of the results for each topic and an in-depth discussion was carried out based on the survey results. This study also sheds light on other crucial areas of education where blockchain technology might help.
A Comparative Study on Various Methodologies Used in Sentiment Analysis for Indian Languages

Sentiment analysis is one of the applications of Natural Language Processing (NLP). There are so many social networking sites on the internet where people are free to express their opinions or ideas on platforms like blogs, internet forums, and tweets through Twitter about a particular entity. Opinion or sentiment has a very strong impact on people’s lives and their mental health. Sentiment analysis is the problem of sentiment mining from the available data and extracting opinions of a particular entity in three categories: positive, neutral, and negative. Sentiment analysis seems to be an area of text data mining. Sentiment analysis is carried out by data gathering, analysis of data or text, and aggregating the text. This paper will provide a detailed survey of different techniques and a comparative study of different approaches applied in sentiment classification to understand the level of work.
Classification Using Marginalized Maximum Likelihood Estimation and Black-Box Variational Inference

Based upon variational inference (VI) a new set of classification algorithms has recently emerged. This set of algorithms aims (A) to increase generalization power, (B) to decrease computational complexity. However, the complex math and implementation considerations have led to the emergence of black-box variational inference methods (BBVI). Relying on these principles, we assume the existence of a set of latent variables during the generation of data points. We subsequently marginalize the conventional maximum likelihood objective function w.r.t this set of latent variables and then apply black-box variational inference to estimate the model’s parameters. We evaluate the performance of the proposed method by comparing the results obtained from the application of our method to real-world and synthetic data sets with those obtained using basic and state-of-art classification algorithms. We proceed and scrutinize the impact of: (1) the existence of non-informative features at various dimensionalities, (2) the imbalanced data representation, (3) non-linear data sets, and (4) different data set size on the performance of algorithms under consideration. The results obtained prove to be encouraging and effective.
Rethinking Probabilistic Topic Modeling from the Point of View of Classical Non-Bayesian Regularization

Probabilistic Topic Modeling with hundreds of its models and applications has been an efficient text analysis technique for almost 20 years. This research area has evolved mostly within the frame of the Bayesian learning theory. For a long time, the possibility of learning topic models with a simpler conventional (non-Bayesian) regularization remained underestimated and rarely used. The framework of Additive Regularization for Topic Modeling (ARTM) fills this gap. It dramatically simplifies the model inference and opens up new possibilities for combining topic models by just adding their regularizers. This makes the ARTM a tool for synthesizing models with desired properties and gives rise to developing the fast online algorithms in the BigARTM open-source environment equipped with a modular extensible library of regularizers. In this paper, a general iterative process is proposed that maximizes a smooth function on unit simplices. This process can be used as inference mechanism for a wide variety of topic models. This approach is believed to be useful not only for rethinking probabilistic topic modeling, but also for building the neural topic models increasingly popular in recent years.
Deep Learning-Based Sentiment Analysis of Customer Reviews on Hotels Through Tweets

Tweets are continually produced and short. Sentiments play a vital role in comprehending personal feelings. Multitudes show their opinions on any subject or object on social media. The public’s sentiment is split into different categories namely, positive, negative and neutral. In this paper, data is acquired from hotel reviews on Twitter using Python’s Tweepy library and pre-processed using Python scripts. Inconsistent data or noise, retweets, tags, URLs along with hashtag symbols and duplicate entries are removed. The tweets are upsampled and split by using scikit-learn in Python. The textual data is converted into vectors using Keras Tokenizer in Python. Bi-Sense Emoji Embedding (BSEE) is used for performing SA. The sentiments are categorized using Support Vector Machine (SVM) and Random forest (RF), and performance is compared with BSEE based on Accuracy, Recall, F-measure, Precision and Time period. It is seen that the proposed classifier offers better results.
Robots, Artificial Intelligence and Service Automation in Tourism and Quality of Life

This chapter explores the ways in which Robots, Artificial Intelligence, and Service Automation (“RAISA”) will influence the quality of life of tourists, tourism and hospitality employees, and local residents due to the increasing integration of automation technologies into travel, tourism, and hospitality (“TTH”). In order to explain this, the authors explore what is currently defined as quality life, breaking down the concept into generalizable constructs that have a tangible and critical impact upon the well-being of humans. Then, the authors analyze how the incorporation of RAISA will impact upon the quality of life for tourism and hospitality employees, tourists, and local residents at destinations. The chapter ends with a discussion of the trajectory of RAISA and its influence upon the quality of life of consumers, illustrating the net impact of increasing automation of travel-related services will be positive for humanity.
Virtual Tourism and Consumer Wellbeing: A Critical Review, Practices, and New Perspectives

Virtual tourism aided by revolutionary technologies—including virtual reality (VR), augmented reality (AR), and livestreaming (LS)—has been the topic of increased discussion among practitioners and academics as an innovative yet sustainable means of achieving tourism recovery. The emergence of virtual tourism provides a means of traveling and explorations of new places and has opened the possibilities for people to experience places and activities they may not have had the opportunity to have before. With these cutting-edge technologies, consumers’ wellbeing is creatively achieved and enhanced. Given the scarcity of literature on this topic, this chapter elaborates on the scope of virtual tourism, provides exemplary cases of its applications, synthesizes existing knowledge on this topic, and postulates new perspectives and priorities for future research endeavors. Furthermore, this chapter thoroughly examines the intricate connections between virtual tourism and consumer wellbeing, which has attracted considerable attention both in practice and in academia.
HTTP, WebSocket, and SignalR: A Comparison of Real-Time Online Communication Protocols

Real-time web applications are widely implemented and used past few years. Different web applications like sports, taxi booking, stocks, tracking system needs to refresh itself without any human request. Different business and organization demand automate refresh of web pages to give users a better experience. Hyper Text Transfer Protocol (HTTP) is a basic and slow technology for information exchange between client and server. It is not appropriate for real time changes and updates. WebSocket is communication protocol with persistent connection. It has event driven architecture and duplex communication that makes it suitable for real time web. Further, SignalR is a library with automatic reconnection feature and fallback transports features. This research paper discusses HTTP, WebSocket and SignalR in terms of their data transfer, connection, performance, error handling, and reliability. This study gives an idea of three protocols performance and benefits in different use cases.