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In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review highlights the role of machine learning in managing research regarding Hadith studies that have been published and categorizing it by their research topics, language & corpus, and the machine-learning algorithms. This article review has been conducted on 48 previously published hadith study journals. Then, we summarize existing trends, including trending topics, common language & corpus, and general algorithms often used in previous hadith-related reviews. This article aims to give new insight to help the broad community of researchers interested in these narrations to create fresh and further research with the uncommon topic, language & corpus, and algorithms. Furthermore, this article is also expected to contribute to academics and practitioners as a guide for conducting future research on the application of computer science in Hadith studies. We conclude that the most frequently discussed topic is Hadith Classification at 33.33%, the most widely used language is Arabic at 43.75%, and the most commonly used algorithm is SVM at 12.5%. In addition, the dataset mainly used is a public dataset by Al-Bukhari at 30.53%.

We present the results of an ongoing collaboration between computer science and psychology researchers that employs Natural Language Processing (NLP) methods to examine the trajectory of semantic space used during group idea generation sessions. Specifically, we track and estimate the region of semantic space being used and the degree to which new ideas expand that space. We present a visualization of this space mapping endeavor and compare human ratings of creativity dimensions (i.e., novelty, task-relevance, and elaboration) to algorithm-based estimations of those same dimensions. The semantic space mapping and algorithm development projects can be used to deliver real-time feedback to human creative groups in order to optimize the collaborative creativity process. The overall goal of this research is to increase the "survival" of novel ideas and their elaboration in the collaborative ideation and subsequent decision processes.

We introduce "Ricerca Operativa Applicazioni Reali" (ROAR; in English, "Real Applications of Operations Research"), a three-year project for higher secondary schools. Its main aim is to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. ROAR offers examples and problems closely connected with students' everyday life or with the industrial reality, balancing mathematical modeling and algorithmics. The project is composed of three teaching units, addressed to grades 10, 11, and 12. The implementation of the first teaching unit took place in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). In particular, in this paper, we provide a full description of this first teaching unit in terms of objectives, prerequisites, topics and methods, organization of the lectures, and digital technologies used. Moreover, we analyze the feedback received from students and teachers involved in the experimentation, and we discuss advantages and disadvantages related to distance learning that we had to adopt because of the COVID-19 pandemic.

The main objective of this paper is the conceptual design of a computational AI system that emulates human creativity. To do this, extensive research has been done on recent discoveries about the neural bases of human creativity. As a result, eleven neurocognitive factors have been identified on which the tremendous creative capacity of the human brain is based. The integration of these factors has made it possible to create a neurocognitive model of human creativity. The analysis of each of creative neurocognitive structures separately suggests certain characteristics that must be present in the structure of a creative computational system to emulate brain creativity. Properly integrating these characteristics, a general architecture for a creative multi-agent system has been proposed, as well as the guidelines for the correct representation of knowledge. Based on this general architecture a creative computational system can be implemented for a specific field of knowledge, or general purpose.

The TETRAD for constraint-based aids to causal model specification project and related work in computer science aims to apply standards of rigor and precision to the problem of using data and background knowledge to make inferences about a model's specifications. Several algorithms that are implemented in the TETRAD II program are presented. (SLD)

In the past, algorithm animation has yielded mixed results as a teaching tool in computer science. This article presents a study in which undergraduates at George Washington University (DC) viewed algorithm animations and then programmed the same algorithms. Results suggest that combing the two is an effective way to use animation and to produce transfer effects for general recursion problem solving. (PEN)

Discussion of text compression focuses on a method to reduce the amount of storage needed to represent a Markov model with an extended alphabet, by applying a clustering scheme that brings together similar states. Highlights include probability vectors; algorithms; implementation details; and experimental data with natural languages. (Author/LRW)

Presented is a scheduling algorithm that uses all the busses at each step for any rectangular array. Included are two lemmas, proofs, a theorem, the solution, and variations on this problem. (KR)

Reviews the development of artificial intelligence systems and the mechanisms used, including knowledge representation, programing languages, and problem processing systems. Eleven books and 6 journals are listed as sources of information on artificial intelligence. (23 references) (CLB)

Computer science education for the liberal arts student has both a practical value (creating an intelligent consumer) and an appreciative value (teaching algorithmic thinking). A computer literacy course can be structured to harmonize with the aims of liberal education. (SK)

Describes the nature and use of computer algebra and its applications to various physical sciences. Includes diagrams illustrating, among others, a computer algebra system and flow chart of operation of the Euclidean algorithm. (SK)

A traditional question is whether or not computers shall ever think like humans. This question is redirected to a discussion of whether computers shall ever be truly creative. Creativity is defined and a program is described that is designed to complete creatively a series problem in mathematics. (MP)

When problems defy solution, mathematicians can settle for "almost." This branch of mathematics (called computational complexity) encompasses both the number of parts involved in a problem and the intricacy of their interrelatedness. Current research in this area is discussed, considering various computer applications involved. (JN)

The repetitious nature of music is thought to be very similar to the repetitious nature of computer algorithms. Subroutines are seen to be very effectively applied to music notation, through reducing repetitious entry and the amount of memory required to represent a musical score. Examples of subroutine use are provided. (MP)

The problem of computing the longest common subsequence of two sequences (LCS for short) is a classical and fundamental problem in computer science. In this article, we study four variants of LCS : the Repetition-Bounded Longest Common Subsequence problem (RBLCS), the Multiset-Restricted Common Subsequence problem (MRCS), the Two-Side-Filled Longest Common Subsequence problem (2FLCS), and the One-Side-Filled Longest Common Subsequence problem (1FLCS). Although the original LCS can be solved in polynomial time, all these four variants are known to be NP-hard. Recently, an exact, O (1. 4422 5 n) -time, dynamic programming (DP) based algorithm for RBLCS was proposed, where the two input sequences have lengths n and p o l y (n). Here, we first establish that each of MRCS , 1FLCS , and 2FLCS is polynomially equivalent to RBLCS. Then, we design a refined DP-based algorithm for RBLCS that runs in O (1. 4142 2 n) time, which implies that MRCS , 1FLCS , and 2FLCS can also be solved in O (1. 4142 2 n) time. Finally, we give a polynomial-time 2-approximation algorithm for 2FLCS. [ABSTRACT FROM AUTHOR]