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The International Journal of Semantic Computing has published a special issue on Semantic Computing, which includes five selected papers from the 17th IEEE International Conference on Semantic Computing. Semantic Computing focuses on the derivation, description, integration, and use of semantics for various resources. The papers in the special issue cover topics such as intent detection, constructing probabilistic models from knowledge graphs, unsupervised estimation of subjective content descriptions, the influence of noisy labels on question answering systems, and automatic domain-adaptive sentiment analysis. Each paper presents innovative approaches and findings in their respective areas of research. [Extracted from the article]

Currently, there are many research studies that apply and improve mutation testing techniques including traditional mutation testing or first-order mutation testing, and higher-order mutation testing (HOMT) for evaluating the quality of the set of test data in particular, and the quality of test suites in general. The results of those studies have proven the effectiveness of mutation testing in the field of software testing. Mutation testing allows the quality of test cases to be automatically evaluated, thereby helping the testers to improve the quality in the design and execution of the software testing. Besides, these studies have also pointed out the main barriers in applying mutation testing techniques in practice. However, we are the first to introduce a method that can reduce the cost, but keep the quality of testing activity based on evaluating the quality of the mutation operator as well as the quality of the test cases. In this paper, we concentrate on two problems regarding higher-order mutation testing: Evaluating the quality of mutation operators as well as generated mutants and prioritizing test cases based upon its capability of killing mutants. This may help developers allocate suitably their resources during testing phase. The study of this paper is an extended version of our previous study titled "Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing", which is published in the proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 (V. N. Do, Q. V. Nguyen and T. B. Nguyen, Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing, in Intelligent Information and Database Systems. ACIIDS 2021, eds. N. T. Nguyen, S. Chittayasothorn, D. Niyato and B. Trawiński. Lecture Notes in Computer Science, Vol. 12672 (Springer, Cham), https://doi.org/10.1007/978-3-030-73280-6%5f66) to confirm the usefulness of our proposed method. [ABSTRACT FROM AUTHOR]

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning. [ABSTRACT FROM AUTHOR]

ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data), and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring, and pipelining using ChatGPT from the perspective of computational biologists in this paper. [ABSTRACT FROM AUTHOR]

In general, models of universe problems in almost all fields, such as engineering, mathematics, medical sciences, computer science, physics, management sciences, artificial intelligence, and operations research, are practically full of complexities and include various types of uncertainties when dealing with them on numerous occasions. Different theories, such as probability, rough sets, fuzzy sets, soft ideals, and so on, have been created to deal with these uncertainties. An algebraic structure, AG-groupoid, is an intermediate structure between two types of structures: commutative semigroup and groupoid. This structure has a very close relationship to a commutative semigroup since a commutative AG-groupoid is always a semigroup. These structures have so many applications in flocks theory, geometry, topology, and many more. We explore several structural properties of an AG-groupoid by using hybrid structures in this paper. The main motivation behind this paper is to present the concepts of hybrid ideals, hybrid bi-ideals and hybrid interior ideals of an AG-groupoid and characterize AG-groupoid in terms of hybrid structures. Also, we show that the hybrid intersection and hybrid product structures will coincide under certain conditions. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Large-scale benchmark datasets are crucial in advancing research within the computer science communities. They enable the development of more sophisticated AI models and serve as "golden" benchmarks for evaluating their performance. Thus, ensuring the quality of these datasets is of utmost importance for academic research and the progress of AI systems. For the emerging vision-language tasks, some datasets have been created and frequently used, such as Flickr30k, COCO, and NoCaps, which typically contain a large number of images paired with their ground-truth textual descriptions. In this paper, an automatic method is proposed to assess the quality of large-scale benchmark datasets designed for vision-language tasks. In particular, a new cross-modal matching model is developed, which is capable of automatically scoring the textual descriptions of visual images. Subsequently, this model is employed to evaluate the quality of vision-language datasets by automatically assigning a score to each 'ground-truth' description for every image picture. With a good agreement between manual and automated scoring results on the datasets, our findings reveal significant disparities in the quality of the ground-truth descriptions included in the benchmark datasets. Even more surprising, it is evident that a small portion of the descriptions are unsuitable for serving as reliable ground-truth references. These discoveries emphasize the need for careful utilization of these publicly accessible benchmark databases. [ABSTRACT FROM AUTHOR]

Deoxyribonucleic Acid (DNA) coding technology is a new research field developed by the combination of computer science and molecular biology, that has been gradually applied in the field of image encryption in recent years. Furthermore, sensitivity to initial conditions, pseudo-random properties, and state ergodicity of coupled chaotic maps can help produce good pseudo-random number generators and meet the requirements of an image encryption system well. In this paper, an image encryption algorithm based on high-dimensional coupled chaotic maps and DNA coding is proposed. A pseudo-random sequence is generated by a long short-term memory (LSTM) architecture using the proposed maps and evaluated through a set of statistical tests to show the high performance of the proposed generator. All intensity values of an input image are converted to a binary sequence, which is scrambled globally by the high-dimensional coupled chaotic maps. The DNA operations are performed on the scrambled binary sequences instead of binary operations to increase the algorithm efficiency. Simulation results and performance analyses demonstrate that the proposed encryption scheme is extremely sensitive to small changes in secret keys, provides high security and can resist differential attack. [ABSTRACT FROM AUTHOR]

Low-rank matrix completion, which aims to recover a matrix with many missing values, has attracted much attention in many fields of computer science. A low-rank matrix fitting (LMaFit) method has been proposed for fast matrix completion recently. However, this method cannot converge accurately on matrices of real-world images. For improving the accuracy of LMaFit method, an improved low-rank matrix fitting (ILMF) method based on the weighted L 1 , p norm minimization is proposed in this paper, where the L 1 , p norm is the summation of the p -power (0 < p < 2) of L 1 norms of rows in a matrix. In the proposed method, i.e. the ILMF method, the incomplete matrix that may be corrupted by noises is decomposed into the summation of a low-rank matrix and a noise matrix at first. Then, a weighted L 1 , p norm minimization problem is solved by using an alternating direction method for improving the accuracy of matrix completion. Experimental results on real-world images show that the ILMF method has much better performances in terms of both the convergence accuracy and convergence speed than the compared methods. [ABSTRACT FROM AUTHOR]

Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences. We focus on four scenarios: (1) linear sequences without duplicated numbers; (2) circular sequences without duplicated numbers; (3) linear sequences with duplicated numbers; (4) circular sequences with duplicated numbers. We develop corresponding algorithms and apply them to gene sequencing data. [ABSTRACT FROM AUTHOR]

A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we introduce the concept of cubic soft graph, complete cubic soft graph, (internal, external) cubic soft graphs and investigate some of their properties. Then we deal with fundamental operations, union, intersection, Cartesian product, composition of cubic soft graphs and illustrate these notions by several examples. We prove that cubic soft graphs under these operations are also a cubic soft graph. Finally, we describe an application of cubic soft graphs in decision making. [ABSTRACT FROM AUTHOR]

With the untimely passing of Patrick Dehornoy in September 2019, the world of mathematics lost a brilliant scholar who made profound contributions to set theory, algebra, topology, and even computer science and cryptography. And I lost a dear friend and a strong influence in the direction of my own research in mathematics. In this paper, I will concentrate on his remarkable discovery that the braid groups are left-orderable, and its consequences, and its strong influence on my own research. I'll begin by describing how I learned of his work. [ABSTRACT FROM AUTHOR]

The maximum matching problem is of great interest in many areas, for example, statistical physics and theoretical computer science. However, precise determining of maximum matchings in general graphs is a challenge and calculationally intractable. In this paper, we study analytically maximum matching problem in the pseudofractal scale-free web. We derive exact expressions for size and number of maximum matchings of the network based on self-similar structure and iterative construction of network, and obtain asymptotic incremental constant for the number of maximum matchings. The obtained results are helpful to understand the structural characteristics of scale-free networks with pseudofractality. [ABSTRACT FROM AUTHOR]

We are fully aware that an ordered semigroup has a very close relation with theoretical computer science, especially with the theory of pattern recognition, decision processes, artificial intelligence, information retrieval and so on. We are eager to introduce the new concepts of hybrid interior ideals and hybrid simple in an ordered semigroup in this paper. We discuss characteristic hybrid structures using ideals and interior ideals, and characterize ordered semigroup in terms of different hybrid ideal structures. Further, we establish the equivalent condition for an ordered semigroup to be simple. [ABSTRACT FROM AUTHOR]

Given n men, n women, and n dogs, each man has an incomplete preference list of women, each woman has an incomplete preference list of dogs, and each dog has an incomplete preference list of men. We understand a family as a triple consisting of one man, one woman, and one dog such that the dog belongs to the preference list of the woman, who, in turn, belongs to the preference list of the man, while the latter belongs to the preference list of the dog. We understand a matching as a collection of nonintersecting families (some agents, possibly, remain single). A matching is said to be nonstable, if one can find a man, a woman, and a dog who do not live together currently but each of them would become "happier" if they do. Otherwise, the matching is said to be stable (a weakly stable matching). We give an example of this problem for n = 3 where no stable matching exists. Moreover, we prove the absence of such an example for n < 3. Such an example was known earlier only for n = 6 [P. Biró and E. McDermid, Three-sided stable matchings with cyclic preferences, Algorithmica 58 (2010) 5–18]. The constructed examples also allow one to halve the size of the recently constructed analogous example for complete preference lists [C.-K. Lam and C.G. Plaxton, On the existence of three-dimensional stable matchings with cyclic preferences, in Algorithmic Game Theory, Lecture Notes in Computer Science, Vol. 11801 (Springer, 2019), pp. 329–342]. [ABSTRACT FROM AUTHOR]

Reciprocal Recommender Systems (RRS) use bilateral preferences to satisfy the needs of both the parties involved. Popularity bias is a critical problem in RRS, which arises when the RRS tend to prefer few popular users over others and thus exhibit biased behavior towards popular users. It can have adverse effect on both the parties involved. Popular users may become swamped by the requests received from a large number of users and cease to acknowledge, which can make it difficult for other users to make contact. To address this challenge, we propose Popularity-aware Siamese Bi-directional Gated Recurrent Units (PSBiGRU) with the proposed popularity-aware reciprocal score (ParS)-based re-ranking that uses semantic similarity between explicit user profiles. The proposed model is evaluated on two reciprocal environments, namely, online recruitment and online dating. Experimental findings demonstrate that PSBiGRU surpasses the compared state-of-the-art methodologies and illustrate its viability. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Time series is acknowledged as one of the most common crucial data types in our daily lives. Among the time series mining tasks, rule discovery is important to provide valuable knowledge that brings us a profound insight view of relationships between different objects through time. One challenge is that when the number of objects and their lengths increase, it easily leads to a combinatorial explosion. Therefore, we propose a temporal inter-object association rule mining algorithm, NPTR, to discover new informative temporal inter-object association rules from time series and overcome the challenge with parallelization. Another remarkable point is that NPTR defines a concurrent approach by performing the frequent pattern mining process and rule mining one simultaneously. From the experiments on real-world data, NPTR returns the rules exactly with less time and memory costs than others do. Those rules can be further utilized for other tasks such as prediction, classification, and clustering. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA... [ABSTRACT FROM AUTHOR]