Pub Date : 2022-05-31DOI: 10.1142/s0129054122420047
S. Raghavendra, A. Harshavardhan, S. Neelakandan, R. Partheepan, Ranjan Walia, V. Chandra Shekhar Rao
One of the deadliest diseases in the world is brain cancer. Children and adults are also susceptible to this malignancy. It also has the poorest rate of survival and comes in a variety of shapes, textures, and sizes, depending on where it is found. Bad things will happen if the tumour brain is misclassified. As a reason, early detection of the right kind and grade of tumour is critical in determining the best treatment strategy. Brain tumours may be identified by looking at magnetic resonance imaging (MRI) pictures of the patient’s brain. The manual method becomes time-consuming and may lead to human mistakes due to the huge quantities of data and the different kinds of brain tumours. As a result, a computer-assisted diagnostic (CAD) system is needed. Image categorization methods have advanced significantly in recent years, particularly deep learning networks, which have achieved success in this field. In this case, we used a multilayer stacked probabilistic belief network to accurately classify brain tumors. Here the MRI brain images are Pre-processed using the Hybrid Butter worth Anisotropic filter and contrast Blow up Histogram Equalization. Followed by pre-processing, the denoised image can be segmented by using the bounding box U-NET segmentation methodology. Then after segmenting the target, the specialized features regarding the tumor can be extracted using the In-depth atom embedding method. Then they obtained can reduce feature dimensionality by using the Backward feature eliminating green wing optimization. The extracted features can be given as input for the classification process. A Multilayer stacked probabilistic belief network is then used to classify the tumour as malignant or benign. The suggested system’s efficacy was tested on the BraTS dataset, which yielded a high level of accuracy. Subjective comparison study is also performed out among the suggested technique and certain state-of-the-art methods, according to the work presented. Experiments show that the proposed system outperforms current methods in terms of assisting radiologists in identifying the size, shape, and location of tumors in the human brain.
{"title":"Multilayer Stacked Probabilistic Belief Network-Based Brain Tumor Segmentation and Classification","authors":"S. Raghavendra, A. Harshavardhan, S. Neelakandan, R. Partheepan, Ranjan Walia, V. Chandra Shekhar Rao","doi":"10.1142/s0129054122420047","DOIUrl":"https://doi.org/10.1142/s0129054122420047","url":null,"abstract":"One of the deadliest diseases in the world is brain cancer. Children and adults are also susceptible to this malignancy. It also has the poorest rate of survival and comes in a variety of shapes, textures, and sizes, depending on where it is found. Bad things will happen if the tumour brain is misclassified. As a reason, early detection of the right kind and grade of tumour is critical in determining the best treatment strategy. Brain tumours may be identified by looking at magnetic resonance imaging (MRI) pictures of the patient’s brain. The manual method becomes time-consuming and may lead to human mistakes due to the huge quantities of data and the different kinds of brain tumours. As a result, a computer-assisted diagnostic (CAD) system is needed. Image categorization methods have advanced significantly in recent years, particularly deep learning networks, which have achieved success in this field. In this case, we used a multilayer stacked probabilistic belief network to accurately classify brain tumors. Here the MRI brain images are Pre-processed using the Hybrid Butter worth Anisotropic filter and contrast Blow up Histogram Equalization. Followed by pre-processing, the denoised image can be segmented by using the bounding box U-NET segmentation methodology. Then after segmenting the target, the specialized features regarding the tumor can be extracted using the In-depth atom embedding method. Then they obtained can reduce feature dimensionality by using the Backward feature eliminating green wing optimization. The extracted features can be given as input for the classification process. A Multilayer stacked probabilistic belief network is then used to classify the tumour as malignant or benign. The suggested system’s efficacy was tested on the BraTS dataset, which yielded a high level of accuracy. Subjective comparison study is also performed out among the suggested technique and certain state-of-the-art methods, according to the work presented. Experiments show that the proposed system outperforms current methods in terms of assisting radiologists in identifying the size, shape, and location of tumors in the human brain.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1142/s0129054122420059
Changwei Liu, Kexin Wang, Aman Wu
Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn’t being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior Graph Convolutional Neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model’s order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach.
{"title":"Management and Monitoring of Multi-Behavior Recommendation Systems Using Graph Convolutional Neural Networks","authors":"Changwei Liu, Kexin Wang, Aman Wu","doi":"10.1142/s0129054122420059","DOIUrl":"https://doi.org/10.1142/s0129054122420059","url":null,"abstract":"Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn’t being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior Graph Convolutional Neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model’s order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125068647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1142/s0129054122500125
Xiaoqing Liu, Shuming Zhou, Hong Zhang, Baohua Niu
The vertex (edge) connectivity has been regularly used to measure the fault tolerance and reliability of interconnection networks, while it has defects in the assumption that all neighbors of one node will fail concurrently. To overcome this deficiency, some new generalizations of traditional connectivity have been suggested to quantize the size or the number of the connected components of the survival graph. The [Formula: see text]-component (edge) connectivity, one generalization of vertex (edge) connectivity, has been proposed to characterize the vulnerability of multiprocessor systems based on the number of components of the survival graph. In this paper, we determine the [Formula: see text]-component (edge) connectivity of a family of networks, called the round matching composition networks [Formula: see text], which are a class of networks composed of [Formula: see text] ([Formula: see text]) clusters with the same order, linked by [Formula: see text] perfect matchings. By exploring the combinatorial properties and fault-tolerance of [Formula: see text], we establish the [Formula: see text]-component (edge) connectivity [Formula: see text] for [Formula: see text] and [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text].
{"title":"The Component (Edge) Connectivity of Round Matching Composition Networks","authors":"Xiaoqing Liu, Shuming Zhou, Hong Zhang, Baohua Niu","doi":"10.1142/s0129054122500125","DOIUrl":"https://doi.org/10.1142/s0129054122500125","url":null,"abstract":"The vertex (edge) connectivity has been regularly used to measure the fault tolerance and reliability of interconnection networks, while it has defects in the assumption that all neighbors of one node will fail concurrently. To overcome this deficiency, some new generalizations of traditional connectivity have been suggested to quantize the size or the number of the connected components of the survival graph. The [Formula: see text]-component (edge) connectivity, one generalization of vertex (edge) connectivity, has been proposed to characterize the vulnerability of multiprocessor systems based on the number of components of the survival graph. In this paper, we determine the [Formula: see text]-component (edge) connectivity of a family of networks, called the round matching composition networks [Formula: see text], which are a class of networks composed of [Formula: see text] ([Formula: see text]) clusters with the same order, linked by [Formula: see text] perfect matchings. By exploring the combinatorial properties and fault-tolerance of [Formula: see text], we establish the [Formula: see text]-component (edge) connectivity [Formula: see text] for [Formula: see text] and [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text].","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130392587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1142/s0129054122500113
B. Rovan, A. Varga
A new framework to measure distances (similarity) between formal languages and between grammars based on distances between words is introduced. It is based on approximating languages by their finite subsets and using monotone sequences of such finite approximations to define an infinite language in the limit. Distances between finite languages are defined and extended to distances between monotone sequences of finite languages leading to distances between infinite languages. The framework captures several distances studied in the literature. Context-free grammars with energy are introduced to enable finite approximations emphasizing “syntactically important” parts of words. Grammars with energy are also used to extend distances between monotone sequences of finite languages to distances between context-free grammars. A basic toolkit for monotone sequences of finite languages and distances between languages resp. grammars is provided. As part of this toolkit a non-symmetric version of distances is defined, providing additional characterisation of distances in general. Additional properties of distances between grammars are derived by restricting the“energy use” of grammars with energy. Some methods of estimating the distances are presented to be used in cases where the distance is not computable or difficult to compute.
{"title":"Finite Approximations and Similarity of Languages","authors":"B. Rovan, A. Varga","doi":"10.1142/s0129054122500113","DOIUrl":"https://doi.org/10.1142/s0129054122500113","url":null,"abstract":"A new framework to measure distances (similarity) between formal languages and between grammars based on distances between words is introduced. It is based on approximating languages by their finite subsets and using monotone sequences of such finite approximations to define an infinite language in the limit. Distances between finite languages are defined and extended to distances between monotone sequences of finite languages leading to distances between infinite languages. The framework captures several distances studied in the literature. Context-free grammars with energy are introduced to enable finite approximations emphasizing “syntactically important” parts of words. Grammars with energy are also used to extend distances between monotone sequences of finite languages to distances between context-free grammars. A basic toolkit for monotone sequences of finite languages and distances between languages resp. grammars is provided. As part of this toolkit a non-symmetric version of distances is defined, providing additional characterisation of distances in general. Additional properties of distances between grammars are derived by restricting the“energy use” of grammars with energy. Some methods of estimating the distances are presented to be used in cases where the distance is not computable or difficult to compute.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127802185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-28DOI: 10.1142/s0129054122500137
Zhengqi Yu, Shuming Zhou, Hong Zhang
With the rapid development and advances of very large scale integration technology and wafer-scale integration technology, multiprocessor systems, taking interconnection networks as underlying topologies, have been widely designed and used in big data era. The topology of an interconnection network is usually represented as a graph. If any two distinct vertices [Formula: see text] in a connected graph [Formula: see text] are connected by min[Formula: see text] vertex (edge)-disjoint paths, then [Formula: see text] is called strongly Menger (edge) connected. In 1996, Opatrny et al. [16] introduced the DCC (Disjoint Consecutive Cycle) linear congruential graph, which consists of [Formula: see text] nodes and is generated by a set of linear functions [Formula: see text] with special properties. In this work, we investigate the strong Menger connectivity of the DCC linear congruential graph [Formula: see text] with faulty vertices or edges, where [Formula: see text], [Formula: see text], gcd[Formula: see text] and [Formula: see text] is a multiple of [Formula: see text]. In detail, we show that [Formula: see text] is strongly Menger connected if [Formula: see text] for any [Formula: see text]. Moreover, we determine that [Formula: see text] is strongly Menger edge connected if [Formula: see text] for any [Formula: see text]. Furthermore, we prove that, under the restricted condition [Formula: see text], [Formula: see text] is strongly Menger edge connected if [Formula: see text] and [Formula: see text] for any [Formula: see text]. In addition, we present some empirical examples to show that the bounds are all optimal in the sense of the maximum number of tolerable edge faults.
随着超大规模集成技术和晶圆级集成技术的快速发展和进步，以互联网络为底层拓扑结构的多处理器系统在大数据时代得到了广泛的设计和应用。互连网络的拓扑结构通常用图表示。如果连通图[公式:见文]中任意两个不同的顶点[公式:见文]通过最小[公式:见文]顶点(边)不相交的路径相连，则称为[公式:见文]强门格尔(边)连通。1996年，Opatrny等[16]引入了DCC (Disjoint continuous Cycle)线性同余图，该图由[公式:见文]个节点组成，由一组具有特殊性质的线性函数[公式:见文]生成。在这项工作中，我们研究了具有错误顶点或边的DCC线性同余图[公式:见文]的强门格尔连通性，其中[公式:见文]，[公式:见文]，gcd[公式:见文]和[公式:见文]是[公式:见文]的倍数。详细地说，我们证明了[Formula: see text]是强门格尔连接的，如果[Formula: see text]对于任何[Formula: see text]。此外，我们确定对于任何[公式:见文]，如果[公式:见文]为[公式:见文]，则[公式:见文]为强门格尔边连通。进一步证明了在受限条件下[公式:见文]，对于任意[公式:见文]，如果[公式:见文]和[公式:见文]，[公式:见文]是强门格尔边连通的。此外，我们给出了一些经验例子来证明，在可容忍边缘故障的最大数目意义上，边界都是最优的。
{"title":"Fault-Tolerant Strong Menger (Edge) Connectivity of DCC Linear Congruential Graphs","authors":"Zhengqi Yu, Shuming Zhou, Hong Zhang","doi":"10.1142/s0129054122500137","DOIUrl":"https://doi.org/10.1142/s0129054122500137","url":null,"abstract":"With the rapid development and advances of very large scale integration technology and wafer-scale integration technology, multiprocessor systems, taking interconnection networks as underlying topologies, have been widely designed and used in big data era. The topology of an interconnection network is usually represented as a graph. If any two distinct vertices [Formula: see text] in a connected graph [Formula: see text] are connected by min[Formula: see text] vertex (edge)-disjoint paths, then [Formula: see text] is called strongly Menger (edge) connected. In 1996, Opatrny et al. [16] introduced the DCC (Disjoint Consecutive Cycle) linear congruential graph, which consists of [Formula: see text] nodes and is generated by a set of linear functions [Formula: see text] with special properties. In this work, we investigate the strong Menger connectivity of the DCC linear congruential graph [Formula: see text] with faulty vertices or edges, where [Formula: see text], [Formula: see text], gcd[Formula: see text] and [Formula: see text] is a multiple of [Formula: see text]. In detail, we show that [Formula: see text] is strongly Menger connected if [Formula: see text] for any [Formula: see text]. Moreover, we determine that [Formula: see text] is strongly Menger edge connected if [Formula: see text] for any [Formula: see text]. Furthermore, we prove that, under the restricted condition [Formula: see text], [Formula: see text] is strongly Menger edge connected if [Formula: see text] and [Formula: see text] for any [Formula: see text]. In addition, we present some empirical examples to show that the bounds are all optimal in the sense of the maximum number of tolerable edge faults.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124338274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1142/s0129054122500101
S. Sushanth Kumar, Harshdeep Singh, Gaurav Mittal
Boolean functions are fundamental bricks in the development of various applications in Cryptography and Coding theory by making benefit from the weights of related Boolean functions (Walsh spectrum). Towards this, the discrete Fourier transform (Walsh–Hadamard) plays a pivotal tool. The work in this paper is dedicated towards the algebraic and numerical degrees, together with the relationship between weights of Boolean function and their Walsh transforms. We introduce Walsh matrices and generalize them to any arbitrary Boolean function. This improves the complexity in computation of Walsh–Hadamard and Fourier transform in certain cases. We also discuss some useful results related to the degree of the algebraic normal form using Walsh–Hadamard transform.
{"title":"A Novel Approach Towards Degree and Walsh-Transform of Boolean Functions","authors":"S. Sushanth Kumar, Harshdeep Singh, Gaurav Mittal","doi":"10.1142/s0129054122500101","DOIUrl":"https://doi.org/10.1142/s0129054122500101","url":null,"abstract":"Boolean functions are fundamental bricks in the development of various applications in Cryptography and Coding theory by making benefit from the weights of related Boolean functions (Walsh spectrum). Towards this, the discrete Fourier transform (Walsh–Hadamard) plays a pivotal tool. The work in this paper is dedicated towards the algebraic and numerical degrees, together with the relationship between weights of Boolean function and their Walsh transforms. We introduce Walsh matrices and generalize them to any arbitrary Boolean function. This improves the complexity in computation of Walsh–Hadamard and Fourier transform in certain cases. We also discuss some useful results related to the degree of the algebraic normal form using Walsh–Hadamard transform.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123755110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-12DOI: 10.1142/s0129054122500083
H. Yildirim, Zeynep Nihan Berberler
The robustness evaluates the capability of networks in resisting failures or attacks on some parts of networks. The concept of vulnerability is very important in network analysis. Isolated rupture degree is a novel graph-theoretic concept defined as a measure of network vulnerability. In this paper, the relationships between isolated rupture degree and some other vulnerability parameters such as isolated scattering number and isolated toughness are established. Exact values for isolated rupture degree of thorny networks are obtained.
{"title":"Isolated Rupture in Thorny Networks","authors":"H. Yildirim, Zeynep Nihan Berberler","doi":"10.1142/s0129054122500083","DOIUrl":"https://doi.org/10.1142/s0129054122500083","url":null,"abstract":"The robustness evaluates the capability of networks in resisting failures or attacks on some parts of networks. The concept of vulnerability is very important in network analysis. Isolated rupture degree is a novel graph-theoretic concept defined as a measure of network vulnerability. In this paper, the relationships between isolated rupture degree and some other vulnerability parameters such as isolated scattering number and isolated toughness are established. Exact values for isolated rupture degree of thorny networks are obtained.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131643861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-12DOI: 10.1142/s0129054122410040
Kitti Gelle, Szabolcs Iván
Reversible forms of computations are often interesting from an energy efficiency point of view. When the computation device in question is an automaton, it is known that the minimal reversible automaton recognizing a given language is not necessarily unique, moreover, there are languages having arbitrarily large reversible recognizers possessing no nontrivial “reversible” congruence. Building atop on our earlier result, we show that the corresponding decision problem is [Formula: see text]-complete, and that even in the case when there are only finitely many such reversible recognizers, the largest one among them can be exponentially larger than the minimal automaton. Both results hold for the case of binary alphabets.
{"title":"Descriptive Complexity of Reversible Languages Having Finitely Many Reduced Automata","authors":"Kitti Gelle, Szabolcs Iván","doi":"10.1142/s0129054122410040","DOIUrl":"https://doi.org/10.1142/s0129054122410040","url":null,"abstract":"Reversible forms of computations are often interesting from an energy efficiency point of view. When the computation device in question is an automaton, it is known that the minimal reversible automaton recognizing a given language is not necessarily unique, moreover, there are languages having arbitrarily large reversible recognizers possessing no nontrivial “reversible” congruence. Building atop on our earlier result, we show that the corresponding decision problem is [Formula: see text]-complete, and that even in the case when there are only finitely many such reversible recognizers, the largest one among them can be exponentially larger than the minimal automaton. Both results hold for the case of binary alphabets.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116551488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-30DOI: 10.1142/s0129054122410106
Martin Plátek, F. Otto, F. Mráz
Here we establish and study some rigorous tools suitable for the lexicalized syntactic analysis (LSA) of natural and formal languages. Motivated by the linguistic method of analysis by reduction, we are interested in correctness preserving LSA. We introduce a suitable model of automata, the h-lexicalized one-way restarting automata (h-RRWW), and compare the properties of their input languages, which are the languages considered traditionally in automata theory, to the properties of the so-called basic and h-proper languages. These languages form the basic components for LSA. With respect to their input languages, h-RRWW-automata are not sensitive to the size of the read/write window and they allow computations that are far from being correctness preserving. On the other hand, for their basic and h-proper languages, h-RRWW-automata ensure that the resulting computations are completely correctness preserving, and they yield infinite ascending hierarchies of language classes within the regular, the context-free, and the context-sensitive languages that are based on the size of the read/write window.
{"title":"One-Way Restarting Automata and Their Sensitivitys","authors":"Martin Plátek, F. Otto, F. Mráz","doi":"10.1142/s0129054122410106","DOIUrl":"https://doi.org/10.1142/s0129054122410106","url":null,"abstract":"Here we establish and study some rigorous tools suitable for the lexicalized syntactic analysis (LSA) of natural and formal languages. Motivated by the linguistic method of analysis by reduction, we are interested in correctness preserving LSA. We introduce a suitable model of automata, the h-lexicalized one-way restarting automata (h-RRWW), and compare the properties of their input languages, which are the languages considered traditionally in automata theory, to the properties of the so-called basic and h-proper languages. These languages form the basic components for LSA. With respect to their input languages, h-RRWW-automata are not sensitive to the size of the read/write window and they allow computations that are far from being correctness preserving. On the other hand, for their basic and h-proper languages, h-RRWW-automata ensure that the resulting computations are completely correctness preserving, and they yield infinite ascending hierarchies of language classes within the regular, the context-free, and the context-sensitive languages that are based on the size of the read/write window.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129279940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-04-18DOI: 10.1142/s0129054122500095
K. Feng
The [Formula: see text]-star graph [Formula: see text], which is introduced to address scaling issues of the star graph, is recognized as an attractive interconnection network topology for building multiprocessor systems because of its desirable properties. Let [Formula: see text] be the minimum number of faulty vertices that make every subgraph isomorphic to [Formula: see text] faulty in [Formula: see text] under vertex-failure model, where [Formula: see text]. In this paper, we prove that [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for [Formula: see text] and [Formula: see text].
{"title":"Subnetwork Preclusion of (n, k)-Star Networks","authors":"K. Feng","doi":"10.1142/s0129054122500095","DOIUrl":"https://doi.org/10.1142/s0129054122500095","url":null,"abstract":"The [Formula: see text]-star graph [Formula: see text], which is introduced to address scaling issues of the star graph, is recognized as an attractive interconnection network topology for building multiprocessor systems because of its desirable properties. Let [Formula: see text] be the minimum number of faulty vertices that make every subgraph isomorphic to [Formula: see text] faulty in [Formula: see text] under vertex-failure model, where [Formula: see text]. In this paper, we prove that [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for [Formula: see text] and [Formula: see text].","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125071345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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