Pub Date : 2022-10-31DOI: 10.1007/978-3-030-81508-0_14
Vesa Halava, T. Harju, R. Niskanen, I. Potapov
{"title":"Integer Weighted Automata on Infinite Words","authors":"Vesa Halava, T. Harju, R. Niskanen, I. Potapov","doi":"10.1007/978-3-030-81508-0_14","DOIUrl":"https://doi.org/10.1007/978-3-030-81508-0_14","url":null,"abstract":"","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123012746","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 : 2022-09-15DOI: 10.1142/s0129054122500228
Hong Zhang, Shuming Zhou, Qifan Zhang
Connectivity is an important index to evaluate the reliability and fault tolerance of a graph. As a natural extension of the connectivity of graphs, the [Formula: see text]-component connectivity of a graph [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices whose removal from [Formula: see text] results in a disconnected graph with at least [Formula: see text] components. It is a scientific issue to determine the exact values of [Formula: see text] for distinguishing the fault tolerability of networks. However, [Formula: see text]-component connectivity of many well-known interconnection networks has not been explored even for small [Formula: see text]. For the [Formula: see text]-dimensional alternating group networks [Formula: see text] and [Formula: see text]-dimensional godan graphs [Formula: see text], we show that [Formula: see text] for [Formula: see text], and [Formula: see text] for [Formula: see text] and [Formula: see text].
{"title":"Component Connectivity of Alternating Group Networks and Godan Graphs","authors":"Hong Zhang, Shuming Zhou, Qifan Zhang","doi":"10.1142/s0129054122500228","DOIUrl":"https://doi.org/10.1142/s0129054122500228","url":null,"abstract":"Connectivity is an important index to evaluate the reliability and fault tolerance of a graph. As a natural extension of the connectivity of graphs, the [Formula: see text]-component connectivity of a graph [Formula: see text], denoted by [Formula: see text], is the minimum number of vertices whose removal from [Formula: see text] results in a disconnected graph with at least [Formula: see text] components. It is a scientific issue to determine the exact values of [Formula: see text] for distinguishing the fault tolerability of networks. However, [Formula: see text]-component connectivity of many well-known interconnection networks has not been explored even for small [Formula: see text]. For the [Formula: see text]-dimensional alternating group networks [Formula: see text] and [Formula: see text]-dimensional godan graphs [Formula: see text], we show that [Formula: see text] for [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":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116793045","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 : 2022-09-10DOI: 10.1142/s012905412250023x
Zsolt Gazdag, S. Vágvölgyi
We denote by [Formula: see text] the class of tree languages generated by chain-free distributed regular tree grammars of [Formula: see text] components cooperating with terminal strategy. Dányi and Fülöp [2] showed that the hierarchy [Formula: see text], [Formula: see text] contains an infinite proper hierarchy. We improve their result showing that for each [Formula: see text], [Formula: see text].
我们用[公式:见文]表示由[公式:见文]组件配合终端策略的无链分布式规则树语法生成的树语言类。Dányi和Fülöp[2]表明层次[Formula: see text]， [Formula: see text]包含了无限的固有层次。我们改进了他们的结果，显示每个[公式:见文本]，[公式:见文本]。
{"title":"The Component Hierarchy of Chain-Free Cooperating Distributed Regular Tree Grammars Revisited","authors":"Zsolt Gazdag, S. Vágvölgyi","doi":"10.1142/s012905412250023x","DOIUrl":"https://doi.org/10.1142/s012905412250023x","url":null,"abstract":"We denote by [Formula: see text] the class of tree languages generated by chain-free distributed regular tree grammars of [Formula: see text] components cooperating with terminal strategy. Dányi and Fülöp [2] showed that the hierarchy [Formula: see text], [Formula: see text] contains an infinite proper hierarchy. We improve their result showing that for each [Formula: see text], [Formula: see text].","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129581176","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 : 2022-09-10DOI: 10.1142/s012905412242014x
Jian Wu, Chaoyu Yang
Coal mining work has always been a high-risk job, although mining technology is now regularly very mature, many accidents still occur every year in various countries around the world, most of which are due to gas explosions, poisoning, asphyxiation and other accidents. Therefore it is important to monitor and predict both underground mine air quality. In this paper, we use the GCN spatio-temporal graph convolution method based on spectral domain for multivariate time series prediction of underground mine air environment. The correlation of these sequences is learned by a self-attentive mechanism, without a priori graph, and the adjacency matrix with an attention mechanism is created dynamically. The temporal and spatial features are learned by graph Fourier transform and inverse Fourier transform in TC module (temporal convolution) and GC module (graph convolution), respectively. Besides, the corresponding experimental predictions are performed on other public datasets. And a new loss function is designed based on the idea of residuals, which greatly improves the prediction accuracy. In addition, the corresponding experimental predictions were performed on other public datasets. The results show that this model has outstanding prediction ability and high prediction accuracy on most time-series prediction data sets. Through experimental verification, this model has high prediction accuracy for dealing with multivariate time series prediction problems, both for long-term and short-term prediction.
{"title":"Graph Convolutional Network-Guided Mine Gas Concentration Predictor","authors":"Jian Wu, Chaoyu Yang","doi":"10.1142/s012905412242014x","DOIUrl":"https://doi.org/10.1142/s012905412242014x","url":null,"abstract":"Coal mining work has always been a high-risk job, although mining technology is now regularly very mature, many accidents still occur every year in various countries around the world, most of which are due to gas explosions, poisoning, asphyxiation and other accidents. Therefore it is important to monitor and predict both underground mine air quality. In this paper, we use the GCN spatio-temporal graph convolution method based on spectral domain for multivariate time series prediction of underground mine air environment. The correlation of these sequences is learned by a self-attentive mechanism, without a priori graph, and the adjacency matrix with an attention mechanism is created dynamically. The temporal and spatial features are learned by graph Fourier transform and inverse Fourier transform in TC module (temporal convolution) and GC module (graph convolution), respectively. Besides, the corresponding experimental predictions are performed on other public datasets. And a new loss function is designed based on the idea of residuals, which greatly improves the prediction accuracy. In addition, the corresponding experimental predictions were performed on other public datasets. The results show that this model has outstanding prediction ability and high prediction accuracy on most time-series prediction data sets. Through experimental verification, this model has high prediction accuracy for dealing with multivariate time series prediction problems, both for long-term and short-term prediction.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"374 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122438221","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 : 2022-09-10DOI: 10.1142/s0129054122420229
Salman Mukhtar, M. Salman, A. D. Maden, M. U. Rehman
The non-commuting graph associated to a group has non-central elements of the graph as vertices and two elements [Formula: see text] and [Formula: see text] do not form an edge if and only if [Formula: see text]. In this paper, we consider non-commuting graphs associated to dihedral and semidihedral groups. We investigate their metric properties such as center, periphery, eccentric graph, closure and interior. We also perform various types of metric identifications on these graphs. Moreover, we generate metric and metric-degree polynomials of these graphs.
{"title":"Metric Properties of Non-Commuting Graph Associated to Two Groups","authors":"Salman Mukhtar, M. Salman, A. D. Maden, M. U. Rehman","doi":"10.1142/s0129054122420229","DOIUrl":"https://doi.org/10.1142/s0129054122420229","url":null,"abstract":"The non-commuting graph associated to a group has non-central elements of the graph as vertices and two elements [Formula: see text] and [Formula: see text] do not form an edge if and only if [Formula: see text]. In this paper, we consider non-commuting graphs associated to dihedral and semidihedral groups. We investigate their metric properties such as center, periphery, eccentric graph, closure and interior. We also perform various types of metric identifications on these graphs. Moreover, we generate metric and metric-degree polynomials of these graphs.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126944129","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}
For the strategy of investing in gold and Bitcoin, first collect the historical prices of two types of investment products in the market, and use the wavelet neural network model and WT-LSTM model to model and analyze to predict the future price trends of gold and Bitcoin. Second, considering the difference in price fluctuations between gold and Bitcoin, based on the GARCH-EVT model to increase the risk uncertainty of financial assets, proposes how to achieve the best trading strategy under risk characteristics. Finally, considering the influence of transaction rate on income, we use particle swarm algorithm and genetic algorithm to study what kind of transaction rate can achieve maximum income. The study found that although traders can predict future trends based on daily price changes, due to the different risk factors of gold and Bitcoin, and the different sensitivity of the two financial assets to transaction costs, trading strategies will be very different.
{"title":"Algorithm Design for Asset Trading Under Multiple Factors","authors":"Li-Jun Xu, Shou-Yu Wei, Xiao-Qing Lu, Ze-Hua He, Jia-Ming Zhu","doi":"10.1142/s0129054122420199","DOIUrl":"https://doi.org/10.1142/s0129054122420199","url":null,"abstract":"For the strategy of investing in gold and Bitcoin, first collect the historical prices of two types of investment products in the market, and use the wavelet neural network model and WT-LSTM model to model and analyze to predict the future price trends of gold and Bitcoin. Second, considering the difference in price fluctuations between gold and Bitcoin, based on the GARCH-EVT model to increase the risk uncertainty of financial assets, proposes how to achieve the best trading strategy under risk characteristics. Finally, considering the influence of transaction rate on income, we use particle swarm algorithm and genetic algorithm to study what kind of transaction rate can achieve maximum income. The study found that although traders can predict future trends based on daily price changes, due to the different risk factors of gold and Bitcoin, and the different sensitivity of the two financial assets to transaction costs, trading strategies will be very different.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"8 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132221265","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 : 2022-09-08DOI: 10.1142/s0129054122420205
Bin Yang, Jie Wang
The existing helmet detection algorithms have disadvantages such as difficulty in detecting occluded targets, small targets, etc. To address those problems, a YOLO V4-based helmet detection improvement algorithm has been proposed. Firstly, the model’s backbone structure is improved, and the backbone’s multi-scale feature extraction capability is enhanced by using MCM modules with different sized convolutional kernels, the FSM channel attention module is used to guide the model to dynamically focus on the channel features of extracted small targets and obscured target information. Secondly, in order to optimize the model training, the latest loss function Eiou is used to replace Ciou for anchor frame regression prediction to improve the convergence speed and regression accuracy of the model. Finally, a helmet dataset is constructed from this paper, and a K-means clustering algorithm is used to cluster the helmet dataset and select the appropriate a priori candidate frames. The experimental results show that the improved algorithm has a significant improvement in detection accuracy compared with the original YOLO V4 algorithm, and can have a positive detection effect on small targets and obscured targets.
{"title":"An Improved Helmet Detection Algorithm Based on YOLO V4","authors":"Bin Yang, Jie Wang","doi":"10.1142/s0129054122420205","DOIUrl":"https://doi.org/10.1142/s0129054122420205","url":null,"abstract":"The existing helmet detection algorithms have disadvantages such as difficulty in detecting occluded targets, small targets, etc. To address those problems, a YOLO V4-based helmet detection improvement algorithm has been proposed. Firstly, the model’s backbone structure is improved, and the backbone’s multi-scale feature extraction capability is enhanced by using MCM modules with different sized convolutional kernels, the FSM channel attention module is used to guide the model to dynamically focus on the channel features of extracted small targets and obscured target information. Secondly, in order to optimize the model training, the latest loss function Eiou is used to replace Ciou for anchor frame regression prediction to improve the convergence speed and regression accuracy of the model. Finally, a helmet dataset is constructed from this paper, and a K-means clustering algorithm is used to cluster the helmet dataset and select the appropriate a priori candidate frames. The experimental results show that the improved algorithm has a significant improvement in detection accuracy compared with the original YOLO V4 algorithm, and can have a positive detection effect on small targets and obscured targets.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122757887","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}
Recently, a framework for employing the Gowers [Formula: see text] norm in the context of (generalized) Boolean functions with cryptographic significance was introduced. In this paper, we first give tight bounds on the Gowers [Formula: see text] norm of generalized Boolean functions via the (generalized) sum-of-squares indicator. Secondly, we provide a framework for the generalized signal-to-noise ratio ([Formula: see text]) of generalized [Formula: see text]-functions. We characterize the [Formula: see text] in terms of the Gowers [Formula: see text] norm. In particular, we present a direct link between the [Formula: see text] of a class of generalized Boolean functions and the [Formula: see text] of its component Boolean functions. Finally, the expressions of the Gowers [Formula: see text] norm of generalized Boolean functions from some well-known secondary constructions (the concatenation and Carlet’s construction) are obtained.
{"title":"Results on the Gowers U2 Norm of Generalized Boolean Functions","authors":"Zhiyao Yang, Pinhui Ke, Zhixiong Chen, Chenhuang Wu","doi":"10.1142/s0129054122500216","DOIUrl":"https://doi.org/10.1142/s0129054122500216","url":null,"abstract":"Recently, a framework for employing the Gowers [Formula: see text] norm in the context of (generalized) Boolean functions with cryptographic significance was introduced. In this paper, we first give tight bounds on the Gowers [Formula: see text] norm of generalized Boolean functions via the (generalized) sum-of-squares indicator. Secondly, we provide a framework for the generalized signal-to-noise ratio ([Formula: see text]) of generalized [Formula: see text]-functions. We characterize the [Formula: see text] in terms of the Gowers [Formula: see text] norm. In particular, we present a direct link between the [Formula: see text] of a class of generalized Boolean functions and the [Formula: see text] of its component Boolean functions. Finally, the expressions of the Gowers [Formula: see text] norm of generalized Boolean functions from some well-known secondary constructions (the concatenation and Carlet’s construction) are obtained.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125217329","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}
To analyse speech intention based on discussion texts in online collaborative discussions, automatic classification of discussion texts is conducted to assist teachers improve their abilities to diagnose and analyse the discussion process. The current study proposes a deep learning network model that incorporates multi-head attention mechanism with bidirectional long short-term memory (MA-BiLSTM). The proposed algorithm acquires contextual semantic connections from a global perspective and the role of key feature words within sentences from a local perspective to further strengthen the semantic features of the texts. The proposed model was employed to classify 12,000 interactive texts generated during online collaborative discussion activities. Results show that MA-BiLSTM achieved an overall classification accuracy of 83.25%, which is at least 2.83% higher than those of other baseline models. However, the classification of consultative and administrative interactive texts is minimally effective. MA-BiLSTM achieved better than the existing classification methods for interactive text classification.
{"title":"Optimizing the Online Learners' Verbal Intention Classification Efficiency Based on the Multi-Head Attention Mechanism Algorithm","authors":"Yangfeng Zheng, Zheng Shao, Zhanghao Gao, Mingming Deng, Xuesong Zhai","doi":"10.1142/s0129054122420114","DOIUrl":"https://doi.org/10.1142/s0129054122420114","url":null,"abstract":"To analyse speech intention based on discussion texts in online collaborative discussions, automatic classification of discussion texts is conducted to assist teachers improve their abilities to diagnose and analyse the discussion process. The current study proposes a deep learning network model that incorporates multi-head attention mechanism with bidirectional long short-term memory (MA-BiLSTM). The proposed algorithm acquires contextual semantic connections from a global perspective and the role of key feature words within sentences from a local perspective to further strengthen the semantic features of the texts. The proposed model was employed to classify 12,000 interactive texts generated during online collaborative discussion activities. Results show that MA-BiLSTM achieved an overall classification accuracy of 83.25%, which is at least 2.83% higher than those of other baseline models. However, the classification of consultative and administrative interactive texts is minimally effective. MA-BiLSTM achieved better than the existing classification methods for interactive text classification.","PeriodicalId":192109,"journal":{"name":"Int. J. Found. Comput. Sci.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876087","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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