Pub Date : 2023-03-06DOI: 10.7494/csci.2023.24.1.4643
J. Odoom, Xiaofang Huang, S. Danso, Benedicta Nana Esi Nyarko
Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub.
{"title":"Privacy Preservation for Transaction initiators: Stronger Key Image Ring Signature and Smart Contract-based Framework","authors":"J. Odoom, Xiaofang Huang, S. Danso, Benedicta Nana Esi Nyarko","doi":"10.7494/csci.2023.24.1.4643","DOIUrl":"https://doi.org/10.7494/csci.2023.24.1.4643","url":null,"abstract":"Recently, blockchain technology has garnered support. However, an attenuating factor to its global adoption in certain use cases is privacy-preservation owing to its inherent transparency. A widely explored cryptographic option to address this challenge has been ring signature which aside its privacy guarantee must be double spending resistant. In this paper, we identify and prove a catastrophic flaw for double-spending attack in a Lightweight Ring Signature scheme and proceed to construct a new, fortified commitment scheme using the signer’s entire private key. Subsequently, we compute a stronger key image to yield a double-spending-resistant signature scheme solidly backed by formal proof. Inherent in our solution is a novel, zero-knowledge-based, secured and cost-effective smart contract for public key aggregation. We test our solution on a private blockchain as well as Kovan testnet along with performance analysis attesting to efficiency and usability and make the code publicly available on GitHub.","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78679313","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 : 2023-03-06DOI: 10.7494/csci.2023.24.1.4494
F. O. Asahiah, Mary Taiwo Onifade, Abayomi Emmanuel Adegunlehin, Adekemisola Olufunmilayo Asahiah, Adekemi Olawunmi Amoo
Spell checking and correction is still in infancy for Yorùbá language, and existing tools cannot be applied directly to address the problem because Yorùbá language requires extensive use of diacritics for marking phonemes and tone. We addressed this problem by collecting data from on-line sources and from optical character recognition of hard copy of books. The features relevant to spell checking and correction in this language that marks tones (and underdot) were identified through the review of existing spell checking solutions, analysis of the data collected and consultation with relevant Yorùbá Linguistics textbooks. A conceptual model was formulated as a parallel combination of a unigram language model and a language diacritic model to form a dictionary sub-model that is used by Error Detection and Candidate Generation modules. The candidate generation module was implemented as an inverse Levensthein edit-distance algorithm. The system was evaluated using Detection Accuracy (calculated from Precision and Recall) and Suggestion Accuracy (SA) as metrics.Our experimental setups compared the performance of the component subsystems when used alone with the their combination into a unified model. The detection accuracies for each of the models were 93.23%, 94.10% and 95.01% respectively while their suggestion accuracies were 26.94%, 72.10% and 65.89% respectively. In relation to the size of training corpus, the unified model was able to achieve a increase of 1.83% in detection accuracy and 5.27% in suggestion accuracy for 70% increase in size of corpus. The results indicated that each of the sub-models in the dictionary played different roles while the increase in training data does not give a linear proportional increase in performance of the spell checker. The study also showed that spell checking a Yorùbá text was better when attention is paid to the diacritical aspect of the language
{"title":"Diacritic-Aware YorùBá Spell Checker","authors":"F. O. Asahiah, Mary Taiwo Onifade, Abayomi Emmanuel Adegunlehin, Adekemisola Olufunmilayo Asahiah, Adekemi Olawunmi Amoo","doi":"10.7494/csci.2023.24.1.4494","DOIUrl":"https://doi.org/10.7494/csci.2023.24.1.4494","url":null,"abstract":"Spell checking and correction is still in infancy for Yorùbá language, and existing tools cannot be applied directly to address the problem because Yorùbá language requires extensive use of diacritics for marking phonemes and tone. We addressed this problem by collecting data from on-line sources and from optical character recognition of hard copy of books. The features relevant to spell checking and correction in this language that marks tones (and underdot) were identified through the review of existing spell checking solutions, analysis of the data collected and consultation with relevant Yorùbá Linguistics textbooks. A conceptual model was formulated as a parallel combination of a unigram language model and a language diacritic model to form a dictionary sub-model that is used by Error Detection and Candidate Generation modules. The candidate generation module was implemented as an inverse Levensthein edit-distance algorithm. \u0000The system was evaluated using Detection Accuracy (calculated from Precision and Recall) and Suggestion Accuracy (SA) as metrics.Our experimental setups compared the performance of the component subsystems when used alone with the their combination into a unified model. The detection accuracies for each of the models were 93.23%, 94.10% and 95.01% respectively while their suggestion accuracies were 26.94%, 72.10% and 65.89% respectively. In relation to the size of training corpus, the unified model was able to achieve a increase of 1.83% in detection accuracy and 5.27% in suggestion accuracy for 70% increase in size of corpus. The results indicated that each of the sub-models in the dictionary played different roles while the increase in training data does not give a linear proportional increase in performance of the spell checker. The study also showed that spell checking a Yorùbá text was better when attention is paid to the diacritical aspect of the language","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78847599","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 : 2023-03-06DOI: 10.7494/csci.2023.24.1.4551
Sagar Dhanraj Pande, A. Khamparia
The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as the global explainability for the enhancement of interpretations in the case of intrusion detection systems. The proposed model was implemented using the popular dataset, NSL-KDD, and the performance of the framework evaluated using accuracy, precision, recall, and F1-score. The accuracy of the framework is achieved by about 99.99%. The proposed framework able to identify the top 4 features using local explainability and the top 20 features using global explainability.
{"title":"Explainable Deep Neural Network based Analysis on Intrusion Detection Systems","authors":"Sagar Dhanraj Pande, A. Khamparia","doi":"10.7494/csci.2023.24.1.4551","DOIUrl":"https://doi.org/10.7494/csci.2023.24.1.4551","url":null,"abstract":"The research on Intrusion Detection Systems (IDSs) have been increasing in recent years. Particularly, the research which are widely utilizing machine learning concepts, and it is proven that these concepts were effective with IDSs, particularly, deep neural network-based models enhanced the rate of detections of IDSs. At the same instance, the models are turning out to be very highly complex, users are unable to track down the explanations for the decisions made which indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, the article deals with the proposed model that able to explain the obtained predictions. The proposed framework is a combination of a conventional intrusion detection system with the aid of a deep neural network and interpretability of the model predictions. The proposed model utilizes Shapley Additive Explanations (SHAP) that mixes with the local explainability as well as the global explainability for the enhancement of interpretations in the case of intrusion detection systems. The proposed model was implemented using the popular dataset, NSL-KDD, and the performance of the framework evaluated using accuracy, precision, recall, and F1-score. The accuracy of the framework is achieved by about 99.99%. The proposed framework able to identify the top 4 features using local explainability and the top 20 features using global explainability.","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77630318","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 : 2023-03-06DOI: 10.7494/csci.2023.24.1.4654
S. Sayed, A. Elkorany, Sabah Sayed
Millions of people around the world have been affected and some have died during the global pandemic Corona (COVID-19). This pandemic has created a global threat to people's lives and medical systems. The constraints of hospital resources and the pressures on healthcare workers during this period are among the reasons for wrong decisions and medical deterioration. Anticipating severe patients is an urgent matter of resource consumption by prioritizing patients at high risk to save their lives. This paper introduces an early prognostic model to predict the severity of patients and detect the most significant features based on clinical blood data. The proposed model predicts ICU severity within the first 2 hours of hospital admission, seeks to assist clinicians in decision-making and facilitates efficient use of hospital resources. The Hunger Game Search (HGS) meta-heuristic algorithm and the SVM are hybridized for building the proposed prediction model. Furthermore, they have been used for selecting the most informative features from the blood test data. Experiments have shown that using HGS for selecting the features with the SVM classifier achieved excellent results compared with the other four meta-heuristic algorithms. The model using the features selected by the HGS algorithm accomplished the topmost results, 98.6% and 96.5% for the best and mean accuracy, respectively, compared with using all features and features selected by other popular optimization algorithms.
在全球大流行冠状病毒(COVID-19)期间，全球数百万人受到影响，有些人死亡。这次大流行对人们的生命和医疗系统造成了全球性威胁。在此期间，医院资源的限制和医护人员的压力是导致错误决策和医疗恶化的原因之一。通过优先考虑高危患者以挽救其生命，预测重症患者是一项紧迫的资源消耗问题。本文介绍了一种基于临床血液数据的早期预后模型，用于预测患者的严重程度并检测最显著的特征。该模型可在入院前2小时内预测重症监护病房的严重程度，旨在帮助临床医生做出决策，并促进医院资源的有效利用。将饥饿游戏搜索(Hunger Game Search, HGS)元启发式算法与支持向量机(SVM)相结合，建立预测模型。此外，它们还被用于从血液检测数据中选择信息最丰富的特征。实验表明，与其他四种元启发式算法相比，使用HGS与SVM分类器进行特征选择取得了较好的效果。与使用所有特征和其他流行的优化算法选择的特征相比，使用HGS算法选择的特征模型的最佳和平均准确率分别为98.6%和96.5%。
{"title":"Applying hunger Game Search (HGS) for Selecting Significant blood indicators for Early Prediction of ICU CoViD-19 severity","authors":"S. Sayed, A. Elkorany, Sabah Sayed","doi":"10.7494/csci.2023.24.1.4654","DOIUrl":"https://doi.org/10.7494/csci.2023.24.1.4654","url":null,"abstract":"Millions of people around the world have been affected and some have died during the global pandemic Corona (COVID-19). This pandemic has created a global threat to people's lives and medical systems. The constraints of hospital resources and the pressures on healthcare workers during this period are among the reasons for wrong decisions and medical deterioration. Anticipating severe patients is an urgent matter of resource consumption by prioritizing patients at high risk to save their lives. This paper introduces an early prognostic model to predict the severity of patients and detect the most significant features based on clinical blood data. The proposed model predicts ICU severity within the first 2 hours of hospital admission, seeks to assist clinicians in decision-making and facilitates efficient use of hospital resources. The Hunger Game Search (HGS) meta-heuristic algorithm and the SVM are hybridized for building the proposed prediction model. Furthermore, they have been used for selecting the most informative features from the blood test data. Experiments have shown that using HGS for selecting the features with the SVM classifier achieved excellent results compared with the other four meta-heuristic algorithms. The model using the features selected by the HGS algorithm accomplished the topmost results, 98.6% and 96.5% for the best and mean accuracy, respectively, compared with using all features and features selected by other popular optimization algorithms.","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88454485","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 : 2023-03-01DOI: 10.1007/978-3-031-20350-3_7
Xueyang Zhao, Binghao Yan, P. Zhang
{"title":"New Algorithms for a Simple Measure of Network Partitioning","authors":"Xueyang Zhao, Binghao Yan, P. Zhang","doi":"10.1007/978-3-031-20350-3_7","DOIUrl":"https://doi.org/10.1007/978-3-031-20350-3_7","url":null,"abstract":"","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"1 1","pages":"113846"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90085008","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}
{"title":"Complexity assessments for decidable fragments of Set Theory. III: Testers for crucial, polynomial-maximal decidable Boolean languages","authors":"D. Cantone, P. Maugeri, E. Omodeo","doi":"10.2139/ssrn.4163424","DOIUrl":"https://doi.org/10.2139/ssrn.4163424","url":null,"abstract":"","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"1 1","pages":"113786"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83226137","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 : 2023-03-01DOI: 10.1007/978-3-030-91081-5_18
Quentin Bramas, Anissa Lamani, S. Tixeuil
{"title":"The Agreement Power of Disagreement","authors":"Quentin Bramas, Anissa Lamani, S. Tixeuil","doi":"10.1007/978-3-030-91081-5_18","DOIUrl":"https://doi.org/10.1007/978-3-030-91081-5_18","url":null,"abstract":"","PeriodicalId":23063,"journal":{"name":"Theor. Comput. Sci.","volume":"105 1","pages":"113772"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73030210","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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