Social media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing Multi‐Pop, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics.
{"title":"Multi‐Pop: Enhancing user engagement with content‐based multimodal popularity prediction in social media","authors":"Jiyoon Kim, Hyeongjin Ahn, Eunil Park","doi":"10.1111/exsy.13707","DOIUrl":"https://doi.org/10.1111/exsy.13707","url":null,"abstract":"Social media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing <jats:italic>Multi‐Pop</jats:italic>, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Conversational assistants (CAs) and Task‐oriented ones, in particular, are designed to interact with users in a natural language manner, assisting them in completing specific tasks or providing relevant information. These systems employ advanced natural language understanding (NLU) and dialogue management techniques to comprehend user inputs, infer their intentions, and generate appropriate responses or actions. Over time, the CAs have gradually diversified to today touch various fields such as e‐commerce, healthcare, tourism, fashion, travel, and many other sectors. NLU is fundamental in the natural language processing (NLP) field. Identifying user intents from natural language utterances is a sub‐task of NLU that is crucial for conversational systems. The diversity in user utterances makes intent detection (ID) even a challenging problem. Recently, with the emergence of Deep Neural Networks. New State of the Art (SOA) results have been achieved for different NLP tasks. Recurrent neural networks (RNNs) and Transformer architectures are two major players in those improvements. RNNs have significantly contributed to sequence modelling across various application areas. Conversely, Transformer models represent a newer architecture leveraging attention mechanisms, extensive training data sets, and computational power. This review paper begins with a detailed exploration of RNN and Transformer models. Subsequently, it conducts a comparative analysis of their performance in intent recognition for Task‐oriented (CAs). Finally, it concludes by addressing the main challenges and outlining future research directions.
{"title":"Intent detection for task‐oriented conversational agents: A comparative study of recurrent neural networks and transformer models","authors":"Mourad Jbene, Abdellah Chehri, Rachid Saadane, Smail Tigani, Gwanggil Jeon","doi":"10.1111/exsy.13712","DOIUrl":"https://doi.org/10.1111/exsy.13712","url":null,"abstract":"Conversational assistants (CAs) and Task‐oriented ones, in particular, are designed to interact with users in a natural language manner, assisting them in completing specific tasks or providing relevant information. These systems employ advanced natural language understanding (NLU) and dialogue management techniques to comprehend user inputs, infer their intentions, and generate appropriate responses or actions. Over time, the CAs have gradually diversified to today touch various fields such as e‐commerce, healthcare, tourism, fashion, travel, and many other sectors. NLU is fundamental in the natural language processing (NLP) field. Identifying user intents from natural language utterances is a sub‐task of NLU that is crucial for conversational systems. The diversity in user utterances makes intent detection (ID) even a challenging problem. Recently, with the emergence of Deep Neural Networks. New State of the Art (SOA) results have been achieved for different NLP tasks. Recurrent neural networks (RNNs) and Transformer architectures are two major players in those improvements. RNNs have significantly contributed to sequence modelling across various application areas. Conversely, Transformer models represent a newer architecture leveraging attention mechanisms, extensive training data sets, and computational power. This review paper begins with a detailed exploration of RNN and Transformer models. Subsequently, it conducts a comparative analysis of their performance in intent recognition for Task‐oriented (CAs). Finally, it concludes by addressing the main challenges and outlining future research directions.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Li Cao, Maocai Wang, Massimiliano Vasile, Guangming Dai
This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results.
{"title":"Multi agent collaborative search algorithm with adaptive weights","authors":"Li Cao, Maocai Wang, Massimiliano Vasile, Guangming Dai","doi":"10.1111/exsy.13709","DOIUrl":"https://doi.org/10.1111/exsy.13709","url":null,"abstract":"This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS‐AW). MACS is a multi‐agent memetic scheme for multi‐objective optimization originally developed to mix local and population‐based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub‐problem the agent had to solve; (ii) the population‐based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS‐AW is compared against some state‐of‐art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS‐AW is applied to the solution of two real‐life optimization problems and compared against MACS2.1. It will be shown that MACS‐AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS‐AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS‐AW and its predecessor obtain same results.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miguel Civit, María José Escalona, Francisco Cuadrado, Salvador Reyes‐de‐Cozar
BackgroundActive Learning with AI‐tutoring in Higher Education tackles dropout rates.ObjectivesTo investigate teaching‐learning methodologies preferred by students. AHP is used to evaluate a ChatGPT‐based studented learning methodology which is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, and help students elect the best strategies according to their preferences.MethodsComparative study of three learning methodologies in a counterbalanced Single‐Group with 33 university students. It follows a pre‐test/post‐test approach using AHP and SAM. HRV and GSR used for the estimation of emotional states.FindingsCriteria related to in‐class experiences valued higher than test‐related criteria. Chat‐GPT integration was well regarded compared to well‐established methodologies. Student emotion self‐assessment correlated with physiological measures, validating used Learning Analytics.ConclusionsProposed model AI‐Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups.
{"title":"Class integration of ChatGPT and learning analytics for higher education","authors":"Miguel Civit, María José Escalona, Francisco Cuadrado, Salvador Reyes‐de‐Cozar","doi":"10.1111/exsy.13703","DOIUrl":"https://doi.org/10.1111/exsy.13703","url":null,"abstract":"BackgroundActive Learning with AI‐tutoring in Higher Education tackles dropout rates.ObjectivesTo investigate teaching‐learning methodologies preferred by students. AHP is used to evaluate a ChatGPT‐based studented learning methodology which is compared to another active learning methodology and a traditional methodology. Study with Learning Analytics to evaluate alternatives, and help students elect the best strategies according to their preferences.MethodsComparative study of three learning methodologies in a counterbalanced Single‐Group with 33 university students. It follows a pre‐test/post‐test approach using AHP and SAM. HRV and GSR used for the estimation of emotional states.FindingsCriteria related to in‐class experiences valued higher than test‐related criteria. Chat‐GPT integration was well regarded compared to well‐established methodologies. Student emotion self‐assessment correlated with physiological measures, validating used Learning Analytics.ConclusionsProposed model AI‐Tutoring classroom integration functions effectively at increasing engagement and avoiding false information. AHP with the physiological measuring allows students to determine preferred learning methodologies, avoiding biases, and acknowledging minority groups.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Energy security constitutes a pivotal determinant in safeguarding the seamless functioning of economies. This research endeavours to shed light on the underlying predilections and potential scopes for enhancement within China's provincial energy security policies. By delving into an array of policy documents procured from the esteemed Legal Information Network of Peking University, it offers a meticulous exploration. Employing sophisticated text analysis methodologies, the study constructs a two‐tier analytical framework, meticulously encapsulating both the policy instruments employed and the intricate processes of their execution. Leveraging the power of Nvivo 12 Plus software, pertinent policy contents are systematically coded, with those aligning with the defined analytical dimensions aggregated for frequency computations. Furthermore, a Policy Measurement and Categorization (PMC) index model is devised, harnessing word frequency statistical data to assign a quantitative assessment to the policies under scrutiny. The empirical results demonstrate a noteworthy disparity in the adoption of policy tools among various provinces, with command‐and‐control mechanisms, economic incentive structures, and societal engagement strategies emerging as the most recurrent policy types. Among the energy security policies scrutinized, approximately 84.85% were categorized as effective, while a smaller yet significant portion, 6.06%, was classified as outstanding. Despite the overall robustness of China's provincial energy security policies, the investigation identifies several avenues for further refinement. The study suggests that the government could bolster these measures through intensified focus on transformative adjustments to energy structures, augmentation of green loan guarantee systems, and fostering enhanced inter‐sectoral collaboration. These strategic enhancements may serve as key levers to propel China's provincial energy security policies towards even greater effectiveness and resilience.
能源安全是保障经济正常运行的关键因素。本研究试图揭示中国省级能源安全政策的基本倾向和潜在改进空间。通过深入研究从北京大学法律信息网上获取的一系列政策文件，本研究进行了细致的探索。研究采用了复杂的文本分析方法，构建了一个双层分析框架，细致地概括了所采用的政策工具及其复杂的执行过程。利用 Nvivo 12 Plus 软件的强大功能，对相关政策内容进行了系统编码，并将符合所定义的分析维度的内容汇总起来进行频率计算。此外，还设计了一个政策衡量和分类（PMC）指数模型，利用词频统计数据对所审查的政策进行量化评估。实证结果表明，各省在采用政策工具方面存在显著差异，指挥控制机制、经济激励结构和社会参与战略是最常见的政策类型。在接受调查的能源安全政策中，约 84.85% 被归类为有效政策，另有 6.06% 的政策被归类为无效政策。尽管中国各省的能源安全政策总体上比较稳健，但调查也发现了一些需要进一步完善的途径。研究建议，政府可以通过加强对能源结构转型调整的关注、扩大绿色贷款担保体系以及促进跨部门合作来强化这些措施。这些战略改进措施可作为关键杠杆，推动中国省级能源安全政策取得更大成效，并增强其韧性。
{"title":"Selection preference and effectiveness quantification of provincial energy security policies in China","authors":"Liangpeng Wu, Yujing Tang, Qingyuan Zhu","doi":"10.1111/exsy.13711","DOIUrl":"https://doi.org/10.1111/exsy.13711","url":null,"abstract":"Energy security constitutes a pivotal determinant in safeguarding the seamless functioning of economies. This research endeavours to shed light on the underlying predilections and potential scopes for enhancement within China's provincial energy security policies. By delving into an array of policy documents procured from the esteemed Legal Information Network of Peking University, it offers a meticulous exploration. Employing sophisticated text analysis methodologies, the study constructs a two‐tier analytical framework, meticulously encapsulating both the policy instruments employed and the intricate processes of their execution. Leveraging the power of Nvivo 12 Plus software, pertinent policy contents are systematically coded, with those aligning with the defined analytical dimensions aggregated for frequency computations. Furthermore, a Policy Measurement and Categorization (PMC) index model is devised, harnessing word frequency statistical data to assign a quantitative assessment to the policies under scrutiny. The empirical results demonstrate a noteworthy disparity in the adoption of policy tools among various provinces, with command‐and‐control mechanisms, economic incentive structures, and societal engagement strategies emerging as the most recurrent policy types. Among the energy security policies scrutinized, approximately 84.85% were categorized as effective, while a smaller yet significant portion, 6.06%, was classified as outstanding. Despite the overall robustness of China's provincial energy security policies, the investigation identifies several avenues for further refinement. The study suggests that the government could bolster these measures through intensified focus on transformative adjustments to energy structures, augmentation of green loan guarantee systems, and fostering enhanced inter‐sectoral collaboration. These strategic enhancements may serve as key levers to propel China's provincial energy security policies towards even greater effectiveness and resilience.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In picture books, readers can obtain different emotional perceptions according to different image style attributes. Artists often use different combinations of colours, textures, materials, and other style elements in images to convey different emotions in their creations. Especially in picture books for children, there is a strong correlation between the perceived effect of the work and the accuracy and degree of emotional expression. In the process of creating picture books, various factors will affect the efficiency of artists trying to transfer styles to meet their creative needs. With the development of image style transfer technology based on a deep convolutional neural network, artists can use this technology to create works with different styles of emotional changes efficiently. In this paper, we select illustrations of picture books and use deep convolutional neural networks to transfer image styles from three aspects: colour style transfer, texture style, and material style transfer. Through sampling survey experiments, we discuss the changes in image attributes, emotional expression, and emotional perception in picture books for children. The survey results found that the most direct and evident influence on the emotional changes of picture book images is the transfer of colour style attributes, material style attributes, and texture style attributes. The results of this study can provide a valuable reference for improving the accuracy of emotional expression, the depth of meaning extension, and the height of artistic value in picture books for children during the process of an artist's creation. This research stands out by systematically analysing the distinct impact of each style attribute transfer, offering a comprehensive framework that can be utilized by artists and technologists alike to enhance the emotional and artistic quality of children's picture books.
{"title":"Application of visual attribute transfer technology in analysing changes in emotional expression in picture books","authors":"Yue Wang, Yin Wang, Yansu Qi, Sheng Miao, Weijun Gao","doi":"10.1111/exsy.13677","DOIUrl":"https://doi.org/10.1111/exsy.13677","url":null,"abstract":"In picture books, readers can obtain different emotional perceptions according to different image style attributes. Artists often use different combinations of colours, textures, materials, and other style elements in images to convey different emotions in their creations. Especially in picture books for children, there is a strong correlation between the perceived effect of the work and the accuracy and degree of emotional expression. In the process of creating picture books, various factors will affect the efficiency of artists trying to transfer styles to meet their creative needs. With the development of image style transfer technology based on a deep convolutional neural network, artists can use this technology to create works with different styles of emotional changes efficiently. In this paper, we select illustrations of picture books and use deep convolutional neural networks to transfer image styles from three aspects: colour style transfer, texture style, and material style transfer. Through sampling survey experiments, we discuss the changes in image attributes, emotional expression, and emotional perception in picture books for children. The survey results found that the most direct and evident influence on the emotional changes of picture book images is the transfer of colour style attributes, material style attributes, and texture style attributes. The results of this study can provide a valuable reference for improving the accuracy of emotional expression, the depth of meaning extension, and the height of artistic value in picture books for children during the process of an artist's creation. This research stands out by systematically analysing the distinct impact of each style attribute transfer, offering a comprehensive framework that can be utilized by artists and technologists alike to enhance the emotional and artistic quality of children's picture books.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on‐going advancements in diagnostic methods and encourages further exploration in this field.
{"title":"Prospect of large language models and natural language processing for lung cancer diagnosis: A systematic review","authors":"Arushi Garg, Smridhi Gupta, Soumya Vats, Palak Handa, Nidhi Goel","doi":"10.1111/exsy.13697","DOIUrl":"https://doi.org/10.1111/exsy.13697","url":null,"abstract":"Lung cancer, a leading cause of global mortality, demands a combat for its effective prevention, early diagnosis, and advanced treatment methods. Traditional diagnostic methods face limitations in accuracy and efficiency, necessitating innovative solutions. Large Language Models (LLMs) and Natural Language Processing (NLP) offer promising avenues for overcoming these challenges by providing comprehensive insights into medical data and personalizing treatment plans. This systematic review explores the transformative potential of LLMs and NLP in automating lung cancer diagnosis. It evaluates their applications, particularly in medical imaging and the interpretation of complex medical data, and assesses achievements and associated challenges. Emphasizing the critical role of Artificial Intelligence (AI) in medical imaging, the review highlights advancements in lung cancer screening and deep learning approaches. Furthermore, it underscores the importance of on‐going advancements in diagnostic methods and encourages further exploration in this field.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity O(n).
{"title":"CATcAFSMs: Context‐based adaptive trust calculation for attack detection in fog computing based smart medical systems","authors":"Alishba Nawaz, Waseem Iqbal, Ayesha Altaf, Abrar Almjally, Hatoon AlSagri, Bayan Alabdullah","doi":"10.1111/exsy.13687","DOIUrl":"https://doi.org/10.1111/exsy.13687","url":null,"abstract":"Fog's basic distributed nature and ability to process data in transit—that is, to make decisions in real time—make it a good fit for scenarios involving several distributed devices that need to communicate, provide real‐time data analysis, and carry out storage functions. The majority of fog computing applications are driven by the user's demands and/or their desire for functioning services, either neglecting or giving security considerations second attention. Fog computing security issues have not received enough attention. Fog computing could be exploitable due to the security difficulties associated with cloud computing. Due to its flexibility to function near the end user and independence from a centralized design, fog computing provides the dependability required by time‐sensitive smart healthcare systems. There is a need for enhanced security and privacy solutions for fog computing, where trust is essential, due to the importance of healthcare data. This research aims to develop a context‐based adaptive trust solution for the smart healthcare environment utilizing Bayesian approaches and similarity measures against bad mouthing and ballot stuffing, while context‐dependent trust solutions for fogs remain an unexplored area of study. The proposed trust model has been simulated in Contiki‐Cooja to evaluate our findings. In contrast to static weighting, adaptive weights are provided to direct and indirect trust using entropy values that ensure the least degree of trust bias, and context similarity calculations eliminate recommender nodes with malicious intent by leveraging server, colleague, and service similarities. The proposed model protects smart healthcare systems from attacks using similarity metrics, incorporates context, and also uses adaptive weighting for trust calculation. By eliminating trust bias and also detecting attacks, this solution enhances the trust calculation by 10% as compared to the previous solution. This paradigm is efficient due to its small trust computation overhead and linear complexity <jats:italic>O</jats:italic>(<jats:italic>n</jats:italic>).","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meltem Kurt Pehlivanoğlu, Robera Tadesse Gobosho, Muhammad Abdan Syakura, Vimal Shanmuganathan, Luis de‐la‐Fuente‐Valentín
Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well‐formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved. However, the availability of high‐quality paraphrase datasets has been limited, particularly for machine‐generated sentences. In this paper, we present ParaGPT, a new paraphrase dataset of 81,000 machine‐generated sentence pairs, including 27,000 reference sentences (ChatGPT‐generated sentences), and 81,000 paraphrases obtained by using three different large language models (LLMs): ChatGPT, GPT‐3, and T5. We used ChatGPT to generate 27,000 sentences that cover a diverse array of topics and sentence structures, thus providing diverse inputs for the models. In addition, we evaluated the quality of the generated paraphrases using various automatic evaluation metrics. Furthermore, we provide insights into the strengths and drawbacks of each LLM in generating paraphrases by conducting a comparative analysis of the paraphrasing performance of the three LLMs. According to our findings, ChatGPT's performance, as per the evaluation metrics provided, was deemed impressive and commendable, owing to its higher‐than‐average scores for semantic similarity, which implies a higher degree of similarity between the generated paraphrase and the reference sentence, and its relatively lower scores for syntactic diversity, indicating a greater diversity of syntactic structures in the generated paraphrase. ParaGPT is a valuable resource for researchers working on NLP tasks like paraphrasing, text simplification, and text generation. We make the ParaGPT dataset publicly accessible to researchers, and as far as we are aware, this is the first paraphrase dataset produced based on ChatGPT.
{"title":"Comparative analysis of paraphrasing performance of ChatGPT, GPT‐3, and T5 language models using a new ChatGPT generated dataset: ParaGPT","authors":"Meltem Kurt Pehlivanoğlu, Robera Tadesse Gobosho, Muhammad Abdan Syakura, Vimal Shanmuganathan, Luis de‐la‐Fuente‐Valentín","doi":"10.1111/exsy.13699","DOIUrl":"https://doi.org/10.1111/exsy.13699","url":null,"abstract":"Paraphrase generation is a fundamental natural language processing (NLP) task that refers to the process of generating a well‐formed and coherent output sentence that exhibits both syntactic and/or lexical diversity from the input sentence, while simultaneously ensuring that the semantic similarity between the two sentences is preserved. However, the availability of high‐quality paraphrase datasets has been limited, particularly for machine‐generated sentences. In this paper, we present ParaGPT, a new paraphrase dataset of 81,000 machine‐generated sentence pairs, including 27,000 reference sentences (ChatGPT‐generated sentences), and 81,000 paraphrases obtained by using three different large language models (LLMs): ChatGPT, GPT‐3, and T5. We used ChatGPT to generate 27,000 sentences that cover a diverse array of topics and sentence structures, thus providing diverse inputs for the models. In addition, we evaluated the quality of the generated paraphrases using various automatic evaluation metrics. Furthermore, we provide insights into the strengths and drawbacks of each LLM in generating paraphrases by conducting a comparative analysis of the paraphrasing performance of the three LLMs. According to our findings, ChatGPT's performance, as per the evaluation metrics provided, was deemed impressive and commendable, owing to its higher‐than‐average scores for semantic similarity, which implies a higher degree of similarity between the generated paraphrase and the reference sentence, and its relatively lower scores for syntactic diversity, indicating a greater diversity of syntactic structures in the generated paraphrase. ParaGPT is a valuable resource for researchers working on NLP tasks like paraphrasing, text simplification, and text generation. We make the ParaGPT dataset publicly accessible to researchers, and as far as we are aware, this is the first paraphrase dataset produced based on ChatGPT.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The task of a semantic community query is to obtain a subgraph based on a given query vertex (or vertex set) and other query parameters in an attributed graph such that belongs to , contains and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs.
语义社区查询的任务是根据给定的查询顶点（或顶点集）和其他查询参数，在归属图中获取一个子图，该子图属于 ，包含 并满足预定义的社区内聚力模型。在大多数情况下，基于传统归属网络的网络结构的现有社区查询模型通常缺乏社区语义。然而，归属图中很少考虑顶点属性特征，尤其是与社区语义密切相关的查询顶点属性。现有的基于结构内聚性和属性内聚性的社区查询算法通常不把查询顶点的属性作为社区内聚性模型的重要因素，从而导致社区语义薄弱。本文提出了一种以大规模属性图命名的语义社区查询方法。首先，我们采用 k 核结构模型作为社区查询模型的结构内聚度，从而得到原始图的子图。其次，我们根据查询顶点与其他顶点在社区属性方面的平均距离来定义属性内聚度，从而剪切出子图，得到语义社区。为了提高大规模属性图中的社区查询效率，我们应用了两种启发式剪枝策略。实验结果表明，我们的方法在多个评价指标上都优于现有的社区查询方法，是在大规模属性图中查询语义社区的理想方法。
{"title":"Semantic community query in a large‐scale attributed graph based on an attribute cohesiveness optimization strategy","authors":"Jinhuan Ge, Heli Sun, Yezhi Lin, Liang He","doi":"10.1111/exsy.13704","DOIUrl":"https://doi.org/10.1111/exsy.13704","url":null,"abstract":"The task of a semantic community query is to obtain a subgraph based on a given query vertex (or vertex set) and other query parameters in an attributed graph such that belongs to , contains and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named in a large‐scale attributed graph. First, the <jats:italic>k</jats:italic>‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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