2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)最新文献

{"title":"LMs go Phishing: Adapting Pre-trained Language Models to Detect Phishing Emails","authors":"Kanishka Misra, J. Rayz","doi":"10.1109/WI-IAT55865.2022.00028","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00028","url":null,"abstract":"Despite decades of research, the problem of Phishing in everyday email communication is ever so prevalent. Traditionally viewed as a text-classification task, the task of phishing detection is an active defense against phishing attempts. Mean-while, progress in natural language processing has established the universal usefulness of adapting pre-trained language models to perform downstream tasks, in a paradigm known as pre-train-then-fine-tune. In this work, we build on this paradigm, and propose two language models that are adapted on 725k emails containing phishing and legitimate messages. We use these two models in two ways: 1) by performing classification-based fine-tuning, and 2) by developing a simple priming-based approach. Our approaches achieve empirical gains over a good deal of prior work, achieving near perfect performance on in-domain data, and relative improvements on out-of-domain emails.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132992432","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":"Emotional Coloring of Kazakh People’s Names in the Semantic Knowledge Database of \"Fascinating Onomastics\" Mobile Application","authors":"G. Bekmanova, Gaziza Yelibayeva, B. Yergesh, Laura Orynbay, Ayaulym Sairanbekova, Z. Kaderkeyeva","doi":"10.1109/WI-IAT55865.2022.00105","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00105","url":null,"abstract":"The article discusses the definition of semantic features of the knowledge base of Kazakh names and its creation. A broad analysis of names and their origin is carried out as well as features of emotions in the Kazakh names are introduced. The survey results of people’s emotional perception of emotionally colored names are analyzed. The results of this work will be used in the development of the mobile application \"Fascinating onomastics\".","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133408378","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":"Agent-Based Modeling for Studying the Spontaneous Emergence of Money","authors":"Mattia Di Russo, Z. Babutsidze, Célia Da Costa Pereira, M. Iacopetta, A. Tettamanzi","doi":"10.1109/WI-IAT55865.2022.00057","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00057","url":null,"abstract":"A central question in economics is how a society accepts money, defined as a commodity used as a medium of exchange, as an unplanned outcome of the individual interactions. This question has been approached theoretically in the literature and investigated by means of agent-based modeling. While an important aspect of the theory is the individual’s speculative behavior, that is, the acceptance of money despite a potential short-term loss, previous work has been unable to reproduce it with boundedly rational agents. We investigate the reasons for the failure of previous work to have boundedly rational agents learn speculative strategies. Starting with an agent-based model proposed in the literature, where the intelligence of the agents is guided by a learning classifier system that is shown to be capable of learning trade strategies (core strategies) that involve short sequences of trades, we test several modifications of the original model and we come up with a set of assumptions that enable the spontaneous emergence of speculative strategies, which explain the emergence of money even when the agents have bounded rationality.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132721788","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":"A Contact Matrix-Based Approach for Predicting COVID-19 Using Influenza Data","authors":"Bing Liu, Tao Li, Zili Zhang","doi":"10.1109/WI-IAT55865.2022.00129","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00129","url":null,"abstract":"The global pandemic Corona Virus Disease 2019 (COVID-19) has become one of the deadliest epidemics in human history, bringing enormous harm to human society. To help health policymakers respond to the threat of COVID-19, prediction of outbreaks is needed. Research on COVID-19 prediction usually uses data-driven models and mechanism models. However, in the early stages of the epidemic, there were not enough data to establish a data-driven model. The inadequate understanding of the virus that causes COVID-19, SARS-COV-2, has also led to the inaccuracies of the mechanism model. This has left the government with the toughest Non-pharmaceutical interventions (NPIs) to curb the spread of the virus, such as the lockdown of Wuhan in 2020. Yet man is a social animal, and social relations and interactions are necessary for his existence. The novel coronavirus and containment measures have challenged human and community interactions, affecting the lives of individuals and collective societies. To help governments take appropriate and necessary actions in the early stages of an epidemic, and to mitigate its impact on people’s psychology and lives, we used the COVID-19 pandemic as an example to develop a model that uses surveillance data from one epidemic to predict the development trend of another. Based on the fact that both influenza and COVID-19 are transmitted through infectious respiratory droplets, we hypothesized that they may have the same underlying contact structure, and we proposed the influenza data-based COVID-19 prediction (ICP) model. In this model, the underlying contact pattern is firstly inferred by using a singular value decomposition method from influenza surveillance data. Then the contact matrix was used to simulate the influenza virus transmission through close contact of people, and the influenza virus transmission model was established. In order to be able to simulate the spread of COVID-19 virus using influenza transmission models, we used influenza contact matrix and COVID-19 infection data to estimate the risk of a population contracting COVID-19, i.e. force of infection of COVID-19. Finally, we used force of infection and influenza virus transmission model to simulate and predict the spread of COVID-19 in the population. We obtained age-disaggregated influenza and COVID-19 infection data for the United States in 2020, as well as data for Europe, which was not disaggregated by age. We use correlation coefficients as an evaluation indicator, and the final results prove that the predicted value and the actual value are positively correlated. So, the development trend of COVID-19 can be predicted using influenza surveillance data.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324517","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":"Obtaining and Providing Partial Information in Binary Contests","authors":"Noam Simon, Priel Levy, David Sarne","doi":"10.1109/WI-IAT55865.2022.00035","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00035","url":null,"abstract":"Contests are commonly used as a mechanism for eliciting effort and participation in multi-agent settings. Naturally, and much like with various other mechanisms, the information provided to the agents prior to and throughout the contest fundamentally influences its outcomes. In this paper we study the problem of information providing whenever the contest organizer does not initially hold the information and obtaining it is potentially costly. As the underlying contest mechanism for our model we use the binary contest, where contestants’ strategy is captured by their decision whether or not to participate in the contest in the first place. Here, it is often the case that the contest organizer can proactively obtain and provide contestants information related to their expected performance in the contest. We provide a comprehensive equilibrium analysis of the model, showing that even when such information is costless, it is not necessarily the case that the contest organizer will prefer to obtain and provide it to all agents, let alone when the information is costly.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114730254","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":"GrS Algorithm for Solving Gas Transmission Compressor Design Problem","authors":"Lei Dai, Liming Zhang, Zehua Chen","doi":"10.1109/WI-IAT55865.2022.00137","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00137","url":null,"abstract":"This paper is a continuous study of our recently proposed gradient-free deterministic method, named granular sieving (GrS), for its application exploration. GrS is developed to solve global optimization problems for Lipschitz continuous functions defined in arbitrary path-wise connected compact sets in Euclidean spaces. It can be regarded as granular sieving with synchronous analysis in both the domain and range of the objective function. The algorithm is easy to implement with moderate computational cost. Although the effectiveness of the algorithm has been verified on the benchmark databases, its feasibility in real optimization problems remains to be explored. This paper applies GrS in a well-known real-world engineering optimization problem, gas transmission compressor design (GTCD), which requires to determine the minimum cost for a gas pipeline transmission system per day. The experimental results are promising compared with some classic algorithms.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125755615","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":"Educational Decision Support System Adopting Sentiment Analysis on Student Feedback","authors":"T. Shaik, Xiaohui Tao, Christopher Dann, Carol Quadrelli, Y. Li, S. O’Neill","doi":"10.1109/WI-IAT55865.2022.00062","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00062","url":null,"abstract":"Educational institutions are constantly analyzing their teaching practice and learning environments to provide a better learning experience for their students. Engaging with all students’ feedback and analyzing manually is almost impossible due to the amount of textual data. Sentiment analysis has the potential to analyze students’ feedback and extract their opinion or sentiment toward courses, teaching, and infrastructure. In this study, a conceptual framework is proposed to analyze qualitative feedback from students and classify them into 19 predefined aspects of Biggs’ model. Student feedback can be preprocessed using tokenization, stemming, and stopword removal. TextBlob was used to categorize the sentiment of students’ comments on each course using polarity and subjectivity. For the classification problem, a word embedding layer is used to transform the plain English words into vector representation and feed them to the deep learning model Bi-LSTM with forwarding and backward propagation. Deep learning is evaluated for its performance in multi-label classification. A case study with a desktop application adopting the proposed framework to analyze student comments of an education institution and illustrating the framework results in bar graphs. This would assist an educational institute in verifying its existing systems and improving its services to students. Overall, an application was designed for an educational institute to check and enhance teaching and learning practices.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129945987","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":"Oscillation Patterns of A Complex Exponential Neural Network","authors":"Lei Zhang","doi":"10.1109/WI-IAT55865.2022.00069","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00069","url":null,"abstract":"The paper presents the design and evaluation of a complex exponential neural network model. The development of the model is inspired by the exponential form of general solutions to nonlinear differential equations that describe dynamical systems. The research goal is to develop a mathematical representation for neural oscillation and reduce the amount of computation in the neural network to improve computational efficiency. In particular, the weighted sum of two complex exponential neurons is evaluated to demonstrate that the difference of oscillation frequencies between the two neurons is the dominant parameter that determines the oscillation patterns of the neural network.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132280650","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":"Multiple Neighbor Relation Enhanced Graph Collaborative Filtering","authors":"Riwei Lai, Shitong Xiao, R. Chen, Li Chen, Qilong Han, Li Li","doi":"10.1109/WI-IAT55865.2022.00016","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00016","url":null,"abstract":"Graph convolutional networks (GCNs) have substantially advanced state-of-the-art collaborative filtering (CF) methods. Recent GCN-based CF methods have started to explore potential neighbor relations instead of only focusing on direct user-item interactions. Despite the encouraging progress, they still suffer from two notable limitations: (1) only one type of potential neighbor relations is explored, i.e., co-interacting with the same item/user, neglecting the fact that user-item interactions are associated with various attributes and thus there can exist multiple potential neighbor relations from different aspects; (2) the distinction between information from direct user-item interactions and potential neighbor relations and their different extents of influence are not fully considered, which represent very different aspects of a user or an item. In this paper, we propose a novel Multiple Neighbor Relation enhanced method for Graph Collaborative Filtering (MNR-GCF) to address these two limitations. First, in order to capture multiple potential neighbor relations, we introduce a new construction of heterogeneous information networks with multiple types of edges to account for multiple neighbor relations, and a multi-relation aggregation mechanism to effectively integrate relation-aware information. We then enhance CF with a degree-aware dynamic routing mechanism to dynamically and adaptively fuse information from direct user-item interactions and potential neighbor relations at each aggregation layer. Our extensive experimental results show that our solution consistently and substantially outperforms a large number of state-of-the-art CF methods on three public benchmark datasets.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130855157","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":"PHIHNE: predicting Phage-Host Interaction through Heterogeneous Network Embedding","authors":"Qiang Zhu, Qing-yang Dai, R. He, Junjie Huang","doi":"10.1109/WI-IAT55865.2022.00148","DOIUrl":"https://doi.org/10.1109/WI-IAT55865.2022.00148","url":null,"abstract":"The volumes of novel phages obtained by metagenomics demand computational tools to predict phage–host interactions. Compared with the experimental approach, the identification of phage–host interactions by computational method can save time and reduce costs. In this paper, we present a computational method for predicting potential phage-host interactions by network fusion and graph mining, named PHIHNE. Unlike existing methods, PHIHNE constructs two different viral host heterogeneous networks by similarity network fusion and graph embedding techniques. Then, PHIHNE introduces two meta-path scores to extract features from each viral host heterogeneous graph. Based on this graph mining approach, a hybrid feature vector of phage-host pairs can be obtained to predict potential phage-host interactions using a machine learning classifier. PHIHNE is validated on four datasets and its performance shows the potential of PHIHNE in predicting phage-host interaction. Some of the novel phage-host interactions predicted by PHIHNE have been verified by existing in biological experiments.","PeriodicalId":345445,"journal":{"name":"2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130485374","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}