Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology最新文献_第7页

Can We Replace Programming Languages by Natural Instruction? 我们可以用自然指令代替编程语言吗?

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.0000003

Tom Michael Mitchell

引用次数: 0

Efficient Query Obfuscation with Keyqueries 使用键查询进行有效的查询混淆

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493950

Maik Fröbe, Eric Schmidt, Matthias Hagen

{"title":"Efficient Query Obfuscation with Keyqueries","authors":"Maik Fröbe, Eric Schmidt, Matthias Hagen","doi":"10.1145/3486622.3493950","DOIUrl":"https://doi.org/10.1145/3486622.3493950","url":null,"abstract":"Search engine users who do not want a sensitive query to actually appear in a search engine’s query log can use query obfuscation or scrambling techniques to keep their information need private. However, the practical applicability of the state-of-the-art obfuscation technique is rather limited since it compares hundreds of thousands of candidate queries on a local corpus to select the final obfuscated queries. We propose a new approach to query obfuscation combining an efficient enumeration algorithm with so-called keyqueries. Generating only hundreds of candidate queries, our approach is orders of magnitude faster and makes close to real-time obfuscation of sensitive information needs feasible. Our experiments in TREC scenarios on the ClueWeb corpora show that our approach achieves a retrieval effectiveness comparable to the previous exhaustive candidate generation at a run time of only seconds instead of hours. Overall, 75% of the private information needs can be obfuscated while retrieving at least one relevant document of the original private query—that itself will not appear in the search engine logs. To further improve a user’s privacy, the query obfuscation can easily be combined with other client-side tools like TrackMeNot or PEAS fake queries, and TOR routing.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91379355","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}

引用次数: 3

A Computational Agent Model for Temporal Dynamic Analysis in Virtual Community Cohesion 虚拟社区内聚时间动态分析的计算Agent模型

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498925

A. A. Aziz, N. Yusop, Zahurin Mat Aji, Z. Dahalin

{"title":"A Computational Agent Model for Temporal Dynamic Analysis in Virtual Community Cohesion","authors":"A. A. Aziz, N. Yusop, Zahurin Mat Aji, Z. Dahalin","doi":"10.1145/3498851.3498925","DOIUrl":"https://doi.org/10.1145/3498851.3498925","url":null,"abstract":"Community cohesion is a broad concept that commonly connotes better relationships among people of different backgrounds. It has resulted in long-term interactions amongst the participating community members, commonly in the physical community. The relationships among agents were represented as a conceptual model for virtual community cohesion, and those relationships were then modelled using a set of formal specifications. Later, the simulator was used to explore exciting patterns and traces that explain the agent model's behaviour related to virtual community cohesion as asserted in related literature. These traces show the necessary what-if conditions as the resulting input-output collaboration patterns in the entire virtual community cohesion emerge. Moreover, this computational agent model has been evaluated using mathematical analysis (whereby theoretical and value substitution analyses are performed) and automated logical verification (internal validation based on previous empirical studies). The simulation traces and patterns provide indications that the internal validation has been successful.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"253 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82927470","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}

引用次数: 0

Analyzing Neural Correlations Between Numerical Induction and Letter Induction Based on Data-Brain Driven Integration Evidence 基于数据-脑驱动集成证据的数字归纳法与字母归纳法神经关联分析

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498969

Lianfang Ma, Jianhui Chen, Ning Zhong

{"title":"Analyzing Neural Correlations Between Numerical Induction and Letter Induction Based on Data-Brain Driven Integration Evidence","authors":"Lianfang Ma, Jianhui Chen, Ning Zhong","doi":"10.1145/3498851.3498969","DOIUrl":"https://doi.org/10.1145/3498851.3498969","url":null,"abstract":"Numerical induction and letter induction are two kinds of important subtypes of induction. Analyzing their neural correlations is very important for understanding the common mechanism of induction. Previous comparative studies on number cognition and letter comprehension were mainly based on a group of comparative experiment designs and their neuroimaging data. However, because of the many-to-many structure-function relationships, it is difficult to understand neural correlations between number cognition and letter comprehension, especially in complex cognitive functions, such as induction, only based on single-task or few-task neuroimaging data within an experimental lab. This paper proposes a systematic approach to analyze the similarity and disimilarity of neural pattern between numerical and letter induction by using Data-Brain driven integration evidence. Under the four dimensions of Data-Brain, a group of internal and external evidence is collected. A three stages multi-task analytical method is proposed to understand the similarity and disimilarity of neural pattern between numerical and letter induction, by combining meta-analysis and representational similarity. Results show that more activation specific for inductive reasoning is left MFG and IFG. And number inductive reasoning and letter inductive reasoning have high neural pattern similarity in the IFG and MFG, and a significant main effect of inductive reasoning is in the left MFG. Other hand, the method can supplementary proof some results, it has important implications for understand the brain mechanism of information processing.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89922389","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}

引用次数: 0

Cross-Domain Attentive Sequential Recommendations based on General and Current User Preferences (CD-ASR) 基于一般和当前用户偏好的跨域注意顺序推荐(CD-ASR)

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493949

Nawaf Alharbi, Doina Caragea

{"title":"Cross-Domain Attentive Sequential Recommendations based on General and Current User Preferences (CD-ASR)","authors":"Nawaf Alharbi, Doina Caragea","doi":"10.1145/3486622.3493949","DOIUrl":"https://doi.org/10.1145/3486622.3493949","url":null,"abstract":"Sequential Recommendations (SR) have become increasingly important because of their accuracy and consistency with real world scenarios, where a user interacts with a sequence of items over time. SR systems have the capability of modeling temporal information to extract user’s current preferences. However, data sparsity is a real challenge for SR models, causing them to sometimes function poorly and generate inaccurate recommendations. A practical solution to this problem is to transfer information from multiple source domains to tackle the sparsity in the target domain, an approach known as Cross-Domain Recommendations (CDR). Extracting users’ preferences from different domains in a sequential manner can help generate an effective CDR for sequential models. In this paper, we propose a Cross-Domain Attentive Sequential Recommendation model based on general and current user preferences (CD-ASR). We assume the user information from the source domains to be a user’s general information, and apply a general attention model to aggregate user source representational vectors. At the same time, we apply a self-attention sequential model to obtain user’s current preferences in the target domain. Implicitly, we utilize the aggregated user’s source vectors to transfer knowledge to produce more precise recommendation in the target domain. Our model shows superiority over other state-of-the-art SR models using three datasets extracted from Amazon dataset.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89952865","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}

引用次数: 1

VBNet: A VLC Enabled Hybrid Data Center Network VBNet:支持VLC的混合数据中心网络

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3498851.3498999

Jie Li, Xiaoyu Du, Zhijie Han

引用次数: 1

CURIOCITY Framework: Managing Heterogeneous Cultural Heritage Data curocity框架:管理异质文化遗产数据

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3494001

Alexander Pinto-De la Gala, Yudith Cardinale, Irvin Dongo

引用次数: 1

A personalized agent-based chatbot for nutritional coaching 一个个性化的基于代理的聊天机器人，用于营养指导

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493992

D. Calvaresi, Stefan Eggenschwiler, J. Calbimonte, Gaetano Manzo, M. Schumacher

引用次数: 4

Blockchain-Based Self-Sovereign Identity: Survey, Requirements, Use-Cases, and Comparative Study 基于区块链的自我主权身份:调查、需求、用例和比较研究

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493917

Razieh Nokhbeh Zaeem, Kai-Chi Chang, Teng-Chieh Huang, David Liau, Wenting Song, Aditya Tyagi, Manah M. Khalil, Michael R. Lamison, Siddharth Pandey, K. S. Barber

{"title":"Blockchain-Based Self-Sovereign Identity: Survey, Requirements, Use-Cases, and Comparative Study","authors":"Razieh Nokhbeh Zaeem, Kai-Chi Chang, Teng-Chieh Huang, David Liau, Wenting Song, Aditya Tyagi, Manah M. Khalil, Michael R. Lamison, Siddharth Pandey, K. S. Barber","doi":"10.1145/3486622.3493917","DOIUrl":"https://doi.org/10.1145/3486622.3493917","url":null,"abstract":"Identity is at the heart of digital transformation. Successful digital transformation requires confidence in and protection of digital identities. On the Internet, however, there is no unique and standard identity layer. Consequently, a variety of digital identities have emerged over years, leading to privacy risks, security vulnerabilities, risks for identity owners, and liability for identity issuers and those relying on digital identities to grant access to goods and services. Self-Sovereign Identity (SSI) and similar forms of identity management on the blockchain distributed ledger are novel technologies that recognize the need to keep user identity privately stored in user-owned devices, securely verified by identity issuers, and only revealed to verifiers as needed. There is limited academic literature defining the prerequisite SSI functional and non-functional requirements and comparing SSI technologies. Often those SSI technologies reviewed in the literature lack behind current advances. We present the first work that compiles a comprehensive list of functional and non-functional requirements of SSI and compares an extensive number of existing SSI/blockchain-based identity management solutions with respect to these requirements. Our work sheds light on the state-of-the-art SSI development and paves the way for future, more informed analysis and development of novel identity management and SSI solutions.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79063121","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}

引用次数: 6

Segmentation-based Phishing URL Detection 基于分段的网络钓鱼URL检测

Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Pub Date : 2021-12-14 DOI: 10.1145/3486622.3493983

Eint Sandi Aung, H. Yamana

{"title":"Segmentation-based Phishing URL Detection","authors":"Eint Sandi Aung, H. Yamana","doi":"10.1145/3486622.3493983","DOIUrl":"https://doi.org/10.1145/3486622.3493983","url":null,"abstract":"Uniform resource locators (URLs), used for referencing web pages, play a vital role in cyber fraud because of their complicated structure; phishers, or in other words, attackers, employ tricky by-passing techniques to deceive users. Thus, information extracted from URLs might indicate significant and meaningful patterns essential for phishing detection. To enhance the accuracy of URL-based phishing detection, we need an accurate word segmentation technique to split URLs correctly. However, in contrast to traditional word segmentation techniques used in natural language processing (NLP), URL segmentation requires meticulous attention, as tokenization, the process of turning meaningless data into meaningful data, is not as easy to apply as in NLP. In our work, we concentrate on URL segmentation to propose a novel tokenization method, named URL-Tokenizer, by combining the Bert tokenizer and WordSegment tokenizer, in addition to adopting character-level and word-level segmentations simultaneously. Our experimental evaluations in detecting the phishing URLs show that our proposed method achieves a high accuracy of 95.7% with a balanced dataset, and 97.7% with an imbalanced dataset, whereas baseline models achieved 85.4% with a balanced dataset and 85.1% with an imbalanced dataset.","PeriodicalId":89230,"journal":{"name":"Proceedings. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":"263 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79692982","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}

引用次数: 0