The article considers a variant of the intersection method proposed for solving basic problems in columns-based intelligent systems. In these systems, basic problems serve as the basis for solving ...all other problems. Basic concepts and definitions are given. The formulation of basic problems is described and their solution is given using a general universal method based on element-by-element comparison of patterns. Further, the solution of basic problems using the intersection method for patterns in the form of finite unordered sets and finite sequences or vectors is considered The type of intersection used, the memorization operations are considered in detail, the necessary conditions are indicated, under which the correctness of the operation of the intersection method is proved. An estimate of the computational efficiency of the intersection method in comparison with the method based on element-by-element pattern comparison is given. In conclusion, the possibility of parallelizing calculations in solving basic problems, including basic problems for pattern regions, is shown.
Upon concluding a meeting, participants can occasionally leave with different understandings of what had been discussed. Detecting inconsistencies in understanding is a desired capability for an ...intelligent system designed to monitor meetings and provide feedback to spur stronger shared understanding. In this paper, we present a computational model for the automatic prediction of consistency among team members' understanding of their grou's decisions. The model utilizes dialogue features focused on the dynamics of group decision-making. We trained a hidden Markov model using the AMI meeting corpus and achieved a prediction accuracy of 64.2%, as well as robustness across different meeting phases. We, then, implemented our model in an intelligent system that participated in human team planning about a hypothetical emergency response mission. The system suggested topics that the team would derive the most benefit from reviewing with one another. Through an experiment with 30 participants, we evaluated the utility of such a feedback system and observed a statistically significant increase of 17.5% in objective measures of the teams' understanding compared with that obtained using a baseline interactive system.
INNOVAZIONE E SVILUPPO INDUSTRIALE de Saint Mihiel, Alessandro Claudi
Techne (Florence, Italy : 2011),
01/2020, Letnik:
20
Journal Article
Recenzirano
Odprti dostop
Questa nuova nozione applicata a superfici trasparenti manifesta il livello di contaminazione in atto: la "superficie-limite" favorisce processi di osmosi, di interazione e di comunicazione fra gli ...ambienti da essa interfacciati. Il legame tra facciate di edifici complessi ed energia prodotta da fonti di energia rinnovabili e spesso imprescindibile, anche con riferimento al raggiungimento del sempre piü prossimo obiettivo "zero energy". Il sistema e basato sull'integrazione della tradizionale tistica e con le tecnologie degli intelligent systems finalizzate ad assicurare alti livelli di comfort indoor con ridotti consumi energetici. Da un lato si rinvengono le tematiche della progettazione ambientale in cui la concezione dell'habitat non e limitata ai soli aspetti fisico-formali, ma anche alle determinazioni immateriali del progetto e orientata a un'idea di governance ambientale; dall'altro, si individuano le complesse problematiche delle tecniche e dei materiali innovativi oltre che dei processi, delle metodologie, delle procedure e dei topics per il progetto sostenibile, sviluppate secondo le implicazioni sul progetto e le necessarie modalita del suo controllo tecnico. Obiettivi e risultati Lobiettivo generale e sviluppare un processo che realizzi un prodotto competitivo sul mercato, capace d'integrare la tradizionale componentistica e meccanica con le tecnologie informatiche e degli intelligent systems.
•Adaptive Neuro-Fuzzy Inference System based robust scheme provide more accuracy•Combine features text, images & frames for phishing detection proof more detection•This is the first work that ...reflects the best unified text, image and frame feature•Using SVM for phishing web classification and relate the use with the current result•The proposed solution achieves 98.3% accuracies
A phishing attack is one of the most significant problems faced by online users because of its enormous effect on the online activities performed. In recent years, phishing attacks continue to escalate in frequency, severity and impact. Several solutions, using various methodologies, have been proposed in the literature to counter the web-phishing threats. Notwithstanding, the existing technology cannot detect the new phishing attacks accurately due to the insufficient integration of features of the text, image and frame in the evaluation process. The use of related features of images, frames and text of legitimate and non-legitimate websites and associated artificial intelligence algorithms to develop an integrated method to address these together. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) based robust scheme using the integrated features of the text, images and frames for web-phishing detection and protection. The proposed solution achieves 98.3% accuracies. To our best knowledge, this is the first work that considers the best-integrated text, image and frame feature based solution for phishing detection scheme.
IEEE Intelligent Systems is promoting young and aspiring artificial intelligence (AI) scientists and recognizing the rising stars as “AI‘s 10 Watch.” This biennial 2022 edition is slightly different ...from the previous editions: We solicited submissions from individuals who had obtained their Ph.D. up to 10 years prior (as opposed to 5 years in all of the previous editions). This led to more applications of the highest quality. The selection committee finally had to select 10 outstanding contributors from a pool of 30+ highly competitive and strong nominations, which made the selection decisions rather difficult. After a careful and detailed selection process through many rounds of discussions via e-mails and live meetings, the committee voted unanimously on a short list of 10 top candidates who have all demonstrated outstanding achievements in different areas of AI. The selection was based solely on scientific quality, reputation, impact, and expert endorsements accumulated since their Ph.D. It is our honor and privilege to announce the following 2022 class of “AI’s 10 to Watch.”• Bo Li. She is working on trustworthy machine learning (ML) at the intersection of ML, security and privacy, and game theory. She was able to integrate domain knowledge and logical reasoning abilities into data-driven statistical ML models to improve learning robustness with guarantees, and she has designed scalable privacy-preserving data-publishing frameworks for high-dimensional data. Her work has provided rigorous guarantees for the trustworthiness of learning systems and been deployed in industrial applications. She is an assistant professor with the University of Illinois at Urbana-Champaign.• Tongliang Liu. He is working in the fields of trustworthy ML. His work in theories and algorithms of ML with noisy labels has led to significant contributions and influence in the fields of ML, computer vision, natural language processing (NLP), and data mining, as large-scale datasets in those fields are prone to suffering severe label errors. He is a senior lecturer at the School of Computer Science, University of Sydney, and a visiting associate professor at the Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence.• Liqiang Nie. He is the dean of and a professor with the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). He works on multimedia content analysis and search, with a particular emphasis on data-driven multimodal learning and knowledge-guided multimodal reasoning. He pioneered the explicit modeling of consistent, complementary, and partial alignment relationships among modalities.• Soujanya Poria. He is an assistant professor at Singapore University of Technology and Design (SUTD). His seminal research on fusing information from textual, audio, and visual modalities for diverse behavioral and affective tasks significantly improved systems reliant on multimodal data, paving the way to various novel research avenues. His latest works are on information extraction, vision–language reasoning, and understanding human conversations in terms of common sense-based, context-grounded causal explanations.• Deqing Sun. He is a staff research scientist at Google. He has made significant contributions to computer vision, in particular in motion estimation. His work on optical flow (“Classic+NL” and “PWC-Net”) has been very influential and has been powering commercial applications such as Super SloMo in NVIDIA’s RTX platform, Face Unblur, and Fusion Zoom on Google’s Pixel phone.• Yizhou Sun. She is a pioneer in heterogeneous information network (HIN) mining, with a recent focus on deep graph learning, neural symbolic reasoning, and providing neural solutions to multiagent dynamical systems. Her work has a wide spectrum of applications, ranging from e-commerce, health care, and material science to hardware design. She is currently an associate professor at the University of California, Los Angeles (UCLA).• Jiliang Tang. He is a University Foundation Professor at Michigan State University. He works on graph ML and trustworthy AI and their applications in education and biology. His contributions to these fields include highly cited algorithms, well-received systems, and popular books.• Zhangyang “Atlas” Wang. He works on efficient and reliable ML. Recently, his core research theme is to leverage, understand, and expand the role of sparsity, from classical optimization to modern neural networks (NNs), whose impacts span the efficient training/inference of large-foundation models, robustness and trustworthiness, generative AI, graph learning, and more.• Hongzhi Yin. He has worked on trustworthy data intelligence to turn data into privacy-preserving, robust, explainable, and fair intelligent services in various industries and scenarios. He is also a leading expert researching and developing next-generation intelligent systems and algorithms for lightweight on-device predictive analytics as well as recommendation and decentralized ML on massive and heterogeneous data. He is an associate professor and ARC Future Fellow at the University of Queensland.• Liang Zheng. He is a senior lecturer at the Australian National University and works on data-centric computer vision, where he seeks to improve the quality of training and validation data, predict test data difficulty without labels, and more. These efforts provide a complementary perspective to model-centric developments. He has also made significant contributions to object re-identification and the broader smart city initiative through the introduction of widely used benchmarks and baseline methods.
Tactile sensors are an important medium for artificial intelligence systems to perceive their external environment. With the rapid development of smart robots, wearable devices, and human-computer ...interaction interfaces, flexible tactile sensing has attracted extensive attention. An overview of the recent development in high-performance tactile sensors used for smart systems is introduced. The main transduction mechanisms of flexible tactile sensors including piezoresistive, capacitive, piezoelectric, and triboelectric sensors are discussed in detail. The development status of flexible tactile sensors with high resolution, high sensitive, self-powered, and visual capabilities are focused on. Then, for intelligent systems, the wide application prospects of flexible tactile sensors in the fields of wearable electronics, intelligent robots, human-computer interaction interfaces, and implantable electronics are systematically discussed. Finally, the future prospects of flexible tactile sensors for intelligent systems are proposed.
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