IEEE International Conference on Cognitive Informatics and Cognitive Computing最新文献

{"title":"Mono-, Multi-, And Poly-scale Analyses AND Fractional Operators for Cognitive Systems","authors":"W. Kinsner","doi":"10.1109/ICCI-CC.2018.8482076","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2018.8482076","url":null,"abstract":"Cognitive systems are evolving system as governed by perception-action processes, memory, attention, and intelligence. Classical dynamic systems have either no memory, or very short memory, as signified by the Markovian assumption of exponential relaxation. This talk provides an overview of mono-scale, multi-scale and poly-scale modeling of such systems, with emphasis on poly-scale measures and fractional-order differential and integral equations as the basis for modeling of dynamical systems with short-term, long-term, and any other-term memories.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133287550","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":"Status, challenges and future trends of brain inspired computing","authors":"Luping Shi","doi":"10.1109/ICCI-CC.2015.7259426","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259426","url":null,"abstract":"Brain inspired computing system is very important for building up real intelligent systems. In this talk, the current status and the recent progress of brain inspired computing research are introduced. The key challenges of this research including fundamental theory, neuromorphic chip design and fabrication, and software environment are analyzed. The possible solutions, development strategy and roadmap, and future trends of this research are discussed.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123770528","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":"Cognitive robotics and mathematical engineering","authors":"Yingxu Wang","doi":"10.1109/ICCI-CC.2015.7259425","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259425","url":null,"abstract":"It is recognized that the core problems across contemporary disciplines such as cognitive science, intelligence science, robotics, knowledge science, brain science, and computational intelligence are a fundamental mathematical problem where none of them may be simply reduced onto any type of numbers. This keynote lecture presents an emerging field known as mathematical engineering (ME) underpinning cognitive robotics. ME is a contemporary form of abstract engineering that studies formal structural models and functions of complex, abstract, and mental objects and their systematic and rigorous manipulations. ME is embodied by denotational mathematics (DM) supplement to traditional analytic mathematics. DM is a category of novel mathematical structures as function of functions on hyperstructures beyond those of real numbers and bits, in order to formalize rigorous expressions and inferences. ME powered by DMs provides a novel approach to solve complex and intelligent computing problems centric in the development of cognitive robots towards autonomous perception, inference, and learning mimicking the cognitive mechanisms of the brain.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032853","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":"Artificial intelligence in the web age","authors":"Bo Zhang","doi":"10.1109/ICCI-CC.2015.7259424","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259424","url":null,"abstract":"Due to the characteristics of web data and the change of men-machine interaction mode in the web age, artificial intelligence and information processing encounter new challenges and requirements. The new demand is that information processing has to deal with the meaning or semantics of information. Traditional information processing only treats the form of information rather than the meaning. Artificial intelligence intends to handle information as that of human beings, which makes machines to deal with the meaning or to understand the information. This talk presents new artificial intelligence technologies in the context of text and image processing, as well as intelligent robots. We will discuss how artificial intelligence may face the opportunity and challenge, and what strategy we will adopt to deal with them.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117214536","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":"The improvement and implementation of clustering algorithm based on multi-core computing","authors":"Liangyu Dong, Dongping Xu, Zhenzhen Liu, Shasha Wang","doi":"10.1109/ICCI-CC.2015.7259417","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259417","url":null,"abstract":"Clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. By appropriately representing the abstract objects in a vector space, the similarity among objects is equivalent to that among vectors. Hence, the problems, such as the clustering of limited data, clustering accuracy and efficiency, can be solved properly via calculating the similarity among vectors. As the research on clustering algorithm of limited data objects has been furthered and refined, it has been applied to various fields throughout commerce, industry, daily life, and national defense etc. When it comes to the pursue for higher efficiency of these applications, the amount of data will be expanded from limited to mass, accordingly the clustering of limited data will be massively enlarged. Thus, the implementation of the traditional serial programming algorithm, i.e. the goals of clustering will be encountered with a devastating challenge. The arising of Hadoop cloud computing platform throws light on the computing of mass data clustering. Nonetheless, under the new circumstances, the issues, like the efficiency and accuracy of clustering calculation, are still the focuses of information specialists. The essay proposes a K-means parallel clustering algorithm based on Hadoop platform and MapReduce programming model aiming at improving the traditional serial K-means clustering algorithm, which also improves the random selection of initial clustering center in K-means algorithm combined with Canopy algorithm. The experimental result shows that the improved algorithm reduces the time complexity. Moreover, the accuracy of the results and the execution efficiency have increased by 40% respectively.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126919232","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":"BEP performance of two typical relay selection strategies based on Rayleigh channel","authors":"Shaoling Hu, Xuanli Wu, Zhongzhao Zhang","doi":"10.1109/ICCI-CC.2015.7259418","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259418","url":null,"abstract":"In this paper, we provide an analysis of bit error probability (BEP) of two typical relay selection strategies based on threshold-based selection cooperation in detail, at arbitrary signal to noise ratios (SNRs) and number of available relays. We assume that all relays adopt decode-and forward (DF) scheme and all channels follow the Rayleigh fading. One of these two typical strategies has the best the source-to-relay channel (FHSC) and the other has the best the relay-to-destination channel (SHSC). Usually, an approximation will be taken when the selected relay does a wrong decoding. But in this paper, both the situations in which the data, at the relay node, are decoded correctly and not are taken into consideration, precisely. Numerical results demonstrate that when the threshold is low, the source-to-destination link is well and the SNR is relatively high, FHSC will outperform SHSC; otherwise, SHSC will have an advantage over FHSC.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116902002","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":"Visualization of big data","authors":"S. Kung","doi":"10.1109/ICCI-CC.2015.7259428","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259428","url":null,"abstract":"Big data has many divergent types of sources, from physical (sensor/IoT) to social and cyber (web) types, rendering it messy, imprecise, and incomplete. Due to its quantitative (volume and velocity) and qualitative (variety) challenges, big data to the users resembles something like “the elephant to the blind men”. It is imperative to enact a major paradigm shift in data mining and learning tools so that information from diversified sources must be integrated together to unravel information hidden in the massive and messy big data, so that, metaphorically speaking, it would let the blind men “see” the elephant. This talk will address yet another vital “V”-paradigm: “Visualization”. Visualization tools are meant to supplement (instead of replace) the domain expertise (e.g. a cardiologist) and provide a big picture to help users formulate critical questions and subsequently postulate heuristic and insightful answers.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121385760","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":"An improved collaborative filtering recommendation algorithm not based on item rating","authors":"Zhisheng Zhong, Yong Sun, Yue Wang, Pengfei Zhu, Yue Gao, Huanle Lv, Xiaolin Zhu","doi":"10.1109/ICCI-CC.2015.7259390","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259390","url":null,"abstract":"As e-commerce grows fast nowadays, recommender systems have become an integral part of every electricity business. A number of the recommendation algorithms need score matrix (i.e., matrix that is used to record the data of the score that users value the item) as a mean of input. However, in many cases, the data only obtained the user's record matrix (i.e., matrix that contained only whether users have purchased or downloaded the item, without a score that is about a particular range), instead of the users' score matrix. Under this circumstance, the record matrix fails to reflect the preference of the user, the function of the recommendation algorithm declined. The feature of the improved algorithm the paper presents that, by recording a neighbor user (i.e., a similar user) data of purchase or download history, the current users' preference of the item can be predicted, and by record matrix authors can predict users' preferences of an item, thereby improve the effectiveness of recommendation algorithm which requires score matrix as an input.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121025732","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":"An ICA with reference based on artificial fish swarm algorithm","authors":"Yanfei Jia, Liquan Zhao, L. Xu, Xiaodong Yang","doi":"10.1109/ICCI-CC.2015.7259369","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259369","url":null,"abstract":"The independent component analysis with reference algorithm uses gradient method to optimize the cost function, this makes it easily fall into local optimal solution. To overcome the problem, this paper proposes a new independent component analysis with reference algorithm that has global convergence. The new algorithm uses artificial fish swarm algorithm with global convergence to optimize cost function of independent component analysis algorithm with reference. It accords to the behavior of artificial fish preying, swarming, following and food consistence to update artificial fish position, which is to update the separation matrix and research the separation matrix optimal solution of independent component analysis algorithm with reference. Comparing with the original algorithm based on gradient method, the new algorithm does not need to calculate the gradient of cost function and has higher accuracy. Simulation results show that the new method is effective.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123693656","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":"Rethinking Artificial Intelligence","authors":"N. Howard","doi":"10.1109/ICCI-CC.2015.7259423","DOIUrl":"https://doi.org/10.1109/ICCI-CC.2015.7259423","url":null,"abstract":"The modern school of Artificial Intelligence was originally expected to provide a full working model of intelligence as a set of procedures. Scholars implemented these procedures over time to conceptualize the notion of an intelligent machine. Computer scientists rushed to implement working models that would allegedly reach beyond many limits. Perhaps the most debilitating act was equating what is efficient in procedures to what is artificialized in intelligence. Equally debilitating was interpreting the speed of arithmetic calculations as a quantifier: it led to teams being interpreting speed and accuracy as reflections of intelligence. In order to reach an artificial form of intelligence that is faithful to the amalgam of biological, physical and chemical that it seeks to imitate; scholars of AI must reach a deeper synthesis of its integrative nature, leading to the creation of many artificial synthetic forms of Intelligence, instead of a single vision of intelligence that simply focuses on matching the performance of the human brain. Having said that, we can clearly concur that most of the AI Modern School's limitations have been discovered and are well-documented and known to the AI community. Our aim is to discuss a number of these issues, particularly the limits previously described. We avow that these limits emerged from epistemological misunderstandings on the perceived meanings of intelligence itself, leading to the limits imposed in the current interpretations of AI. Future work in AI, or alternatively coined Synthetic Intelligence, must revisit fundamental assumptions about the nature of the brain, cognition, computing, and intelligence. Synthetic Intelligence focuses on the phenomena such as intelligence and consciousness, and mapping them to the physics of the brain and models of brain processes at each of its multiple levels. It is the ‘stack’ of brain subsystems at multiple levels, from cortical down to molecular, joined by a common thread, that make up a mind. What we need are mathematically described mechanisms and information structures to integrate computational discourse analysis, value systems, mapping of cognitive structures to neuron interactions and to the molecular mechanisms of such interactions. The key to this discovery will be the study of emergence of intelligence and consciousness in engineered systems - implemented in silico or in vitro.","PeriodicalId":167843,"journal":{"name":"IEEE International Conference on Cognitive Informatics and Cognitive Computing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586740","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}