2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)最新文献

{"title":"A Cognitive Model Based Framework and Multi-layer Storage Architecture for Associative Memory","authors":"Jiandong Li, Runhe Huang, K. Wang","doi":"10.1109/ICCICC50026.2020.9450213","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450213","url":null,"abstract":"Memory is the foundation of intelligence. KID model covers multiple human cognitive processes such as learning and memory. This paper refines its memory process, especially focusing on associative memory of long-term memory. An associative memory framework with novel neural network storage architectures is presented to simulate human-like associative memory ability for machine intelligence. The presented framework involves an associative memory repository and two abstract functions, Assimilation() for knowledge encoding and storage, and Instantiation() for knowledge recall and application. The proposed novel storage architecture has two storage structures which both includes three kinds of layers: input layer, competitive layer and associative memory layer. Its design integrates multiple associative memory related neuroscience theories. It is characterized by chaotic feature, self-organization, self-adjustment, self-growth and associative recall. With the encapsulation of presented associative memory framework and novel storage architecture, the KID model can incorporate associative memory and be applied to various fields like intelligent information and knowledge management systems, personized products development and robotic intelligence.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124952258","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":"Towards Energy-Efficient Systems for Artificial Intelligence in the Future","authors":"Yu Wang","doi":"10.1109/ICCICC50026.2020.9450224","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450224","url":null,"abstract":"Cognitive Robotics & Machine Learning powered by Artificial Intelligence (AI) are now playing significant roles in various domains. The rapid development of AI is propelled by three crucial components: large-scale data, AI algorithms, and computation circuits and systems. The circuits and systems provide fundamental computation capability for analyzing data and executing algorithms. Specific and heterogeneous circuits and systems are propping up current AI computation capability. However, with the volume of data becoming larger and larger, and the slowing down of Moore's Law, currently circuits and systems for AI is now facing great challenges in the future. From the data perspective, large-scale data are organized using sparse structures including graphs, networks, time series, and spiking signals. However, nowadays circuits and systems are highly structured, which are far away from analyzing and handling large-scale sparse data efficiently. From the algorithm perspective, collaborative intelligence becomes a promising way to surpassing the computation capability limitation of a single node, while currently the performance of collaborative intelligence algorithms is constrained by limited communication resources, complex data dependency, and lacking automation tools.To overcome these problems and provide energy-efficient circuits and systems to propel future AI, the structured sparse design and collaborative perception/decision methods will be introduced. The hardware-software co-design idea is introduced in the structured sparse design to map and process unstructured sparse data on current structured circuits and systems efficiently. While the variable center framework is adopted in the collaborative intelligence systems to realize collaborative perception/decision by multiple agents. All these designs will propel the development of AI in various domains in the future, including autonomous driving, recommendation systems, and etc.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115258725","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":"Power Allocation for Maritime Cognitive Satellite-UAV-Terrestrial Networks","authors":"Xinran Fang, Yanmin Wang, W. Feng, Yunfei Chen, B. Ai","doi":"10.1109/ICCICC50026.2020.9450217","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450217","url":null,"abstract":"In this paper, we investigate hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks for maritime coverage enhancement. We adopt tethered UAVs to provide aerial base station (BS) sites, and orchestrate onshore and UAV-mounted BSs in a user-centric manner. To address the spectrum scarcity problem, all available spectrum is shared among satellites, UAVs and terrestrial base stations (TBSs). This generates undesirable challenging co-channel interference (CCI) under the irregular cell-free system topology. We establish a cognitive framework to sense not only the spectrum status but also ships’ position information. According to the cognitive information, user-centric virtual clusters are self organized by a group of UAVs and onshore BSs. Besides, location-dependent large-scale channel state information (CSI) can be obtained through the cognitive position information. We thus optimize the power allocation strategy with only the large-scale CSI, to further mitigate both the inter-cluster interference and leakage interference to satellite users. The problem is non-convex. By using the random matrix theory and successive convex optimization methods, we solve it in an iterative way. Simulation results corroborate the efficiency of the proposed cognitive framework and the presented power allocation scheme.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"541 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127647805","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":"[Front matter]","authors":"","doi":"10.1109/iccicc50026.2020.9450223","DOIUrl":"https://doi.org/10.1109/iccicc50026.2020.9450223","url":null,"abstract":"","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115641897","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":"QAM: Question Answering System Based on Knowledge Graph in the Military","authors":"Xueling Dai, Jike Ge, Hongyue Zhong, Dong Chen, Jun Peng","doi":"10.1109/ICCICC50026.2020.9450261","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450261","url":null,"abstract":"To automatically collect information from the Internet and provide it to users for query, much of the existing search engines have been focused on massive and sprawling information. However, we note that the search engines produce a host of answers to specific questions with little accuracy or intelligence. Therefore, we consider the approach of question answering (QA) systems based on the knowledge graph (KG). Resent works are widely shared that the KG can be deeper for the field of medicine and finance, etc. Nowadays, QA system in the military is sorely needed with the dramatic growth of the technology. In this paper, we propose a QA system based on the KG in the military (QAM). Which applies semantic Web technology, and allows a deep analysis of military questions and documents at both representation and interrogation levels. The experimental results reveal that KG applied in the military domain, can make up the inadequacy of the general search engine, return to refine and accurate results, and provide conveniently knowledge service in the military field.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123977476","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":"Safety Helmet Detection Algorithm based on Color and HOG Features","authors":"Miao Jin, Jun Zhang, Xiwen Chen, Quan Wang, Bing Lu, Wei Zhou, Gaoning Nie, Xu Wang","doi":"10.1109/ICCICC50026.2020.9450264","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450264","url":null,"abstract":"There are many unstable factors in the working environment of electrical workers, which threaten their safety. Therefore, how to protect electrical workers is a problem worth thinking about. Safety helmet protects worker’s head from injury when they fall. This paper presents a method to detect whether electrical workers wear a safety helmet or not. This method is based on Support Vector Machine (SVM), the grids of Histograms of Oriented Gradient (HOG) features, and color features. Firstly, we get information about the worker’s work scene image. According to the acquired image, we use the Deformable Parts Model (DPM) algorithm to extract the worker’s area. In the area where the worker exists, we use the method of color space conversion and color feature matching to extract the area where the safety helmet may exist. In this area, we use the SVM trained by HOG features to detect the safety helmet and ultimately to realize the judgment of workers wearing a safety helmet. In the experimental part, the effectiveness of our method is demonstrated. Compared with the Color + CHT and Color + Number of Pixels, our method has been improved by 3 to 4 percentage points.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"82 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128988632","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":"Human Capability Augmentation through Cognitive and Autonomous Systems","authors":"Ming Hou","doi":"10.1109/ICCICC50026.2020.9450269","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450269","url":null,"abstract":"The Covid-19 pandemic reminds us again about our limited knowledge and understanding in the nature including both micro and macro worlds. We have been developing a variety of tools such as automation, robotics, internet, and artificial intelligence (AI), etc. to augment human capability for improved safety, quality, and productivity in work and life, but human lives are still vulnerable over 100 years since the last Spanish Flu in 1918. We are even more vulnerable when the tools we developed (e.g., automation and AI) do not understand human intent or follow human instructions. Recent accidents to the Boeing 737 Max passengers ring the alarm again about the imperative needs of appropriate design concepts and scientific methodologies for developing safety critical cognitive and/or autonomous systems or AI functions and collaborative partnership of human and intelligent systems. With AI and its related technologies reach their bottleneck, it is even more vital to follow scientific and systematic methodology to understand well about capacity and limitation of both human intelligence and machine intelligence so that their strengths can be optimized for a collaborative partnership when dealing with safety critical situations. This talk discusses about the needs for the researchers, designers, developers, and all practitioners who are interested in building and using 21st century human-autonomy symbiosis technologies (Why). It touches the topics of proper analytical methodologies for functional requirements of the intelligent systems, design methodologies, implementation strategies, evaluation approaches, and trusted relationships (How). These aspects will be explained with real-world examples when considering contextual constraints of technology, human capability and limitations, and functionalities that AI and autonomous systems should achieve (When). Audience will gain insights of context-based and interaction-centered design approach for developing a safe, trusted, and collaborative partnership between human and technology by optimizing the interaction between human intelligence and AI. The challenges and potential issues will also be discussed for guiding future research and development activities when augmenting human capabilities with AI, and cognitive and/or autonomous systems.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133313129","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":"Heart rate analysis for pre-performance routine in darts game","authors":"H. Hiraishi","doi":"10.1109/ICCICC50026.2020.9450215","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450215","url":null,"abstract":"This paper presents the analysis of the heart rate data for the pre-performance routine (PPR) to improve throwing accuracy in a darts game. We conducted a detailed study to establish a relation between the heart rate change during PPR, timing of heartbeat, and throwing accuracy. It was confirmed that heart rate tends to increase by performing a set of actions just before the game, leading to higher scores. This set of actions is considered as PPR. It was also observed that PPR-led throwing at the timing of heartbeat resulted in improved scores. A system that provides a user with the sound of a heartbeat was developed using a smart watch. The effectiveness of the system was verified and compared in an experiment with a metronome that produces audible sounds at regular intervals and is not related to heartbeat.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124790596","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 Cognitive Model of the Tactile Vibration Sense and Experiments on a Touch Simulation System","authors":"Junjie Bai, Jiajie Li, Jun Peng, Kan Luo, Shuai Gao, Jianfeng Cai, Yingxu Wang","doi":"10.1109/ICCICC50026.2020.9450267","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450267","url":null,"abstract":"The tactile sensing and interaction technologies mimicking human skin and sensor mechanisms are a highly demanded research in both theories and applications. This paper presents the design of an experimental system for sensing the tactile vibration sensations by mimicking human skin. An experimental device for vibration and tactile perception is developed based on LabVIEW technologies, which can control the vibration intensity, time interval and sequence of five vibration sources. By specifying a set of psychophysical parameters on the device, a series of vibration and tactile experiments are carried out on fingers, arms, abdomen and other parts of the body. The tactile perceptions on vibration mechanisms have been tested by different vibration intensity, sequence, and interval. The experimental results have enabled applications in tactile perception and interaction interfaces towards a portable blind guide system.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"73 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106955","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":"Designing a Cost Function to Assess a Neural Network to Detect Distributed Denial of Service Attacks","authors":"M. Ghanbari, W. Kinsner","doi":"10.1109/ICCICC50026.2020.9450259","DOIUrl":"https://doi.org/10.1109/ICCICC50026.2020.9450259","url":null,"abstract":"This paper presents a model for designing a cost function for neural networks. The proposed procedure consists of enriching a basis cost function with distinguishable features as the coefficients to create a highly sensitive cost function. Since the Internet traffic data that contains distributed denial of service is not balanced, exaggerating the anomalous part of data leads to a better classification and data class separation. To develop the proposed cost function, sensitivity analysis is used as a measure to assess and test the cost function’s parameters. The principle components of the most sensitive cost function are extracted. The most efficient principle component with the highest variance is selected as the weights for the selected cost function. Therefore, the highest separation between two normal and anomalous clusters can be gained.","PeriodicalId":212248,"journal":{"name":"2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"385 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132817254","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}