2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)最新文献_第3页

Emerging Technologies: Connecting Millennials and Manufacturing 新兴技术:连接千禧一代和制造业

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00034

J. Acharya, Yasutaka Serizawa, S. Gaur

{"title":"Emerging Technologies: Connecting Millennials and Manufacturing","authors":"J. Acharya, Yasutaka Serizawa, S. Gaur","doi":"10.1109/CogMI48466.2019.00034","DOIUrl":"https://doi.org/10.1109/CogMI48466.2019.00034","url":null,"abstract":"The manufacturing industry is in a state of flux due to both external and internal factors. External causes include pressure to reduce costs due to global competition and shift towards low volume, high mix production due to increasingly diverse customer base who demand autonomy of choice. The biggest internal cause is the retirement of skilled workers with decades of acquired knowledge and the inability to find like-for-like replacements. However new opportunities also arise with every challenge and manufacturing is no exception. There is an unique opportunity now to reshape and optimize all aspects of manufacturing from production, sales and operations planning, logistics and end to end supply chain management. Various technology enablers such as IoT, AI and robotics will have to work in synergy to achieve such optimized manufacturing. This is seen in recent manufacturing initiatives such as Industry 4.0 which are also in alignment with bigger global trends such as the shift towards circular economy, waste reduction and environmental preservation. And this will also attract a new breed or workers, namely the millennial and Gen-Z who will find this new manufacturing more tuned to their ethos, way of thinking and technology preferences which will finally solve the aging worker problem","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"11 17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123694378","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: 4

Towards Generating Consumer Labels for Machine Learning Models 为机器学习模型生成消费者标签

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00033

C. Seifert, Stefanie Scherzinger, L. Wiese

引用次数: 13

Machine Learning and Human Cognition Combined to Enhance Knowledge Discovery Fidelity 机器学习与人类认知相结合提高知识发现的保真度

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00010

S. Chujfi, C. Meinel

{"title":"Machine Learning and Human Cognition Combined to Enhance Knowledge Discovery Fidelity","authors":"S. Chujfi, C. Meinel","doi":"10.1109/CogMI48466.2019.00010","DOIUrl":"https://doi.org/10.1109/CogMI48466.2019.00010","url":null,"abstract":"The objective of this work is knowledge discovery in large-scale audio files by performing a Cognitive Analysis – CA –, where the knowledge is extracted from transcribed customer service conversations taking into consideration individual cognitive styles to mimic the human cognitive process and maximize the correct meaning interpretation information in a given context. We make the following three contributions: (i) integrate a Cyber Cognitive Identity model – CCI – that states the cognitive profile an individual has for interacting in cyberspace, which yields superior fidelity to identify the meaning of spoken sentences following Sternberg's Thinking Style Inventory (TSI). In particular it guides an analysis grounded in peers' cognitive styles to index words by dimension; (ii) a novel method that extends the Latent Dirichlet Allocation (LDA) approach to a multidimensional partially supervised machine learning model with the help of the psychological activation theory Adaptive Control of Thought – ACT; (iii) an improvement of the Exploratory Data Analysis – EDA–suggested by De Mast and Trip, envisioned as an extended approach to obtain high-fidelity data where topics of a three-dimensional corpus are clustered according to cognitive categorizations. Using speech-to-text software, we transcribed and evaluated 27 500 calls from 206 German-speaking teleworkers combining these three complementary methods and achieved significant fidelity to generate a hypothesis based on individuals' cognitive affinities.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126107547","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

[Title page iii] [标题页iii]

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/cogmi48466.2019.00002

引用次数: 0

Fractal Base Antennas Effects on Wi-Fi Harvesting Technologies 分形基天线对Wi-Fi采集技术的影响

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/cogmi48466.2019.00028

R. Parekh, K. George

{"title":"Fractal Base Antennas Effects on Wi-Fi Harvesting Technologies","authors":"R. Parekh, K. George","doi":"10.1109/cogmi48466.2019.00028","DOIUrl":"https://doi.org/10.1109/cogmi48466.2019.00028","url":null,"abstract":"The world is moving towards wireless technologies. Wireless charging is common these days. However, the existing system requires contact between the charging point and the equipment to be charged. True wireless and contactless charging are still under research. In this paper, the aim is to devise a method of charging low powered devices using energy harvested from RF signals given by Wi-Fi routers. Specifically, to show how a fractal-based antenna performs compared to a regular 2.4 GHz Wi-Fi energy harvester. The method of harvesting used here involves a hybrid rectification circuit that boosts the output voltage. For the circuit design, the proposal comprises of a bridgeless converter and a diode bridge. The data is collected using the newly devised method of two fractal-based monopole antennas and two others with varied capacitance. The same test was repeated with another antenna as well as by putting the router at a distance from the antenna and observing the performance of the charging. The third test was performed by placing two routers together at different positions and orientations to check the results with two fractal antennae designs. The first capacitive load used was 4.7uF, which charged to 2.46V in 6 minutes with a 3D Fractal tree antenna. The second capacitor used had a capacitance of 1000uF, and this capacitor took approximately 37 hours to charge to 1.9V with the same antenna.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132928984","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

Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE annette:一个嵌入到Eclipse IDE中的智能辅导伙伴

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00018

Melissa Day, Manohara Rao Penumala, Javier Gonzalez-Sanchez

{"title":"Annete: An Intelligent Tutoring Companion Embedded into the Eclipse IDE","authors":"Melissa Day, Manohara Rao Penumala, Javier Gonzalez-Sanchez","doi":"10.1109/CogMI48466.2019.00018","DOIUrl":"https://doi.org/10.1109/CogMI48466.2019.00018","url":null,"abstract":"With Computer Science (CS) class sizes that are often large, it is challenging to provide effective personalized feedback to students. Intelligent Tutoring Companions can provide such feedback and improve CS students' experience. This work describes the construction of a Tutoring Companion, Annete, designed to support students in a university Java programming course by providing them with intelligent feedback generated by a neural network. Annete is embedded into the Eclipse Integrated Development Environment (IDE), which is an environment that is already familiar to students in programming courses. Embedding Annete into Eclipse improves her effectiveness, as the students do not need to learn how to use an additional tool. While the student works in Eclipse, Annete collects 21 pieces of data from the student's code, including whether certain key words are used, error messages from the compiler, and cyclomatic complexity. When a run attempt, debug attempt, or a request for help occurs in Eclipse, Annete uses the data available to infer a feedback message to show to the student. Our approach is evaluated among 28 CS students completing a programming assignment while Annete assists them. Results suggest that students feel supported while working with Annete and show potential for using neural network modeling with embedded tutoring companions in the future. Challenges are discussed, as well as opportunities for future work.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122116127","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: 8

[Copyright notice] (版权)

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/cogmi48466.2019.00003

引用次数: 0

Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks 鲁棒、可扩展和快速:多相机网络中车辆跟踪的分组分类器

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00013

Abhijit Suprem, Rodrigo Alves Lima, Bruno Padilha, J. E. Ferreira, C. Pu

{"title":"Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks","authors":"Abhijit Suprem, Rodrigo Alves Lima, Bruno Padilha, J. E. Ferreira, C. Pu","doi":"10.1109/CogMI48466.2019.00013","DOIUrl":"https://doi.org/10.1109/CogMI48466.2019.00013","url":null,"abstract":"As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and surveillance, where we implement a system that performs automated tracking of all vehicles all the time. Our evaluations show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.","PeriodicalId":116160,"journal":{"name":"2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123765735","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

Perennial, Permuted, and Pervasive Search in Ambient Intelligence 环境智能中的多年生、排列和普遍搜索

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00027

Javed Mostafa, Xiaopeng Lu, Sandeep Avula

引用次数: 0

How Network Analysis Can Improve the Reliability of Modern Software Ecosystems 网络分析如何提高现代软件生态系统的可靠性

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI) Pub Date : 2019-12-01 DOI: 10.1109/CogMI48466.2019.00032

P. Boldi

引用次数: 1