Towards Cyber-Phenomenology: Aesthetics and Natural Computing in Multi-Level Information Systems

2014 ◽  
pp. 69-86
Author(s):  
Marcin J. Schroeder
Keyword(s):  
Information Systems ◽  
Natural Computing ◽  
Level Information ◽  
Multi Level

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Dual Attention Multiscale Network for Vessel Segmentation in Fundus Photography

2021 ◽  
Author(s):  
Pengshuai Yin ◽  
Yupeng Fang ◽  
Qingyao Wu ◽  
QiLin Wan
Keyword(s):  
Disease Diagnosis ◽  
Fundus Photography ◽  
Essential Step ◽  
Input Size ◽  
Real World Applications ◽  
Vessel Structure ◽  
Level Information ◽  
Multi Level ◽  
Sensitivity Specificity ◽  
To Receive

Abstract Background: Automatic vessel structure segmentation is an essential step towards an automatic disease diagnosis system. The task is challenging due to the variance shapes and sizes of vessels across populations.Methods: A multiscale network with dual attention is proposed to segment vessels in different sizes. The network injects spatial attention module and channel attention module on feature map which size is 1 8 of the input size. The network also uses multiscale input to receive multi-level information, and the network uses the multiscale output to gain more supervision. Results: The proposed method is tested on two publicly available datasets: DRIVE and CHASEDB1. The accuracy, AUC, sensitivity, specificity on DRIVE dataset is 0.9615, 0.9866, 0.7693, and 0.9851, respectively. On the CHASEDB1 dataset, the metrics are 0.9797, 0.9895, 0.8432, and 0.9863 respectively. The ablative study further shows effectiveness for each part of the network. Conclusions: Multiscale and dual attention mechanism both improves the performance. The proposed architecture is simple and effective. The inference time is 12ms on a GPU and has potential for real-world applications. The code will be made publicly available.

Application of multi-level information fusion for wear particle recognition of ferrographic images

2018 ◽  
Vol 26 (6) ◽  
pp. 1551-1560
Author(s):  
徐 斌 XU Bin ◽  
温广瑞 WEN Guang-rui ◽  
苏 宇 SU Yu ◽  
张志芬 ZHANG Zhi-fen ◽  
陈 峰 CHEN Feng ◽  
...  
Keyword(s):  
Information Fusion ◽  
Wear Particle ◽  
Particle Recognition ◽  
Level Information ◽  
Multi Level

scholarly journals A Set of Best Practices to Design Face-to-face Teaching Sessions for Technology-centered University-level Computing Courses

2014 ◽  
Vol 4 (4) ◽  
pp. 59
Author(s):  
Ilse Baumgartner
Keyword(s):  
Higher Education ◽  
Information Systems ◽  
Best Practices ◽  
Business Processes ◽  
Information Systems Management ◽  
Computing Education ◽  
Face To Face ◽  
Level Information ◽  
Enterprise Level ◽  
Working Set

Since more than a decade, all kinds of businesses and organisations are intensively exploring enterprise-level information systems to better integrate their business processes, information flows and people. Consequently, the industry demands for technically skilled, but also “business-savvy” IT professionals are permanently growing. To meet this need, more and more computing education programs try to incorporate enterprise-level information systems into their curricula. While there is some computing education research done to investigate the need for this new type of IT-business professional and to analyse general implications for higher education, only very few research works or practice papers exist which report on concrete attempts to design and deliver higher education computing courses which intensively use enterprise-level systems. In this paper, the author reports on a series of experiences made within the Bachelor of Science (Information Systems Management) degree program offered by the School of Information Systems (SIS) at the Singapore Management University (SMU). The primary focus of this paper is put on establishing a working set of best practices for the design of an effective structure of the face-to-face teaching sessions for courses which use enterprise-level systems and applications in their curricula. While this paper is principally based on education experiences made within the frame of an Information Systems program, the best practices presented in this paper are equally applicable to any other computing education field or even to the engineering education in general.

Multi-level information fusion to alleviate network congestion

2020 ◽  
Vol 63 ◽  
pp. 248-255 ◽  
Author(s):  
Joel Weijia Lai ◽  
Jie Chang ◽  
L. K. Ang ◽  
Kang Hao Cheong
Keyword(s):  
Information Fusion ◽  
Network Congestion ◽  
Level Information ◽  
Multi Level

scholarly journals Integrated parametric multi-level information and numerical modelling of mechanised tunnelling projects

2020 ◽  
Vol 43 ◽  
pp. 101011 ◽  
Author(s):  
Jelena Ninić ◽  
Christian Koch ◽  
Andre Vonthron ◽  
Walid Tizani ◽  
Markus König
Keyword(s):  
Numerical Modelling ◽  
Level Information ◽  
Multi Level ◽  
Mechanised Tunnelling

scholarly journals Multi-Level Information Storage Using Engineered Electromechanical Resonances of Piezoelectric Wafers: A Concept Piezoelectric Quick Response (PQR) Code

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6344
Author(s):  
Christopher Hakoda ◽  
Eric S. Davis ◽  
Cristian Pantea ◽  
Vamshi Krishna Chillara
Keyword(s):  
Information Storage ◽  
Cumulative Number ◽  
Quick Response ◽  
Multiple Frequency ◽  
Potential Applications ◽  
Level Information ◽  
Multi Level ◽  
Storage Technologies ◽  
Piezoelectric Wafers ◽  
Polarization Profiles

A piezoelectric-based method for information storage is presented. It involves engineering the polarization profiles of multiple piezoelectric wafers to enhance/suppress specific electromechanical resonances. These enhanced/suppressed resonances can be used to represent multiple frequency-dependent bits, thus enabling multi-level information storage. This multi-level information storage is demonstrated by achieving three information states for a ternary encoding. Using the three information states, we present an approach to encode and decode information from a 2-by-3 array of piezoelectric wafers that we refer to as a concept Piezoelectric Quick Response (PQR) code. The scaling relation between the number of wafers used and the cumulative number of information states that can be achieved with the proposed methodology is briefly discussed. Potential applications of this methodology include tamper-evident devices, embedded product tags in manufacturing/inventory tracking, and additional layers of security with existing information storage technologies.

Cancer differentiation analysis technology as a novel technology for cerebral cancer screening.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2040-2040
Author(s):  
Hongmei Tao ◽  
Xing Tang ◽  
Yue Lin ◽  
Chris Chang Yu ◽  
Xuedong Du
Keyword(s):  
Cancer Screening ◽  
Sensitivity And Specificity ◽  
Physical Property ◽  
Screening Methods ◽  
Potential Candidate ◽  
Healthy Individuals ◽  
Level Information ◽  
Area Under Roc Curve ◽  
Multi Level ◽  
Initial Results

2040 Background: While the current cancer screening methods mostly failed to detect cerebral cancer, a novel, promising technology named cancer differentiation analysis (CDA) technology has been developed to measure novel bio-physical properties to obtain valuable multi-level and multi-parameter information including protein, cellular and molecular level information. Initial results showed that CDA technology is capable of detecting cerebral cancer with a high degree of sensitivity and specificity. Methods: In this study, samples from 78 cerebral cancer patients and 321 healthy individuals were measured. Peripheral blood of each individual was drawn in EDTA tubes. One class of bio-physical property in blood samples was utilized for CDA tests. CDA data were conducted using SPSS, and the results were shown in table. Results: The average CDA values of cerebral cancer and control groups were 52.30 and 33.38 (rel. units) respectively. The results indicated that cerebral cancer could be significantly distinguished from the control (p < 0.001). Area under ROC curve (AUC) was 0.980, and sensitivity and specificity was 92.3% and 96.6% respectively. Conclusions: Initial results showed that CDA technology could effectively distinguish cerebral cancer from healthy individuals. As a novel bio-physical based cancer detection approach with multi-level and multi-parameter expressions, CDA could be a potential candidate for cerebral cancer screening. Results from Statistical Analysis of CDA. [Table: see text]

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