False Analog Data Injection Attack Towards Topology Errors: Formulation and Feasibility Analysis

In this paper, we propose a class of false analog data injection attack that can misguide the system as if topology errors had occurred. By utilizing the measurement redundancy with respect to the state variables, the adversary who knows the system configuration is shown to be capable of computing the corresponding measurement value with the intentionally misguided topology. The attack is designed such that the state as well as residue distribution after state estimation will converge to those in the system with a topology error. It is shown that the attack can be launched even if the attacker is constrained to some specific meters. The attack is detrimental to the system since manipulation of analog data will lead to a forged digital topology status, and the state after the error is identified and modified will be significantly biased with the intended wrong topology. The feasibility of the proposed attack is demonstrated with an IEEE 14-bus system.Comment: 5 pages, 7 figures, Proc. of 2018 IEEE Power and Energy Society General Meetin

Improving Micro-Expression Recognition with Shift Matrices and Database Combination

Micro-expressions are brief, subtle changes in facial expressions associated with emotional responses, and researchers have worked for decades on automatic recognition of them. As convolutional neural networks have been widely used in many areas of computer vision, such as image recognition and motion detection, it has also drawn the attention of scientists to use it for micro-expression recognition. However, none of them have been able to achieve an accuracy high enough for practical use. One of the biggest problems is the limited number of available datasets. The most popular datasets are SMIC, CASME, CASMEII, and SAMM. Most groups have worked on the datasets separately, but few have tried to combine them. In our approach, we combined the datasets and extracted the shared features. If new datasets under the same classifying rules (FACS) are created in the future, they can easily be combined using our approach. In addition to this novel approach for combining datasets, we use a new way of extracting the features instead of the Local Binary Pattern from Three Orthogonal Planes (LBP-TOP). To be more specific, we create shift matrices, the changing pattern of pixels, to keep the spatial information of the videos. Our highest recorded accuracy from 100 experiments was 88 percent, but we chose to report 72.5 percent. This is the median accuracy and a more convincing result though it’s a little bit lower than the best result to date. However, our f1 score is 72.3 percent and higher than the best result to date. Our paper presents an extendable approach to micro-expression recognition that should increase in accuracy as more datasets become available

Learning Rich Geographical Representations: Predicting Colorectal Cancer Survival in the State of Iowa

Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival curves for patients in the state of Iowa, spanning the years 1989 to 2012. Specifically, we compare model performance using a newly defined metric -- area between the curves (ABC) -- to assess (a) whether survival curves can be reasonably predicted for colorectal cancer patients in the state of Iowa, (b) whether geographical features improve predictive performance, and (c) whether a simple binary representation or richer, spectral clustering-based representation perform better. Our findings suggest that survival curves can be reasonably estimated on average, with predictive performance deviating at the five-year survival mark. We also find that geographical features improve predictive performance, and that the best performance is obtained using richer, spectral analysis-elicited features.Comment: 8 page

Research on Logistics Service Quality Improvement Based on A-KANO Model: An Application of Express Industry in China

Literatures indicate that service management has become a strategic issue for companies in the new millennium. By improving logistics service quality (LSQ), logistics companies can increase customer satisfaction (CS) and gain market shares. This study aims to establish a logistics service attributes analysis model to extract customer knowledge for logistics service quality improvement based on analytical Kano (A-Kano) model. By the building of Kano quantitative satisfaction index and importance index, an objective classification method and the decision-making rule to improve LSQ are proposed. It can be seen from the results that different value propositions should be given to LSQ attributes among the same Kano classification according to the decision index. At last, the method has been demonstrated by means of a real case application, which refers to a Chinese express company