On augmented OBDD and performability for sensor networks

The expected hop count (EHC) or performability of a wireless sensor network (WSN) with probabilistic node failures provides the expected number of operational nodes a message traverses from a set of sensors to reach its target station. This paper proposes a novel approach for computing the EHC of a practical communication model for WSN, k-of-all-sources to any-terminal (k-of-S,t). Techniques based on factoring and Boolean techniques solve the EHC when k=1 for |S| greater than/equal to 1 However, they fail to scale with large WSN and are not useful for computing the EHC with k>1. To overcome these problems, we propose an Augmented Ordered Binary Decision Diagram (OBDD-A) approach, which obtains the EHC for all cases of (k-of-S,t). We use randomly generated wireless networks and grid networks having up to 4.6x1020 (s,t)-minpaths to generate results. Results show that OBDD-A can obtain the EHC for networks that are unsolvable with existing approaches

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

An overview of decision table literature 1982-1995.

This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

CompDP: A Framework for Simultaneous Subgraph Counting Under Connectivity Constraints

The subgraph counting problem computes the number of subgraphs of a given graph that satisfy some constraints. Among various constraints imposed on a graph, those regarding the connectivity of vertices, such as "these two vertices must be connected," have great importance since they are indispensable for determining various graph substructures, e.g., paths, Steiner trees, and rooted spanning forests. In this view, the subgraph counting problem under connectivity constraints is also important because counting such substructures often corresponds to measuring the importance of a vertex in network infrastructures. However, we must solve the subgraph counting problems multiple times to compute such an importance measure for every vertex. Conventionally, they are solved separately by constructing decision diagrams such as BDD and ZDD for each problem. However, even solving a single subgraph counting is a computationally hard task, preventing us from solving it multiple times in a reasonable time. In this paper, we propose a dynamic programming framework that simultaneously counts subgraphs for every vertex by focusing on similar connectivity constraints. Experimental results show that the proposed method solved multiple subgraph counting problems about 10-20 times faster than the existing approach for many problem settings

The Role of Data Quality and Heterogeneity on the Calibration of Neural Networks

Neural networks have been widely studied and used in recent years due to its highclassification accuracy and training efficiency. With the increase of network depth, however,the models become worse calibrated, meaning they cannot reflect the true probabilities. Onthe other hand, in many applications such as medical diagnosis, facial recognition and selfdriving cars, the calibrated output probabilities are of critical importance. Therefore, theunderstanding of the cause of deep neural network uncalibration is of much concern.The influence of model structures on the output calibration has been explored.However, the impact of the training dataset quality and heterogeneity, such as dataset sizeand label noise remains unclear. In this thesis, the impact of data quality and heterogeneityon the output calibration is investigated theoretically and experimentally. Afterwards, thedefect of calibration methods using single global parameter are discussed. To overcomethe calibration issues resulting from the dataset heterogeneity, we propose an improvedcalibration technique that can give better performance

An overview of decision table literature.

The present report contains an overview of the literature on decision tables since its origin. The goal is to analyze the dissemination of decision tables in different areas of knowledge, countries and languages, especially showing these that present the most interest on decision table use. In the first part a description of the scope of the overview is given. Next, the classification results by topic are explained. An abstract and some keywords are included for each reference, normally provided by the authors. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. Other examined topics are the theoretical or practical feature of each document, as well as its origin country and language. Finally, the main body of the paper consists of the ordered list of publications with abstract, classification and comments.