Network Analysis with Stochastic Grammars

Digital forensics requires significant manual effort to identify items of evidentiary interest from the ever-increasing volume of data in modern computing systems. One of the tasks digital forensic examiners conduct is mentally extracting and constructing insights from unstructured sequences of events. This research assists examiners with the association and individualization analysis processes that make up this task with the development of a Stochastic Context -Free Grammars (SCFG) knowledge representation for digital forensics analysis of computer network traffic. SCFG is leveraged to provide context to the low-level data collected as evidence and to build behavior profiles. Upon discovering patterns, the analyst can begin the association or individualization process to answer criminal investigative questions. Three contributions resulted from this research. First , domain characteristics suitable for SCFG representation were identified and a step -by- step approach to adapt SCFG to novel domains was developed. Second, a novel iterative graph-based method of identifying similarities in context-free grammars was developed to compare behavior patterns represented as grammars. Finally, the SCFG capabilities were demonstrated in performing association and individualization in reducing the suspect pool and reducing the volume of evidence to examine in a computer network traffic analysis use case

Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Deep Learning for Computer Vision in Smart Cities

[EN] The Digital Age has caused a rapid shift from traditional industry to an economy mainly based upon information technology. According to recent studies, 74 zettabytes (ZB) of data have been generated, captured and replicated in the world in 2021, with video accounting for 82% of internet traffic. This figure has been amplified due to the coronavirus pandemic, and it is expected to keep increasing, reaching 149 ZB by 2024. Processing this impressive amount of information is one of the main scientific challenges of our time. Against this backdrop, Machine Learning (ML) and two related paradigms have emerged: big data and deep learning. These disciplines take advantage of mathematical optimization methods, bioinspiration and modern Graphics Processing Units (GPUs) to manage large datasets efficiently and effectively. Cities from around the world have adapted the previous methods to make use of the newly available data, promoting themselves as “smart”. Apart from aiming to integrate innovative technologies in their daily operation, Smart Cities (SCs) aim to attract new residents and external investors. Some of the key motivations of the Horizon projects and NextGenerationEU funds are precisely to make cities more digital, greener, healthier and robust. Artificial Intelligence (AI) can greatly contribute to the achievement of those objectives. Several lines of action have been identified in SCs, such as: smart mobility, smart environment, smart people, smart living and smart economy. This dissertation focuses on vision applications of deep learning within the scope of SCs. Theoretical and practical research gaps are identified and suitable solutions are proposed. As a result, the state of the art has been pushed forward and new use cases have been successfully implemented. A novel solution is proposed for each of the identified lines of action. Two models have been designed and evaluated with special attention to efficiency and scalability, and a third model has been created and tested focusing on accuracy within a high-resource environment. Moreover, two novel methods have been developed: a method for automatising crucial healthcare challenges, making early diagnosis an option; and another method for automatic unbiased cadastral categorization

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications

Fundamental Retribution Error: Criminal Justice and the Social Psychology of Blame

At least since the M\u27Naghten case of the 1840s,\u27 Anglo- American criminal law has concerned itself closely, famously, and contentiously with the psychology of the accused. Another significant body of scholarship addresses the psychology of juries, and other valuable research has approached some of the rules of criminal evidence from the perspective of social and cognitive psychology. There has, however, yet to be a general investigation of what social cognition research might teach us about the criminal law\u27s pervasive concern with blameworthiness. This Article undertakes that investigation. It brings research on the psychology of social cognition to bear on the decision-making processes of public officials charged with the administration of criminal justice. The psychological research suggests that these decision makers, like most other human beings, are likely to overestimate the causal significance of personal choice, and to correspondingly underestimate the causal significance of situational factors in the behavior of others. My thesis is that this observer\u27s tendency to attribute conduct and its consequences to personality, rather than to situation, has important and disturbing implications for the theory and practice of criminal law

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts