Enhancing Application Identification By Means Of Sequential Testing

Abstract. One of the most important challenges for network administrators is the identification of applications behind the Internet traffic. This identification serves for many purposes as in network security, traffic engineering and monitoring. The classical methods based on standard port numbers or deep packet inspection are unfortunately becoming less and less efficient because of encryption and the utilization of non standard ports. In this paper we come up with an online iterative probabilistic method that identifies applications quickly and accurately by only using the size of packets. Our method associates a configurable confidence level to the port number carried in the transport header and is able to consider a variable number of packets at the beginning of a flow. By verification on real traces we observe that even in the case of no confidence in the port number, a very high accuracy can be obtained for well known applications after few packets were examined

Chemical Enhancement of Bloody Footwear Impressions from Buried Substrates

Footwear impressions are regarded as one of the most common forensic evidence types left at crime scenes. A review of research to date describes previous tests on the survival of footwear impressions in a range of contaminants on a myriad of surfaces. None, however, examined the effects of the burial environment on such impressions. Using human blood as a contaminant, footwear impressions were made on samples of white cotton, newspaper, and black plastic trash bags and were buried for specific time frames, from one to four weeks. The study examines the subsequent development of the surviving impressions postexcavation, using chemical enhancement techniques of ninhydrin, acid black 1, leucocrystal violet (LCV), and Bluestar. The majority of impressions recovered were from the substrates that were in the soil for the shortest period. Poor recovery rates and loss of impressions were observed on substrates buried for more than two weeks. LCV and Bluestar proved most effective for enhancing and retrieving impressions. Impressions were able to be examined by a trained forensic footwear investigator to identify class, individual, and wear characteristics of the impression itself. Potential survival of such identifying features is of paramount importance to an investigation

Fingerprinting Smart Devices Through Embedded Acoustic Components

The widespread use of smart devices gives rise to both security and privacy concerns. Fingerprinting smart devices can assist in authenticating physical devices, but it can also jeopardize privacy by allowing remote identification without user awareness. We propose a novel fingerprinting approach that uses the microphones and speakers of smart phones to uniquely identify an individual device. During fabrication, subtle imperfections arise in device microphones and speakers which induce anomalies in produced and received sounds. We exploit this observation to fingerprint smart devices through playback and recording of audio samples. We use audio-metric tools to analyze and explore different acoustic features and analyze their ability to successfully fingerprint smart devices. Our experiments show that it is even possible to fingerprint devices that have the same vendor and model; we were able to accurately distinguish over 93% of all recorded audio clips from 15 different units of the same model. Our study identifies the prominent acoustic features capable of fingerprinting devices with high success rate and examines the effect of background noise and other variables on fingerprinting accuracy

PRODUCTIVITY AND LAND ENHANCING TECHNOLOGIES IN NORTHERN ETHIOPIA: HEALTH, PUBLIC INVESTMENTS, AND SEQUENTIAL ADOPTION

The adoption of more efficient farming practices and technologies that enhance agricultural productivity and improve environmental sustainability is instrumental for achieving economic growth, food security and poverty alleviation in sub-Saharan Africa. Our research examines the interaction between public investments, community health, and adoption of productivity and land enhancing technologies by households in the northern Ethiopian state of Tigray. Agricultural technology adoption decisions are modeled as a sequential process where the timing of choices can matter. We find that time spent sick and opportunity costs of caring for sick family members are significant factors in adoption. Sickness, through its impact on household income and labor allocation decisions for healthcare and other activities, significantly reduces the likelihood of technology adoption. Our findings suggest that agencies working to improve agricultural productivity and land resource conservation should consider not only the financial status of potential adopters, but also their related health situation.Research and Development/Tech Change/Emerging Technologies,

Shrinkage Estimators in Online Experiments

We develop and analyze empirical Bayes Stein-type estimators for use in the estimation of causal effects in large-scale online experiments. While online experiments are generally thought to be distinguished by their large sample size, we focus on the multiplicity of treatment groups. The typical analysis practice is to use simple differences-in-means (perhaps with covariate adjustment) as if all treatment arms were independent. In this work we develop consistent, small bias, shrinkage estimators for this setting. In addition to achieving lower mean squared error these estimators retain important frequentist properties such as coverage under most reasonable scenarios. Modern sequential methods of experimentation and optimization such as multi-armed bandit optimization (where treatment allocations adapt over time to prior responses) benefit from the use of our shrinkage estimators. Exploration under empirical Bayes focuses more efficiently on near-optimal arms, improving the resulting decisions made under uncertainty. We demonstrate these properties by examining seventeen large-scale experiments conducted on Facebook from April to June 2017

The "fuzzy front end" of innovation

The fast transformation of technologies into new products or processes is one of the core challenges for any technology-based enterprise. Within the innovation process, we believe, the early phases (fuzzy front end) to have the highest impact on the whole process and the result (Input-Output Process), since it will influence the design and total costs of the innovation extremely. However the Fuzzy Front End is unfortunately the least-well structured part of the innovation process, both in theory and in practice. The focus of the present chapter is on methods and tools to manage the fuzzy front end of the innovation process. Firstly, the activities, characteristics, and challenges of the front end are described. Secondly, a framework of the application fields for different methods and tools is presented: Since a product upgrade requires a different approach compared to radical innovation, where the market is unknown and a new technology is applied, we believe such a framework to be useful for practitioners. Thirdly, a selection of methods and tools that can be applied to the fuzzy front end are presented and allocated within the framework. The methods selected here address process improvements, concept generation, and concept testing. --fuzzy front end,innovation management,stage-gate process,frontloading,triz,dsm-matrix,lead user

Application of Sparse Identification of Nonlinear Dynamics for Physics-Informed Learning

Advances in machine learning and deep neural networks has enabled complex engineering tasks like image recognition, anomaly detection, regression, and multi-objective optimization, to name but a few. The complexity of the algorithm architecture, e.g., the number of hidden layers in a deep neural network, typically grows with the complexity of the problems they are required to solve, leaving little room for interpreting (or explaining) the path that results in a specific solution. This drawback is particularly relevant for autonomous aerospace and aviation systems, where certifications require a complete understanding of the algorithm behavior in all possible scenarios. Including physics knowledge in such data-driven tools may improve the interpretability of the algorithms, thus enhancing model validation against events with low probability but relevant for system certification. Such events include, for example, spacecraft or aircraft sub-system failures, for which data may not be available in the training phase. This paper investigates a recent physics-informed learning algorithm for identification of system dynamics, and shows how the governing equations of a system can be extracted from data using sparse regression. The learned relationships can be utilized as a surrogate model which, unlike typical data-driven surrogate models, relies on the learned underlying dynamics of the system rather than large number of fitting parameters. The work shows that the algorithm can reconstruct the differential equations underlying the observed dynamics using a single trajectory when no uncertainty is involved. However, the training set size must increase when dealing with stochastic systems, e.g., nonlinear dynamics with random initial conditions

Chemical enhancement of soil based footwear impressions on fabric

This study investigates the enhancement of footwear impressions prepared with soils from different locations on a variety of fabric surfaces with different morphology. Preliminary experiments using seventeen techniques were carried out and the best responding reagents were evaluated further. Results indicated that the soils investigated (a cross-section of soils from Scotland) are more likely to respond to reagents that target iron ions rather than calcium, aluminium or phosphorus ions. Furthermore, the concentration of iron and soil pH did not appear to have an effect on the performance of the enhancement techniques. For the techniques tested, colour enhancement was observed on all light coloured substrates while enhancement on dark coloured fabrics, denim and leatherette was limited due to poor contrast with the background. Of the chemical enhancement reagents tested, 2,20-dipyridil was a suitable replacement for the more common enhancement technique using potassium thiocyanate. The main advantages are the use of less toxic and flammable solvents and improved clarity and sharpness of the enhanced impression. The surface morphology of the fabrics did not have a significant effect on the enhancement ability of the reagents apart from a slight tendency for diffusion to occur on less porous fabrics such as polyester and nylon/lycra blends