I Know What You Did Last Summer: Your Smart Home Internet of Things and Your iPhone Forensically Ratting You Out

The adoption of smart home Internet of Things (IoT) devices continues to grow. What if your devices can snitch on you and let us know where you are at any given point in time? In this work we examined the forensic artifacts produced by Nest devices, and in specific, we examined the logical backup structure of an iPhone used to control a Nest thermostat, Nest Indoor Camera and a Nest Outdoor Camera. We also integrated the Google Home Mini as another method of controlling the studied Smart Home devices. Our work is the primary account for the examination of Nest artifacts produced by an iPhone, and is also the first open source research to produce a usable forensics tool we name the Forensic Evidence Acquisition and Analysis System (FEAAS). FEAAS consolidates evidentiary data into a readable report that can infer user events (like entering or leaving a home) and what triggered an event (whether it was the Google Assistant through a voice command, or the use of an iPhone application). Our results are important for the advancement of digital forensics, as there are cases starting to emerge in which smart home IoT devices have already been used as culpatory evidence

Heuristic Evaluation for Novice Programming Systems

The past few years has seen a proliferation of novice programming tools. The availability of a large number of systems has made it difficult for many users to choose among them. Even for education researchers, comparing the relative quality of these tools, or judging their respective suitability for a given context, is hard in many instances. For designers of such systems, assessing the respective quality of competing design decisions can be equally difficult. Heuristic evaluation provides a practical method of assessing the quality of alternatives in these situations and of identifying potential problems with existing systems for a given target group or context. Existing sets of heuristics, however, are not specific to the domain of novice programming and thus do not evaluate all aspects of interest to us in this specialised application domain. In this article, we propose a set of heuristics to be used in heuristic evaluations of novice programming systems. These heuristics have the potential to allow a useful assessment of the quality of a given system with lower cost than full formal user studies and greater precision than the use of existing sets of heuristics. The heuristics are described and discussed in detail. We present an evaluation of the effectiveness of the heuristics that suggests that the new set of heuristics provides additional useful information to designers not obtained with existing heuristics sets

Flowback cleanup mechanisms of post-hydraulic fracturing in unconventional natural gas reservoirs

This work investigates the fracturing fluid cleanup mechanisms of post-hydraulic fracturing in unconventional gas formations by studying a large number of wide-ranging parameters simultaneously. In this work, different scenarios of the cleanup operation of the hydraulic fracturing process are considered. This study consists of investigating the post-fracturing cleanup operation of hydraulically fractured vertical wells (VW) and multiple fractured horizontal wells (MFHWs). Additionally, the impact of soaking time, the range of the matrix permeability, applied drawdown pressure, injected fracturing fluid (FF) volume, fracture spacing and horizontal well length has been investigated by running different sets. Results show that that the trend of the impact of relevant parameters for VWs and MFHWs are analogous excepting the matrix permeability, km. That is, in the MFHW base reference set, the effect of matrix permeability on capillary pressure is more significant than that on fluid flow while the reverse is observed for VW. The difference in the impact of km in VWs and MFHWs is attributed to the geometry of the fluid flow towards the production well and different well completion scheme. It is also concluded that the impact of parameters affecting the capillary pressure in the matrix is more significant for MFHWs whereas matrix and fracture mobility pertinent parameters are more important for VWs than MFHWs. As a result, larger matrix capillary pressure values are more vital in the cleanup of MFHWs because of more imbibition of FF into the matrix and subsequently lower conflict between the flow of gas and FF in the fracture. The other part of this research concentrates on the impact of IFT reducing agents on the post-fracturing production in different formations. In hydraulic fracturing operations, these agents are commonly used as an additive in fracturing fluid to facilitate its backflow by reducing Pc and subsequently enhancing gas production. The results of this work recommend that using such agents enhances the gas production rate for ultratight formations but not for tight formations (it reduces the gas production rate). Therefore it is not suggested to use such agents in tight formations. The findings of this work improve the understanding of fracture cleanup leading to better design of hydraulic fracturing operations in unconventional formations

Typilus: Neural Type Hints

Type inference over partial contexts in dynamically typed languages is challenging. In this work, we present a graph neural network model that predicts types by probabilistically reasoning over a program's structure, names, and patterns. The network uses deep similarity learning to learn a TypeSpace -- a continuous relaxation of the discrete space of types -- and how to embed the type properties of a symbol (i.e. identifier) into it. Importantly, our model can employ one-shot learning to predict an open vocabulary of types, including rare and user-defined ones. We realise our approach in Typilus for Python that combines the TypeSpace with an optional type checker. We show that Typilus accurately predicts types. Typilus confidently predicts types for 70% of all annotatable symbols; when it predicts a type, that type optionally type checks 95% of the time. Typilus can also find incorrect type annotations; two important and popular open source libraries, fairseq and allennlp, accepted our pull requests that fixed the annotation errors Typilus discovered.Comment: Accepted to PLDI 202

Prediction and Analysis of Ground Stops with Machine Learning

A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common type of delays. Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops (GS), issued at affected airports. Ground Stops are implemented to control air traffic volume to specific airports where the projected traffic demand is expected to exceed the airports’ acceptance rate over a short period of time due to conditions such as inclement weather, volume constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic Management Initiative (TMI), particularly because all flights destined to affected airports are grounded until conditions improve. Efforts have been made over the years to reduce the impact of Traffic Management Initiatives on airports and flight operations. However, these efforts have largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP), due to their frequency and duration compared to Ground Stops. Limited work has also been carried out on Ground Stops because of the limited amount of time that traffic management personnel often have between planning and implementing Ground Stops and external factors that influence decisions of traffic management personnel. Consequently, this research primarily focuses on the prediction of weather-related Ground Stops at Newark Liberty International (EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors that influence their occurrence. It is expected that this research will provide stakeholders with further insights into factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by benchmarking Machine Learning algorithms in order to identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning algorithms to predict the occurrence of weather-related Ground Stops. The performance of these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR and LGA. Further analysis also revealed that model performance is significantly better when using balanced datasets compared to imbalanced datasets

Dasymetric distribution of votes in a dense city

[EN] A large proportion of electoral analyses using geography are performed on a small area basis, such as polling units. Unfortunately, polling units are frequently redrawn, provoking breaks in their data series. Previous electoral results play a key role in many analyses. They are used by political party workers and journalists to present quick assessments of outcomes, by political scientists and electoral geographers to perform detailed scrutinizes and by pollsters and forecasters to anticipate electoral results. In this paper, we study to what extent more complex geographical approaches (based on a proper location of electors on the territory using dasymetric techniques) are of value in comparison to simple methods (like areal weighting) for the problem of reallocating votes in a large, dense city. Barcelona is such a city and, having recently redrawn the boundaries of its census sections, it is an ideal candidate for further scrutiny. Although previous studies show the approaches based on dasymetric techniques outperforming simpler solutions for interpolating census figures, our results show that improvements in the process of reallocating votes are marginal. This brings into question the extra effort that entails introducing ancillary sources of information in a dense urban area for this kind of data. Additional research is required to know whether and when these results are extendable. (C) 2017 Elsevier Ltd. All rights reserved.This work was supported by the Spanish Ministry of Economics and Competitiveness under Grant CSO2013-43054-R.Pavia, JM.; Cantarino-Martí, I. (2017). Dasymetric distribution of votes in a dense city. Applied Geography. 86:22-31. https://doi.org/10.1016/j.apgeog.2017.06.021S22318

AcetoBase: a functional gene repository and database for formyltetrahydrofolate synthetase sequences

Acetogenic bacteria are imperative to environmental carbon cycling and diverse biotechnological applications, but their extensive physiological and taxonomical diversity is an impediment to systematic taxonomic studies. Acetogens are chemolithoautotrophic bacteria that perform reductive carbon fixation under anaerobic conditions through the Wood–Ljungdahl pathway (WLP)/acetyl-coenzyme A pathway. The gene-encoding formyltetrahydrofolate synthetase (FTHFS), a key enzyme of this pathway, is highly conserved and can be used as a molecular marker to probe acetogenic communities. However, there is a lack of systematic collection of FTHFS sequence data at nucleotide and protein levels. In an attempt to streamline investigations on acetogens, we developed AcetoBase - a repository and database for systematically collecting and organizing information related to FTHFS sequences. AcetoBase also provides an opportunity to submit data and obtain accession numbers, perform homology searches for sequence identification and access a customized blast database of submitted sequences. AcetoBase provides the prospect to identify potential acetogenic bacteria, based on metadata information related to genome content and the WLP, supplemented with FTHFS sequence accessions, and can be an important tool in the study of acetogenic communities. AcetoBase can be publicly accessed at https://acetobase.molbio.slu.se

Harmful algal blooms and climate change: exploring future distribution changes

Harmful algae can cause death in fish, shellfish, marine mammals, and humans, via their toxins or from effects associated with their sheer quantity. There are many species, which cause a variety of problems around north-west Europe, and the frequency and distribution of algal blooms have altered in the recent past. Species distribution modelling was used to understand how harmful algal species may respond in the future to climate change, by considering environmental preferences and how these may shift. Most distribution studies to date use low resolution global model outputs. In this study, high resolution, downscaled shelf seas climate projections for the north-west European shelf were nested within lower resolution global projections, to understand how the distribution of harmful algae may change by the mid to end of century. Projections suggest that the habitat of most species (defined by temperature, salinity, depth, and stratification) will shift north this century, with suitability increasing in the central and northern North Sea. An increase in occurrence here might lead to more frequent detrimental blooms if wind, irradiance and nutrient levels are also suitable. Prioritizing monitoring of species in these susceptible areas could help in establishing early-warning systems for aquaculture and health protection schemes