Center for Research on Sustainable Forests 2017 Annual Report

Ongoing development within the CRSF to be the region’s research data portal and geospatial observatory for forests of the Northeastern US. In addition to updating the CRSF home website, we continue to support three online tools for forest resources professionals and the public: Northeast Forest Information System (NEFIS) – an online, opensource, web portal for applied forestry information (http://www.nefismembers.org). More than 1,000 documents were uploaded over the year on a wide range of topics, user numbers have doubled, and monthly page views have reached nearly 5,000. Maine Forest Spatial Tool – displays a wide variety of geospatial data on forest resources across the State of Maine for both forest resource professionals and the public (http://mfst.acg.maine.edu). Maine Forest Dashboard – The Dashboard was launched in Spring 2017 and can be accessed at http://www.maineforestdashboard.com. The site provides customizable forest statistics and changes using long-term data from the Maine Forest Service and has had nearly 100 page views since its release in early May. CRSF scientists continue to provide a strong return for every dollar provided by the Maine Economic Improvement Fund (MEIF) to support CRSF research. In the past year, there has been over $21 in return for every$1 invested in

Cooperative Forestry Research Unit Annual Report 2016

Cooperative forestry research unit annual repor

Connectivity modelling for a species-driven nature recovery network in Oxfordshire

The development of England’s new Nature Recovery Network has been piloted in several counties in the country, but few have systematically mapped connectivity based on species dispersal. This study proposes and evaluates a novel modelling framework that integrates various layers of species information into a spatial conservation prioritization analysis. It aims to strategically identify optimal zones for nature recovery that can maximize species connectivity in Oxfordshire, using bats as a focal species. The framework was able to not only identify key landscape corridors but also stepping stone habitats for bats and emphasized how well-placed, small-scale green and blue infrastructure, such as hedgerows and ponds, can be just as effective as larger reserves. It also found that the current coverage of protected areas may not adequately be protecting woodland habitat needed for connectivity. Next steps for Oxfordshire’s NRN should scale up the application of this connectivity framework to address these areas of priority in the landscape

Analyzing Social and Stylometric Features to Identify Spear phishing Emails

Spear phishing is a complex targeted attack in which, an attacker harvests information about the victim prior to the attack. This information is then used to create sophisticated, genuine-looking attack vectors, drawing the victim to compromise confidential information. What makes spear phishing different, and more powerful than normal phishing, is this contextual information about the victim. Online social media services can be one such source for gathering vital information about an individual. In this paper, we characterize and examine a true positive dataset of spear phishing, spam, and normal phishing emails from Symantec's enterprise email scanning service. We then present a model to detect spear phishing emails sent to employees of 14 international organizations, by using social features extracted from LinkedIn. Our dataset consists of 4,742 targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack emails sent to 5,912 non victims; and publicly available information from their LinkedIn profiles. We applied various machine learning algorithms to this labeled data, and achieved an overall maximum accuracy of 97.76% in identifying spear phishing emails. We used a combination of social features from LinkedIn profiles, and stylometric features extracted from email subjects, bodies, and attachments. However, we achieved a slightly better accuracy of 98.28% without the social features. Our analysis revealed that social features extracted from LinkedIn do not help in identifying spear phishing emails. To the best of our knowledge, this is one of the first attempts to make use of a combination of stylometric features extracted from emails, and social features extracted from an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature

FinBook: literary content as digital commodity

This short essay explains the significance of the FinBook intervention, and invites the reader to participate. We have associated each chapter within this book with a financial robot (FinBot), and created a market whereby book content will be traded with financial securities. As human labour increasingly consists of unstable and uncertain work practices and as algorithms replace people on the virtual trading floors of the worlds markets, we see members of society taking advantage of FinBots to invest and make extra funds. Bots of all kinds are making financial decisions for us, searching online on our behalf to help us invest, to consume products and services. Our contribution to this compilation is to turn the collection of chapters in this book into a dynamic investment portfolio, and thereby play out what might happen to the process of buying and consuming literature in the not-so-distant future. By attaching identities (through QR codes) to each chapter, we create a market in which the chapter can ‘perform’. Our FinBots will trade based on features extracted from the authors’ words in this book: the political, ethical and cultural values embedded in the work, and the extent to which the FinBots share authors’ concerns; and the performance of chapters amongst those human and non-human actors that make up the market, and readership. In short, the FinBook model turns our work and the work of our co-authors into an investment portfolio, mediated by the market and the attention of readers. By creating a digital economy specifically around the content of online texts, our chapter and the FinBook platform aims to challenge the reader to consider how their personal values align them with individual articles, and how these become contested as they perform different value judgements about the financial performance of each chapter and the book as a whole. At the same time, by introducing ‘autonomous’ trading bots, we also explore the different ‘network’ affordances that differ between paper based books that’s scarcity is developed through analogue form, and digital forms of books whose uniqueness is reached through encryption. We thereby speak to wider questions about the conditions of an aggressive market in which algorithms subject cultural and intellectual items – books – to economic parameters, and the increasing ubiquity of data bots as actors in our social, political, economic and cultural lives. We understand that our marketization of literature may be an uncomfortable juxtaposition against the conventionally-imagined way a book is created, enjoyed and shared: it is intended to be

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Network Intrusion Detection System:A systematic study of Machine Learning and Deep Learning approaches

The rapid advances in the internet and communication fields have resulted in ahuge increase in the network size and the corresponding data. As a result, manynovel attacks are being generated and have posed challenges for network secu-rity to accurately detect intrusions. Furthermore, the presence of the intruderswiththeaimtolaunchvariousattackswithinthenetworkcannotbeignored.Anintrusion detection system (IDS) is one such tool that prevents the network frompossible intrusions by inspecting the network traffic, to ensure its confidential-ity, integrity, and availability. Despite enormous efforts by the researchers, IDSstillfaceschallengesinimprovingdetectionaccuracywhilereducingfalsealarmrates and in detecting novel intrusions. Recently, machine learning (ML) anddeep learning (DL)-based IDS systems are being deployed as potential solutionsto detect intrusions across the network in an efficient manner. This article firstclarifiestheconceptofIDSandthenprovidesthetaxonomybasedonthenotableML and DL techniques adopted in designing network-based IDS (NIDS) sys-tems. A comprehensive review of the recent NIDS-based articles is provided bydiscussing the strengths and limitations of the proposed solutions. Then, recenttrends and advancements of ML and DL-based NIDS are provided in terms ofthe proposed methodology, evaluation metrics, and dataset selection. Using theshortcomings of the proposed methods, we highlighted various research chal-lenges and provided the future scope for the research in improving ML andDL-based NIDS

Insectivorous bats in Indian rice fields respond to moonlight, temperature, and insect activity

ABSTRACT: Context - Rice, India’s most widely grown crop, suffers substantial and increasing yield loss to insect pests. Insectivorous bats are known suppressors of insect pests, providing significant economic value to agricultural systems worldwide, yet their ecology in Indian agricultural landscapes is poorly understood. Objectives - We assess the influence of key biotic and abiotic factors on the activity of insectivorous bats over the growing season and within a night in a rice cultivation landscape. Methods - Passive acoustic recorders were used to track bat activity in a rice field in the Sonitpur district of Assam, India. We used generalised linear mixed models to analyse the effect of temperature, insect activity, and moonlight intensity on the activity of six bat sonotypes. We also used a multimodal analysis to describe the within-night activity patterns of these sonotypes. Results - Minimum nightly temperature and moonlight intensity had a positive and negative influence, respectively, on the activity of six bat sonotypes, while the activity of four bat sonotypes increased with insect activity. Within-night activity showed one of two patterns: three sonotypes displayed a dusk peak in activity, while the three other sonotypes were active through the night. Conclusion - The potential to maximise natural pest control in agricultural landscapes can only be realised through understanding the ecology of natural enemies in these landscapes. Our findings suggest that bats in rice fields are tracking insects over a season and within a night, pointing to a valuable ecosystem service in Indian agriculture that is yet to be quantified.info:eu-repo/semantics/publishedVersio

Using surveillance of animal bite patients to decipher potential risks of rabies exposure from domestic animals and wildlife in Brazil

Direct contact with domestic animals and wildlife is linked to zoonotic spillover risk. Patients presenting with animal-bite injuries provide a potentially valuable source of surveillance data on rabies viruses that are transmitted primarily by animal bites. Here, we used passive surveillance data of bite patients to identify areas with high potential risk of rabies transmission to humans across Brazil, a highly diverse and populous country, where rabies circulates in a range of species. We analyzed one decade of bite patient data from the national health information system (SINAN) comprising over 500,000 patients attending public health facilities after being bitten by a domestic or wild animal. Our analyses show that, between 2008 and 2016, patients were mostly bitten by domestic dogs (average annual dog bite patients: 502,043 [436,391–544,564], annual incidence per state: 258 dog bites/100,000 persons) and cats (76,512 [56,588–97,580] cat bites, 41 cat bites/100,000/year), but bites from bats (4,172 [3,351–5,365] bat bites, 2.3/100,000/year), primates (3,320 [3,013–3,710] primate bites, 2.0/100,000/year), herbivores (1,908 [1,492–2,298] herbivore bites, 0.9/100,000/year) and foxes (883 [609–1,086] fox bites, 0.6/100,000/year) were also considerable. Incidence of bites due to dogs and herbivores remained relatively stable over the last decade. In contrast bites by cats and bats increased while bites by primates and foxes decreased. Bites by wild animals occurred in all states but were more frequent in the North and Northeast of Brazil, with over 3-fold differences in incidence between states across all animal groups. Most bites reported from domestic animals and wildlife occurred in urban settings (71%), except for bites from foxes, which were higher in rural settings (57%). Based upon the Ministry of Health guidelines, only half of patients received the correct Post-Exposure Prophylaxis following a bite by a suspect rabid animal. We identified areas and species of high-risk for potential zoonotic transmission of rabies in Brazil and reveal that, despite increasing human encroachment into natural ecosystems, only patients reporting bites by bats increased. Our study calls for future research to identity the socio-ecological factors underlying bites and the preventive measures needed to reduce their incidence and potential risk of rabies transmission