Miracle-dz/UniFAR-FTUs huggingface dataset:
Applicability of Remote Sensing Data to Rapidly Assess River Restoration Sites in the Palo Verde Valley

Identifying sites for river restoration is a complex process, drawing from many fields of science. Field data is often collected to assess if a site is suitable for restoration. Because field data collection can be time and resource intensive, using remote sensing data could potentially speed up the process. I used remote sensing data to assess restoration potential in 106 sites in the Palo Verde Valley on the Colorado River. I identified sites for restoration and determined how the remotely sensed data matched with field data. Each site was classified as LEV, STB or IRR. LEV sites consisted of small buffers of land surrounding the river channel. STB sites were large sites that were setback from the river. IRR sites contained irrigation channels inside them. In ArcGIS, I created layers for water features, the sites, elevation and land use, and assigned weights to each of these features. Using suitability analysis, I created a model to quantify the restorability of each site. Overall, 65 sites were very promising for restoration. I found that LEV sites had the best restoration potential, with a mean score of 0.5, while IRR sites had the least potential with a mean score of 2.3. In addition, I found that remote sensing data did not vary much from field data. However, the results were not statistically significant. Remote sensing can be used as a preliminary process in assessing restoration sites. Future studies may want to look into this relationship more closely.
Paolini Convex approximation of an inhomogeneous anisotropic functional

— The numerical minimization of the functional &(u) = <£>(x, vu) \Du\ + [xudX ~ — Q 30 — I Kudx, u e BV(Q; { — 1, l} ) , is addressed. The function <p is continuous, has linear growth, and is confi vex and positively homogeneous of degree one in the second variable. We prove that & can be equivalently minimized on the convex set BV(Q; [ 1 , 1]) and then regularized with a sequence { ^ (# )} , of stricdy convex functionals defined on BV(Q; [ 1 , 1]). Then both & and &, can be discretized by continuous linear finite elements. The convexity property of the functionals on BV(Q; [ — 1, 1]) is useful in the numerical minimization of &. The r — L 1 (D)-convergence of the discrete functionals {&h}h and {&t,b}*,b t o &> a s w e ^ a s the compactness of any sequence of discrete absolute minimizers, are proven.
Spatio-Temporal Planning for a Reconfigurable Multi-Robot System

In this paper we introduce a spatio-temporal planning and scheduling approach for collaborative multi-robot systems. In particular, we are targeting an application to physically reconfigurable systems in order to take advantage of morphological changes. The planning approach relies on an ontology to model the functionalities individual physical agents offer within the multirobot system, while an implicit domain representation is given. An inference layer on top of a knowledge-based system allows to account for superadditive effects from physically combining two or more robots. We present a formulation of the domain-specific planning problem and outline our spatio-temporal planning approach. This approach combines the use of constraint-based satisfaction techniques with linear optimization to solve a multicommodity min-cost flow problem to deal with the transportation of immobile robotic systems. The paper describes the findings after implementing core features and evaluates the approach based on a fictitious scientific mission. We close with a discussion of the current limitations of the illustrated approach.
New Faculty Orientation And Seminar Series: Emphasis On Teaching And Learning

Traditional new faculty orientations focus on university services, fringe benefits, and research activities. If teaching is discussed at all, it is minimal. The orientation w rkshop and seminar series presented in this paper has a new look. The emphasis is on teaching. Topics discussed include: how to be a successful and happy faculty member, the Michigan Technological University (MTU) student body, total student development, university learning centers, and preparing for the first day. The orien tatio is followed by weekly seminars throughout the year. The weekly seminars, particularly in the fall, discuss just in time topics such as preparing for quizzes and exams, dealing with marginal student performance, student evaluations, classroom assessment, grading, active vs. passive learning, reflective teaching, etc. The orientation is directed at new faculty, i.e., faculty at the university for three years or less. The weekly seminar series includes more experienced t achers as well. The goal is to encourage an exchange of ideas about teaching between new and experienced professors. The orientation and seminar series were initiated for the first time in Fall of 1995. The attendance, participation, and response has been very good. The evaluation and feedback from faculty in ttendance has been encouraging.
How to Target Critical Infrastructure: The Adversary Return on Investment from an Industrial Control System

Imagine a device that could decrypt all encryption—within seconds. A box with this capability could be one of the most valuable pieces of equipment for an organization, but even more valuable to an adversary. What if that box only worked against American encryption? If true, a particular market would be ripe for the harvest. A device that powerful could be used to decrypt secrets and data in transit, making encrypted data an adversary might have access to, extremely valuable. Similarly, Critical Infrastructure is a target for some because of the yield that a successful attack could result in. Death, disruption or damage is a real possibility. The Return on Investment (ROI) and Return on Security Investment (ROSI) fall short in actually determining the level of protection required for an organization striving to protect the most sensitive data or system. The Adversary Return on Investment (AROI) is the missing piece to the equation. From the adversary’s vantage point, data, infrastructure or systems have value. By understanding this value an organization can more appropriately align its security strategy; especially, for the most critical infrastructure.
Analysis of Regional Development Disparities in Ukraine Using Fuzzy Clustering

Disparities in the development of regions in any country affect the entire national economy. Detecting the disparities can help formulate the proper economic policies for each region by taking action against the factors that slow down the economic growth. This study was conducted with the aim of applying clustering methods to analyse regional disparities based on the economic development indicators of the regions of Ukraine. There were considered fuzzy clustering methods, which generalize partition clustering methods by allowing objects to be partially classified into more than one cluster. Fuzzy clustering technique was applied using R packages to the data sets with the statistic indicators concerned to the economic activities in all administrative regions of Ukraine in 2017. Sets of development indicators for different sectors of economic activity, such as industry, agriculture, construction and services, were reviewed and analysed. The study showed that the regional cluster classification results strongly depend on the input development indicators and the clustering technique used for this purpose. Consideration of different partitions into fuzzy clusters opens up new opportunities in developing recommendations on how to differentiate economic policies in order to achieve maximum growth for the regions and the entire country.
The Role of Technology in Increasing Motivation of Millennial Women Entrepreneurs Starting a Business in the Digital Era

The development of entrepreneurship in Indonesia proves that entrepreneurship is one of the important aspects needed to improve the country's economy. The benefits of entrepreneurship are one of them to improve the standard of living and the economy of the community. Technological development greatly helped the development of entrepreneurs in Indonesia, especially in the culinary field. The presence of online delivery applications, namely Gofood and Grabfood has had a big impact on changes that initially bought food conventionally but now it can through the online delivery application. This research was using questionnaires and interviews with young women with 33 respondents aged 17-30 years old who has her own culinary business. The data was collected around five weeks and for data, analysis used Statistical Package for Social Sciences (SPSS) Software version 21 to analysis the data. Young women in the digital era were greatly helped by technological advancements. Delivery through Gofood and Grabfood makes it easy for consumers to buy food without having to go to the food restaurant. Simply use the application and select the food they want and will be delivered to their food and food and will be delivered to their location. Desire to open food business is currently there Gofood and Grabfood, so it is very helpful in expanding the reach of its customers, Gofood and Grabfood is a free application for business owners and very profitable in increasing sales.
Intelligent System for Visualized Data Analytics

The chapter 1 discusses about the importance of the social media in the day to day life and how users are utilizing their services. It summarizes why web users opted social media for data analysis and for daily decision making process. This chapter discussed about the definitions of the sentiment analysis and opinion mining. This chapter explains how sentiments or opinions are expressed, by web users. The chapter 2 discusses about the literature study of this research domain on the popular social networking sites, taking some example sites like twitter, amazon, flipkart etc., and various tools available for the task of sentiment analysis. This chapter discusses about, what is intelligent system. We have shown different sentiment classification levels and sentiment analysis methodology. The chapter 3 discusses about the system design for this research domain. This chapter depicts a model diagram of an intelligent system that processing the twitter data, and the complete diagram of the intelligent system for visualized data analytics and the data flow diagram of the sentiment analysis variation of social data.
Fe 3 +-EDTA-zinc oxide nano-diagnostics : Synthesis and in vitro cellular evaluation

Resulting from many efforts in opportune recognizing and correct treating briefl y molecular imaging and therapy, some methods or molecules have been developed by now to overcome any unwanted defaults in imaging and therapy, specifi cally nanoparticles. Generally, synthesis new compounds, for example Zinc Oxide-Iron nano-complex consisting non-toxic paramagnetic ion [Fe3+] and its cellular uptake vehicle [zinc oxide] as a lowering risk of toxicity and increase in cellular uptake liability, could be useful and noticeable in molecular imaging purposes. The isolated nano-contrast was structurally analyzed by variety of techniques such as EDAX, AFM, Zeta and size measuring, SEM, FTIR and UV spectrums. Finally for monitoring the nano-complex toxicity and cellular uptake on human embryonic kidney cells named as HEK 293 was assessed respectively. The analytical result showed a very good promising size at nanoscale and zeta potential as well as iron content for the suggested ferric containing nano-complex as well as paramagnetic properties. Also outcome of cell study were resulted in no signifi cant cellular toxicity comparing to 62% toxicity of control drug Magnevist and higher cellular uptake of 56% comparing to 9% for that of Magnevist as well. In summary, it seems that the proposed ZnO-Fe nano-complex may be useful as a novel low risk contrast agent to increase resolution in molecular Imaging like MRI and improve the current situation with the minimum cost.
Analyzing the Visual Grounding of "Referring Relationships"

There have been numerous efforts to accomplish the task of visual grounding (Deng et al., 2018, Johnson et al., 2015, Krishna et al., 2018), the act of matching regions or objects within an image with natural language queries. But with each method released, there is a growing uncertainty about the effectiveness of the machine’s learning. Are computers learning what we expect, and are datasets properly testing this learning? (Cirik et al., 2018). In this thesis, I analyze the visual grounding method of “Referring Relationships” (RR) by Krishna et al. (2018). I find that RR’s relationship information does not have a significant positive impact on performance as compared to a baseline model that only detects objects. In addition, I find that the Visual Relationship Detection dataset (VRD), one of the datasets used in the original paper, exhibits bias. In other words, it allows methods that do not utilize relationships to perform well, showing that the VRD dataset is not able to properly test the RR method.
End-to-End Online Speech Recognition with Recurrent Neural Networks

Recurrent neural networks (RNNs) have shown outstanding sequence to sequence modeling performance. Thanks to recent advances in end-to-end training approaches for automatic speech recognition (ASR), RNNs can learn direct mapping functions from the sequence of audio features to the sequence of output characters or words without any intermediate phoneme or lexicon layers. So far, majority of studies on end-to-end ASR have been focused on increasing the accuracy of speech recognition to the level of traditional state-of-the-art models. However, although the end-to-end ASR models have reached the accuracy of the traditional systems, their application has usually been limited to utterance-level speech recognition with pre-segmented audio instead of online speech recognition with continuous audio. This is because the RNNs cannot be easily generalized to very long streams of audio when they are trained with segmented audio. To address this problem, we propose an RNN training approach on training sequences with virtually infinite length. Specifically, we describe an efficient GPUbased RNN training framework for the truncated backpropagation through time (BPTT) algorithm, which is suitable for online (continuous) training. Then, we present an online version of the connectionist temporal classification (CTC) loss computation algorithm, where the original CTC loss is estimated with partial sliding window. This modified CTC algorithm can be directly employed for truncated BPTT based RNN training.
BotProfiler++: Detecting Malware-Infected Hosts using Templates of Time-Series HTTP Request Patterns

Malware-infected hosts are one of the most serious threats in network services. An attacker controls many malware-infected hosts via command and control (C&C) servers to carry out cyber attacks. The victim of the attack observes only the packets from the malware-infected hosts. Thus, it is difficult to identify the attacker. One approach to mitigating such attacks is to detect malware-infected hosts. BotProfiler is one of the methods that detect malware-infected hosts. BotProfiler focuses on the communications between malware-infected hosts and C&C servers. Most malware use HTTP as their C&C protocol. Thus, BotProfiler generates the templates of HTTP requests sent by malware-infected hosts and detects malware-infected hosts by comparing the monitored HTTP requests and the templates. BotProfiler avoids misdetections by introducing the rarity which is calculated by the frequency of the HTTP requests monitored at the deployment network. However, BotProfiler may cause a large number of misdetections before a sufficient amount of benign traffic are monitored. Moreover, even if a sufficient amount of benign traffic to calculate rarity are monitored, the rarity may cause the undetected C&C communications; the C&C communications whose rarity scores are low are not detected even if they exactly match the templates of the C&C communications. In this thesis, we propose a method that detects malware-infected hosts with high detection rate and low false detection rate without using the data on benign communications. Based on the fact that many malware-infected hosts generate multiple HTTP requests, we propose a method using the templates of the sets of the HTTP requests. This method generates a template called group template for each malware that is constructed of the set of templates of HTTP requests the malware generates. Then, it detects malware-infected
It Takes a Village: Peer Effects and Externalities in Technology Adoption

Do social networks matter for the adoption of new forms of political participation? We develop a formal model showing that the quality of communication that takes place in social networks is central to understanding whether a community will adopt forms of political participation where benefits are uncertain and where there are positive externalities associated with participation. Early adopters may exaggerate benefits, leading others to discount information about the technology's value. Thus, peer effects are likely to emerge only where informal institutions support truthful communication. We collect social network data for sixteen Ugandan villages where an innovative mobile-based reporting platform was introduced. Consistent with our model, we find variation across villages in the extent of peer effects on technology adoption, as well as evidence supporting additional observable implications. Impediments to social diffusion may help explain the varied uptake of new and increasingly common political communication technologies around the world.
Whalewatching boats disrupt the foraging activities of Minke whales in Faxaflói bay , Iceland

We compared Minke whale behaviour in the presence and absence of whalewatching boats in Faxaflói Bay, Iceland, a feeding ground for the North Atlantic population. Individual focal follows were conducted from land (control) and from commercial whalewatching boats (impact). Minke whale activity states were inferred from movement metric data (inter-breath interval and directness index) and Linear Mixed Effects (LME) models were used to investigate the effect of whalewatching boats on the activity state of Minke whales. We identified three distinct activity states: surface feeding (conspicuous events of feeding observed at the surface), foraging (long dives and sinuous movement) and travelling (short dives and straight movement). The presence of boats influenced the interaction between dive interval and directness index. Minke whales avoided whalewatching boats by decreasing their inter-breath intervals (performing shorter dives) and decreasing their directness index (increasing circular movement). An increase in respiration rate in the presence of whalewatching boats could reflect an increase in metabolic rate caused by avoidance behaviour. This could lead to an increase in energy expenditure of Minke whale during interactions with whalewatching boats. In addition, the long and relatively sinuous dives observed during foraging behaviour were absent during interactions with whalewatching boats. This indicates that whalewatching boats disrupt the foraging behaviour of Minke whales. Foraging is a biologically important activity for Minke whales in the feeding grounds, as the energy acquired in the feeding ground will set the limit to the amount of energy available for reproduction and lactation in the breeding ground. Thus the foraging disruption observed in this study could be of biological importance.
Ecological indicators of mammal exposure to Ebolavirus

Much of the basic ecology of Ebolavirus remains unresolved despite accumulating disease outbreaks, viral strains and evidence of animal hosts. Because human Ebolavirus epidemics have been linked to contact with wild mammals other than bats, traits shared by species that have been infected by Ebolavirus and their phylogenetic distribution could suggest ecological mechanisms contributing to human Ebolavirus spillovers. We compiled data on Ebolavirus exposure in mammals and corresponding data on life-history traits, movement, and diet, and used boosted regression trees (BRT) to identify predictors of exposure and infection for 119 species (hereafter hosts). Mapping the phylogenetic distribution of presumptive Ebolavirus hosts reveals that they are scattered across several distinct mammal clades, but concentrated among Old World fruit bats, primates and artiodactyls. While sampling effort was the most important predictor, explaining nearly as much of the variation among hosts as traits, BRT models distinguished hosts from all other species with greater than 97% accuracy, and revealed probable Ebolavirus hosts as large-bodied, frugivorous, and with slow life histories. Provisionally, results suggest that some insectivorous bat genera, Old World monkeys and forest antelopes should receive priority in Ebolavirus survey efforts. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
Confronting models with data: the challenges of estimating disease spillover

For pathogens known to transmit across host species, strategic investment in disease control requires knowledge about where and when spillover transmission is likely. One approach to estimating spillover is to directly correlate observed spillover events with covariates. An alternative is to mechanistically combine information on host density, distribution and pathogen prevalence to predict where and when spillover events are expected to occur. We use several case studies at the wildlife–livestock disease interface to highlight the challenges, and potential solutions, to estimating spatio-temporal variation in spillover risk. Datasets on multiple host species often do not align in space, time or resolution, and may have no estimates of observation error. Linking these datasets requires they be related to a common spatial and temporal resolution and appropriately propagating errors in predictions can be difficult. Hierarchical models are one potential solution, but for fine-resolution predictions at broad spatial scales, many models become computationally challenging. Despite these limitations, the confrontation of mechanistic predictions with observed events is an important avenue for developing a better understanding of pathogen spillover. Systems where data have been collected at all levels in the spillover process are rare, or non-existent, and require investment and sustained effort across disciplines. This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.
Ngwevu intloko: a new early sauropodomorph dinosaur from the Lower Jurassic Elliot Formation of South Africa and comments on cranial ontogeny in Massospondylus carinatus

Our knowledge of Early Jurassic palaeobiodiversity in the upper Elliot Formation of South Africa has increased markedly in recent years with the discovery of new fossils, re-assessments of previously collected material and a better understanding of Stormberg Group stratigraphy. Here, Ngwevu intloko, a new genus of upper Elliot basal sauropodomorph is named on the basis of a complete skull and partial skeleton (BP/1/4779) previously assigned to Massospondylus carinatus. It can be distinguished from all other basal sauropodomorphs by a combination of 16 cranial and six postcranial characters. The new species is compared to a small ontogenetic series of M. carinatus as well as to a range of closely related taxa. Taphonomic deformation, sexual dimorphism and ontogeny are rejected as possible explanations for the morphological differences present between BP/1/4779 and other taxa. Osteohistological examination reveals that BP/1/4779 had nearly reached adult size at the time of its death at a minimum age of 10 years.