Miracle-dz/UniFAR-FTUs huggingface dataset:
The use of Vis‐NIR spectral reflectance for determining root density: evaluation of ryegrass roots in a glasshouse trial

This paper reports the use of visible/near‐infrared reflectance spectroscopy (Vis‐NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm−3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm−3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm−3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r2 cross‐validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross‐validation (RMSECV) = 0.60 mg cm−3; with removing outliers, r2 cross‐validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm−3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.
A Probabilistic Approach to Error Detection&Correction for Tree-Mapping Grammars

Rule-based natural language generation denotes the process of converting a semantic input structure into a surface representation by means of a grammar. In the following, we assume that this grammar is handcrafted and not automatically created for instance by a deep neural network. Such a grammar might comprise of a large set of rules. A single error in these rules can already have a large impact on the quality of the generated sentences, potentially causing even a complete failure of the entire generation process. Searching for errors in these rules can be quite tedious and time-consuming due to potentially complex and recursive dependencies. This work proposes a statistical approach to recognizing errors and providing suggestions for correcting certain kinds of errors by cross-checking the grammar with the semantic input structure. The basic assumption is the correctness of the latter, which is usually a valid hypothesis due to the fact that these input structures are often automatically created. Our evaluation reveals that in many cases an automatic error detection and correction is indeed possible.
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection

Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks. However, extending state-of-the-art saliency detectors from image to video is challenging. The performance of salient object detection suffers from object or camera motion and the dramatic change of the appearance contrast in videos. In this paper, we present flow guided recurrent neural encoder (FGRNE), an accurate and end-to-end learning framework for video salient object detection. It works by enhancing the temporal coherence of the per-frame feature by exploiting both motion information in terms of optical flow and sequential feature evolution encoding in terms of LSTM networks. It can be considered as a universal framework to extend any FCN based static saliency detector to video salient object detection. Intensive experimental results verify the effectiveness of each part of FGRNE and confirm that our proposed method significantly outperforms state-of-the-art methods on the public benchmarks of DAVIS and FBMS.
Dynamic Consistency Checking of Cloud Computing Patterns

Cloud computing patterns can be used to build cloud applications based on proven and tested solutions. However, the correct implementation of these patterns is not always warranted during the whole life cycle of an application. Inconsistencies between implementation and cloud computing patterns can result in architectural drift and negatively impact the quality attributes of an application. Therefore, this thesis presents a method to check the consistency of cloud computing patterns at runtime. Nine cloud computing patterns are selected and analyzed in detail for structural and semantic constraints. A formal notation is developed to express the constraints and enable automatic consistency checking of the patterns. Furthermore, a framework is designed which enables monitoring of cloud applications and checking of constraints for cloud computing patterns at runtime. To show the feasibility of this method, the framework is implemented and successfully tested in a cloud computing environment, with a simulated workload on virtual machines, for the patterns Static Workload, Continuously Changing Workload, Elasticity Manager, and Watchdog.
Collecte, intégration et visualisation de données Web incertaines sur des objets mobiles

Nombreuses sont aujourd’hui les applications de veille sur objets mobiles : voitures, trains, avions, bateaux, personnes, ou, plus globalement, populations ou phénomènes tels que cyclones. De façon classique, cela exige la collecte de données à partir de réseaux de capteurs, d’analyse d’images ou de vidéos, ou l’utilisation de ressources spécifiques à une application cible. Nous montrons dans ce papier de démonstration comment le contenu Web peut être à la place exploité pour collecter des informations (trajectoires, métadonnées) concernant certains objets mobiles. Cependant, l’incertitude et les incohérences vont de pair avec les données Web. Nous développons ainsi une méthodologie pour l’estimation de l’incertitude et le filtrage des données extraites. En guise de démonstration, nous présentons sous forme d’une application Web un système construisant des trajectoires de bateaux à partir de données issues de réseaux sociaux, en présentant à l’utilisateur les trajectoires inférées, des méta-informations, ainsi que leurs niveaux d’incertitude.
Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation

Tropical deforestation continues to be a major driver of biodiversity loss and greenhouse gas emissions. Remote sensing technology is increasingly used to assess changes in forest cover, species distributions and carbon stocks. However, satellite and airborne sensors can be prohibitively costly and inaccessible for researchers in developing countries. Here, we describe the development and use of an inexpensive (<$2,000) unmanned aerial vehicle for surveying and mapping forests and biodiversity (referred to as ‘Conservation Drone’ hereafter). Our prototype drone is able to fly pre-programmed missions autonomously for a total flight time of ~25 minutes and over a distance of ~15 km. Non-technical operators can program each mission by defining waypoints along a flight path using an open-source software. This drone can record videos at up to 1080 pixel resolution (high definition), and acquire aerial photographs of <10 cm pixel resolution. Aerial photographs can be stitched together to produce real-time geo-referenced land use/cover maps of surveyed areas. We evaluate the performance of this prototype Conservation Drone based on a series of test flights in Aras Napal, Sumatra, Indonesia. We discuss the further development of Conservation Drone 2.0, which will have a bigger payload and longer range. Initial tests suggest a flight time of ~50 minutes and a range of ~25 km. Finally, we highlight the potential of this system for environmental and conservation applications, which include near real-time mapping of local land cover, monitoring of illegal forest activities, and surveying of large animal species.
Achieving Secure and Effective Search Services in Cloud Computing

One critical challenge of today's cloud services is how to provide an effective search service while preserving user privacy. In this paper, we propose a wildcard-based multi-keyword fuzzy search (WMFS) scheme over the encrypted data, which tolerates keyword misspellings by exploiting the indecomposable property of primes. Compared with existing secure fuzzy search schemes, our WMFS scheme has the following merits: 1) Efficiency. It eliminates the requirement of a predefined dictionary and thus supports updates efficiently. 2) High accuracy. It eliminates the false positive and false negative introduced by specific data structures and thus allows the user to retrieve files as accurate as possible. 3) Flexibility. It gives the user great flexibility to specify different search patterns including keyword and substring matching. Extensive experiments on a real data set demonstrate the effectiveness and efficiency of our scheme.
Touch the Page and Mimic Me: Evaluation of a Talking-Pen Learning Tool.

International Christian University High School abbie.odakura@gmail.com The purpose of this paper is to evaluate the talking-pen device called the Mimic Me, an educational technology product of a large English conversation school in Japan, as an educational learning tool. The product will be reviewed in terms of its developmental appropriateness for the target audience based on current research. Although the Mimic Me uses behaviorist learning theories to teach vocabulary and short phrases, it could be further enhanced to live up to its full potential as a learning tool. The author proposes alternative ways that the product could be used, as well as possible design modifications that would improve the effectiveness of the product.
Characterizing the surface heterogeneity of fire effects using multi‐temporal reflective wavelength data

The relationship between changes observed in multi‐temporal remotely sensed data and disturbance processes are increasingly being studied in support of various land process modelling and management decision applications. The possibility of mapping both the location and degree of change and retrieving information concerning the disturbance process are primary goals. This paper studies changes in reflective wavelength data caused by the action of fire. We consider the heterogeneity of fire effects in terms of the fraction of the observation that burned (f) and the combustion completeness (cc). A spectral mixture model and field and satellite observations of prescribed fires are used to examine the relationship between change in reflectance, and cc and f. The prescribed fires were lit in different South African savannah types during the SAFARI 2000 dry season campaign. Implications for the development of methods to retrieve cc and f, and for the development of methods to map the spatial extent of fire‐affected areas with known detection capabilities are discussed.
Klein tunneling in the α − T 3 model

We investigate Klein tunneling for the α-T3 model, which interpolates between graphene and the dice lattice via parameter α. We study transmission across two types of electrostatic interfaces: sharp potential steps and sharp potential barriers. We find both interfaces to be perfectly transparent for normal incidence for the full range of the parameter α for both interfaces. For other angles of incidence, we find that transmission is enhanced with increasing α. For the dice lattice, we find perfect, all-angle transmission across a potential step for incoming electrons with energy equal to half of the height of the potential step. This is analogous to the “super”, all-angle transmission reported for the dice lattice for Klein tunneling across a potential barrier.
Predicting forest stand characteristics with airborne scanning lidar

Currently, commercial forestry applications of airborne scanning lidar are limited to geo-technical applications such as creation of digital terrain models for layout of roads or logging systems. We investigated the feasibility of predicting characteristics of forest stands with lidar data in a universityindustry partnership. Lidar lends itself well to such applications because it allows direct measurement of important structural characteristics of height and canopy closure. We found that lidar data can be used to predict the stand characteristics of height, basal area, and volume quite well. The potential for commercial applications appears bright. Lidar data can be used to estimate stand characteristics over large areas or entire forests. After the process is streamlined, it should be possible to provide maps of height, basal area, and volume in such areas within a few weeks of the lidar collection flight.
The Era of Sustainability: Promises, Pitfalls and Prospects for Sustainable Buildings and the Built Environment

Following 25 years of efforts in the field, the author discusses the situation of the construction sector by reflecting on the 3Ps of the era of sustainability: the promises of sustainability; the pitfalls in the interpretation of sustainability for construction; the prospects for sustainable buildings and the built environment in the future. The paper is organized into five sections. The first section introduces the emergence of sustainable construction, its promises and challenges for architects and engineers. The second section considers how sustainability has been interpreted in practice in construction, i.e., primarily through the process of greening buildings and the built environment. The third section describes the main pitfalls that such interpretation has determined, including the role played by evaluation and assessment systems for sustainable buildings. The fourth section examines prospective paths to overcoming such pitfalls, with particular concerns for new professionalisms and the ensuing role for higher education. The fifth section concludes by supporting the idea that the era of sustainability is still necessary, if sustainability itself becomes a sort of ‘container concept’ of all that is necessary for long-lasting natural and human life on Earth.
Genetic Diversity Among For ty Coffee Var ieties Assessed by RAPD Markers Associated with Restr iction Digestion

The genetic variability of 40 accessions of C. arabica was evaluated using a combination of the random amplified polymorphic DNA (RAPD) technique and restriction digestion of genomic DNA. The genetic variability and the relatedness among all accessions were initially evaluated using 195 RAPD primers which revealed a very low level of genetic variation. To improve the efficiency in the detection of polymorphism, the genomic DNA of all accessions were submitted to digestion with restriction endonucleases prior to PCR amplification. A total of 24 primers combined with restriction digestion of DNA rendered 318 bands, of which 266 (83.65%) were polymorphic. The associations among genotypes were estimated using UPGMA-clustering analysis. The accessions were properly clustered according to pedigree and agronomic features. The abil ity to distinguish among coffee accessions was greater for RAPD plus restriction digestion than for RAPD alone, providing evidences that the combination of the techniques was very efficient for the estimation of genetic relationship among C. arabica genotypes.
SAW

Token-based proximity authentication methods that authenticate users based on physical proximity are effortless, but lack explicit user intentionality, which may result in accidental logins. For example, a user may get logged in when she is near a computer or just passing by, even if she does not intend to use that computer. Lack of user intentionality in proximity-based methods makes them less suitable for multi-user shared computer environments, despite their desired usability benefits over passwords. We present an authentication method for desktops called Seamless Authentication using Wristbands (SAW), which addresses the lack of intentionality limitation of proximity-based methods. SAW uses a low-effort user input step for explicitly conveying user intentionality, while keeping the overall usability of the method better than password-based methods. In SAW, a user wears a wristband that acts as the user's identity token, and to authenticate to a desktop, the user provides a low-effort input by tapping a key on the keyboard multiple times or wiggling the mouse with the wristband hand. This input to the desktop conveys that someone wishes to log in to the desktop, and SAW verifies the user who wishes to log in by confirming the user's proximity and correlating the received keyboard or mouse inputs with the user's wrist movement, as measured by the wristband. In our feasibility user study (n=17), SAW proved quick to authenticate (within two seconds), with a low false-negative rate of 2.5% and worst-case false-positive rate of 1.8%. In our user perception study (n=16), a majority of the participants rated it as more usable than passwords.
Grey matter networks and clinical progression in subjects with predementia Alzheimer ’ s disease

We studied whether grey matter network parameters are associated with rate of clinical progression in non-demented subjects who have abnormal amyloid markers in the cerebrospinal fluid (CSF), that is, predementia Alzheimer’s disease. Non-demented subjects (62 with subjective cognitive decline; 160 with mild cognitive impairment (MCI); age = 68 ± 8 years; mini-mental state examination = 28 ± 2.4) were selected from the Amsterdam Dementia Cohort when they had abnormal amyloid in CSF (< 640 pg/mL). Networks were extracted from grey matter structural MRI, and nine parameters were calculated. Cox proportional hazards models were used to test associations between each connectivity predictor and rate of progression to MCI or dementia. After a median time of 2.2 years, 122 (55%) subjects showed clinical progression. Lower network parameter values were associated with increased risk for progression, with the strongest hazard ratio of 0.29 for clustering (95% confidence interval = 0.12 0.70; p < 0.01). Results remained after correcting for tau, hippocampal volume, and MMSE scores. Our results suggest that at predementia stages, grey matter network parameters may have use to identify subjects who will show fast clinical progression. m.tenkate-layout.indd 232 26/07/2018 17:32 233 Grey matter networks and clinical progression
Ethics as Aesthetic: A Computational Creativity Approach to Ethical Behavior

We address the question of how to build AI agents that behave ethically by appealing to a computational creativity framework in which output artifacts are agent behaviors and candidate behaviors are evaluated using a normative ethics as the aesthetic measure. We then appeal again to computational creativity to address the meta-level question of which normative ethics the system should employ as its aesthetic, where now output meta-artifacts are normative ethics and candidate ethics are evaluated using a meta-level-ethics-based aesthetic. We consider brieﬂy some of the issues raised by such a proposal as well as how the hybrid base-meta-level system might be evaluated from three different perspectives: creative, behavioral and ethical.
A 78 dB SNDR 87 mW 20 MHz Bandwidth Continuous-Time � ADC With VCO-Based Integrator and Quantizer Implemented in 0 . 13 � m CMOS

The use of a VCO-based integrator and quantizer within a continuous-time (CT) analog-to-digital converter (ADC) structure is explored, and a custom prototype in a 0.13 m CMOS with a measured performance of 81.2/78.1 dB SNR/SNDR over a 20 MHz bandwidth while consuming 87 mW from a 1.5 V supply and occupying an active area of 0.45 mm demonstrated. A key innovation is the explicit use of the oscillator’s output phase to avoid the signal distortion that had severely limited the performance of earlier VCO-based ADCs, which had made use of its output frequency only. The proposed VCO-based integrator and quantizer structure enables fourth-order noise shaping with only three opamp-based integrators.
How accurate are estimates of glacier ice thickness? Results from ITMIX, the Ice Thickness Models Intercomparison eXperiment

Abstract. Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.
A real-time logo detection system using data offloading on mobile devices

ABSTRACT In the past few years, mobile augmented reality (AR) has attracted a great deal of attention. It presents us a live, direct or indirect view of a real-world environment whose elements are augmented (or supplemented) by computer-generated sensory inputs such as sound, video, graphics or GPS data. Also, deep learning has the potential to improve the performance of current AR systems. In this paper, we propose a distributed mobile logo detection framework. Our system consists of mobile AR devices and a back-end server. Mobile AR devices can capture real-time videos and locally decide which frame should be sent to the back-end server for logo detection. The server schedules all detection jobs to minimise the maximum latency. We implement our system on the Google Nexus 5 and a desktop with a wireless network interface. Evaluation results show that our system can detect the view change activity with an accuracy of and successfully process 40 image processing jobs before deadline.
Distorted Voronoi languages

In a recent paper, Jager, Metzger, and Riedel (2011) study communication games of common interest when signals are simple and types complex. They characterize strict Nash equilibria as so-called Voronoi languages that consist of Voronoi tesselations of the type set and Bayesian estimators on the side of receivers. In this note, we introduce conflicts of interest in the same setting. We characterize strict Nash equilibria as distorted Voronoi languages that use all messages. For large conflicts, such informative equilibria need not exist. If the bias is sufficiently small, however, these equilibria do exist. This establishes the robustness of the results in Jager, Metzger, and Riedel (2011) to biased interests. We finally give examples of strict Nash equilibria, one of them using simulations to illustrate an equilibrium with many messages and non-uniformly distributed types.