Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting and measuring individual trees using an airborne laser scanner

High-resolution airborne laser scanner data offer the possibility to detect and measure individual trees. In this study, an algorithm which estimated position, height, and crown diameter of individual trees was validated with field measurements. Because all the trees in this study were measured on the ground with high accuracy, their positions could be linked with laser measurements, making validation on an individual tree basis possible. In total, 71 percent of the trees were correctly detected using laser scanner data. Because a large portion of the undetected trees had a small stem diameter, 91 percent of the total stem volume was detected. Height and crown diameter of detected trees could be estimated with a root-mean-square error (RMSE) of 0.63 m and 0.61 m, respectively. Stem diameter was estimated, using laser measured tree height and crown diameter, with an RMSE of 3.8 cm. Different laser beam diameters (0.26 to 3.68 m) were also tested, the smallest beam size showing a better detection rate in dense forest. However, estimates of tree height and crown diameter were not affected much by different beam size.
Effective Schools–Effective Superintendents: The Emerging Instructional Leadership Role

The national commission reports, which launched the most intensive and sustained effort to improve schools in America's history, confirmed the importance of instructional leadership. Although the role of the principal was initially emphasized, research studies on instructionally effective schools indicate that superintendents use their “bureaucratic” positions in the formal organization to improve instruction. They enact their instructional leadership roles through a broad array of activities including staff selection, principal supervision, establishing clear instructional goals, monitoring instruction, and financial planning for instruction to improve instruction. The concept of instructional leadership has moved beyond a simple description of the principal's role to understanding it as a multi-level, multidimensional, and highly interactive activity that may require a more consultative leadership style.
6-bit CMOS Digital Attenuators With Low Phase Variations for $X$-Band Phased-Array Systems

This paper presents 6-bit CMOS digital step attenuators with low phase variations. To mitigate the insertion phase variation of conventional switched Pi/T attenuators, the proposed attenuators employ a compensation circuit. This includes a low-pass filter for phase/amplitude correction. Analysis and comparison of two types of Pi-attenuators with the inductive and capacitive correction circuits are described. The two types of attenuators are fabricated using a 0.18-μm CMOS process. The attenuators have a maximum attenuation range of 31.5 dB with 0.5-dB steps (64 states). The attenuators with the inductive and capacitive correction structures, respectively, exhibit root mean square (rms) amplitude errors of less than 0.3 and 0.4 dB, and rms phase errors of less than 3.5° and 2° at 8-12 GHz. The insertion losses are 8.7 and 10.5 dB at 10 GHz, respectively. The input 1-dB compression points are 15 and 13 dBm at 10 GHz, and the total chip sizes, excluding pads, are 1.25 × 0.4 mm2 and 0.67 × 0.5 mm2.
Evaluation and selection of SaaS product based on user preferences

Software as a Service (SaaS) is a software delivery paradigm in which the product is not installed on-premise, but it is available on Internet and Web. The customers do not pay to possess the software itself but rather to use it. This concept of pay per use is very attractive. Hence, we see increasing number of organizations adopting SaaS. However, each customer is unique, which leads to a very large variation in the requirements off the software. As several suppliers propose SaaS products, the choice of this latter becomes a major issue. When multiple criteria are involved in decision making, we talk about a problem of "Multi-Criteria Decision-Making" (MCDM). Therefore, this paper presents a method to help customers to choose a better SaaS product satisfying most of their conditions and alternatives. Also, we know that a good method of adaptive selection should be based on the correct definition of the different parameters of choice. This is why we started by extraction and analysis the various parameters involved in the process of the selection of a SaaS application.
Visualizing Product Line Domain Variability by Aspect-Oriented Modeling

Modeling variability is a core problem in software product line engineering. The relationship between variability and commonality in a software product line bears strong similarities to the relationship between crosscutting concerns and core concerns in aspect-oriented modeling. So modeling variability with aspect-oriented techniques is an obvious idea which has been exploited before to some extent. In this paper, we propose a new approach to modeling and visualizing variability by a combination of aspect-oriented variability modeling with table-based modeling of configuration possibilities and constraints. As a modeling language, we use a slightly extended version of the ADORA language. Our main contributions are a visual, integrated model comprising both the commonality and the variability of the product line and a novel mechanism for synthesizing products from this model based on the aspect weaving capabilities of ADORA.
Examining Vision Sensor for Cm-Scale Robots

The capabilities of the Centeye Stonyman Vision Chip were examined on the Parallax BOE-bot platform. This paper describes the tests that were performed to determine the sensor’s suitability for controlling navigation in an unknown environment. M. Srinivasan’s Image Interpolation Algorithm was used to compute 1D optical flow. Parameters that affect the optical flow’s signal-to-noise ratio were manipulated to acquire a sufficiently strong signal and navigation of a testing arena and collision avoidance were demonstrated. Keywords—robot vision; miniature robot; obstacle avoidance
Genetic Mechanisms Underlying the Pathogenicity of Cold-Stressed Salmonella enterica Serovar Typhimurium in Cultured Intestinal Epithelial Cells

ABSTRACT Salmonella encounters various stresses in the environment and in the host during infection. The effects of cold (5°C, 48 h), peroxide (5 mM H2O2, 5 h) and acid stress (pH 4.0, 90 min) were tested on pathogenicity of Salmonella. Prior exposure of Salmonella to cold stress significantly (P < 0.05) increased adhesion and invasion of cultured intestinal epithelial (Caco-2) cells. This increased Salmonella-host cell association was also correlated with significant induction of several virulence-associated genes, implying an increased potential of cold-stressed Salmonella to cause an infection. In Caco-2 cells infected with cold-stressed Salmonella, genes involved in the electron transfer chain were significantly induced, but no simultaneous significant increase in expression of antioxidant genes that neutralize the effect of superoxide radicals or reactive oxygen species was observed. Increased production of caspase 9 and caspase 3/7 was confirmed during host cell infection with cold-stressed Salmonella. Further, a prophage gene, STM2699, induced in cold-stressed Salmonella and a spectrin gene, SPTAN1, induced in Salmonella-infected intestinal epithelial cells were found to have a significant contribution in increased adhesion and invasion of cold-stressed Salmonella in epithelial cells.
Application of Set Pair Analysis-Based Similarity Forecast Model and Wavelet Denoising for Runoff Forecasting

This study presents the application of a set pair analysis-based similarity forecast (SPA-SF) model and wavelet denoising to forecast annual runoff. The SPA-SF model was built from identical, discrepant and contrary viewpoints. The similarity between estimated and historical data can be obtained. The weighted average of the annual runoff values characterized by the highest connection coefficients was regarded as the predicted value of the estimated annual runoff. In addition, runoff time series were decomposed using wavelet transforms to acquire approximate and detailed runoff signals at various resolution levels. At each resolution level, threshold quantifications were performed by setting the values of a detailed signal below a fixed threshold to zero. The denoised runoff time series data were obtained from the approximation at the final resolution level and processed detailed signals using threshold quantification at all resolution levels of runoff by wavelet reconstruction. Instead of using the original annual runoff, the denoised annual runoff was applied to compute the similarity between estimated and historical data for model calibration. The original data were used for model calibration and validation; the denoised runoff data were used as input data to calibrate the model (obtaining different connection coefficients) that is then applied for validation purposes by using as benchmark the same original data. To verify the accuracy of the proposed method, the annual runoff data of six stations in Eastern Taiwan were analyzed. Based on a root mean square error (RMSE) criterion, the analytical results demonstrated that, for all six stations, the proposed method using denoised annual runoff outperformed the traditional SPA-SF model, using original annual runoff, because noise was effectively removed from the detailed data, using a constant threshold, thus enhancing the accuracy of the annual runoff forecasting for the SPA-SF model.
Semantic Web Techniques for Searching and Navigating Video Shots in BBC Rushes

In this paper we describe our approaches to creating content-based search and navigation systems that help a TV program or movie maker reuse rushes. We developed two prototypes: the first one uses traditional content-based search with fast browsing and the second one combines AJAX Web technology with Semantic Web technologies (RDF, SKOS) and content based multimedia information annotation and retrieval techniques (MPEG-7)
Utilization-aware load balancing for the energy efficient operation of the big.LITTLE processor

ARM's big.LITTLE architecture introduces the opportunity to optimize power consumption by selecting the core type most suitable for a level of processing demand. To take advantage of this new axis of optimization, we introduce processor utilization into the Linux kernel's load balancing algorithm. Our method improves the Linux kernel's ability to schedule tasks in an energy efficient manner without making it directly aware of the available core types. Experimental results show an energy consumption improvement over the standard Linux scheduler up to 11.35% with almost no reduction in performance.
Mobile robot emotion expression with motion based on MACE-GRACE model

We report in this paper an experiment conducted to evaluate the ability to express emotions of a mobile robot. Via the experiment, we show that a robot with musical-related motions could convey quite well its emotions to human, even better with appropriately accompanying music. Moreover, the result obtained suggests that even without facial expression, a robot could communicate quite well its emotion to human if its motion is well designed.
Fault Tolerance in the Cloud

Cloud Computing is an emerging and innovative platform, which makes computing and storage available to the end-users as services. The cloud is a “blob” of unstructured resources that are classified into three domains: (a) applications (or software), (b) platform, and (c) infrastructure. The cloud is a merger of business and computing models, which makes it a very important scientific and business medium for the end-users. Cloud Computing has established a widespread adoption in various domains, such as research, business, health, e-commerce, agriculture, and social life. Recently, cloud computing has increasingly been employed for a wide range of applications in various research domains, such as agriculture, smart grids, e-commerce, scientific applications, healthcare, and nuclear science. In the “Market Trends” report by Gartner, it is estimated that the cloud-based business services and Software-as-a-Service (SaaS) market will increase from $13.4 to $32.2 billion from 2011 to 2016. Similarly, Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) market is estimated to grow from $7.6 billion to $35.5 billion from 2011 to 2016. The cloud investments have delivered around $4 billion benefit yield in the last five years.
A CMA-ES super-fit scheme for the re-sampled inheritance search

The super-fit scheme, consisting of injecting an individual with high fitness into the initial population of an algorithm, has shown to be a simple and effective way to enhance the algorithmic performance of the population-based algorithm. Whether the super-fit individual is based on some prior knowledge on the optimization problem or is derived from an initial step of pre-processing, e.g. a local search, this mechanism has been applied successfully in various examples of evolutionary and swarm intelligence algorithms. This paper presents an unconventional application of this super-fit scheme, where the super-fit individual is obtained by means of the Covariance Adaptation Matrix Evolution Strategy (CMA-ES), and fed to a single solution local search which perturbs iteratively each variable. Thus, compared to other super-fit schemes, the roles of super-fit individual generator and global optimizer are switched. To prevent premature convergence, the local search employs a re-sampling mechanism which inherits parts of the best individual while randomly sampling the remaining variables. We refer to such local search as Re-sampled Inheritance Search (RIS). Tested on the CEC 2013 optimization benchmark, the proposed algorithm, named CMA-ES-RIS, displays a respectable performance and a good balance between exploration and exploitation, resulting into a versatile and robust optimization tool.
Robust stability analysis of FJR composite controller with a supervisory loop

In this paper, a controller design method for flexible joint robots (FJR), considering actuator saturation is proposed and its robust stability is thoroughly analyzed. This method consists of a composite control structure, with a PD controller on the fast dynamics and a PID controller on slow dynamics. Moreover, the need of powerful actuator is removed by decreasing the bandwidth of the fast controller during critical occasions, with the use of a supervisory loop. Fuzzy logic is used in the supervisory law, in order to adjust the proper gain in the forward path. It is then shown that UUB stability of the overall system is guaranteed in presence of uncertainties, provided that the PD and the PID gains are tuned to satisfy certain conditions.
Mean-field transport in stratified and/or rotating turbulence (Corrigendum)

Fig. 1. The coefficients α⊥, β⊥, β‖, and μ, as well as β⊥ and β C ‖ for the Roberts flow, calculated in the second-order correlation approximation, as functions of k⊥/kf , where kf = √ 2k0 is the effective wavenumber of the flow. Results obtained with kx = ky and k‖/k⊥ = 1/ √ 2 ≈ 0.7 or k‖/k⊥ = 1/16 √ 2 ≈ 0.004 are represented by open squares and dotted lines or by open diamonds and dashed lines, respectively. Results with kx/ky = 0.75 [k⊥ = (3, 4, 0)k1] or kx/ky = 5 [k⊥ = (5, 1, 0)k1] and k‖/k⊥ = 0.2 are indicated by open or filled circles, respectively. Orange and black symbols correspond to the first and second expressions for β⊥ and μ in (21) or for β⊥ in (26).
Acceptable Contradictions : Pragmatics or Semantics ?

Naive speakers find some logical contradictions acceptable, specifically borderline contradictions involving vague predicates such as Joe is and isn’t tall. In a recent paper, Cobreros et al. (J Philos Logic, 2012) suggest a pragmatic account of the acceptability of borderline contradictions. We show, however, that the pragmatic account predicts the wrong truth conditions for some examples with disjunction. As a remedy, we propose a semantic analysis instead. The analysis is close to a variant of fuzzy logic, but conjunction and disjunction are interpreted as intensional operators.
Modified Adaptive Filtering with Averaging Algorithm for Colored Noise Cancellation in Speech Signals

In many applications of noise cancellation, it has been noticed that the signal characteristics change quite rapidly. So noise cancellers, with short training time and high tracking rates, are needed. These requirements turn our attention to the utilization of adaptive algorithms with high convergence rates. The recursive least squares (RLS) algorithm is said to be the best choice, from this point of view, but this algorithm has high computational complexity and stability problems. Adaptive filtering with averaging (AFA) is used in such conditions, because of its high adaptation rate and low computational complexity as compared to RLS. A modification in existing AFA algorithm to improve the performance in terms of signal to noise ratio is examined (compared with AFA) and is illustrated on colored noise added to speech signals. KeywordsAdaptive Filtering with Averaging (AFA), Modified Adaptive Filtering with Averaging, Colored noise.
Biological drugs targeting the immune response in the therapy of psoriasis

Chronic plaque psoriasis affects more than 2% of world population, has a chronic recurrent behavior, gives a heavy burden to the patients’ quality of life, and hence remains a huge medical and social problem. The clinical results of conventional therapies of psoriasis are not satisfactory. According to the current knowledge of the molecular and cellular basis of psoriasis, it is defined as an immune-mediated chronic inflammatory and hyperproliferative skin disease. A new generation of biological drugs, targeting molecules and cells involved into perturbed pro-inflammatory immune response in the psoriatic skin and joints, has been recently designed and applied clinically. These biological agents are bioengineered proteins such as chimeric and humanized antibodies and fusion proteins. In particular, they comprise the antitumor necrosis factor-α agents etanercept, infliximab, and adalimumab, with clinical efficacy in both moderate-severe psoriasis and psoriatic arthritis, and the anti-CD11a efalizumab with selective therapeutic action exclusively in the skin. Here, we overview recent findings on the molecular pathways relevant to the inflammatory response in psoriasis and present our clinical experience with the drugs currently employed in the dermatologic manifestations, namely etanercept, infliximab, and efalizumab. The growing body of clinical data on the efficacy and safety of antipsoriasis biological drugs is reviewed as well. Particular focus is given to long-term safety concerns and feasibility of combined therapeutic protocols to ameliorate clinical results.
Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones

Previous work on the recognition of human movement patterns has mainly focused on movements of individuals. This paper addresses the joint identification of the indoor movement of multiple persons forming a cohesive whole - specifically a flock - with clustering approaches operating on features derived from multiple sensor modalities of modern smartphones. Automatic detection of flocks has several important applications, including evacuation management and socially aware computing. The novelty of this paper is, firstly, to use data fusion techniques to combine several sensor modalities (WiFi, accelerometer and compass) to improve recognition accuracy over previous unimodal approaches. Secondly, improve the recognition of flocks using hierarchical clustering. We use a dataset comprising 16 subjects forming one to four flocks walking in a building on single and multiple floors. With the best settings, we achieve a F-score accuracy of up to 87 percent an improvement of up to twelve percent points over existing approaches.
Rejecting mismatches of visual words by contextual descriptors

The Bag-of-Visual-Words model has become a popular model in image retrieval and computer vision. But when the local features of the Interest Points (IPs) are transformed into visual words in this model, the discriminative power of the local features are reduced or compromised. To address this issue, in this paper, we propose a novel contextual descriptor for local features to improve its discriminative power. The proposed contextual descriptors encode the dominant orientation and directional relationships between the reference interest point (IP) and its context. A compact Boolean array is used to represent these contextual descriptors. Our experimental results show that the proposed contextual descriptors are more robust and compact than the existing contextual descriptors, and improve the matching accuracy of visual words, thus make the Bag-of-Visual-Words model become more suitable for image retrieval and computer vision tasks.
A quantum secret ballot

The paper concerns the protection of the secrecy of ballots, so that the identity of the voters cannot be matched with their vote. To achieve this we use an entangled quantum state to represent the ballots. Each ballot includes the identity of the voter, explicitly marked on the "envelope" containing it. Measuring the content of the envelope yields a random number which reveals no information about the vote. However, the outcome of the elections can be unambiguously decided after adding the random numbers from all envelopes. We consider a few versions of the protocol and their complexity of implementation.
A Routing Method by Reactive Energy Decision in Wireless Sensor Networks

The wireless sensor network (WSN) was extensively deployed and researched recently. While taking the advantage of smaller dimension, lower cost and simple structure of the sensor nodes, more restrictions come with sensors. Therefore, the limited power of sensor nodes is the most direct and difficult problem we meet. The limitation on the energy of sensor nodes creates the bottlenecks when designing the suitable routing protocols. In order to solve the problem of limited energy, the loading of nodes has to be distributed as possible as it could. If the energy consumption can be shared averagely by most nodes, then the lifetime of sensor networks will be enlarged. Thus we propose the routing protocol called Reactive Energy Decision Routing Protocol (REDRP) for the sensor network which considers several representative routing protocols in different structures. This protocol will create the routes in a reactive routing method to transmit the data gathered from nodes and the REDRP will use the residual energy of nodes as the routing decision for energy-aware. The results of simulation show that the more fairness usage of sensor nodes, the more energy consumption of entire network will be distributed by our protocol and the longer the lifetime of nodes.