Miracle-dz/UniFAR-FTUs huggingface dataset:
DRIFT: Differentiated RF Power Transmission for Wireless Sensor Network deployment in the smart grid

Smart grid calls for low-cost, fine-grained and long-lasting monitoring solutions, to be able to provide reliable service to customers, enhance situational awareness capabilities and refine the operation of the grid and the microgrids, in addition to enabling prompt utility reaction to emergencies. Wireless Sensor Networks (WSNs) are promising candidates for monitoring the smart grid, given their capability to cover a large geographic region at low-cost. However, they do not provide long-lasting operation capability due to the limited battery lifetime of the sensors. Particularly, when sensors are deployed in hard-to-reach or hazardous environments, replacing the batteries of the sensors increase the cost of monitoring significantly. In the literature, energy-efficient protocols and ambient energy harvesting have been proposed to extend the lifetime of the sensors while neither of those offer a concrete solution for the smart grid. In this context, recent advances in Radio Frequency (RF) energy harvesting offers a unique solution to make WSNs operationally ready for smart grid monitoring tasks. Studies on RF energy harvesting have focused on uniform power delivery to all sensors, however it becomes essential to differentiate between critical zones and less critical zones in smart grid monitoring tasks. In this paper, we propose the Differentiated RF Power Transmission (DRIFT) scheme which is based on an Integer Linear Programming (ILP) model that maximizes the power received by the high priority sensor nodes. We compare the performance of DRIFT with a path length minimizing approach, namely Sustainable wireless Rechargeable Sensor network (SuReSense). We show that DRIFT is able to provide more power to high priority nodes than SuReSense. We also show that there is a tradeoff between power maximization and path length minimization.
Simulated Multispectral Imagery for Tree Species Classification Using Support Vector Machines

The information content of remotely sensed data depends primarily on the spatial and spectral properties of the imaging device. This paper focuses on the classification performance of the different spectral features (hyper- and multispectral measurements) with respect to three tree species. The Support Vector Machine was chosen as the classification algorithm for these features. A simulated optical radiation model was constructed to evaluate the identification performance of the given multispectral system for the tree species, and the effects of spectral-band selection and data preprocessing were studied in this setting. Simulations were based on the reflectance measurements of the pine (Pinus sylvestris L.), spruce [ Picea abies (L.) H. Karst. ], and birch trees (Betula pubescens Ehrh. and Betula pendula Roth). Leica ADS80 airborne sensor with four spectral bands (channels) was used as a fixed multispectral sensor system that leads to response values for the at-sensor radiance signal. Results suggest that this four-band system has inadequate classification performance for the three tree species. The simulations demonstrate on average a 5-15 percentage points improvement in classification performance when the Leica system is combined with one additional spectral band. It is also demonstrated for the Leica data that feature mapping through a Mahalanobis kernel leads to a 5-10 percentage points improvement in classification performance when compared with other kernels.
Structural Basis for TatA Oligomerization: An NMR Study of Escherichia coli TatA Dimeric Structure

Many proteins are transported across lipid membranes by protein translocation systems in living cells. The twin-arginine transport (Tat) system identified in bacteria and plant chloroplasts is a unique system that transports proteins across membranes in their fully-folded states. Up to date, the detailed molecular mechanism of this process remains largely unclear. The Escherichia coli Tat system consists of three essential transmembrane proteins: TatA, TatB and TatC. Among them, TatB and TatC form a tight complex and function in substrate recognition. The major component TatA contains a single transmembrane helix followed by an amphipathic helix, and is suggested to form the translocation pore via self-oligomerization. Since the TatA oligomer has to accommodate substrate proteins of various sizes and shapes, the process of its assembly stands essential for understanding the translocation mechanism. A structure model of TatA oligomer was recently proposed based on NMR and EPR observations, revealing contacts between the transmembrane helices from adjacent subunits. Herein we report the construction and stabilization of a dimeric TatA, as well as the structure determination by solution NMR spectroscopy. In addition to more extensive inter-subunit contacts between the transmembrane helices, we were also able to observe interactions between neighbouring amphipathic helices. The side-by-side packing of the amphipathic helices extends the solvent-exposed hydrophilic surface of the protein, which might be favourable for interactions with substrate proteins. The dimeric TatA structure offers more detailed information of TatA oligomeric interface and provides new insights on Tat translocation mechanism.
Dense optical flow in stabilized scenes for moving object detection from a moving camera

This paper proposes a method for detecting moving objects appeared in video captured by a moving camera. The proposed method relies on dense optical flow to differentiate moving objects from static background. Whenever video taken from a static camera is used, the dense optical flow itself is sufficient to determine the moving object in the scenes. However, in a non-static camera, all pixels are moving making which lead to incapability of optical flow to differentiate the moving objects from the static background. In order to solve this problem, a stabilization method is incorporated by the mean of global motion extraction, which can be done by analyzing the homography transformation between two consequtive frames. Finally, by applying a threshold on the dense optical flow, the region of moving object is acquired. The proposed method has been evaluated in the experiments and produce satisfying results with 98% accuracy.
Paraphyly of the Subgenus Sintonius (Diptera, Psychodidae, Sergentomyia): Status of the Malagasy Species. Creation of a New Subgenus and Description of a New Species

During an inventory of Phlebotomine sand flies carried out in Madagascar, we have identified some specimens showing morphological characters related to the subgenus Sintonius of the genus Sergentomyia. We started a molecular study based on cytochrome b mtDNA and on D1–D2 and D8 domains of the rDNA. The sampling includes all the Sergentomyia species available and also S. (Sergentomyia) schwetzi, S. (Parrotomyia) magna, and the following species belonging to the subgenus Sintonius: S. clydei, S. christophersi, S. affinis vorax, S. adleri and S. meilloni. The Sintonius subgenus (sensu Theodor) is paraphyletic. The Malagasy specimens morphologically Sintonius-like are never clustered with the continental Sintonius. We propose a new subgenus to include them: Trouilletomyia subg. nov. Due to the lack of mesanepisternal setae, the species huberti is removed from the genus Phlebotomus and we propose here a new combination: Sergentomyia huberti comb. nov. The male of S. huberti is pinpointed and described for the first time. Lastly, a new species for Science is described on one female: Sergentomyia (Trouilletomyia) boironis n. sp.
A Cloud Based Architecture for Distributed Real Time Processing of Continuous Queries

With the rapid pace of technological advancements in smart device, sensor and actuator technologies, the Internet of Things (IoT) domain has received significant attention. These advances have brought us closer to the ubiquitous computing vision. However, in order to fully realize this vision, devices and applications should rapidly adapt to the changes in the environment and other nearby devices. Most of the existing applications store collected data in a data store and allow users to query stored data to notice and react to such changes. Many of the applications utilize cloud and big data technologies for scalability. Nevertheless, the responsiveness of such IoT applications is largely limited due to polling based queries. Therefore, in this paper, we propose a centrally managed distributed infrastructure based on the state of the art big data technologies. We focus primarily on the problem of how to process multiple continuous queries in real time and notify users timely. Our main contribution is specifying a generic and scalable architecture to process a multitude of real time queries. We provide a prototype implementation to demonstrate the applicability of the approach. The scalability of the architecture is evaluated by conducting several experiments on the prototype implementation.
The curious case of parallel connections in HTTP/2

Web pages and web-based services are becoming more and more complex. The average page size for the Alexa top 1000 websites in 2016 has reached 2.1 MB and fetching a page requires requests for 128 different objects. Although the bandwidth has been increasing exponentially in the last few years, the web experience is not improving at the same pace because of latency issues in HTTP/1. The HTTP/2 protocol aims to solve these issues by allowing clients and servers to multiplex HTTP requests and responses on a single TCP connection. If HTTP/2 is widely adopted, it can have enormous benefits not only for the user experience, but also for the servers and the network. Since clients do not have to open multiple parallel connections to avoid the problem of head-of-line blocking in HTTP/1.1, the number of concurrent TCP sessions can be significantly reduced. However, although multiplexing is one of the main features of HTTP/2, nothing actually prevents a client from opening multiple HTTP/2 connections to a server. In this paper we investigate the behavior of HTTP/2 traffic in the wild. We perform experiments to examine if web browsers use a single connection per domain over HTTP/2 in practice. Contrary to popular belief, our experiments on the traffic of a large university campus network and a residential network show that a significant number of HTTP/2 accesses are performed using parallel connections to a single domain on a server. We present two possible hypotheses for this behavior and discuss its implications for the future of the web.
Developmental Interactive Bibliotherapy: Feminist Methodology in an Undergraduate Course

The recent emphasis on the diverse needs of learners underscores the necessity for using alternative practices in undergraduate content class rooms (Kussrow 1997). Cognitive modes of instruction typically regulate the acquiring of knowledge. If students are to use their full potential to learn subject content, however, they must also make conscious use of their affective inner resources such as personal responses, intrinsic motivation, and prior experiences (F? rber and Armaline 1992; Frager 1993). Feminist pedagogy has been instru mental in promoting the notion of the complex learner while providing inclu sive and holistic theoretical perspectives and educational practices. The essential relationship between the affective and cognitive dimensions of learning is cen tral to feminist education. In particular, Maher (1985) stated that "feminist peda gogy stresses the need for students to draw on their own personal and intellec tual experiences to build a satisfying ver sion of the subject, one that they can use productively in their own lives" (30). That important connection between the personal and the intellectual emphasized in feminist education also exists in devel
School Choice: Do We Know Enough?

Martin Carnoy's article ("School Choice? Or is it Privatization? " October 2000 Educational Researcher) is an informative, well-constructed piece that complements my earlier review of the literature on school choice ("School Choice: An Examination of the Empirical Evidence on Achievement, Parental Decision Making, and Equity," December 1999 Educational Researcher). In particular, Carnoy asks many important questions about how publicly funded, privately delivered education (e.g., through a voucher system) would impact educational achievement and equity. The issues raised in Carnoy's article on privatization are important in informing the policy debate about vouchers. However, given the paucity of evidence on the impact of vouchers, I disagree with Carnoy's view that increased school choice and competition "likely does little or nothing to improve overall student achievement" (p. 19). I do not necessarily think that increased choice and competition would lead to better outcomes; rather I maintain that we do not know enough about how different voucher designs would impact educational and social outcomes in various contexts to form a conclusion pro or con. In this response to the Carnoy article, I argue that it is premature to judge choice and competition, and further, that we should not be bounded by perceived political constraints when analyzing public policies designed to increase choice and competition.
WilcoxCV: an R package for fast variable selection in cross-validation

UNLABELLED In the last few years, numerous methods have been proposed for microarray-based class prediction. Although many of them have been designed especially for the case n << p (much more variables than observations), preliminary variable selection is almost always necessary when the number of genes reaches several tens of thousands, as usual in recent data sets. In the two-class setting, the Wilcoxon rank sum test statistic is, with the t-statistic, one of the standard approaches for variable selection. It is well known that the variable selection step must be seen as a part of classifier construction and, as such, be performed based on training data only. When classifier accuracy is evaluated via cross-validation or Monte-Carlo cross-validation, it means that we have to perform p Wilcoxon or t-tests for each iteration, which becomes a daunting task for increasing p. As a consequence, many authors often perform variable selection only once using all the available data, which can induce a dramatic underestimation of error rate and thus lead to misleadingly reporting predictive power. We propose a very fast implementation of variable selection based on the Wilcoxon test for use in cross-validation and Monte Carlo cross-validation (also known as random splitting into learning and test sets). This implementation is based on a simple mathematical formula using only the ranks calculated from the original data set. AVAILABILITY Our method is implemented in the freely available R package WilcoxCV which can be downloaded from the Comprehensive R Archive Network at http://cran.r-project.org/src/contrib/Descriptions/WilcoxCV.html.
Background Modelling on Tensor Field for Foreground Segmentation

The paper proposes a new method to perform foreground detection by means of background modeling using the tensor concept. Sometimes, statistical modelling directly on image values is not enough to achieve a good discrimination. Thus the image may be converted into a more information rich form, such as a tensor field, to yield latent discriminating features. Taking into account the theoretically well-founded differential geometrical properties of the Riemannian manifold where tensors lie, we propose a new approach for foreground detection on tensor field based on data modeling by means of Gaussians mixtures directly on tensor domain. We introduced a online Kmeans approximation of the Expectation Maximization algorithm to estimate the parameters based on an Affine-Invariant metric. This metric has excellent theoretical properties but essentially due to the space curvature the computational burden is high. We propose a novel Kmeans algorithm based on a new family of metrics, called Log-Euclidean, in order to speed up the process, while conserving the same theoretical properties. Contrary to the affine case, we obtain a space with a null curvature. Hence, classical statistical tools usually reserved to vectors are efficiently generalized to tensors in the Log-Euclidean framework. Theoretical aspects are presented and the Affine-Invariant and Log-Euclidean frameworks are compared experimentally. From a practical point of view, results are similar to those of the Affine-Invariant framework but are obtained much faster. Theoretic analysis and experimental results demonstrate the promise and effectiveness of the proposed framework.
RMIT University at TREC 2008: Legal Track

Abstract : This paper reports on the participation of RMIT university in the 2008 TREC Legal Track Ad Hoc task. OCR errors can corrupt the document view formed by an information retrieval system, and substantially hinder the successful retrieval of relevant documents for user queries. In previous research, the presence of errors in OCR text was observed to lead to unstable and unpredictable retrieval effectiveness. In this study, we investigate the effects of OCR error minimization - through de-hyphenation of terms, and the removal of corrupted or "noise" terms - on retrieval performance. Our results indicate that removing noise terms can lead to significant savings in terms of index size.
Joint pronunciation modelling of non-native speakers using data-driven methods

Modelling non-native speakers with different mother tongues is a difficult task for automatic speech recognition due to the large variation among speakers. One possibility for jointly modelling all speakers is to use the same speaker independent acoustic models and a joint lexicon to capture the variation. We have modified the reference lexicon using pronunciation rules that are derived in a totally data-driven manner from a set of adaptation data using the reference recognizer and the reference lexicon. Deriving common rules for such diverse sources simultaneously is difficult. The challenge is to combine these rules to a common set without increasing the confusability. In this paper we compare several methods of combining the individual rules to form a common lexicon for all speakers. Using a new log likelihood rule pruning measure presented in this paper, we achieved improved performance compared with more traditional rule pruning methods based on rule probability, and with much fewer rules. With a confusability reduction scheme we reduced the number of rules even further.
Parallel Estimation of Distribution Algorithms

The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the generality of various probabilistic models by comparing the properties of underlying mappings. Also, some convergence issues are discussed and theoretical ways for further improvements are proposed. 2. Development of new probabilistic model and methods capable of dealing with continuous parameters. The resulting Mixed Bayesian Optimization Algorithm (MBOA) uses a set of decision trees to express the probability model. Its main advantage against the mostly used IDEA and EGNA approach is its backward compatibility with discrete domains, so it is uniquely capable of learning linkage between mixed continuous-discrete genes. MBOA handles the discretization of continuous parameters as an integral part of the learning process, which outperforms the histogram-based approach. The original metrics for MBOA is derived as the incremental version of Bayesian Dirichlet metrics (BDe). Its usefulness for modelling of Boolean functions is also investigated and confirmed. 3. Parallelization of EDA algorithms. Different versions of parallel EDA algorithms are proposed for fine-grained, coarse-grained and multithreaded environment. All these algorithms use the original principle of restricted ordering of nodes in Bayesian network to minimize the communication between parallel processes: • The pipelined Parallel Bayesian Optimization algorithm (PBOA) is proposed and simulated for fine-grained type of parallelism. The target platform for its implementation can be Transputers, or one-purpose MIMD processor. • Distributed Bayesian optimization algorithm (DBOA) is proposed and implemented for coarse-grained type of parallelism. The experiments are running on the uniform cluster of Sun Ultra 5 workstations connected by fast Ethernet switch. Both PBOA and DBOA are based on the parallelization of classical Pelikan's BOA code. • The multithreaded MBOA algorithm, which uses original MBOA code with mixed decision trees model, was simulated in Transim tool. In all parallel algorithms the additional progress towards realtime performance can be achieved by choosing the KBOA version of BOA algorithm. The concept of KBOA is based on the partial (local) information about the linkage. This information is used to adjust the prior of probabilistic model and injection of building blocks is used to improve the quality of initial population. 4. Extension …
Approximating the Independence Number via the -function Extended Abstract

We study the relationship between the independence number of a graph and its semi-deenite relaxation, the Lovv asz-function. We deduce an improved approximation algorithm for the independence number. If a graph on n vertices has an independence number n=k + m, for some xed integer k 3 and some m > 0, the algorithm nds, in random polynomial time, an independent set of size ~ (m 3=(k+1)). This is the rst improvement upon the Ramsey Theory based algorithm of Boppana and Halldorsson that nds an independent set of size (m 1=(k? 1)) in such a graph. The algorithm is based on semi-deenite programming, some properties of the-function, and the recent algorithm of Karger, Motwani and Sudan for approximating the chromatic number of a graph. If the-function of an n vertex graph is at least Mn 1? 2=h , for some absolute constant M, we describe another, related algorithm that nds an independent set of size h. Finally, while it is easy to see that the condition (G) n(1 +)=2, > 0, guarantees the existence of linear-sized independent subsets, we describe graphs G with (G) n(1 ? )=2 and sublinear independence number.
Scalable clustering and applications

Large scale machine learning is becoming an active research area recently. Most of the existing clustering algorithms cannot handle big data due to its high time and space complexity. Among the clustering algorithms, eigen vector based clustering, such as Spectral clustering, shows very good accuracy, but it has cubic time complexity. There are various methods proposed to reduce the time and space complexity for eigen decomposition such as Nyström method, Lanc-zos method etc. Nyström method has linear time complexity in terms of number of data points, but has cubic time complexity in terms of number of sampling points. To reduce this, various Rank k approximation methods also proposed, but which are less efficient compare to the normalized spectral clustering. In this paper we propose a two step algorithm for spectral clustering to reduce the time complexity toO(nmk + m2k'), by combining both Nyström and Lanczos method, where k is the number of clusters and k' is the rank k approximation of the sampling matrix (k < k' << m << n). It shows very good results, with various data sets, image segmentation problems and churn prediction of a telecommunication data set, even with very low sampling (for 10 Million × 10 Million matrix, sampled only 100 columns) with lesser time, which confirms the validity of the algorithm.