Miracle-dz/UniFAR-FTUs huggingface dataset:
Receiver architectures for UWB-based transmit-only sensor networks

We are interested in designing a low-cost, ultra-wide-band (UWB) sensor network, that consists of a large number of sensor nodes (SNs), which sense the environment and transmit the resulting data via a wireless radio link to so-called cluster heads (CHs). We focus on low-cost transmit-only SNs; the resulting uncoordinated multiuser-access causes packet collisions. To improve the robustness to packet collision, we propose two novel low-complexity noncoherent receiver architectures: (i) the adaptive CH receiver (CHR) that receives only packets whose signal strength is larger than an adaptive threshold; and (ii) the switching CHR that allows the focus to be switched from a packet of a given SN to a packet from another SN with higher signal strength. Using simulations, we show that the number of received packets during burst periods can be dramatically increased with the adaptive CHR, and even more so with the switching CHR.
A comparative study of fuzzy and neural network approaches to discriminant analysis with linguistic variables

This paper proposes a fuzzy discriminant analysis to solve the two -group classification problem where the measured variables are linguistic in nature. Especially u nder imprecise framework, the linguistic variables capture more information although vagueness is inhe rent. In analogy to classical statistics, a fuzzy linear discriminant function is introduced here, which directly deals with continuous fuzzy numbers as t he representative of linguistic values to obtain fuzzy scores for classification. To make a comparative study, the backpropagation neural ne twork approach has also been studied in this paper. Finally admission to management programme is considered as an example of the application on two-level classification problem of the proposed method.
Using Patterns for Keyword Search in RDF Graphs

An increasing number of RDF datasets are available on the Web. Querying RDF data requires the knowledge of a query language such as SPARQL; it also requires some information describing the content of these datasets. The goal of our work is to facilitate the querying of RDF datasets, and we present an approach for enabling users to search in RDF data using keywords. We introduce the notion of pattern to integrate external knowledge in the search process, which increases the quality of the results.
Essays on agricultural externalities and benefit transfer of recreational fishing value

This dissertation explores the physical and economic aspects of agricultural externalities and the economic value of marine and stream recreational fishing. The effects of pesticide use and tillage practice on water treatment costs and pesticide contamination in treated water are empirically investigated in the first essay. The economic value of marine recreational fishing, the value of access to fishing sites and fishing quality improvements, is examined in the second essay. The third essay examines the economic value of stream recreational fishing: the value of fishing trips and water quality improvements. To evaluate the value of recreational fishing, both second and third essays use benefit transfer techniques. The first essay, Empirical Investigation of Agricultural Externalities: Effects of Pesticide Use and Tillage System on Surface Water Quality and Treatment Costs, focuses on the off-farm water quality and water treatment cost effects of upstream and nearby agricultural practices (pesticide use and tillage system), specifically on the pesticide contamination in finished public surface water and water treatment costs in the Maumee River Basin, a major tributary to Lake Erie, located in northwestern Ohio, northeastern Indiana, and southeastern Michigan. Pesticide contamination level in treated surface water and average chemical cost of treating surface water are related to farming
Application of SVD-based sparsity in compressed sensing susceptibility weighted imaging

Long scan time has hampered susceptibility weighted imaging (SWI) in routine clinical application to diagnose brain diseases related to venous vasculature. Compressed sensing (CS) was demonstrated to significantly reduce scan time of SWI by exploiting signal sparsity in wavelet domain. However the reconstruction time of CS based on wavelet sparsity is usually time consuming. In this study, the feasibility of applying CS in SWI with singular value decomposition (SVD)-based sparsity basis was investigated. It was found that CS reconstruction based on SVD sparsity basis can achieve reasonably high computing speed than that of wavelet-based sparsity basis, while still achieving accurate image reconstruction.
The Effect of Corporate Break-Ups on Information Asymmetry: A Market Microstructure Analysis

This paper investigates the information environment during and after a corporate break-up utilizing direct measures of information asymmetry developed in the market microstructure literature. The analysis is based on all corporate break-ups in the United States in the period 1995-2005. The results document that information asymmetry declines significantly as a result of a break-up. However, this reduction takes place not at the time of its announcement or its completion, but after it has been fully consummated. At the same time, not all investors are equally affected, but informed investors who generate private information by skilled analysis of public information come to play a more important role compared to traditional corporate insiders. This might explain why financial advisors promote break-ups among their corporate clients, as they are likely beneficiaries. The positive stock-market reaction to break-up announcements is significantly related to reductions in insider-related information asymmetry, indicating that the advantage of skilled information analysts does not offset the overall improvement in the information environment due to a break-up.
International Conference on Intelligent and Advanced Systems 2007 Multi Objective Optimization of Anfis Structure

This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). This approach based on multi objective optimization mechanism for training parameters in antecedent part. It considers two cost functions as the objectives which are the maximum difference measurements between the real nonlinear system and the nonlinear model, and training mean square error (MSE). The NSGA-II is the multi objective optimization algorithm which employed for this purpose. So we use gradient decent (GD) method for training all parameters in conclusion part. Finally we show simulation results of applied this method to some nonlinear identification system.
The Performance Optimization of Hadoop during Mining Online Education Packets for Malware Detection

Network security is becoming more and more important because there is a lot of anomaly traffic information in some websites such as online education. In order to detect the anomaly traffic effectively, we can use some cloud computing tools such as Hadoop. For the sake of good performance of Hadoop, we need to do performance tuning. This paper mainly focused on the two aspects of performance tuning: 1. This paper deeply researched the phases of Hadoop (like Map and Reduce) and the effective of Hadoop had been greatly improved with the help of performance tuning. 2. This paper pointed out a compressed Trie algorithm to replace the hash table during the map phase and solved the problem of low efficiency.
Vouchers and Caseworkers in Public Training Programs: Evidence from the Hartz Reform in Germany

This paper studies the role of training vouchers and caseworkers in public training programs. Using a rich administrative data set, we apply matching and regression methods to measure the effect of the Hartz reform in Germany, which introduced training vouchers and imposed more selective criteria on participants. Besides estimating the overall reform effect, we isolate the effect induced by changes in the composition of program participants due to stricter selection by the caseworkers (selection effect) from the effect based on the introduction of vouchers (voucher effect). Analyzing the most important type of training in Germany, we find a slightly positive impact of the reform. Our decomposition results suggest that the selection effect is ? if at all ? slightly negative, and that the voucher effect increased both, the employment probability and earnings of the participants.
Cloud-hosted key sharing towards secure and scalable mobile applications in clouds

User data may be stored in a cloud to take advantage of its scalability, accessibility, and economics. However, data of a sensitive nature must be protected from being read in the clear by an untrusted cloud provider. It is also beneficial to provide finite time limits on access to the data by users. A key management scheme is proposed where encrypted key shares are stored in the cloud and automatically deleted based on passage of time or user activity. The accessibility of the data gradually expires and revocation occurs as a result of the loss of sufficient key shares. The process does not require additional coordination by the data owner, which is of advantage to a very large population of resource-constrained mobile users. The rate of expiration may be controlled through the initial allocation of shares and the heuristics for removal. Subscription to user data is maintained through regular re-generation of shares. A simulation of the scheme and also its implementation on commercial mobile and cloud platforms demonstrate its practical performance.
Long-term results of intraoperative electron beam radiation therapy for nonmetastatic locally advanced pancreatic cancer

AbstractTo assess prognostic benefits of intraoperative electron beam radiation therapy (IOERT) in patients with nonmetastatic locally advanced pancreatic cancer (LAPC) and evaluate optimal adjuvant treatment after IOERT.A retrospective cohort study using prospectively collected data was conducted at the Cancer Hospital of the Chinese Academy of Medical Sciences, China National Cancer Center.Two hundred forty-seven consecutive patients with nonmetastatic LAPC who underwent IOERT between January 2008 and May 2015 were identified and included in the study. Overall survival (OS) was calculated from the day of IOERT. Prognostic factors were examined using Cox proportional hazards models. The 1-, 2-, and 3-year actuarial survival rates were 40%, 14%, and 7.2%, respectively, with a median OS of 9.0 months. On multivariate analysis, an IOERT applicator diameter < 6 cm (hazards ratio [HR], 0.67; 95% confidence interval [CI], 0.47–0.97), no intraoperative interstitial sustained-release 5-fluorouracil chemotherapy (HR, 0.46; 95% CI, 0.32–0.66), and receipt of postoperative chemoradiotherapy followed by chemotherapy (HR, 0.11; 95% CI, 0.04–0.25) were significantly associated with improved OS. Pain relief after IOERT was achieved in 111 of the 117 patients, with complete remission in 74 and partial remission in 37. Postoperative complications rate and mortality were 14.0% and 0.4%, respectively. Nonmetastatic LAPC patients with smaller size tumors could achieve positive long-term survival outcomes with a treatment strategy incorporating IOERT and postoperative adjuvant treatment.Chemoradiotherapy followed by chemotherapy might be a recommended adjuvant treatment strategy for well-selected cases. Intraoperative interstitial sustained-release 5-fluorouracil chemotherapy should not be recommended for patients with nonmetastatic LAPC.
Effects of anti-poverty programs on electoral behavior : evidence from the Mexican Education, Health, and Nutrition Program

Ever since Latin American economies collapsed in the 1980s and early 1990s, traditional redistributive programs began to coexist with new anti-poverty programs that usually took the form of conditional cash transfers (CCT). I examine the effects of the Mexican Education, Health, and Nutrition program (Progresa), the first and largest CCT implemented in the region, on electoral behavior. I argue that Progresa not only was substantially different from traditional clientelism, but that it challenged local monopolies on political power by increasing voter's income and giving recipients implicit and explicit information about its non-political nature. This weakening of monopolies, in turn, gave political parties incentives to compete for the votes of Progresa recipients. As a consequence, recipients increased their electoral participation, at least in the short term, and clientelism was irrevocably eroded. Despite the increased competition, however, recipients rewarded parties that proposed and retained Progresa. My understanding of Progresa's electoral effects is based on theory, field research on four villages, interviews with Progresa's designers and personnel, and analysis of media sources from 1996 until 2003. To test this argument, I use the Mexico 2000 Panel Study; aggregate data at the municipality level from 1997-2003; and to explicitly deal with the historic correlation between poverty, rural residence, and support for the seventy-year incumbent party, Institutional Revolutionary Party, I take advantage of the fact that early assignment of program benefits included a randomized component originally designed to evaluate the program effects on schooling and health. Thesis Supervisor: Chappell Lawson Title: Associate Professor of Political Science
The effect of condensed tannins in Lotus pedunculatus on the digestion and metabolism of methionine, cystine and inorganic sulphur in sheep

Two experiments were conducted with sheep fed on fresh Lotus pedunculatus containing 50–55 g condensed tannin (CT)/kg dry matter. Effects of CT were assessed by comparing control sheep (CT operating) with sheep receiving a continuous intraruminal infusion of polyethylene glycol (PEG) to bind and inactivate CT. Digestion of methionine and cystine was determined using a continuous intraruminal infusion of indigestible markers, whilst plasma irreversible loss (IRL) of methionine, cystine and inorganic sulphate was determined using 35S labelling. The proportion of microbial non-NH3-N (NAN) in whole rumen digesta NAN and the IRL of reducible S from the rumen were determined using a continuous intraruminal infusion of (NH4)235SO4. The proportion of microbial NAN in whole rumen digesta NAN (0·44 v. 0·71) and the IRL of reducible S from the rumen (0·84 v. 2·49 g S/d) were lower in control than PEG sheep. PEG sheep lost 30% of ingested methionine and cystine across the rumen, whereas the control sheep lost no methionine and cystine across the rumen. Apparent absorption of methionine from the small intestine was 27% higher in control than PEG sheep, but both groups had a similar apparent absorption of cystine. The apparent digestibility of cystine in the small intestine was lower in control (0·42) than PEG (0·53) sheep, whereas the apparent digestibility of methionine was similar (0·78) for both groups. CT had no effect on plasma methionine IRL, but markedly increased the IRL of cystine (39·8 v. 22·4 μmol/min) and reduced the IRL of plasma inorganic sulphate (35·9 v. 50·2 μmol/min). A three-pool model comparing interconversions between the three plasma metabolites showed that CT increased the flow of cystine to body synthetic reactions (36·5 v. 17·3 μmol/min). This was due to trans-sulphuration of methionine to cystine being greater in control than in PEG sheep, whilst the oxidation of both methionine and cystine were reduced in control sheep. It was concluded that CT reduced the proteolysis of forage protein and the degradation of S amino acids to inorganic sulphide in the rumen, resulting in increased net absorption of methionine and increased utilization of cystine for body synthetic reactions in sheep with a high capacity for wool growth (and, hence, high cystine requirement).
The Contribution of Economic Indicator Analysis to Understanding and Forecasting Business Cycles

This paper reviews major features of the development of economic indicator analysis (EIA), notably its contribution to identifying, understanding, explaining and forecasting business cycles. The paper highlights the substantial pioneering role of Dr Geoffrey H. Moore in this development. The paper reviews some key issues regarding the selection and classification of economic indicators; and the methodologies developed to use these indicators to identify and measure business cycles on national, regional and sectoral bases. After making an overall assessment of EIA, acknowledgement is given to the widespread development and applications of EIA around the world to study the co-movements of key economic variables and to forshadow changes in them.
A simple robot paths planning based on Quadtree

Based on traditional methods, according with practical application conditions, and the special structure of Quadtree, this paper gives a practical method of paths planning for the further expansion of Quadtree in the field of paths planning, except for segmentation obstacles. The experimental and simulation results demonstrate that the algorithm described in the text could be able to find all the relevant paths correctly, and make the better choice rapidly.
Productivity and Economic Growth in Latin America: The Stochastic Production Frontier Approach

This paper looks into the total factor productivity performance and economic growth of Latin America. A stochastic production frontier function was estimated as a translog leading to technical inefficiency in a set of 19 Latin American countries over the period 1961â€“1990. Using the Malmquist productivity index, productivity growth was decomposed into two components: variation in technology and change in technical efficiency. The application of this technique makes it possible to quantify the contribution of productivity to economic growth in Latin America, to identify sources of technical production inefficiency and to understand the factors determining the performance of the Latin American economies. The most important conclusion of the study is that the total factor productivity performance was the chief cause of the low economic growth observed in Latin America for the period in question
NGC 2419, M92, and the age gradient in the galactic halo

The WFPC2 camera on HST has been used to obtain deep main sequence photometry of the low-metallicity ([Fe/H]=-2.14), outer-halo globular cluster NGC 2419. A differential fit of the NGC 2419 CMD to that of the similarly metal-poor \ standard cluster M92 shows that they have virtually identical principal sequences and thus the same age to well within 1 Gyr. Since other low-metallicity clusters throughout the Milky Way halo have this same age to within the 1-Gyr precision of the differential age technique, we conclude that the earliest star (or globular cluster) formation began at essentially the same time everywhere in the Galactic halo throughout a region now almost 200 kpc in diameter. Thus for the metal-poorest clusters in the halo there is no detectable age gradient with Galactocentric distance. To estimate the absolute age of NGC 2419 and M92, we fit newly computed isochrones transformed through model-atmosphere calculations to the (M_V,V-I) plane, with assumed distance scales that represent the range currently debated in the literature. Unconstrained isochrone fits give M_V(RR) = 0.55 \pm 0.06 and a resulting age of 14 to 15 Gyr. Incorporating the full effects of helium diffusion would further reduce this estimate by about 1 Gyr. A distance scale as bright as M_V(RR) = 0.15 for [Fe/H] = -2, as has recently been reported, would leave several serious problems which have no obvious solution in the context of current stellar models.
Modelling and estimating the energy consumption of embedded applications and operating systems

This paper presents a multi-layer power and energy estimation approach for embedded applications and operating systems. The multi-layer methodology is an incremental approach where we consider that energy can be summed over the hyper period of the real-time system. At the first layer, we start by estimating energy and power consumption of standalone tasks. At the following layers, we add energy overheads of embedded operating system services such as timer interrupt, inter process communications (IPC)) and peripheral devices accesses. We illustrate our Multi-layer approach by estimating the energy consumption of an M-JPEG encoder running on linux 2.6 and deployed on a XUP Virtex-II pro development board.
Negotiation-based privacy preservation scheme in internet of things platform

Internet of things (IoT) brings forth a coupling among diverse sensors and diverse domains of applications and services. Sensors primarily provide the sensed observations, which are utilized by various applications and services. Applications are meant to serve personalized needs of the user, based on their various requirements. Therefore, in IoT, the challenge is to provide a platform to facilitate the interoperation among the diverse domains of applications, various heterogeneous devices such as sensors, actuators, gateways, and to preserve privacy while receiving and sharing the sensed observation from and with the multiple data sources and sinks. This paper discusses the characteristics and vision of the IoT system and presents a privacy preservation framework as a part of the IoT platform including a data masking tool for both privacy and utility preservation. The proposed framework provides negotiation--based architecture to find a solution for utility-privacy tradeoff in IoT data management. A case study on data privacy problem in e-health is presented along with a discussion for future research scope and conclusion.
Generalized Baum-Welch Algorithm and its Implication to a New Extended Baum-Welch Algorithm

This paper describes how we can use the generalized BaumWelch (GBW) algorithm to develop better extended BaumWelch (EBW) algorithms. Based on GBW, we show that the backoff term in the EBW algorithm comes from KL-divergence which is used as a regularization function. This finding allows us to develop a fast EBW algorithm, which can reduce the time of model space discriminative training by half, without incurring any degradation on recognition accuracy. We compare the performance of the new EBW algorithm with the original one on various large scale systems including Farsi, Iraqi and modern standard Arabic ASR systems. Index Terms: speech recognition, discriminative training
On-line SVM traffic classification

A wide range of traffic classification approaches has been proposed in the last few years by the scientific community. However, the development of complete classification architectures that work directly in real-time on high capacity links is limited. In this paper we present the implementation of a machine-learning technique (SVM), one of the most accurate but most computationally expensive mechanisms, on the CoMo project infrastructure. We show the computational time required to process different traffic traces and the optimization steps we adopted to improve the performance of the system and achieve real-time classification on high-speed links.
AUTOMATED SYSTEM FOR THERMOMETER CALIBRATION

Calibration of thermometers is timeconsuming process that requires short-term temperature stability during multiple measurements at given temperature points. In most laboratories, thermometer calibration process is not automated, requiring the presence of the laboratory assistant during the calibration and for the subsequent calculation of the measurement uncertainty. In this paper, an automated system for calibration of thermometers is presented. The system enables the entire calibration process to be computer driven, after setting calibration parameters. The details are presented on an example of a furnace for temperatures up to 1 000 ° C. The review of the metrology software is given.