Miracle-dz/UniFAR-FTUs huggingface dataset:
BODYF - A Parameterless Broadcasting Protocol over Dynamic Forests

Mobile Ad hoc Networks (MANETs) are composed of mobile devices which spontaneously communicate each other without any previous existing infrastructure. Thus, the resulting network is highly fluctuating and has a dynamically changing topology. Dealing with ad hoc networks, broadcasting is one of the main operations, since many other applications use it very often. In order to avoid the broadcast storm problem in wireless networks, many researchers work on the design of efficient broadcasting algorithms. In this contribution, we present BODYF, a broadcasting protocol that relys on a tree-based topology. The behavior of this algorithm does not depend on any parameter, what makes it challenging and suitable for dealing with MANETs. We compare here our proposal to DFCN, an efficient broadcasting neighbor topology based protocol, and also to Simple Flooding, the most simple broadcasting technique which does not require any knowledge.
SVDD: A proposal for automated credit rating prediction

Credit rating prediction using clustering algorithms has become more and more important in the financial literature. Expanding the ideas of [4] and [5], we propose an approach to generate models for automated credit rating prediction based on support vector domain description (SVDD) and linear regression (LR). The models include the prediction for sovereign and corporate bonds. Another advantage is, the prediction models contain as many groups as rating grades exist, given by rating agencies like S&P, Fitch and Moody's. Our approach is formulated as a step-by-step procedure and all steps are illustrated by an example with artificial data. A numerical example with real data demonstrates the practical usability of our approach.
Why discourse affects speakers’ choice of referring expressions

We propose a language production model that uses dynamic discourse information to account for speakers’ choices of referring expressions. Our model extends previous rational speech act models (Frank and Goodman, 2012) to more naturally distributed linguistic data, instead of assuming a controlled experimental setting. Simulations show a close match between speakers’ utterances and model predictions, indicating that speakers’ behavior can be modeled in a principled way by considering the probabilities of referents in the discourse and the information conveyed by each word.
Two-dimensional interleaving schemes with repetitions: constructions and bounds

The most common approach for dealing with one-dimensional error bursts is interleaving. While the optimal one-dimensional interleaving schemes, both with and without repetitions, are straightforward, in two dimensions, it is not at all obvious how to interleave a minimal number of codewords so that any burst of size up to t can be corrected. Most two-dimensional burst-correcting codes that have been studied in the literature so far correct error bursts of a given rectangular shape. In this work, we assume that a cluster of errors can have an arbitrary shape, as long as it maintains horizontal/vertical connectivity. Given this notion of a cluster, one may define a two-dimensional interleaving scheme A(t,r) of strength t with r repetitions as an infinite array of integers characterized by the property that every integer appears at most r times in any cluster of size t. We introduce the notion of r-dispersion that turns out to be crucial in the design of two-dimensional interleaving schemes with repetitions. The r-dispersion may be thought of as a generalization of the L/sub 1/-distance to a quantity that reflects a property of r points for r/spl ges/2.
Infinite sets of primes with fast primality tests and quick generation of large primes

Infinite sets P and Q of primes are described, P C Q. For any natural number n it can be decided if n E P in (deterministic) time O((logn)9). This answers affirmatively the question of whether there exists an infinite set of primes whose membership can be tested in polynomial time, and is a main result of the paper. Also, for every n E Q, we show how to randomly produce a proof of the primality of n. The expected time is that needed for 1 exponentiations mod n. We also show how to randomly generate k-digit integers which will be in Q with probability proportional to k-1. Combined with the fast verification of n E Q just mentioned, this gives an 0(k4) expected time algorithm to generate and certify primes in a given range and is probably the fastest method to generate large certified primes known to belong to an infinite subset. Finally, it is important that P and Q are relatively dense (at least cn2/3/ log n elements less than n). Elements of Q in a given range may be generated quickly, but it would be costly for an adversary to search Q in this range, a property that could be useful in cryptography.
Effects of Antifouling Biocides on Molecular and Biochemical Defense System in the Gill of the Pacific Oyster Crassostrea gigas

Antifouling biocides such as organotin compounds and their alternatives are potent toxicants in marine ecosystems. In this study, we employed several molecular and biochemical response systems of the Pacific oyster Crassostrea gigas to understand a potential mode of action of antifouling biocides (i.e. tributyltin (TBT), diuron and irgarol) after exposure to different concentrations (0.01, 0.1, and 1 μg L-1) for 96 h. As a result, all the three antifouling biocides strongly induced the antioxidant defense system. TBT reduced both enzymatic activity and mRNA expression of Na+/K+-ATPase and acetylcholinesterase (AChE). Lower levels of both Na+/K+-ATPase activity and AChE mRNA expression were observed in the diuron-exposed oysters compared to the control, while the irgarol treatment reduced only the transcriptional expression of AChE gene. We also analyzed transcript profile of heat shock protein (Hsp) superfamily in same experimental conditions. All antifouling biocides tested in this study significantly modulated mRNA expression of Hsp superfamily with strong induction of Hsp70 family. Taken together, overall results indicate that representative organotin TBT and alternatives have potential hazardous effects on the gill of C. gigas within relatively short time period. Our results also suggest that analyzing a series of molecular and biochemical parameters can be a way of understanding and uncovering the mode of action of emerging antifouling biocides. In particular, it was revealed that Pacific oysters have different sensitivities depend on the antifouling biocides.
Development and Side Effects of Remittances in the CIS Countries: The Case of Belarus

The objective of the present paper is to evaluate the potential development impact and any possible side effects of remittances in Belarus. Our main finding, based on VAR modelling, is that we cannot consider remittances as a driver of economic growth in Belarus: their positive influence on GDP growth is not statistically significant. In fact, in the next period GDP responds negatively to remittances growth (p-value is 0.005). To some extent this may be a result of a productivity decrease conditioned by possible brain-drain effects. Remittances appear to be strongly pro-cyclical with respect to Russian GDP and mildly pro-cyclical with respect to the GDP of Belarus. Analysis shows that negative influence of remittances on GDP is not caused by Dutch disease and inflation: neither exchange rate appreciation nor growth in consumer price is induced by remittances. Instead, lagged REER devaluation Granger causes growth in remittances inflow (Wald test p-value is 0.051): when in a crisis devaluation takes place in Belarus more people go abroad to support their families and more transfers come from abroad. There are no reliable micro-data providing information on remittance receivers; nor are there any reliable data about the ways remittances are spent by receiving households in Belarus. Nevertheless, available statistics and surveys suggest that remittances have only a limited impact in terms of poverty reduction. Remittances are only invested in a limited extent, something in line with their relationship with GDP. At the same time we can conclude that due to remittances supporting household spending, employment remained nearly unchanged, despite an unfavourable economic climate. However, the underdeveloped capital market and lack of investments can result in the failure to modernize so that capacities can react, in a timely fashion, to demand fueled by remittances. Thus, there is a need for effective policies to channel remittances for investment purposes so that they support economic growth. Proposed policy recommendations follow.
Robust fat suppression at 3T in high‐resolution diffusion‐weighted single‐shot echo‐planar imaging of human brain

Single‐shot echo‐planar imaging is the most common acquisition technique for whole‐brain diffusion tensor imaging (DTI) studies in vivo. Higher field MRI systems are readily available and advantageous for acquiring DTI due to increased signal. One of the practical issues for DTI with single‐shot echo‐planar imaging at high‐field is incomplete fat suppression resulting in a chemically shifted fat artifact within the brain image. Unsuppressed fat is especially detrimental in DTI because the diffusion coefficient of fat is two orders of magnitude lower than that of parenchyma, producing brighter appearing fat artifacts with greater diffusion weighting. In this work, several fat suppression techniques were tested alone and in combination with the goal of finding a method that provides robust fat suppression and can be used in high‐resolution single‐shot echo‐planar imaging DTI studies. Combination of chemical shift saturation with slice‐select gradient reversal within a dual‐spin‐echo diffusion preparation period was found to provide robust fat suppression at 3 T. Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc.
Sparse points matching by combining 3D mesh saliency with statistical descriptors

This paper proposes new methodology for the detection and matching of salient points over several views of an object. The process is composed by three main phases. In the first step, detection is carried out by adopting a new perceptually‐inspired 3D saliency measure. Such measure allows the detection of few sparse salient points that characterize distinctive portions of the surface. In the second step, a statistical learning approach is considered to describe salient points across different views. Each salient point is modelled by a Hidden Markov Model (HMM), which is trained in an unsupervised way by using contextual 3D neighborhood information, thus providing a robust and invariant point signature. Finally, in the third step, matching among points of different views is performed by evaluating a pairwise similarity measure among HMMs. An extensive and comparative experimental session has been carried out, considering real objects acquired by a 3D scanner from different points of view, where objects come from standard 3D databases. Results are promising, as the detection of salient points is reliable, and the matching is robust and accurate.
The study and improvement of memory management based on SOS

the memory management of the embedded operating system has its unique traits. And so SOS operating system should have its own suitable methods for the memory management because of its small memory and resource constraint. This paper analyses the memory management of SOS operating system, and proposes a new data structure with the combination of memory management methods about μ C / OSII, then deeply probes into the memory allocation and recovery mechanisms, which provides an effective solution for memory management of SOS operating system.
Combining discriminative and model based approaches for hand pose estimation

In this paper we present an approach to hand pose estimation that combines both discriminative and modelbased methods to overcome the limitations of each technique in isolation. A Randomised Decision Forests (RDF) is used to provide an initial estimate of the regions of the hand. This initial segmentation provides constraints to which a 3D model is fitted using Rigid Body Dynamics. Model fitting is guided using point to surface constraints which bind a kinematic model of the hand to the depth cloud using the segmentation of the discriminative approach. This combines the advantages of both techniques, reducing the training requirements for discriminative classification and simplifying the optimization process involved in model fitting by incorporating physical constraints from the segmentation. Our experiments on two challenging sequences show that this combined method outperforms the current state-of-the-art approach.
Towards utterance-based neural network adaptation in acoustic modeling

Despite the superior classification ability of deep neural networks (DNN), the performance of DNN suffers when there is a mismatch between training and testing conditions. Many speaker adaptation techniques have been proposed for DNN acoustic modeling but in case of environmental robustness the progress is still limited. It is also possible to use techniques developed for adapting speakers to handle the impact of environments at the same time, or to combine both approaches. Directly adapting the large number of DNN parameters is challenging when the adaptation set is small. The learning hidden unit contributions (LHUC) technique for unsupervised speaker adaptation of DNN introduces speaker dependent parameters to the existing speaker independent network to increase the automatic speech recognition (ASR) performance of the target speaker using small amounts of adaptation data. This paper investigates the LHUC to adapt the speech recognizer to target speakers and environments where the impacts of speakers and noise differences are quantified separately. Our finding shows that the LHUC is capable of adapting to both speaker and noise conditions at the same time. Compared to the speaker independent model, about 9% to 13% relative word error rate (WER) improvement are observed for all test conditions using AMI meeting corpus.
A Gaussian Mixture PHD Filter for Jump Markov System Models

The probability hypothesis density (PHD) filter is an attractive approach to tracking an unknown and time-varying number of targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. The PHD filter admits a closed-form solution for a linear Gaussian multi-target model. However, this model is not general enough to accommodate maneuvering targets that switch between several models. In this paper, we generalize the notion of linear jump Markov systems to the multiple target case to accommodate births, deaths, and switching dynamics. We then derive a closed-form solution to the PHD recursion for the proposed linear Gaussian jump Markov multi-target model. Based on this an efficient method for tracking multiple maneuvering targets that switch between a set of linear Gaussian models is developed. An analytic implementation of the PHD filter using statistical linear regression technique is also proposed for targets that switch between a set of nonlinear models. We demonstrate through simulations that the proposed PHD filters are effective in tracking multiple maneuvering targets.
A Direct Construction of a Universal P System

We present a direct universal P system based on splicing. Our approach differs from those shown in previous papers as the P system we construct takes as input an encoding of another P system. Previous results were based on the simulation of universal type-0 grammars or Turing machines. We think that the approach we use can be applied to other variants of P systems.
Motion planning for underactuated bipedal mechanisms with kinematic constraints

The motion planning for a bipedal robotic gait is studied in this paper in the light of its underactuated nature. The generation of motion trajectory and the corresponding actuation command for an underactuated mechanism is a challenging problem, especially when it is required to needs to satisfy a set of equalities forming kinematics constraints. Existing method that utilises forward dynamics approach is computationally expensive since the equation of motion, given the actuation command, needs to be integrated with respect to time prior to obtain the resulting motion before it can be tested against the kinematic constraints. In this paper, the use of virtual actuators on the unactuated joints is introduced and explored. The motion planning is performed by designing motion trajectories for the system that minimises the virtual actuator efforts. This approach was found to significantly increase the possibilities of obtaining admissible solutions and consequently, improves the computational efficiency. The implementation of the proposed method in searching for periodic gaits, optimised against a given cost function, for a simple underactuated bipedal mechanism is described in this paper. The approach was used to reproduce a well established previous study which was conducted on conventional approach and the outcomes were found to be consistent.
Two-Stage Vector Quantization Based Multi-band Models for Speaker Identification

This paper presents an effective method for speaker identification. Based on the wavelet transform, the input speech signal is decomposed into several frequency bands, and then the linear predictive cepstral coefficients (LPCC) of each band are calculated. Furthermore, the cepstral mean normalization technique is applied to all computed features in order to provide similar parameter statistics in all acoustic environments. We propose a multi-band 2-stage vector quantization (VQ) as the recognition model in which different 2-stage VQ classifiers are applied independently to each band and the errors of all 2-stage VQ classifiers are combined to yield total error and a global recognition decision. The experimental results show that the proposed method gives better performance than other recognition models proposed previously in both clean and noisy environments.
Simulation Evaluation of Green Driving Strategies Based on Inter-Vehicle Communications

Transportation system produces a large percentage of local pollutants including hydrocarbons (HC), carbon monoxide (CO), carbon dioxide from switching to alternative fuels, one measure would be to apply information and communication technologies to help us drive more smoothly so as to decrease pollutants emissions. This paper studies potential benefits of two green driving strategies based on inter-vehicle communication (IVC). Here green driving strategies are similar to intelligent speed adaptation , but we assume that an IVC-equipped vehicle is able to receive detailed trajectory information from other such vehicles with the help of IVC. For the purpose of evaluation, we integrate Newell’s car-following model and VT-Micro to establish a simulation platform. Market penetration rates of IVC-equipped vehicles and delivery delays of messages are two prominent features of IVC systems. We simulate stop-and-go traffic to calculate potential reductions in air pollutant emissions and fuel consumption under different market penetration rates and delivery delays. Results show that significant savings under frequent stop-and-go traffic conditions may be obtained with our strategies (HC: -88.3%, CO: -95.8%, NOx: -91.5%, CO2: -36.3%, Fuel Consumption: -71.3%) for the same travel time and almost the same overall travel distance. It is also shown that relatively large savings can be achieved even for a market penetration rate as low as 1% and communication delays larger than 2 minutes. In the future we will investigate environmental benefits of green driving strategies for more traffic scenarios and realistic communication scenarios.
Multi-layered map based navigation and interaction for an intelligent wheelchair

Intelligent wheelchair is a paradigm of assisted living applications for elderly and disabled people. Its autonomous navigation and human-robot interaction is the major challenge. The previous intelligent wheelchair research has been mainly focused on geometric map based navigation, which is computational expensive in a large scale environment. This paper proposes the use of multi-layered maps for navigation and interaction of an intelligent wheelchair. The semantic information can improve the efficiency of path planning and navigation as well as extend the capability of task planning for the wheelchair. Some experimental results are given to demonstrate the feasibility and performance of the proposed approach.
Vision-based obstacle detection and avoidance for the CWRU cutter Autonomous Lawnmower

This paper describes the vision-based obstacle detection system of the CWRU Cutter, an autonomous lawnmower developed for the annual “Institute of Navigation (ION) Autonomous Lawnmower Competition. ” Unlike LIDAR sensors commonly found on autonomous vehicles, computer vision systems can provide similar information at drastically reduced prices. Though significantly more cost-effective than LIDAR, these systems have inherent problems due to changing lighting conditions and shadows. This paper investigates the use of image hue and intensity to create a robust, real-time vision-based obstacle detection system for use during the ION competition. Data abstraction methods used to process incoming images for easy combination of information from multiple sensors are also discussed. Using this system, CWRU Cutter correctly identified obstacles in 89% of frames containing fence, 78% of frames containing flowerbeds, and 84% of frames containing boundary lines.
Local color distribution projection filtering using a homogeneous coordinate system

In this paper, we present a denoising method of using multiple image composition based on the color transform of local color distribution of patches. In the method, the color of a guidance image is transformed so as to get close to a noisy input image by a patch-wise color transform. As a result, a noiseless image is obtained. Actually this method can be regarded as the generalized version of our previous method [1] and the guided filter [2], and gives improved images with more clear appearance. The existing methods have a restriction that a filter output form is expressed only by a linear transform, i.e., a first order polynomial. In this paper, we introduce a more general formula for this category of filtering with the guidance image. We deal with a quadratic formula for realizing more flexible transform instead of the previous linear transform, and moreover introduce more simple and flexible regularization.
Automatic Evaluation of Parser Robustness: Eliminating Manual Labor and Annotated Resources

This article describes an automatic evaluation procedure for NLP system robustness under the strain of noisy and ill-formed input. The procedure requires no manual work or annotated resources. It is language and annotation scheme independent and produces reliable estimates on the robustness and accuracy of NLP systems. The procedure was applied to ﬁve parsers and one part-of-speech tagger on Swedish text. To establish the reliability of the procedure, a comparative evaluation involving annotated resources was carried out on the tagger and three of the parsers.