Miracle-dz/UniFAR-FTUs huggingface dataset:
An ontology-based model to determine the automation level of an automated vehicle for co-driving

Full autonomy of ground vehicles is a major goal of the ITS (Intelligent Transportation Systems) community. However, reaching such highest autonomy level in all situations (weather, traffic, .) may seem difficult in practice, despite recent results regarding driverless cars (e.g., Google Cars). In addition, an automated vehicle should also self-assess its own perception abilities, and not only perceive its environment. In this paper, we propose an intermediate approach towards full automation, by defining a spectrum of automation layers, from fully manual (the car is driven by a driver) to fully automated (the car is driven by a computer), based on an ontological model for representing knowledge. We also propose a second ontology for situation assessment (what does the automated car perceive? ), including the sensors/actuators state, environmental conditions and driver's state. Finally, we also define inference rules to link the situation assessment ontology to the automation level one. Both ontological models have been built and first results are presented.
Multi-Space Evolutionary Search for Large-Scale Optimization

In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This paper thus proposes a new search paradigm, namely the multi-space evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to four state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems.
Mapping of multi-floor buildings: A barometric approach

This paper presents a new method for mapping multi-floor buildings. The method combines laser range sensor for metric mapping and barometric pressure sensor for detecting floor transitions and map segmentation. We exploit the fact that the barometric pressure is a function of the elevation, and it varies between different floors. The method is tested with a real robot in a typical indoor environment, and the results show that physically consistent multi5floor representations are achievable.
Modelação numérica de escoamento MHD em veículos de reentrada na atmosfera

Radio blackout is an issue that arises during the reentry period, during which the vehicle is unable to send and receive communication, telemetry and GPS signals. Since this problem arises during one of the mission's most delicate phases, blackout mitigation is a crucial factor for the development of hypersonic and reentry vehicles. So far, different blockout mitigation techniques have been proposed, amongst which the imposition of a stationary magnetic field. A numerical code, capable of calculating the effect of a magnetic field over an electrically conducting flow (e.g. plasma) is proposed, attempting to control the shockwave position. Initially, the existing gas dynamics code is validated, and its robustness and accuracy in calculating shockwaves for different meshes of increasing size is analyzed. Flows with different Mach number, ranging from supersonic to hypersonic velocities, are also analyzed. Afterwards, the magnetohydrodynamic equations are added to the code, assuming a low magnetic Reynolds formulation. The well known Hartmann case is used to validate this implementation, by comparing the numerical results with the analytical solution for different Hartmann numbers. Finally, an application case is studied, wherein the effect of a magnetic dipole in a hypersonic flow over the RAM-C II reentry vehicle is considered. Here it is intended to determine the configuration that allows a higher control range over the shockwave by studying the effect of de magnetic dipole intensity and orientation on the flow.
How Responsible are Modern Cows for Greenhouse Gases

According to the Intergovernmental Panel on Climate Change the main greenhouse gases carbon dioxide, methane, and nitrous oxide started to increase around the early nineteenth century (IPCC, 2006). From then on their increase was exponential particularly during the twentieth century. Methane concentrations have more than doubled since pre-industrial times whereas carbon dioxide and nitrous oxide have increased by roughly 25 and 10%, respectively (Figure 1).
Direct adaptive fuzzy control for a class of MIMO nonlinear systems

This article presents a direct adaptive fuzzy control scheme for a class of uncertain continuous-time multi-input multi-output nonlinear (MIMO) dynamic systems. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller that can achieve control objectives. The adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. The stability analysis of the closed-loop system is performed using a Lyapunov approach. In particular, it is shown that the tracking errors are bounded and converge to a neighborhood of the origin. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance.
Distributed Utility-Based Energy Efficient Cooperative Medium Access Control in MANETS

Cooperative communication, that utilizes near terminals to relay the overhearing information to grasp the variability gains, choices a nice potential to strengthen the transmission potency in wireless networks. to the subsume the hard medium access interactions evoked by relaying and leverage the advantages of such cooperation, associate economical Cooperative Medium Access management (CMAC) protocol is required. throughout this paper, we've got an inclination to tend to propose a completely unique cross-layer Wide unfold Energy-adaptive Location-based CMAC protocol, notably WEAL-CMAC, for Mobile Ad-hoc Networks (MANETs). the design objective of WEAL-CMAC is to strengthen the performance of the MANETs in terms of network amount and energy potency. a wise energy consumption model is used throughout this paper, that takes the energy consumption on each transceiver instrumentation and transmit instrumentation into thought. A distributed utility-based best relay different strategy is incorporated, that selects the most effective relay supported location information and residual energy. moreover, with the aim of enhancing the spacial apply, associate innovative network allocation vector setting is provided to the subsume the variable transmission power of the beginning and relay terminals. we've got an inclination to tend to point that the planned WEAL-CMAC considerably prolongs the network amount below varied circumstances even for prime instrumentation energy consumption cases by comprehensive simulation study
A New Online Detection Approach for Rolling Bearing Incipient Fault via Self-Adaptive Deep Feature Matching

This paper presents a new online detection approach for rolling bearing’s incipient fault based on self-adaptive deep feature matching (SDFM). This approach includes offline and online stages. At the offline stage, a new health state assessment algorithm is first proposed based on singular value decomposition (SVD) and Kurtosis criterion. Based on the assessment results, a kind of deep learning algorithm, i.e., stacked denoising autoencoder (SDAE), is introduced to extract the common deep features of normal state and early fault state. Support vector data description (SVDD) is applied to establish the offline detection model using the obtained features. At the online stage, a self-adaptive matching strategy with 1-Dimensional anchor is proposed. By utilizing the SDAE model established at offline stage, this strategy can extract more representative deep features of the target bearing via generating various proposal fragments and then determining the fault occurrence time in a self-adaptive way by feeding the online features into the SVDD model. Experiments run on the bearing data set of IEEE prognostic and health management (PHM) Challenge 2012. The results show the proposed approach has good detection performance in real time and much lower false alarm rate, with no need to acquire fault characteristic frequency in advance.
Variable Selection via Penalized Likelihood

Maximum likelihood ratio theory contributes tremendous success to parametric inferences, due to the fundamental theory of Wilks (1938). Yet, there is no general applicable approach for nonparametric inferences based on function estimation. Maximum likelihood ratio test statistics in general may not exist in nonparametric function estimation setting. Even if they exist, they are hard to find and can not be optimal as shown in this paper. In this paper, we introduce the sieve likelihood statistics to overcome the drawbacks of nonparametric maximum likelihood ratio statistics. New Wilks' phenomenon is unveiled. We demonstrate that the sieve likelihood statistics are asymptotically distribution free and follow X2-distributions under the null hypotheses for a number of useful hypotheses and a variety of useful models including Caussian white noise models, nonparametric regression models, varying coefficient models and generalized varying coefficient models. We further demonstrate that sieve likelihood ratio statistics are asymptotically optimal in the sense that they achieve optimal rates of convergence given by Ingster (1993). They can even be adaptively optimal in the sense of Spokoiny (1996) by using a simple choice of adaptive smoothing parameter. Our work indicates that the sieve likelihood ratio statistics are indeed general and powerful for nonparametric inferences based on function estimation.
IMPACT OF PRECIPITATION UNCERTAINTY ON SWAT MODEL PERFORMANCE

The accuracy of a model output is, to a large degree, dependent upon the quality of the input data sets including their spatial and temporal resolution. Among those input data sets, precipitation is one of the most important because of its influence on the hydrological model's performance (defined as agreement between measured and simulated values) and its role in determining surface hydrologic processes. Rainfall data are often obtained from rain gage networks, which sometimes may not cover the study area. Usually, precipitation data from the nearest gage stations are used to represent the study area for model hydrological simulations. However, occasionally unusual meteorological conditions can make it necessary to consider precipitation data from gages as far away from the study watershed as the next county. The watershed south of the lake was within 10 miles of two National Oceanic and Atmospheric Administration National Climatic Data Center (NOAA NCDC) precipitation gauges on the north side of the lake, but for year 2010 the SWAT simulated flows using either of these gages as precipitation input data lacked correlation to flows observed at a United States Geology Survey (USGS) gauge on Chickasaw Creek. Due to 2010 being an unusually wet year, an assumption was that additional NOAA NCDC gauges were needed to capture the precipitation events occurred in the watershed; a particular storm occurred in the watershed may be affected by high variability of storm directional paths and variable amounts of rain/snow. By considering additional precipitation gauges within 35 miles of the watershed boundary, model performance as measured by R-squared correlation improved significantly. The nearby precipitation gauges essentially showed no correlation at a coefficient as low as 0.03 for year 2010, but using a combination of the additional gauges to capture the majority of precipitation events raised the coefficient to 0.60 for the same year.
Conjunctive Queries with Comparisons

Conjunctive queries with predicates in the form of comparisons that span multiple relations have regained interest recently, due to their relevance in OLAP queries, spatiotemporal databases, and machine learning over relational data. The standard technique, predicate pushdown, has limited efficacy on such comparisons. A technique by Willard can be used to process short comparisons that are adjacent in the join tree in time linear in the input size plus output size. In this paper, we describe a new algorithm for evaluating conjunctive queries with both short and long comparisons, and identify an acyclic condition under which linear time can be achieved. We have also implemented the new algorithm on top of Spark, and our experimental results demonstrate order-of-magnitude speedups over SparkSQL on a variety of graph pattern and analytical queries.
Optimal Consumption and Portfolio Decision with Heston’s SV Model Under HARA Utility Criterion

Abstract This paper studies the optimal consumption-investment strategy with Heston’s stochastic volatility (SV) model under hyperbolic absolute risk aversion (HARA) utility criterion. The financial market is composed of a risk-less asset and a risky asset, whose price process is supposed to be driven by Heston’s SV model. The risky preference of the individual is assumed to satisfy HARA utility, which recovers power utility, exponential utility and logarithm utility as special cases. HARA utility is of general framework in the utility theory and is seldom studied in the existing literatures. Legendre transform-dual technique along with stochastic dynamic programming principle is presented to deal with our problem and the closed-form solution to the optimal consumption-investment strategy is successfully obtained. Finally, some special cases are derived in detail.
User-centered evaluation of adaptive and adaptable systems: a literature review

Abstract This literature review focuses on user-centered evaluation (UCE) studies of adaptive and adaptable systems. Usability, perceived usefulness and appropriateness of adaptation are the three most commonly assessed variables. Questionnaires appeared to be the most popular method, followed by interviews and data log analysis. The quality of most questionnaires was questionable, and the reporting of interviews and think-aloud protocols was found to be shallow. Furthermore, data logs need triangulation in order to be useful. The reports encountered lack empirical value. The article models the iterative design process for adaptive and adaptable systems, linked to the goals of UCE: supporting decisions, detecting problems and verifying quality. This model summarizes the variables to be assessed at each stage of the process and the relevant methods to assess them.
Enterprise Modelling: A Declarative Approach for FBPML

Enterprise Modelling (EM) methods are well-recognised for their value in describing complex, informal domains in an organised structure. EM methods are used in practice, particularly during the early stages of software system development, e.g. during the phase of business requirements elicitation. The built model, however, has not always provided direct input to software system development. Despite the provision of adequate training to understand and use EM methods, informality is often seen in enterprise models and presents a major obstacle. This paper focuses on one type of EM methods: business process modelling (BPM) methods. We advocate the use of a BPM language within a three-layer framework. The BPM language merges two main and complimentary business process representations, IDEF3 and PSL, to introduce a Fundamental Business Process Modelling Language (FBPML) that is designed for simplicity of use and under-pinned by rich formality that may be used directly to support software and workflow system development. Key-words Business Process Modelling, IDEF3, PSL, Workflow Management, Business Modelling, BSDM, Formal Method, Enterprise Modelling, Collaborative (Web-based) Knowledge Management.
Distributed reachability testing of concurrent programs

Reachability testing is an approach to verifying concurrent programs. During reachability testing, every partially ordered synchronization sequence of a program with a given input is exercised exactly once. In this paper, we present the design and implementation of a distributed reachability testing algorithm for a cluster of workstations. This algorithm allows different test sequences to be exercised concurrently by different workstations without any synchronization, and without any duplication of sequences among workstations. Dynamic load balancing is performed using a work‐stealing scheme. A novel aspect of this scheme is that work‐stealing requests progress in rounds. This round‐based structure identifies overloaded workstations to target for work stealing. Empirical studies show good speedup for four benchmark Java programs and one Lotos specification. Copyright © 2010 John Wiley & Sons, Ltd.
Pricing of Options on Stocks Driven by Multi-dimensional Operator Stable Lévy Processes

We model the price of a stock via a Langévin equation with multi-dimensional fluctuations coupled both in the price in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound Poisson processes with operator stable jump intensities. We derive exact relations for Fourier transforms of the jump intensity in case of different scaling indices E of the process. We express the Fourier transform of the joint probability density of the process to attain given values at several different times and to attain a given maximal value in a given time period through Fourier transforms of the jump intensity. Then we consider a portfolio composed of stocks and of options on stocks and we derive the Fourier transform of a random variable Dt (deviation of the portfolio) that is defined as a small temporal change of the portfolio diminished by the the compound interest earned. We show that if the price of the option at time t is an inverse of a positive, larger than unity, power of a logarithm of the price of the stock at time t then the deviation of the portfolio has a zero mean E [Dt] = 0 and the option pricing problem may have a solution.
Transport equations and plasmid-induced cellular heterogeneity

Autonomously replicating extra-chromosomal elements called plasmids can cause cellular heterogeneity. This thesis deals with the long-time distribution of plasmids in a bacterial population. Vertical gene transfer in a bacterial population structured by the number of plasmids is modeled. The resulting model equations are hyperbolic transport equations with an integral term. We analyze the associated eigenproblems. Finally, horizontal gene transfer of plasmids is included into the model.
Quantum games

Abstract In these lecture notes we investigate the implications of the identification of strategies with quantum operations in game theory beyond the results presented Eisert et al. (1999, Phys. Rev. Lett., 83, 3077). After introducing a general framework, we study quantum games with a classical analogue in order to flesh out the peculiarities of game theoretical settings in the quantum domain. Special emphasis is given to a detailed investigation of different sets of quantum strategies.
Multi-objective Optimization using Evolutionary Algorithms

This is a progress report describing my research during the last one and a half year, performed during part A of my Ph.D. study. The research field is multi-objective optimization using evolutionary algorithms, and the reseach has taken place in a collaboration with Aarhus Univerity, Grundfos and the Alexandra Institute. My research so far has been focused on two main areas, i) multi-objective evolutionary algorithms (MOEAs) with different variation operators, and ii) decreasing the cardinality of the resulting population of MOEAs. The outcome of the former is a comparative analysis of MOEA versions using different variation operators on a suite of test problems. The latter area has given rise to both a new branch of multiobjective optimization (MOO) called MODCO (Multi-Objective Distinct Candidates Optimization) and a new MOEA which makes it possible for the user to directly set the cardinality of the resulting set of solutions. To motivate and cover my previous and future work, the progress report is divided into three main parts:
Trusting Spam Reporters : A Reporter-based Reputation System for Email Filtering

Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trustmaintenance component for users, based on their spam-reporting behavior. The challenge that we consider is that of maintaining a reliable system, not vulnerable to malicious users, that will provide early spam-campaign detection to reduce the costs incurred by users and systems. We report on the utility of a reputation system for spam filtering that makes use of the feedback of trustworthy users. We evaluate our proposed framework, using actual complaint feedback from a large population of users, and validate its spam-filtering performance on a collection of real email traffic over several weeks. To test the broader implication of the system, we create a model of the behavior of malicious reporters, and we simulate the system under various assumptions using a synthetic dataset.
Style-Based Modeling and Refinement of Service-Oriented Architectures ? A graph transformation-based approach

Service-oriented architectures (SOA) provide a flexible and dynamic platform for implementing business solutions. In this paper, we address the modeling of such architectures by refining business-oriented architectures, which abstract from technology aspects, into service-oriented ones, focusing on the ability of dynamic reconfiguration (binding to new services at runtime) typical for SOA. The refinement is based on conceptual models of the platforms involved as architectural styles, formalized by graph transformation systems. Based on a refinement relation between abstract and platform-specific styles we investigate how to realize business-specific scenarios on the SOA platform by automatically deriving refined, SOA-specific reconfiguration scenarios.