Miracle-dz/UniFAR-FTUs huggingface dataset:
A generalized control method for a Tilt-rotor UAV stabilization

Unmanned Aerial Vehicles (UAVs) are currently a very interesting field of research in the modern scientific community, especially in their application to military operations. In this paper we focus on the Tilt-rotor UAV. This is a UAV capable of Vertical Take-Off and Landing (VTOL), with a rotor on each side of its airframe. Each rotor can be tilted independently to provide both lift and forward thrust to the rotor-craft. The Euler-Lagrange formulation is used to develop the model of the UAV. A key aspect in the design is the ability of the UAV to hover in one position. To that end, we design a back-stepping based proportional-derivative (PD) controller. The generalized structure of the proposed controller makes it easy to derive the equations of force and torque. Simulations demonstrate that the controller is capable of stabilizing the UAV. We also highlight its ability to track continuous trajectories.
Effects of pretesting implicit self-determined motivation on behavioral engagement: evidence for the mere measurement effect at the implicit level

Research into individuals’ intended behavior and performance has traditionally adopted explicitly measured, self-report constructs, and outcomes. More recently, research has shown that completing explicit self-report measures of constructs may effect subsequent behavior, termed the “mere measurement” effect. The aim of the present experiment was to investigate whether implicit measures of motivation showed a similar mere measurement effect on subsequent behavior. It may be the case that measuring the implicit systems affects subsequent implicit interventions (e.g., priming), observable on subsequent behavior. Priming manipulations were also given to participants in order to investigate the interaction between measurement and priming of motivation. Initially, a 2 [implicit association test (IAT: present vs. absent) ×2 (Prime: autonomous vs. absent) and a 2 (IAT: present vs. absent) × 2 (Prime: controlled vs. absent)] between participants designs were conducted, these were them combined into a 2 (IAT: present vs. absent) ×3 (Prime: autonomous vs. controlled vs. absent) between participants design, with attempts at a novel task taken as the outcome measure. Implicit measure completion significantly decreased behavioral engagement. Priming autonomous motivation significantly facilitated, and controlled motivation significantly inhibited performance. Finally, there was a significant implicit measurement × priming interaction, such that priming autonomous motivation only improved performance in the absence of the implicit measure. Overall, this research provides an insight into the effects of implicit measurement and priming of motivation and the combined effect of completing both tasks on behavior.
Monte Carlo optimization approach for decentralized estimation networks undercommunication constraints

We consider designing decentralized estimation schemes over bandwidth limited communication links with a particular interest in the tradeoff between the estimation accuracy and the cost of communications due to, e.g., energy consumption. We take two classes of in–network processing strategies into account which yield graph representations through modeling the sensor platforms as the vertices and the communication links by edges as well as a tractable Bayesian risk that comprises the cost of transmissions and penalty for the estimation errors. This approach captures a broad range of possibilities for “online” processing of observations as well as the constraints imposed and enables a rigorous design setting in the form of a constrained optimization problem. Similar schemes as well as the structures exhibited by the solutions to the design problem has been studied previously in the context of decentralized detection. Under reasonable assumptions, the optimization can be carried out in a message passing fashion. We adopt this framework for estimation, however, the corresponding optimization schemes involve integral operators that cannot be evaluated exactly in general. We develop an approximation framework using Monte Carlo methods and obtain particle representations and approximate computational schemes for both classes of in–network processing strategies and their optimization. The proposed Monte Carlo optimization procedures operate in a scalable and efficient fashion and, owing to the non-parametric nature, can produce results for any distributions provided that samples can be produced from the marginals. In addition, this approach exhibits graceful degradation of the estimation accuracy asymptotically as the communication becomes more costly, through a parameterized Bayesian risk.
GlueTK: a framework for multi-modal, multi-display human-machine-interaction

As new input modalities allow interaction not only in front of a single display, but enable interaction in the whole room, application developers face new challenges. They have to handle many new input modalities, each with its own interface and requirements for pre-processing, deal with multiple displays, and applications that are distributed across multiple machines. We present glueTK, a framework that abstracts from the complexities of these input modalities, allows the design of interfaces for a wide range of display sizes, and makes the distribution across multiple machines transparent to the developer as well as the user. With an example application we demonstrate the wide range of input modalities glueTK can support and the functionality this enables. GlueTK moves away from the focus on point and touch like input modalities, enabling the design of applications tailored towards interactive rooms instead of the traditional desktop environment.
Accounting for Purpose in Specifying Requirements for Process Programs

Software process programs, as a group and individually, may be called on to serve multiple purposes, including process specification, implementation, simulation, proxy, and others. This variety of purposes is unusual in comparison to many familiar kinds of application, for which the purpose is typically assumed to be implementation. The variety of purposes and their influence on requirements makes the explicit treatment of alternative purposes important in process-program requirements specification. One important influence that purpose has is in determining useful properties of the software-process system. These systems may be characterized potentially in terms such as consistency, fidelity, precision, verifiability, correspondence, coordination, determinability, and dependence. However, the properties that are actually important to a particular process program can vary significantly depending on the purpose of the program. These properties, in turn, induce specific functional and non-functional requirements on the process program. Conversely, when specific requirements conflict, the analysis of purpose provides a basis for conflict resolution. Methods for process-program requirements specification can be based on the relationships among process-program purpose, process-system properties, and specific kinds of requirements. Such methods may have several potential benefits, including support for the identification, rationale, organization, and analysis of process-program requirements. Additionally, they may facilitate the prevention, detection, and resolution of conflicts among process-program requirements and purposes.
Modeling the Affective Content of Music with a Gaussian Mixture Model

Modeling the association between music and emotion has been considered important for music information retrieval and affective human computer interaction. This paper presents a novel generative model called acoustic emotion Gaussians (AEG) for computational modeling of emotion. Instead of assigning a music excerpt with a deterministic (hard) emotion label, AEG treats the affective content of music as a (soft) probability distribution in the valence-arousal space and parameterizes it with a Gaussian mixture model (GMM). In this way, the subjective nature of emotion perception is explicitly modeled. Specifically, AEG employs two GMMs to characterize the audio and emotion data. The fitting algorithm of the GMM parameters makes the model learning process transparent and interpretable. Based on AEG, a probabilistic graphical structure for predicting the emotion distribution from music audio data is also developed. A comprehensive performance study over two emotion-labeled datasets demonstrates that AEG offers new insights into the relationship between music and emotion (e.g., to assess the “affective diversity” of a corpus) and represents an effective means of emotion modeling. Readers can easily implement AEG via the publicly available codes. As the AEG model is generic, it holds the promise of analyzing any signal that carries affective or other highly subjective information.
Revised spectral types for 64 B-supergiants in the Small Magellanic Cloud: metallicity effects ?

The problem of the classication of metal poor stars, such as occur in the Small Magellanic Cloud (SMC), is dis- cussed with reference to the applicability of the MK system in such an environment. An alternative method is presented here and applied to B-type supergiants in the SMC. A local refer- ence system is rst devised and then a transformation to MK spectral types is determined by comparing the trends of metal line strengths in these two systems. For the determination of the luminosity class, we emphasize the need to use the hydro- gen Balmer line strengths independently of metal line-strength considerations. This method is used to determine new spectral types for 64 supergiants in the SMC, 75% of the sample requir- ing classications different from previous ndings. These new types result in much improved line strength { spectral type cor- relations for He, C, N, O, Mg and Si. Corresponding changes in the distribution of these stars in the Hertzsprung-Russell dia- gram of the SMC reveal more clearly than before the existence of a ridge which may be the SMC analogue of a similar fea- ture found for the LMC by Fitzpatrick & Garmany (1990). The group of very luminous supergiants lying above this ridge in- cludes the LBV AV415 (R40), a property which this object has incommonwithLBVsintheLargeMagellanicCloud.Also,for the rst time, clear examples of BN/BC supergiants are found in the SMC.
Facebook and Privacy: The Balancing Act of Personality, Gender, and Relationship Currency

Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits. We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
Privacy-Integrated Graph Clustering Through Differential Privacy

Data mining tasks like graph clustering can automatically process a large amount of data and retrieve valuable information. However, publishing such graph clustering results also involves privacy risks. In particular, linking the result with available background knowledge can disclose private information of the data set. The strong privacy guarantees of the dierential privacy model allow coping with the arbitrarily large background knowledge of a potential adversary. As current denitions of neighboring graphs do not fulll the needs of graph clustering results, this paper proposes a new one. Furthermore, this paper proposes a graph clustering approach that guarantees 1-edge-dierentia l privacy for its results. Besides giving strong privacy guarantees, our approach is able to calculate usable results. Those guarantees are ensured by perturbing the input graph. We have thoroughly evaluated our approach on synthetic data as well as on real-world graphs.
Fault-Tolerance Analysis of Mixed CAN/Switched Ethernet Architecture

CAN is a well known fieldbus standard used in safety critical a pplications of embedded systems. However, steadily increasing am ount of exchanged information in such systems has led to the use of Switched Eth ernet like solutions. Mixed CAN/Switched Ethernet architectures allow to bypass CAN limitations while preserving the widely used CAN technology. In or der to use this kind of architecture in safety critical applications a complete fault tolerance analysis is mandatory. In this paper, we use a simulation-based fault -injection technique to analyse the impact of different types of errors on the perc entage of application frames missing their deadlines. Results show that differen t types of errors don’t have the same impact on different types of traffic. Moreover, it is shown that the re-emission of corrupted frames can have a negative impa ct on the system’s global performance.
Effectiveness of Artificial Neural Networks in forecasting failure risk for pre-medical students

This research paper evaluates the ability of Artificial Neural Networks (ANN) to predict the performance of the applicants students to Medical Sciences, in order to predict their failure/ risk in their premedical year. Educational institutions in general, consider a variety of factors when making admission decisions. Traditionally, academic researchers have developed several statistical models to predict an applicant's success in the academic programs. An ANN model is designed based on existing academic acceptance criteria for medical college. The Cascade Correlation Learning structure of ANN is used to predict students' final grades in their premedical year. The result of this research shows that the neural network model can predict students' performance even better when the similar characteristics for input data have been maintained.
A Coloured Petri Net Based Approach for Resource Allocation andFault Tolerance for Flexible Manufacturing

In this paper we introduce the architecture of an allocation and management system capable to deal with the complexity of Flexible Manufacturing Systems (FMS). We show how to obtain the model of an FMS based on the fusion of places of Place/Transition nets. Also, we show how this model can be structurally reduced, by using Coloured Petri Nets (CPN). We also determine a general model which is independent of a speciic production trajectory (route), based on CPN. The introduction of fault tolerance in the model of such allocation and management system is also discussed.
3D fingertip and palm tracking in depth image sequences

We present a vision-based approach for robust 3D fingertip and palm tracking on depth images using a single Kinect sensor. First the hand is segmented in the depth images by applying depth and morphological constraints. The palm is located by performing distance transform to the hand contour and tracked with a Kalman filter. The fingertips are detected by combining three depth-based features and tracked with a particle filter over successive frames. Quantitative results on synthetic depth sequences show the proposed scheme can track the fingertips quite accurately. Besides, its capabilities are further demonstrated through a real-life human-computer interaction application.
International Conference on Information Systems ( ICIS ) 2010 ORGANIZATIONAL BOUNDARIES , INDUSTRY FRAGMENTATION , AND ELECTRONIC PERSONAL HEALTH RECORDS

This research-in-progress paper reports on an in-depth case study that integrates literatures from health care policy, information systems, and strategic management to explore how an entrepreneurial firm shapes its organizational boundaries in the context of the complicated healthcare ecosystem in the United States. In doing so, we investigate the implications of boundary shaping decisions on the development and deployment of an electronic personal health record (ePHR) system. The growing call for the use of ePHR systems is based on the logic that providing personalized, timely healthcare information that supports an incentive-based compliance program will not only lower healthcare costs but lead to healthier individuals and improved organizational performance. However, as mentioned in this paper, populating ePHR systems is a huge data integration challenge that requires the successful coordination of many players with potentially competing objectives. By adopting the perspective of a startup firm within this context, we illuminate the impact of industry fragmentation and competing goals and objectives within the health care context, and show the importance of boundary decisions that promote cooperation and tight integration to facilitate the information flows needed to populate employer sponsored ePHR systems.
Half & Half: Multiple Dispatch and Retroactive Abstraction for Java TM†

Software often goes through a variety of extensions during its lifetime: adding new fields or new variants to a data structure, retroactively creating new type abstractions, and adding new operations on a data structure. As characterized by the extensibility problem, it should be possible to apply any combination of these types of extensions in any order. Mainstream object-oriented languages, however, do not well support the latter two. This paper proposes two language mechanisms that facilitate extending existing type hierarchies: multimethod dispatch and retroactive abstraction. For these two mechanisms to coexist, it is necessary to allow method dispatch on parameters of interface types, which presents problems with static type-checking. We present a type-safe solution that combines the two mechanisms by limiting multimethod type checks to package boundaries and by compiling certain packages with multimethods into sealed Jar files.
Performance enhancement and analysis of privacy preservation using slicing approach over hadoop

Nowdays every one is working with web resources, web faces many challenges about preserving data of individual user. Previously anonymization techniques are used to preserve data like Generalization and Bucketization. New technique introduced which partitioned data horizontal as well vertical called as Slicing. Slicing is used to handle huge amount of data but processing time is more. Our Proposed work based on hadoop distributed file system. our contribution in this paper is we implemented slicing techniques over hadoop. Our result shows efficiency of slicing improved over hadoop distributed file system. Slicing approach is useful to preserve Medical and Government data.
Comparing different approaches to model the rotational modulation of the Sun as a star

Context. The space missions MOST, COROT and Kepler are going to provide us with high-precision optical photometry of solar-like stars with time series extending from tens of days to several years. They can be modelled to obtain information on stellar magnetic activity by fitting the rotational modulation of the stellar flux produced by the brightness inhomogeneities associated with photospheric active regions. Aims. The variation of the total solar irradiance provides a good proxy for those photometric time series and can be used to test the performance of different spot modelling approaches. Methods. We test discrete spot models as well as maximum entropy and Tikhonov regularized spot models by comparing the reconstructed total sunspot area variation and longitudinal distributions of sunspot groups with those actually observed in the Sun along activity cycle 23. Appropriate statistical methods are introduced to measure model performance versus the timescale of variation. Results. The maximum entropy regularized spot models show the best agreement with solar observations reproducing the total sunspot area variation on time scales ranging from a few months to the activity cycle, although the model amplitudes are affected by systematic errors during the minimum and the maximum activity phases. The longitudinal distributions derived from the models compare well with the observed sunspot group distributions except during the minimum of activity, when faculae dominate the rotational modulation. The resolution in longitude attainable through the spot modelling is ~$60^{\circ}$, on average. Conclusions. The application of the maximum entropy modelling to solar analogues will provide us with a quite detailed picture of their photospheric magnetic activity that can be the base for comparative and evolutionary studies of solar-like magnetic activity and irradiance variations.
Emotion Regulation, Harsh Parenting, and Teacher Sensitivity Among Socioeconomically Disadvantaged Toddlers in Child Care

ABSTRACT Research Findings: This study examined the transactional nature of harsh parenting and emotion regulation across toddlerhood, including the moderating role of teacher sensitivity in child care. Secondary data analyses were conducted with a subsample of families from the Early Head Start Research and Evaluation Project who participated in center-based child care. Autoregressive cross-lagged path models were used to examine stability and transactional associations between observations of mothers’ harsh parenting behaviors and observers’ ratings of toddler emotion regulation at 14, 24, and 36 months. Teacher sensitivity was observed in children’s child care classrooms and was hypothesized to attenuate the negative impact of harsh parenting on subsequent emotion regulation. Results suggested that poorer emotion regulation and increased harsh parenting at 14 months were particularly salient in setting the stage for worse parent and toddler outcomes at 36 months. Teacher sensitivity was not a significant protective factor. Practice or Policy: Results are discussed in terms of the importance of early parent–toddler interactions that match the developing regulatory needs of young toddlers as well as considering how teacher sensitivity is conceptualized and measured so programs such as Early Head Start can best meet the needs of socioeconomically disadvantaged parents and toddlers.