Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating and Transforming Markets for Sustainability " Reframing Instrumental Stakeholder Theory

Although stakeholder theory has been extensively discussed in academic research, the instrumental version of the theory has only received sparse attention. The purpose of this paper is to start to fill this research void by contributing to the development of instrumental stakeholder theory in three important ways. First, I show that instrumental stakeholder theory has been inconsistently defined in present research, even though most participating authors claim adherence to the definition made by Donaldson & Preston (1995). Two distinct groups of theories – broad instrumental stakeholder theories and narrow instrumental stakeholder theories – can be identified in present research. Second, I present a typology clarifying the distinctions between different broad instrumental stakeholder theories and identify differences between and within groups of theories. Third, I analyze the importance of context for the application of different versions of instrumental stakeholder theories. Finally, I conclude the paper by discussing the implications of these three contributions for future research.
Design and performance of an impedance analyzer for nondestructive moisture content determination of in-shell peanuts

Moisture content (MC) in peanuts is measured at various stages of their processing and storage in the peanut industry. A method was developed earlier that would estimate the MC of a small sample of in-shell peanuts (peanut pods) held between two circular parallel-plates, from the values of capacitance and phase angle measured by a commercially available impedance analyzer, at three frequencies 1, 5 and 9 MHz. These values were used in an empirical equation, developed using the capacitance and phase angle values of samples of known MC levels, to obtain the average MC values of peanut samples with moisture contents in the range of 6 to 20% In the present work, a low-cost impedance analyzer, designed and assembled in our laboratory was used to measure the impedance and phase angles at 1, 5 and 9 MHz. The MC values predicted by the analyzer were compared with those obtained by the standard air-oven method. For 93% of the samples tested in the moisture range between 9% and 20% the MC values were found to be within 1% of the air-oven values. Ability to determine the average MC of in-shell peanuts without shelling and cleaning them, with a low-cost instrument, will be of considerable use in the peanut industry.
A high-performance chip with dataflow architecture for attitude and heading reference system accelerating

Based on parallel processing methodology, a new design of high-performance chip with data flow architecture for accelerating AHRS is presented in this paper. Based gradient descent algorithm, we divided the serial algorithm into four primary pipeline function blocks on chip: Communication Block, Quaternion Initialization Block, Register file Block and Quaternion Update Block. Every block was constructed with several parallel pipeline channels and embedded a number of acceleration units. Acceleration units were the technical-designed cells working for special mathematical operation, high speed reading and writing process, such as floating point operation, multi-dimension vectors computation and high speed cache on chip, which is proven as a great assistance for the data flow process. Compared to the traditional method estimating by serial MCU architecture, this data flow architecture got nearly a 10 times resolution of accuracy. Meanwhile, the speed of estimation could be raised up to over 40 times.
Ambivalent subjectivities: experiences of mothers with disabilities in Russia

Abstract Women with disabilities are still rarely imagined in the role of a mother. Narratives about motherhood that promote traditional gender roles and the primary role of motherhood in women’s lives (pronatalism) in countries like Russia emphasize the value of non-disabled and heteronormative bodies and minds. The lived experiences and the lived citizenship of mothers with disabilities disrupt societal assumptions about motherhood. However, the structural environment of pronatalism inevitably influences the ways in which mothers with disabilities understand motherhood and construct their sense of selves (subjectivities). Drawing on the framework of citizenship and the feminist disability studies literature, this article analyzes how the personal and the political are intertwined. The analysis is based on empirical data obtained from qualitative interviews with 14 mothers with disabilities in a provincial city of Russia.
Conflict management and leadership communication in multiplayer communities

Online multiplayer games often promote long-term cooperation between players. The resulting player groups and communities can be harmonious and long-lived, but it is equally possible that they encounter problems in building trust, managing the community effort, or negotiating values and goals. Conflict management, therefore, is important for the functioning and stability of multiplayer communities. This exploratory study looks at leadership communication and conflict management in the context of multiplayer computer games and the groups and communities that operate within them. By looking at players’ and playerleaders’ perceptions of conflicts and conflict management, a conception of the patterns behind conflicts is formed. In addition, issues of conflict management and leadership communication are discussed.
Policy Privacy in Cryptographic Access Control

Cryptographic access control offers selective access to encrypted data via a combination of key management and functionality-rich cryptographic schemes, such as attribute-based encryption. Using this approach, publicly available meta-data may inadvertently leak information on the access policy that is enforced by cryptography, which renders cryptographic access control unusable in settings where this information is highly sensitive. We begin to address this problem by presenting rigorous definitions for policy privacy in cryptographic access control. For concreteness we set our results in the model of Role-Based Access Control (RBAC), where we identify and formalize several different flavors of privacy, however, our framework should serve as inspiration for other models of access control. Based on our insights we propose a new system which significantly improves on the privacy properties of state-of-the-art constructions. Our design is based on a novel type of privacy-preserving attribute-based encryption, which we introduce and show how to instantiate. We present our results in the context of a cryptographic RBAC system by Ferrara et al. (CSF'13), which uses cryptography to control read access to files, while write access is still delegated to trusted monitors. We give an extension of the construction that permits cryptographic control over write access. Our construction assumes that key management uses out-of-band channels between the policy enforcer and the users but eliminates completely the need for monitoring read/write access to the data.
Shape Analysis via Monotonic Abstraction

We propose a new formalism for reasoning about dynamic memory heaps, using monotonic abstraction and symbolic backward reachability analysis. We represent the heaps as graphs, and introduce an ordering on these graphs. This enables us to represent the violation of a given safety property as the reachability of a finitely representable set of bad graphs. We also describe how to symbolically compute the reachable states in the transition system induced by a program.
An Uncertainty Sampling-Based Active Learning Approach for Support Vector Machines

Support vector machines (SVMs) have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, SVMs is a supervised learning which is based on the assumption that it is straightforward to obtain labeled data, but in reality labeled data can be scarce or expensive to obtain. Active learning (AL) is a way to deal with the above problem by asking for the labels of the most “informative” data points. To reduce the amount of human labeling effort while maintaining the SVMs performance, in this work we propose an uncertainty sampling-based active learning approach for SVMs to annotate the most uncertain unlabeled instances, i.e., an algorithm to select the most informative instances for SVMs learning process. During the SVMs active learning process, to reduce annotation effort while maintaining the SVMs classification performance, we firstly employ the decision margin of SVMs output as the initial uncertainty measure to select the most uncertain instances, to further reduce the number of unlabeled instances to be annotated, we employ the Ratio of Center-Distance to select the boundary vectors of SVMs. We provide a theoretical motivation for the algorithm. To verify the effectiveness and efficiency, we have applied the proposed method on several standard UCI datasets. The experimental results show that employing our active learning method can significantly reduce learning cost while achieving the desired performance.
Discriminative training of Gaussian mixture models for large vocabulary speech recognition systems

Two discriminative techniques are described (and evaluated) for estimating the parameters of the Gaussians in a large vocabulary speech-recognition system. The first technique is based on using a modification of the maximum mutual information (MMI) objective function, and appears to provide no improvement over standard ML estimation. The second technique is based on a heuristic correction of the Gaussian parameters, and is seen to give a 2-5% improvement over ML estimation.
"Financial inclusion does not come easily" : an institutional analysis of the development of the microfinance markets

Microfinance has grown from a niche development intervention in the 1990s to one that commands global influence and donor support. By 2006 microfinance had become part of financial sector development policy through the concept of financial inclusion. At the same time theoretical analysis of economic development increasingly focused on the role of institutions and getting institutions right - including for the financial sector – which has given rise to attempts to theorize gradual institutional change. This convergence of policy, and theoretical emphasis on institutions, raises the central question as to what institutions and institutional changes are necessary for the financial sector to effectively serve poor people. The experience of microfinance sector growth over a decade in two countries has been investigated using a ‘micro-ethnographic’ methodology to respond to this question. The research finds that a focus on institutional functions rather than institutional forms aids definitional precision and allows comparability across markets. Social norms underpinned the development of institutional functions, as theories of social embeddedness suggest. These norms also became integrated into institutional functions through the process of change, adding to critiques of externally imposed ‘best practice’ institutional blueprints. Further, beyond the widely accepted institutional functions which the rest of the financial market needs to operate efficiently, this research highlights the importance of a constitutional function (or law) to include poor people in the formal financial system, appropriate supervision for microfinance providers and support for the development of microfinance. Recent theories of institutional change offer insights beyond path dependency in identifying spaces for change and how changes will ‘stick’. However, to better analyse change at the level of particular institutional arenas, greater elaboration is needed of: how to incorporate multiple sets of agents (including external development agents) and multiple institutional functions; appropriate time-frames for analysis and processes of actor engagement.
Affordance++: Allowing Objects to Communicate Dynamic Use

We propose extending the affordance of objects by allowing them to communicate dynamic use, such as (1) motion (e.g., spray can shakes when touched), (2) multi-step processes (e.g., spray can sprays only after shaking), and (3) behaviors that change over time (e.g., empty spray can does not allow spraying anymore). Rather than enhancing objects directly, however, we implement this concept by enhancing the user. We call this affordance++. By stimulating the user's arms using electrical muscle stimulation, our prototype allows objects not only to make the user actuate them, but also perform required movements while merely approaching the object, such as not to touch objects that do not "want" to be touched. In our user study, affordance++ helped participants to successfully operate devices of poor natural affordance, such as a multi-functional slicer tool or a magnetic nail sweeper, and to stay away from cups filled with hot liquids.
2′-Azido RNA, a Versatile Tool for Chemical Biology: Synthesis, X-ray Structure, siRNA Applications, Click Labeling

Chemical modification can significantly enrich the structural and functional repertoire of ribonucleic acids and endow them with new outstanding properties. Here, we report the syntheses of novel 2′-azido cytidine and 2′-azido guanosine building blocks and demonstrate their efficient site-specific incorporation into RNA by mastering the synthetic challenge of using phosphoramidite chemistry in the presence of azido groups. Our study includes the detailed characterization of 2′-azido nucleoside containing RNA using UV-melting profile analysis and CD and NMR spectroscopy. Importantly, the X-ray crystallographic analysis of 2′-azido uridine and 2′-azido adenosine modified RNAs reveals crucial structural details of this modification within an A-form double helical environment. The 2′-azido group supports the C3′-endo ribose conformation and shows distinct water-bridged hydrogen bonding patterns in the minor groove. Additionally, siRNA induced silencing of the brain acid soluble protein (BASP1) encoding gene in chicken fibroblasts demonstrated that 2′-azido modifications are well tolerated in the guide strand, even directly at the cleavage site. Furthermore, the 2′-azido modifications are compatible with 2′-fluoro and/or 2′-O-methyl modifications to achieve siRNAs of rich modification patterns and tunable properties, such as increased nuclease resistance or additional chemical reactivity. The latter was demonstrated by the utilization of the 2′-azido groups for bioorthogonal Click reactions that allows efficient fluorescent labeling of the RNA. In summary, the present comprehensive investigation on site-specifically modified 2′-azido RNA including all four nucleosides provides a basic rationale behind the physico- and biochemical properties of this flexible and thus far neglected type of RNA modification.
Structure from motion with wide circular field of view cameras

This paper presents a method for fully automatic and robust estimation of two-view geometry, autocalibration, and 3D metric reconstruction from point correspondences in images taken by cameras with wide circular field of view. We focus on cameras which have more than 180deg field of view and for which the standard perspective camera model is not sufficient, e.g., the cameras equipped with circular fish-eye lenses Nikon FC-E8 (183deg), Sigma 8 mm-f4-EX (180deg), or with curved conical mirrors. We assume a circular field of view and axially symmetric image projection to autocalibrate the cameras. Many wide field of view cameras can still be modeled by the central projection followed by a nonlinear image mapping. Examples are the above-mentioned fish-eye lenses and properly assembled catadioptric cameras with conical mirrors. We show that epipolar geometry of these cameras can be estimated from a small number of correspondences by solving a polynomial eigenvalue problem. This allows the use of efficient RANSAC robust estimation to find the image projection model, the epipolar geometry, and the selection of true point correspondences from tentative correspondences contaminated by mismatches. Real catadioptric cameras are often slightly noncentral. We show that the proposed autocalibration with approximate central models is usually good enough to get correct point correspondences which can be used with accurate noncentral models in a bundle adjustment to obtain accurate 3D scene reconstruction. Noncentral camera models are dealt with and results are shown for catadioptric cameras with parabolic and spherical mirrors
Automatic track initiation of manoeuvring target in clutter

Automatic track initiation of manoeuvring targets in clutter is considered and an approximate solution is derived. The proposed approach integrates target existence uncertainty and target dynamical model uncertainty using independent Markov chains within the general Bayesian framework. Using Gaussian approximations an IPDA-IMM algorithm is derived and its performance is compared with other PDA based solutions - IMM-PDA and IMM-IPDA.
Improved accuracy thermal nanostructure

A new high precision superior-order curvature-corrected integrated nanostructure will be presented. In order to improve the temperature behavior of the circuit, a double differential structure will be used, implementing the linear and the superior-order curvature corrections. An original CTAT (Complementary To Absolute Temperature) voltage generator will be proposed, using exclusively MOS transistors biased in weak inversion for a low power operation of the integrated nanostructure, having two great advantages: an important reducing of the circuit silicon area and an improved accuracy (matched resistors being replaced by matched MOS active devices). The superior-order curvature-correction will be implemented by taking the difference between two gate-source voltages of subthreshold-operated MOS transistors, biased at drain currents having different temperature dependencies: PTAT (Proportional To Absolute Temperature) and PTAT2. The SPICE simulations confirm the theoretical estimated results, showing a temperature coefficient under 11ppm / K for an extended input range 223K <T < 423K and for a supply voltage of 1.8V and a current consumption of about 1μA.
Variational Bayesian PHD Filter with Deep Learning Network Updating for Multiple Human Tracking

We propose a robust particle probability hypothesis density (PHD) filter where the variational Bayesian method is applied in joint recursive prediction of the state and the time varying measurement noise parameters. The proposed particle PHD filter is based on forming variational approximation to the joint distribution of states and noise parameters at each frame separately; the state is estimated with a particle PHD filter and the measurement noise variances used in the update step are estimated with a fixed point iteration approach. A deep belief network (DBN) is used in the update step to mitigate the effect of measurement noise on the calculation of particle weights in each frame. The deep learning network is trained based on both colour and oriented gradient histogram (HOG) features and then used to mitigate the measurement noise from the particle selection step, thereby improving the tracking performance. Simulation results using sequences from the CAVIAR dataset show the improvements of the proposed DBN aided variational Bayesian particle PHD filter over the traditional particle PHD filter.
Camera Calibration with Distortion Models and Accuracy Evaluation

A camera model that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortions is presented. The proposed calibration procedure consists of two steps: (1) the calibration parameters are estimated using a closed-form solution based on a distribution-free camera model; and (2) the parameters estimated in the first step are improved iteratively through a nonlinear optimization, taking into account camera distortions. According to minimum variance estimation, the objective function to be minimized is the mean-square discrepancy between the observed image points and their inferred image projections computed with the estimated calibration parameters. The authors introduce a type of measure that can be used to directly evaluate the performance of calibration and compare calibrations among different systems. The validity and performance of the calibration procedure are tested with both synthetic data and real images taken by tele- and wide-angle lenses. >
Effect of Embryo Vitrification on Rabbit Foetal Placenta Proteome during Pregnancy

Very limited information on the post-implantatory effects of vitrification has been published till now. We observed in a previous study that the vitrification procedure for the cryopreservation of embryos introduced transcriptomic and proteomic modifications in the rabbit foetal placenta at the middle of gestation. Now, we have conducted a proteomic study to determine whether protein alterations in the foetal placenta induced by the vitrification procedure remain during pregnancy. In this study, we used 2D-DIGE and mass spectrometry (MALDI-TOF-TOF and LC-MS/MS analysis) to identify the protein changes during middle and late stages of gestation (Day 14 and Day 24, respectively) in rabbit foetal placenta. We identified 11 differentially expressed proteins at Day 14 and 13 proteins at Day 24. Data are available via ProteomeXchange with identifiers PXD001840 and PXD001836. In addition, we demonstrate the presence of three proteins, serum albumin, isocitrate dehydrogenase 1 [NADP+], and phosphoglycerate mutase 1, which were altered during pregnancy. We demonstrate the existence of changes in foetal placental protein during pregnancy induced by the vitrification procedure, which brings into question whether vitrification effects observed during foetal development could lead to physiological and metabolic disorders in adulthood. This effect, taken together with other effects reported in the literature, suggests that embryo cryopreservation is not neutral.
Texture discrimination using local features and count data models

In this paper we consider the problem of textured images modeling and discrimination using local features. Local features are quantized to form a textons visual dictionary. This procedure allows the representation of each textured image by a vector of counts which represent the frequencies of the textons in the texture. Having the count vectors in hand, we introduce a new mixture model for the accurate modeling of these vectors. This mixture model is based on a composition of the Beta-Liouville distribution and the multinomial. The novel proposed model, that we call multinomial Beta-Liouville mixture, is optimized by expectation-maximization (EM) and minimum description length, and strives to achieve a high accuracy of textured image data discrimination. The developed approach is competitive with recent proposed count mixture models and its classification power is demonstrated through experimental results on various textured images data sets.