Miracle-dz/UniFAR-FTUs huggingface dataset:
Market knowledge, social capital and absorptive capacity: An analysis of knowledge spillovers within marketing departments

Acknowledgement. We thank the management and employees of the three research sites for their cooperation, and An analysis of knowledge spillovers within marketing departments This paper investigates how marketing professionals' intra-departmental social network is associated with their knowledge about customers, competitors, and technology. There are three findings. First, we find no consistent effects for ego-network density or central brokerage position, the two network characteristics that have dominated social capital research. Second, however, we find stronger effect for social network exposure to others' knowledge, a rather under-researched facet of social capital which we model using network autoregression. These two findings support recent conjectures that brokerage positions and trust need not always lead to superior outcomes and that, in some contexts, social capital is better conceived as access to others' resources. Our third finding is that individuals' extant knowledge has a positive effect on learning from peers in the technology domain, but a negative effect in the customer and competitor domains. This suggests that the professionals studied were realizing positive returns on their own intellectual capital for learning outside their primary task environment, but negative returns for learning within their primary task environment.
Reusable ontologies, knowledge-acquisition tools, and performance systems: PROTÉGÉ-II solutions to Sisyphus-2

Abstract This paper describes how we applied the PROTEGE-II architecture to build a knowledge-based system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTEGE-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTEGE-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Beginning with the textual description of the elevator-configuration task, we analysed the domain knowledge with respect to PROTEGE-II’s main goal: to build domain-specific knowledge-acquisition tools. We used PROTEGE-II’s suite of tools to construct a knowledge-based system, called ELVIS, that includes a reusable domain ontology, a knowledge-acquisition tool, and a propose-and-revise problem-solving method that is optimized to solve the elevator-configuration task. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that PROTEGE-II generated from the ontologies. After we constructed mapping relations to connect the knowledge base with the method’s code, the final executable problem solver solved the test case provided with the Sisyphus-2 material. We have found that the development of ELVIS has afforded a valuable test case for evaluating PROTEGE-II’s suite of system-building tools. Only projects based on reasonably large problems, such as the Sisyphus-2 task, will allow us to improve the design of PROTEGE-II and its ability to produce reusable components.
Extreme Resistance as a Host Counter-counter Defense against Viral Suppression of RNA Silencing

RNA silencing mediated by small RNAs (sRNAs) is a conserved regulatory process with key antiviral and antimicrobial roles in eukaryotes. A widespread counter-defensive strategy of viruses against RNA silencing is to deploy viral suppressors of RNA silencing (VSRs), epitomized by the P19 protein of tombusviruses, which sequesters sRNAs and compromises their downstream action. Here, we provide evidence that specific Nicotiana species are able to sense and, in turn, antagonize the effects of P19 by activating a highly potent immune response that protects tissues against Tomato bushy stunt virus infection. This immunity is salicylate- and ethylene-dependent, and occurs without microscopic cell death, providing an example of “extreme resistance” (ER). We show that the capacity of P19 to bind sRNA, which is mandatory for its VSR function, is also necessary to induce ER, and that effects downstream of P19-sRNA complex formation are the likely determinants of the induced resistance. Accordingly, VSRs unrelated to P19 that also bind sRNA compromise the onset of P19-elicited defense, but do not alter a resistance phenotype conferred by a viral protein without VSR activity. These results show that plants have evolved specific responses against the damages incurred by VSRs to the cellular silencing machinery, a likely necessary step in the never-ending molecular arms race opposing pathogens to their hosts.
How to Test DoS Defenses

DoS defense evaluation methods influence how well test results predict performance in real deployment. This paper surveys existing approaches and criticizes their simplicity and the lack of realism. We summarize our work on improving DoS evaluation via development of standardized benchmarks and performance metrics. We end with guidelines on efficiently improving DoS evaluation, in the short and in the long term.
PseudoViewer3: generating planar drawings of large-scale RNA structures with pseudoknots

MOTIVATION Pseudoknots in RNA structures make visualization of RNA structures difficult. Even if a pseudoknot itself is represented without a crossing, visualization of the entire RNA structure with a pseudoknot often results in a drawing with crossings between the pseudoknot and other structural elements, and requires additional intervention by the user to ensure that the structure graph is overlap-free. Many programs such as web services prefer to obtain an overlap-free graph in one-shot rather than get a graph with overlaps to be edited. There are few programs for visualizing RNA pseudoknots, and PseudoViewer has been the almost only program that automatically draws RNA secondary structures with pseudoknots. The previous version of PseudoViewer visualizes all the known types of RNA pseudoknots as planar drawings, but visualizes some hypothetical pseudoknots as non-planar drawings. RESULTS We developed a new version of PseudoViewer for efficiently visualizing large RNA structures with any types of pseudoknots, both known and hypothetical, as planar drawings in one-shot. It is about 10 times faster than the previous algorithm, and produces a more compact and aesthetic structure drawing. PseudoViewer3 supports both web services and web applications. AVAILABILITY The new version of PseudoViewer, PseudoViewer3, is available at (http://pseudoviewer.inha.ac.kr).
Using Simulated Annealing for Task Scheduling in Distributed Systems

Task scheduling is one of the key factors in a distributed system. That is, how proper allocating the tasks to the processor of each computer in order to achieve better performance is important. In this problem the reported methods try to minimize Make span and communication cost while maximizing CPU utilization. Since this problem is NP-complete, many genetic algorithms have been proposed. However, except a method based on Tabu search, the existing methods scan the entire solution space regardless to techniques that can reduce the complexity of the optimization. In other words, the main shortcoming of these approaches is to spend much time doing scheduling and, hence, need to exhaustive time. In order to tackle this weakness, in this paper we use memetic algorithm. We apply simulated annealing as local search in our proposed memetic algorithm. Extended experimental results demonstrate efficiency of the proposed method.
Adaptive time horizon for on-line avoidance in dynamic environments

This paper addresses the issue of motion planning in dynamic environments using Velocity Obstacles. Specifically, we propose an adaptive time horizon to truncate the velocity obstacle so that its boundary closely, yet conservatively, approximates the boundary of the set of states from which collision is unavoidable. We wish to develop a representation such that any velocity vector that does not penetrate the velocity obstacle is safe, i.e. an avoidance maneuver exists, and any that does is not. Such clear partitioning between safe and unsafe velocities would allow safe planning with only one step look ahead, and can produce faster trajectories than the conservative trajectories produced when using an infinite time horizon. The computation of the adaptive time horizon is formulated as a minimum time problem, which is solved numerically for each static or moving obstacle. It is used in an on-line planner that generates locally time optimal trajectories to the goal. The planner is demonstrated for static and moving obstacles, and for on-line motion planning in a crowded dynamic environment.
WordNet based Multilingual Text Categorization

This article is essentially dedicated to the problem of Multilingual Text Categorization, that consists in classifying documents in different languages according to the same classification tree. The proposed approach is based on the idea to spread the utilization of WordNet in Text Categorization towards Multilingual Text Categorization. Experimental results of the bi-lingual classification of the ILO corpus (with the documents in English and Spanish) show that the idea we describe are promising and deserve further investigation.
You've Come a Long Way, Baby. Effects of Commuting Times on Couples' Labour Supply

This paper explores the effects of husbands' commuting time on their wives' labour market participation and on family time allocation. We develop a unitary family model of labour supply, which includes commuting times and household production. In a pure leisure model longer commuting time for husbands increases their wives' labour market participation and reduces their own working hours. However, a model that includes household production might determine the exact opposite result. We then examine the sign of these effects by using data from the German Socio-Economic Panel from 1997 to 2010. Employer-induced changes in home to work distances allow us to deal with endogeneity of commuting times. We find that a 1% increase in a husband's commuting distance reduces his wife's probability of participating in the labour force by 1.7 percentage points, 2% over the mean. Moreover, it increases his working hours by 0.2 hours per week. The average effect masks substantial heterogeneity: lower participation rates are concentrated in couples with children and where the husband has higher levels of education.
Relational analysis between technological progress and economic growth: An empirical study in counties from Jiangsu Province

Based on the improved grey T's correlation degree, the paper employs data of 50 counties in Jiangsu Province that participated in the nationwide technological progress assessment from 2003 to 2006, and carries out an empirical analysis on the relation between technical input-output indicators and gross domestic product (GDP). This paper also proposes the identity and difference of how technological progress advances economy in southern, middle and northern Jiangsu from the perspective of the whole province and each of the three regions.
Combining Climate Scenarios and Risk Management Approach—A Finnish Case Study

Climate change impacts on nature and the environment have been widely discussed and studied. Traditionally, a company’s continuity management is based on risk analysis. There are also attempts to implement scenario-based methods in the risk management procedures of companies. For industrial decision makers, it is vital to acknowledge the impacts of climate change with regards to their adaptation strategies. However, a scenario-based approach is not always the most effective way to analyze these risks. This paper investigates the integration of scenario and risk-based methods for a company’s adaptation planning. It considers the uncertainties of the climate change scenarios and the recognized risks as well as suitable adaptation strategies. The paper presents the results of climate risk analysis prepared for two Finnish hydropower plants. The introduced method was first piloted in 2008 and then again in 2015. The update of the analysis pointed out that at the company level, the climate risks and other risks originating from governmental or political decisions form an intertwined wholeness where the origin of the risk is difficult to outline. It seems that, from the business point of view, the main adaptation strategies suggested by the integrated risk and scenarios approach are those that support buying “safety margins” in new investments and reducing decision time horizons. Both of these adaptation strategies provide an advantage in the circumstances where also political decisions and societal changes have a great effect on decision making.
Visual feedback techniques for virtual pointing on stereoscopic displays

The act of pointing to graphical elements is one of the fundamental tasks in Human-Computer Interaction. In this paper we analyze visual feedback techniques for accurate pointing on stereoscopic displays. Virtual feedback techniques must provide precise information about the pointing tool and its spatial relationship with potential targets. We show both analytically and empirically that current approaches provide poor feedback on stereoscopic displays, resulting in low user performance when accurate pointing is required. We propose a new feedback technique following a camera viewfinder metaphor. The key idea is to locally flatten the scene objects around the pointing direction to facilitate their selection. We present the results of a user study comparing cursor-based and ray-based visual feedback techniques with our approach. Our user studies indicate that our viewfinder metaphor clearly outperforms competing techniques in terms of user performance and binocular fusion.
Cooperative Quaternion-based Manipulation without Force/Torque Information

This paper proposes a task-space control protocol for the collaborative manipulation of a single object by two robotic arms. No feedback of the contact forces/torques is required, therefore employment of corresponding sensors is avoided. The proposed methodology is decentralized in the sense that each agent utilizes information associated with its own and the object's dynamic/kinematic parameters and no on-line communication takes place. An adaptive version of the control scheme is also introduced, where the agents' and object's dynamic parameters are considered unknown. Moreover, load sharing coefficients between the agents are employed and internal force regulation is guaranteed. Unit quaternions are also employed to represent the object's orientation. Finally, realistic simulation studies verify the effectiveness of the introduced control scheme.
A Generative Probabilistic OCR Model for NLP Applications

In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system. The model is designed for use in error correction, with a focus on post-processing the output of black-box OCR systems in order to make it more useful for NLP tasks. We present an implementation of the model based on finite-state models, demonstrate the model's ability to significantly reduce character and word error rate, and provide evaluation results involving automatic extraction of translation lexicons from printed text.
A new Combination of Vision-Based and Inertial Tracking for Fully Mobile, Wearable, and Real-Time Operation

Real-time tracking of an object’s pose (position and orientation) is a prerequisite for many potential new applications in mobile systems. We present a new combination of a full six degree of freedom (6 DoF) inertial tracking system with a vision-based tracking system. Inertial tracking is based on silicon micromachined accelerometers and gyroscopes, and the vision-based tracker recovers camera pose solving the perspective n-point problem. Fusion with an Extended Kalman Filter exploits the complementary properties of both systems. Experimental results demonstrate the viability of our approach. Average errors are about several cm in position and 0.5 ◦ in orientation. Our system can be used for mobile, affordable, real-time tracking applications as mobile Augmented Reality (AR).
MMP-2 and sTNF-R1 Variability in Patients with Essential Hypertension: 1-Year Follow-Up Study

The aim of this study is to analyze MMP-2 and sTNF-R1 variability, potent predictors of cardiovascular events, in stable hypertensive patients during a 12-month followup. 234 asymptomatic patients (age 60 ± 13, 136 male) out of 252 patients with essential hypertension were followed up. MMP-2 and sTNF-R1 were measured at baseline and after 12 months (stage I). To compare MMP-2 and sTNF-R1 levels over time interval, we used the statistical method of Bland-Altman. MMP-2 and sTNF-R1 reproducibility was good in our patients for the two intervals with a coefficient of reproducibility of 8.2% and 11.3%, respectively. The percentages of patients within 1.96 × standard deviation of the mean were 93.6% and 92.7%. An elevated coefficient of correlation was obtained for MMP-2, basal versus stage I (r = 0.55, P < 0.0001) and for sTNF-R1 (r = 0.75, P < 0.0001). There is good stability in MMP-2 and sTNF-R1 levels in a followup study of patients with stable hypertension. As a consequence, assessment of its concentrations may be a useful tool for monitoring the follow-up of these patients. Measured variations in MMP-2 and sTNF-R1 levels, exceeding 8.2% and 11.3%, respectively, may indicate an increase in cardiovascular risk, thus, could be used to optimizing treatment than blood pressure control alone.
Effective Use of Water and Increased Dry Matter Partitioned to Grain Contribute to Yield of Common Bean Improved for Drought Resistance

Common bean (Phaseolus vulgaris L.) is the most important food legume in the diet of poor people in the tropics. Drought causes severe yield loss in this crop. Identification of traits associated with drought resistance contributes to improving the process of generating bean genotypes adapted to these conditions. Field studies were conducted at the International Center for Tropical Agriculture (CIAT), Palmira, Colombia, to determine the relationship between grain yield and different parameters such as effective use of water (EUW), canopy biomass, and dry partitioning indices (pod partitioning index, harvest index, and pod harvest index) in elite lines selected for drought resistance over the past decade. Carbon isotope discrimination (CID) was used for estimation of water use efficiency (WUE). The main objectives were: (i) to identify specific morpho-physiological traits that contribute to improved resistance to drought in lines developed over several cycles of breeding and that could be useful as selection criteria in breeding; and (ii) to identify genotypes with desirable traits that could serve as parents in the corresponding breeding programs. A set of 36 bean genotypes belonging to the Middle American gene pool were evaluated under field conditions with two levels of water supply (irrigated and drought) over two seasons. Eight bean lines (NCB 280, NCB 226, SEN 56, SCR 2, SCR 16, SMC 141, RCB 593, and BFS 67) were identified as resistant to drought stress. Resistance to terminal drought stress was positively associated with EUW combined with increased dry matter partitioned to pod and seed production and negatively associated with days to flowering and days to physiological maturity. Differences in genotypic response were observed between grain CID and grain yield under irrigated and drought stress. Based on phenotypic differences in CID, leaf stomatal conductance, canopy biomass, and grain yield under drought stress, the lines tested were classified into two groups, water savers and water spenders. Pod harvest index could be a useful selection criterion in breeding programs to select for drought resistance in common bean.
Match Adaptive Resonance Theory Neural Network for Arabic Alphabet Recognition HOMSI MASUN AND SUKKAR FADEL Department of Computer Science University of Aleppo P . O Box 6502 ALEPPO , SYRIA

Match Adaptive Resonance Theory (MART2) is developed as a modified version of self-organising Adaptive Resonance Theory (ART2) neural network for Arabic alphabet recognition. The new model does not utilise bi-directional synapses, match-reset loops and vigilance parameter. Novel subsystem is added to select the winning F2 node conserving competitive learning concept applied to reset wave. It relies on different sequence of operations of ART2 algorithm, but the classification of the input patterns remains unchanged. In the new architecture, algorithm execution takes almost equal time for each input pattern to be clustered and it has a new strategy in accessing an appropriate node in F2 without having bottom-up connections, generated from F1 to F2. However, top-down connections play an important role in matching and resonance. MART2 classifier of Arabic letters signals is implemented. The raw input signals are segmented and preprocessed depending on two criterions, amplitude average and zero crossings rate which determine voiced and unvoiced frames. Fast Fourier Transform (FFT) is used to transform the signals from time domain to frequency domain. The most important features of the letters are extracted to reduce data size. The reduced data are then presented to MART2 for training and classification. ART2 and MART2 are employed for clustering Arabic letters. Experimental results show that the new algorithm of MART2 generally exhibits faster learning, better clustering performance, lower error level, an improved recognition ability and more accuracy; even without a need of bottom up connections, match-reset loops and a vigilance parameter. That is, a major advantage of a flexible adaptive resonance theory neural network. Key-Words: Self-oraganising, Adaptive Resonance Theory, Match Adaptive Resonance Theory, Long Term Memory, Short Term Memory, Fast Fourier Transform.
FEM simulation and analysis on stator winding inter-turn fault in DFIG

With the development of renewable energy technology, Doubly-Fed Induction Generators (DFIG) are widely used on wind farms. Faults of machines have a direct influence on the safety and effectiveness of the power grid. In order to explore failure mechanism, it is of great importance to study the DFIG fault simulation models and features. In this paper, a Finite Element Method (FEM) simulation model of stator winding inter-turn fault in DFIG is presented by the software of ANSYS Maxwell. Different stator inter-turn short circuit fault conditions are achieved by changing the stator winding connection and the values of end leakage-inductance and resistance. Under normal condition, different branches' currents are the same in each phase and different phase currents are balanced. So the Total Harmonic Distortions (THD) of three-phase stator currents are low and phase differences of stator currents are all about 120°. Additionally, Park's vector trajectory is a circle and its eccentricity is approximately zero. However, as the fault degree enlarges, both the current of faulty-branch and the eccentricity of Park's vector trajectory increase. The phase differences related to faulty-phase and THD of faulty-phase become larger. Therefore, THD, phase differences and the eccentricity can be regarded as three fault features to diagnose fault phase and fault degree.