Miracle-dz/UniFAR-FTUs huggingface dataset:
The triggers of competitiveness: the EFIGE cross-country report. Bruegel Blueprint 17, 17 July 2012

There is a pressing need for Europe to grow out of the crisis, meaning that Europe needs to become more competitive, enabling it to capture growth currently taking place mainly in emerging markets. But what are the triggers of competitiveness? The EFIGE project, led by Bruegel, takes a fresh look by inquiring into the determinants of firm-level international performance – focusing on external competitiveness. In the competitiveness debate, it is crucial to understand not only the macroeconomic challenge, but also to find the right micro-level triggers that will generate growth and exports. The authors identify firm-level total factor productivity as a major determinant of growth and exports. Human capital, research, equity finance and performance based incentives for employees also play their parts. Moreover, size matters and large firms typically are much better exporters than their smaller counterparts. This report builds on previous EFIGE research and studies in depth firm performance in seven countries (Austria, France, Germany, Hungary, Italy, Spain, United Kingdom) to identify the triggers of competitiveness.
Face Verification base-on Hausdorff-Shape Context

This paper proposes a highly robust method for face recognition and authentication. Techniques introduced in this work are composed of two stages. Firstly, the feature of face is to be detected by the principle of Trace Transform. Then, in the second stage, the Hausdorff distance and Shape Context are employed to measure and determine of similarity between models and test images. From the experimental result of 6,325 images, the average of accuracy rate is higher than 84%.
Exponential rate of L_p-convergence of intrinsic martingales in supercritical branching random walks

A.Iksanov, S. Polotsky and U. Rösler were supported by the German Research Foundation (project no. 436UKR 113/93/0-1). The research leading to the present paper has been mainly conducted during visits to University of Kiev (Rösler), to University of Kiel (Iksanov and Polotsky), and to University of Münster (Iksanov). Financial support obtained from these institutions is gratefully acknowledged. e-mail address: gerolda@math.uni-muenster.de Corresponding author; tel. /fax. : +38044 5213202 e-mail address: iksan@unicyb.kiev. ua e-mail address: sergiy.polotskiy@gmail.com e-mail address: roesler@math.uni-kiel.de
Creative patterns and stimulation in conceptual design

Abstract Conceptual design is a creative process. Designers create functions to satisfy customer needs and behaviors and forms to fulfill their functions. Although cognitive processes are at the center in developing new ideas, they are rarely taken into account in research and development of design support methods and systems. It is conceivable that if one understands how cognitive processes are stimulated to generate design ideas, then more effective methods and tools can be developed to support conceptual design. In this article, a cognitive model of conceptual design is developed to capture the relationships among design entities, design operations, and cognitive processes. A protocol analysis is performed to evaluate the model, and a cognitive experiment carried out to study the creative patterns and stimulating relationships. The results show that designers exhibit patterns of creative design behavior, and that these patterns can be captured and instilled into the design process to promote creativity.
More or less supervised supersense tagging of Twitter

We present two Twitter datasets annotated with coarse-grained word senses (supersenses), as well as a series of experiments with three learning scenarios for supersense tagging: weakly supervised learning, as well as unsupervised and supervised domain adaptation. We show that (a) off-the-shelf tools perform poorly on Twitter, (b) models augmented with embeddings learned from Twitter data perform much better, and (c) errors can be reduced using type-constrained inference with distant supervision from WordNet.
The Career Development of Women in Executive Level Positions in Information Technology.

The purpose of this study was to develop an in-depth understanding of career development for women in executive level positions in information technology. The study examined the barriers that hindered and the factors that assisted the women in achieving executive level positions. Furthermore, this study obtained an in-depth understanding of the challenges and opportunities faced by the women in their educational programs and as they moved up the career ladder to executive level positions.
Manipulation of polyhedral parts by rolling

The nonholonomy exhibited by kinematic systems consisting of bodies rolling on top of each other can be used to the purpose of building dexterous mechanism with a minimum hardware complication. Previous work concentrated on manipulation of objects possessing a regular surface. On the other hand, industrial parts are most often irregular, possessing vertices and edges. In this paper we present some results on the description of the set of positions and orientations that polyhedral objects can reach when manipulated by rolling without slipping. An algorithm for planning the manipulation of a polyhedral part from a given configuration to another reachable one, is also presented.
An adaptive model-based real-time voltage control process for Active Distribution Networks using Battery Energy Storage Systems

The paper presents a centralized real-time adaptive and model-based voltage control algorithm for Active Distribution Networks (ADNs). Differently from the available literature, the proposed algorithm merely relies on the control of Battery Energy Storage Systems (BESSs). In this respect, an experimentally model-fitted two-time constant dynamic model of BESS is used. In particular, this model is used to compute the constraints of the BESSs in terms of DC active power at each controller iteration. These constraints are subsequently used in the central controller for the solution of the optimal voltage control problem. The performances of the proposed method are compared with those obtained for the case where BESSs are modeled as ideal energy reservoirs. Such an assessment is carried out using a numerical example referring to IEEE 13 buses distribution test feeder suitably adapted to include stochastic generation and BESSs.
Analysis of gender normalization using MLP and VTLN features

This paper analyzes the capability of multilayer perceptron frontends to perform speaker normalization. We find the context decision tree to be a very useful tool to assess the speaker normalization power of different frontends. We introduce a gender question into the training of the phonetic context decision tree. After the context clustering the gender specific models are counted. We compare this for the following frontends: (1) Bottle-Neck (BN) with and without vocal tract length normalization (VTLN), (2) standard MFCC, (3) stacking of multiple MFCC frames with linear discriminant analysis (LDA). We find the BN-frontend to be even more effective in reducing the number of gender questions than VTLN. From this we conclude that a Bottle-Neck frontend is more effective for gender normalization. Combining VTLN and BN-features reduces the number of gender specific models further.
Robust processing of noisy web-collected data

Crowdsourcing has become an important means for collecting linguistic data. However, the output of web-based experiments is often challenging in terms of spelling, grammar and out-of-dictionary words, and is therefore hard to process with standard NLP tools. Instead of the common practice of discarding data outliers that seem unsuitable for further processing, we introduce an approach that tunes NLP tools such that they can reliably clean and process noisy data collected for a narrow but unknown domain. We demonstrate this by modifying a spell-checker and building a coreference resolution tool to process data for paraphrasing and script learning, and we reach state-of-the-art performance where the original state-of-the-art tools fail.
Testing Wireless Sensor Networks with Hybrid Simulators

Software development for Wireless Sensor Networks (WSNs) is challenging due to characteristics of sensor nodes and the environment they are deployed in. Testing software in a real WSN testbed allows users to get reliable test results. However, real testbeds become more expensive as the number of sensor nodes in the network grows. Simulation tools are alternatives to real testbeds. They are cheaper, faster and repeatable. However, simulation results are not reliable as that of testbeds. Therefore, there is a need for a testing tool that can leverage the advantages of testbeds and simulation tools. These tools are usually called hybrid simulators. In this survey, we discuss several hybrid simulators that use real sensor motes integrated with a simulator to make software development cheaper, repeatable and to make the results more reliable.
Visual Learning by Feature Combination and Feature Construction

In developing a visual learning method, the selection of features highly affects the performance of the method. However, the optimal features generally depend on learning tasks. Therefore, it is necessary for the effective learning to find the optimal features according to the learning task. In this paper, we propose two types of new visual learning methods; the feature combination method and the feature construction method. The former is the simpler method that combines some low-level features into a higher-level feature represented by a linear combination of the low-level features. This method can find more discriminative boundary in the feature space. The latter is the more complex method that combines a variety of low-level features into a constructed feature represented by a tree structure. Although this method is rather complex than the feature combination method, much more discriminative boundary can be found because such a novel feature can form the more discriminative feature space. We compare the performance of our methods based on some experiments with some real-world images.
Ordered Counter-Abstraction

We introduce a new symbolic representation based on an original generalization of counter abstraction. Unlike classical counter abstraction (used in the analysis of parameterized systems with unordered or unstructured topologies) the new representation is tailored for proving properties of linearly ordered parameterized systems, i.e., systems with arbitrary many finite processes placed in an array. The relative positions in the array capture the relative priorities of the processes. Configurations of such systems are finite words of arbitrary lengths. The processes communicate using global transitions constrained by their relative priorities. Intuitively, an element of the symbolic representation has a base and a set of counters. It denotes configurations that respect the constraints imposed by the counters and that have the base as a sub word. We use the new representation in a uniform and automatic Counter Example Guided Refinement scheme. We introduce a relaxation operator that allows a well quasi ordering argument for the termination of each iteration of the refinement loop. We explain how to refine the relaxation to systematically prune out false positives. We implemented a tool to illustrate the approach on a number of parameterized systems.
Association Control for Throughput Maximization and Energy Efficiency for Wireless LANs

Because the access points (APs) and the stations (STAs) of a community network are deployed at the users’ desired places, the APs and STAs tend to concentrate in certain areas. A concentration of STAs often results in the AP(s) and STAs in that particular area suffering from severe congestion. On the other hand, a concentration of APs causes energy wastage. A proper association control can effectively alleviate congestion and improve network throughput. In this paper, we analytically formulate the throughput maximization problem and show that the existing association control schemes do not necessarily maximize throughput. Furthermore, while load balancing tends to use all the existing APs, sufficient performance can be achieved by utilizing fewer APs, especially in AP-concentrated areas. This enables lower energy consumption by putting unused APs in power-saving mode. To this end, we propose an association control scheme that aims at maximizing network throughout and reducing energy consumption. Using both computer simulations and testbed experiments, we confirm that the proposed scheme provides excellent performance and that it is feasible using the off-the-
Feature based approach for review mining using appraisal words

Due to the high increment in the usage of internet nowadays e-commerce is getting very popular. The online shopping is becoming more convenient and prominent. As well as the number of online consumer reviews are increasing very fast. It is a challenge for the organizations and consumers to go through these reviews for the decision making process. Mining the necessary information from these reviews is beneficial to consumers and organizations. Sentiment mining can be very helpful for customer relation management, marketing and identifying the opinion and popularity of the product. In this paper we present a novel framework for review classification using sentiment analysis. The framework uses appraisal words lexicon and product feature extraction for review categorization.
Exploring social media as a driver of sustainable behaviour: case analysis and policy implications

This paper describes an empirical study into the emerging effects of instantly\navailable social media on collective environmentally sustainable behaviour. The\nfirst contribution to the literature is a model whereby social media is positioned as\na means to overcome two important barriers to collective environmentally\nsustainable behaviour. The first barrier can be called fatalism, or a lack of belief\non the part of potential participants that the sustainability initiative will have a\nsignificant impact. This study hypothesizes that social media can help overcome\nthis barrier by presenting evidence to potential participants of the initiative’s goals\nand achievements as well as by helping participants to share this information with\npotential participants in their own social networks. The second barrier is termed\nbusyness, whereby the favourably disposed majority cannot permit themselves the\ntime and energy needed to turn their attitude into behaviour. The second\nhypothesis is that social media can stimulate these people to take action by\nreducing the effort required to act and by helping participants to share their\nexperiences with each other. These mechanisms are related to two outcomes: the\nscale of participation and the impact achieved by the initiative. The second\ncontribution to the literature is an empirical investigation of the relationships\nbetween the aforementioned social media mechanisms and outcomes, whereby\nmore than 30 relevant social media applications are analysed. Initial results show\nthe strong influence of presenting evidence of the group’s goals and achievements\nto potential participants on the scale of participation. A third contribution of this\npaper is to translate the effects of social media enabled collective behaviour into\npolicy implications. This study shows that social media can enable a disruptive\nforce that may affect the power balance between market, government and\nconsumer groups. For these three parties we discuss the implications of this\nchanging power balance.
Modeling and Reasoning about Success, Failure, and Intent of Multi-Agent Activities

Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of individuals. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain. Our evaluation shows that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system can automatically learn an augmented model that is capable of recognizing success, failure, as well as goals of people’s actions with high accuracy. Finally, we demonstrate that explicitly modeling unsuccessful attempts boosts performance on other important recognition tasks. Author Keywords Multi-agent activity recognition, statistical relational learning, structure learning.
Association between Time of Day of Sports-Related Physical Activity and the Onset of Acute Myocardial Infarction in a Chinese Population

Objective To investigate the association between the time of day of sports-related physical activity and the onset of acute myocardial infarction (AMI) in a coronary artery disease (CAD) population in China. Methods Between February 2014 and March 2015, a total of 696 patients from Nanjing, China, who had CAD were studied and divided into two groups (Non-AMI and AMI groups). The work-related activity and sports-related physical activity information were obtained from a self-reporting predesigned patient questionnaire. Results Sports-related physical activity was associated with a lower risk of the onset of AMI, after adjusting the established and potential confounders, with an adjusted odds ratio (OR) of 0.67 (95% CI, 0.47–0.94) compared with those who did not have any sports-related physical activity. A dose–response relationship was observed for intensity, duration, and frequency of sports-related physical activity. Further stratification analysis revealed that the protective effects of sports-related physical activity were significant in the morning and evening groups, and patients who exercised in the evening were at a lower risk of AMI than those doing sports-related physical activity in the morning. The adjusted ORs for doing sports-related physical activity in the morning and evening groups were 0.60(0.36–0.98) and 0.56(0.37–0.87), respectively, compared with inactivity (all P<0.05). On the occurrence of AMI, doing sports-related physical activity in the evening had an adjusted OR of 0.93 (95% CI, 0.54–1.64, P = 0.824) compared with in the morning group. Conclusions Sports-related physical activity is associated with a lower risk of onset of AMI than inactivity in Chinese people. For CAD patients, we suggest they participate in sports-related physical activity of high intensity, long duration, and high frequency. Doing sports-related physical activity in the evening and in the morning have similar benefits on the prevention of the onset of AMI.
Efficiency of Non-Contrast-Enhanced Liver Imaging Sequences Added to Initial Rectal MRI in Rectal Cancer Patients

Purpose The purpose of this study was to estimate the value of addition of liver imaging to initial rectal magnetic resonance imaging (MRI) for detection of liver metastasis and evaluate imaging predictors of a high risk of liver metastasis on rectal MRI. Methods We enrolled 144 patients who from October 2010 to May 2013 underwent rectal MRI with T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) (b values = 50, 500, and 900 s/mm2) of the liver and abdominopelvic computed tomography (APCT) for the initial staging of rectal cancer. Two reviewers scored the possibility of liver metastasis on different sets of liver images (T2WI, DWI, and combined T2WI and DWI) and APCT and reached a conclusion by consensus for different analytic results. Imaging features from rectal MRI were also analyzed. The diagnostic performances of CT and an additional liver scan to detect liver metastasis were compared. Multivariate logistic regression to determine independent predictors of liver metastasis among rectal MRI features and tumor markers was performed. This retrospective study was approved by the Institutional Review Board, and the requirement for informed consent was waived. Results All sets of liver images were more effective than APCT for detecting liver metastasis, and DWI was the most effective. Perivascular stranding and anal sphincter invasion were statistically significant for liver metastasis (p = 0.0077 and p = 0.0471), while extramural vascular invasion based on MRI (mrEMVI) was marginally significant (p = 0.0534). Conclusion The addition of non-contrast-enhanced liver imaging, particularly DWI, to initial rectal MRI in rectal cancer patients could facilitate detection of liver metastasis without APCT. Perivascular stranding, anal sphincter invasion, and mrEMVI detected on rectal MRI were important imaging predictors of liver metastasis.
Engineering of the Embryonic and Adult Stem Cell Niches

Context Stem cells have the potential to generate a renewable source of cells for regenerative medicine due to their ability to self-renew and differentiate to various functional cell types of the adult organism. The extracellular microenvironment plays a pivotal role in controlling stem cell fate responses. Therefore, identification of appropriate environmental stimuli that supports cellular proliferation and lineage-specific differentiation is critical for the clinical application of the stem cell therapies. Evidence Acquisition Traditional methods for stem cells culture offer limited manipulation and control of the extracellular microenvironment. Micro engineering approaches are emerging as powerful tools to control stem cell-microenvironment interactions and for performing high-throughput stem cell experiments. Results In this review, we provided an overview of the application of technologies such as surface micropatterning, microfluidics, and engineered biomaterials for directing stem cell behavior and determining the molecular cues that regulate cell fate decisions. Conclusions Stem cells have enormous potential for therapeutic and pharmaceutical applications, because they can give rise to various cell types. Despite their therapeutic potential, many challenges, including the lack of control of the stem cell microenvironment remain. Thus, a greater understanding of stem cell biology that can be used to expand and differentiate embryonic and adult stem cells in a directed manner offers great potential for tissue repair and regenerative medicine.
An Efficiency and Fairness Based Packet Marking Algorithm

IETF has proposed a service model – Differentiated Services (DiffServ) [1], which for its extensibility, has been widely accepted as a promising model. However, researchers found unfairness in DiffServ networks. To address this problem, this paper analyzed the inherent shortcomings of marker based on time sliding window (TSW) in DiffServ, and proposed a new packet mark algorithm. The author validated this algorithm through simulation experimentation, and compared its performance with some other marking algorithms. The simulation results show that the marking algorithm makes tradeoffs between performance and efficiency.
A novel background subtraction method based on color invariants and grayscale levels

This paper presents a new method for background subtraction which takes advantages of using the color invariants combined with gray color. The proposed method works robustly reducing misclassified foreground objects. Gaussian mixtures are exploited for each pixel through two channels: the color invariants, which are derived from a physical model, and the gray colors obtained as a descriptor of the image. The background models update is performed using a random process selected considering that in many practical situations it is not necessary to update each background pixel model for each new frame. The novel algorithm has been compared to three state-of-the-art methods. Experimental results demonstrate the proposed method achieves a higher robustness, is less sensitive to noise and increases the number of pixel correctly classified as foreground for both indoor and outdoor video sequences.