Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding and managing systemic innovation in project-based industries

Traditional industries (e.g., aerospace and pharmaceuticals) that once organized their activities into functional hierarchies are evolving into project-based forms of organization in which teams of specialists from both inside and outside the firm report to project managers. Emerging industries (e.g., biotechnology and information technology) are also adopting project-based forms of organization. Researchers term this proliferation of organizational forms emerging between market-based organizational interactions and hierarchical organizations as the swollen middle (Hennart, 1993). Though much is known about innovation in traditional, hierarchical organizational structures, little research to date explores the issues associated with innovation in the project-based organizations that populate the swollen middle. As outsourcing of specialized skills increases, product and process innovations with potential to improve overall productivity significantly (e.g., supply chain management, enterprise resource planning, or component prefabrication) often require multiple interdependent firms to change their processes. Although they may hold the promise of significant increases in productivity and profitability, data shows that these systemic innovations diffuse slowly in project-based industries. This research explores the structural mechanisms inherent in project-based forms of organization that impact the diffusion of systemic innovations. Expanding our understanding of this phenomenon is critical as firms and industries continue to evolve into project-based forms of organization. We explore these structural mechanisms in order to develop a proof-of-concept explanatory model for understanding why systemic innovations diffuse more slowly than incremental innovations in project-based industries. Our research design focuses narrowly on innovations in residential building, though we seek to generalize our model to apply it to any project-based firm or industry. In this paper we: 1. Delineate the concepts of incremental and systemic innovations in the project-based industry context. 2. Review the building industry literature on innovation. 3. Present outcome and process evidence from our case-based research on incremental versus systemic innovations in the U.S. residential homebuilding industry. 4. Introduce a proof-of-concept model for systemic innovation in project-based industries. This paper concludes with a research agenda to improve our ability to understand, predict, and influence rates of diffusion for systemic innovations in project-based industries.
AUTOMATIC 3 D LANE MARKING RECONSTRUCTION USING MULTIVIEW AERIAL IMAGERY

The 3D information of road infrastructures are gaining importance with the development of autonomous driving. The exact absolute position and height of lane markings, for example, support lane-accurate localization. Several approaches have been proposed for the 3D reconstruction of line features from multi-view airborne optical imagery. However, standard appearance-based matching approaches for 3D reconstruction are hardly applicable on lane markings due to the similar color profile of all lane markings and the lack of textures in their neighboring areas. We present a workflow for 3D lane markings reconstruction without explicit feature matching process using multi-view aerial imagery. The aim is to optimize the best 3D line location by minimizing the distance from its back projection to the detected 2D line in all the covering images. Firstly, the lane markings are automatically extracted from aerial images using standard line detection algorithms. By projecting these extracted lines onto the Semi-Global Matching (SGM) generated Digital Surface Model (DSM), the approximate 3D line segments are generated. Starting from these approximations, the 3D lines are iteratively refined based on the detected 2D lines in the original images and the viewing geometry. The proposed approach relies on precise detection of 2D lines in image space, a pre-knowledge of the approximate 3D line segments, and it heavily relies on image orientations. Nevertheless, it avoids the problem of non-textured neighborhood and is not limited to lines of finite length. The theoretical precision of 3D reconstruction with the proposed framwork is evaluated.
The surface sediment carbon content of a north norfolk saltmarsh

Carbon sequestration is an important aspect of mitigating climate change and its detrimental environmental and economic effects. Vegetative ecosystems such as rainforests are often praised for their ability to sequester carbon through photosynthesis, however more efficient systems for sequestering carbon such as saltmarshes are often overlooked. Carbon storage was investigated in the developing salt marsh habitats of Holkham Bay, part of the Holkham National Nature Reserve in Norfolk, England between 31/07/2017 and 03/08/2017. This was done to assess the quantity of carbon that may be lost to rising sea levels associated with climate change and be released as CO2, which contributes to the greenhouse effect. Qualitative analyses of vegetation type and sediment texture were carried out using quadrat sampling (n = 70) and sediment cores (n = 35). Quantitative analysis of sediment carbon stock was carried out through loss on ignition. Carbon stock of the salt marsh was graphically represented using GIS mapping and an attempt was made to estimate the total carbon stock of the top 10 cm of the saltmarsh sediments. Measured values of carbon stocks at Holkham were compared with predicted values of carbon stock obtained using the Salt Marsh Carbon Stock Predictor tool. Significant differences in carbon stock between clay and sandy sediments were observed (p < 0.001), whilst vegetation communities showed no statistically significant difference in relation to carbon stock (p > 0.05). Estimates of carbon stock across the marsh (0.43 Km2) stand at 454-444 tonnes of carbon. Measured values of carbon stock were on average 14.18 Tonnes per Hectare lower than those predicted and are potentially due to marsh immaturity. Frequent anthropogenic disturbance at Holkham may be causing a reduction in carbon accumulation by the marsh vegetation. This study shows that substantial quantities of carbon could be lost from Holkham saltmarshes and indicates that larger marshes on the North Norfolk coastline may be substantially larger sinks of carbon, thus requiring
A NOVEL HYBRID APPROACH FOR FEATURE EXTRACTION IN MALAYALAM HANDWRITTEN CHARACTER RECOGNITION

Optical Character Recognition (OCR) is defined as the process of segregating textual scripts from a scanned document. To develop a digitally empowered society, information is made available in digital form. The OCR software assists in digitization of documents in different languages. Many researches are working on digitization of documents particularly to develop effective and error free character recognition models. To develop a digitally empowered society, information should be made digitally available. There arises the need for an OCR software in different languages. Malayalam handwritten character recognition precision is still inhibited around 90% due to the confrontations in Malayalam character set. The omnipresence of two different scripts old and new script, huge character set, ubiquity of similar shaped characters makes Malayalam handwritten character recognition more difficult. Feature extraction for each language may vary depending on various characteristics of the language. By observing the shape patterns in each language, different novel methods are developed to extract features and also to recognize the same. In this research, a novel hybrid approach is proposed which uses a combination of statistical and structural features (SSF). The statistical features are those derived from the statistical dissipating of pixels. Structural features are based on the topological and geometrical properties of the character. This study gives insight to the fact that combination of statistical and structural features gives more accuracy in Malayalam character recognition. Keywords— Optical Character Recognition, Binarization, Feature Extraction, Classification, Machine Recognition, Decision Tree.
A Bayesian Nonparametric Hierarchical Framework for Uncertainty Quantification in Simulation

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Blind image sharpness metric based on edge and texture features

Video is fast becoming the most common medium for media content in the present era. It is especially helpful in security situations for the detection of criminal or threat-related activity. Police routinely use videos as evidence in the analysis of criminal cases. It is important in such applications to get a high-quality still image from such videos. However, there are situations where the images are blurred and have artifacts as they are extracted from moving video repositories. A practical solution to this problem is to sharpen these images using advanced processing techniques to obtain higher display quality. Due to vast amount of data, it is extremely important that any such enhancement technique satisfy real-time processing constraints, in order for it to be usable by the end user. In this paper, a blind image sharpness metric is proposed using a combination of edge and textural features. Edges can be detected using different methods like Canny, Sobel, Prewitt and Roberts that are commonly accepted in the image processing literature. The Canny edge detection method typically provides better results due to extra processing steps and can be effectively used as a model feature extractor for the image. Wavelet processing based on the db2, sym4, and haar is also utilized to extract texture features. The normalized luminance coefficients of natural images are known to obey the generalized Gaussian probability distribution. Consequently, this characteristic is utilized to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. The extracted features are then merged together to obtain the sharpened image. The principle behind image formation is to merge the wavelet decompositions of the two original images using fusion methods applied to the approximation and details coefficients. The two images must be of the same size and are supposed to be associated with indexed images on a common color map. It is worth noting that the image fusion results are more consistent with human subjective visual perception of image quality, ground truth data for which is obtained from publicly available databases. Popular standard images such as Cameraman and Lena are used for the experiments. Results also show that the proposed method provides better objective quality than competing methods.
Impact of CEO characteristics on bank performance The case of a cooperative bank

How do CEO characterisitcs effect the performance of a bank? We use data on 106 autonomous banks that are part of one and the same cooperative European bank over the period 2010–2015 to answer that question. The balance between the homogeneity (e.g., one company) and heterogeneity (e.g., decision freedom of CEOs) of the sample provides an unique setting to test whether CEO turnover and the education and gender of the CEO matter for bank performance. We estimate the panel data with a fixed effects model and include bank and time fixed effects. Moreover, we control for balance sheet and income statement characteristics, the member base and market share of a bank, and local GDP. ∗Electronic addresses: J.A. C.vanToor@tias.edu (Corresponding author), Kees@duvelhok.com, J.Dornigg@gmail.com, J.M. Groeneveld@tias.edu. Due to confidentiality reasons we do not disclose the name of the bank and the country the bank is located in.
PROCUREMENT OVERSIGHT AGENCIES AND GROWTH OF MSES IN TRANS NZOIA COUNTY , IN KENYA

This study sought to explore the relationship between public procurement oversight agencies and growth of Micro and Small Enterprises (MSEs) in Trans Nzoia County. MSEs account for over 80% of employment opportunities and over 70% of many countries’ Gross Domestic Product (GDP). Despite governments’ biggest spending globally, at approximately 9%-45% of GDP, public procurement is controlled by large enterprises. Among the objectives of this study was to evaluate relationship between procurement oversight agencies and growth of MSEs in Trans Nzoia County, in Kenya. It was conducted in the County Government of Trans Nzoia. The respondents were sampled from the MSE within the trading blocs registered with the MSE Authority in the County. The population was 771 firms. The study sample was 155 respondents determined statistically using Cochran formula. Selfadministered questionnaires were used in data collection. Both qualitative and quantitative data analysis techniques were utilized. Descriptive and inferential statistics were utilized in analyzing and presenting the data. The study revealed that there is strong relationship between public procurement and the growth of MSE in Trans Nzoia County at 92% response rate. The relationship between procurement oversight agencies and the growth of MSEs, 75% of respondents posited unsatisfactory performance. The study established that only 37% of MSEs frequently participate in public procurement processes. The study concluded that there exists strong relationship between procurement oversight agencies and the growth of MSE in Trans Nzoia County , in Kenya.
Five-dimensional local positioning using neural networks

In this thesis, a method for real-time transmitter localization is evaluated. An existing system has acted as testbed for the evaluation. This system uses an electromagnetic transmitter and a receiver board with 16 antennas. The antenna values are used to recover the transmitters position and two angles, the five dimensions. The proposed solution is an inverse modelling feed-forward neural network, a multilayer perceptron, which is trained and evaluated with the use of the TensorFlow library. The project resulted in a purely software based estimator which requires no change to the testbed and can act as a drop in replacement for the previous algorithm. The new estimator has accomplished improvements in estimation speed (more than 100× faster), expansion of the volume in which the position can be recovered (27× larger), enlarged range of angles (10% per axis) and has improved the precision of the position estimates (error at the 95th percentile reduced to ~ 1/3 of the previous implementation). The new algorithm is a substantial improvement on the previous implementation, enabling new use cases for the system.
Resistivity Structure of the North West Olkaria Field Using ID Inversion of Magnetotelluric Data

The Northwest Olkaria prospect is part of the Greater Olkaria Geothermal Area located within the Kenyan rift in Nakuru County. It is bounded by the longitudes 36 0 15 E′ and 36 0 12 E′ with an estimated area of 30 Km. The area has been the least studied sector of the Greater Olkaria Geothermal Area due to its remote location and complex terrain. The study used the Magnetotelluric method to determine the electrical resistivity distribution of the subsurface and locate structural features controlling the geothermal distribution in the prospect area. A total of 52 Magnetotelluric soundings were considered in this research project and 47 corresponding central loop Transient Electromagnetic soundings. To allow the static shift correction in the 1D inversion, the Magnetotelluric data were jointly inverted with Transient Electromagnetic data. The results and interpretations of the Joint 1D inversion of Magnetotelluric and central loop Transient Electromagnetic data of Olkaria Northwest prospect were presented inform of resistivity Iso-maps and cross sections. These inversions were achieved by fitting both data kinds using the same 1D resistivity model. The result of the interpretation shows three main resistivity structures below the prospect area: (1) A shallow superficial lying thin high resistivity layer(> 30Ωm) thought to be due to unaltered rock formations and the thick pyroclastic cover is observed across the prospect followed by low resistivity thick dome shaped conductive layer (< 6Ωm) that extends to about 200 m.a.s.l in the North West field and thins towards the Central field becoming almost uniform in the South East and the rest of the field. This layer is presumed to be dominated by low temperature alteration minerals such as smectite and zeolite that defines the clay cap. (2) A high resistivity layer (resistive core) underlain by a deep lying conductor ((>15-50 Ωm) which is thought to be associated with the high temperature secondary minerals such as epidote, chlorite and biotite that are present in the reservoir. (3) A low conductivity layer that extends to about 6,000 m.b.s.l. which is associated with the heat source.Among the many recommendations highlighted, the study revealed that the main reservoir to the western side appears to be deeper than the known situation in the eastern field, at great depths, a prominent low resistive cap is observed and it is probably reflecting the presence of heat source of the geothermal system underneath the Northwest Olkaria prospect.
AN EMBELLISHMENT OF SEMANTIC KNOWLEDGE BASE USING NOVEL CROWD SOURCING AND GRAPH BASED METHODS FOR IMPROVING SENTIMENT ANALYSIS

Opinion Mining is given more attention now-adays, because it helps decision makers to evaluate the success of a newly proposed schemes, new ad campaign or new product launch. There is several classification approaches proposed to classify people’s opinions in Literature. The contextualization and enriched semantic knowledge bases are used to improve the classification accuracy in Opinion Mining. Contextualization recognizes ambiguous terms then adds context information for their disambiguation and enrich the semantic knowledge bases for sentiment analysis using SenticNet. SenticNet is a lexical resource which gives polarity (positive, negative and neutral), semantics and sentic information in sentiment analysis. The process of SenticNet includes recognizes the ambiguous terms, provides context information which is mined from domain specific corpus and ground this contextual information to knowledge sources. But semantically enriched approaches have issues with context and Ambiguous terms occurrence in a same sentence. The concurrences of both the term in same sentence are avoided in this paper by using crowd sourcing method. In crowd sourcing methods, multiple people process each opinion and label them as their skill level, then the large corpora is constructed based on the aggregated crowd sourced labels. The constructed corpus is used to annotate sentence level labels. The combination of human annotation and machine intelligence reduce the time of constructing larger corpora. In proposed novel crowd sourcing method document Meta data is also used along with text features. However labeling using large corpora is not sufficient to obtain high sentiment classification, so that the natural language patterns are used along with text features to improve the sentiment classification. Thus the proposed method yields more generic contextualized lexicons and provides higher classification accuracy. Keywords-Opinion Mining, Sentiment analysis, Contextualization, Disambiguation, Knowledge Extraction
An Indoor Video Surveillance System with Intelligent Fall Detection Capability

This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN) algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.
Mixture Ratio and Thrust Control of a Liquid-Propellant Rocket Engine

Presently, most European launchers’ engines work in open-loop. Not only does this oblige the valves which regulate the mass flow-rates to be calibrated before flight in order to obtain a desired thrust and mixture-ratio, but it also limits performance. Varying operating conditions translate into a drifting equilibrium point. The associated dispersion forces us to carry extra propellants to ensure mission success. The present work implements a closed-loop controller on the VINCI engine, aiming to maintain an optimal equilibrium point despite external perturbations, and also to transition to a 70% thrust regime. To meet these objectives, a reduced linear model of the expander thermodynamic cycle of the engine is obtained through a combination of an analytic linearization approach and a least-squares identification method. A sensitivity study confirms that 5 states suffice to describe the dominant dynamics. Two PID controllers are tuned based on the input-output pairings that minimize coupling within the 2 × 2 MIMO system. Modifications such as feed-forward, anti-windup and measurements filtering are made in order to match the control specifications. Simulations on the non-linear model indicate a single controller to be both capable of maintaining an operating point and of transitioning to the low-thrust regime, all while rejecting perturbations. The robustness to parameter uncertainty is assessed and preliminary results indicate actuator saturation before the controller displays any signs of instability. This work confirms the applicability of a modified PID controller to a naturally stable engine and lays the foundation to the study of other thermodynamic cycles.