Miracle-dz/UniFAR-FTUs huggingface dataset:
HOW MANAGERS USE THE BALANCED SCORECARD TO SUPPORT THE STRATEGY IMPLEMENTATION AND FORMULATION PROCESSES

This paper investigates how the Balanced Scorecard (BSC) is used by managers, to support the strategy implementation and change processes. In the last twenty years the relationship between Management Control Systems (MCS) and strategy has become a relevant issue to management control investigation. The control framework of Simons (1995, 2000) provided a significant contribution assuming that MCS can be used both diagnostically and interactively. This study adopts an exploratory and an explanatory case study approach and focuses on a business unit of a large industrial Portuguese company, describing a true success case of BSC's methodology adoption and use. The results demonstrate that BSC methodology may be used under a diagnosis mode to implement deliberated strategies and, simultaneously, under an interactive mode to promote learning, support strategy revision and provide conditions for new strategies. The research provides insights into the relationship between MCS and strategy, as it identifies the characteristics of using BSC in the several levers of control.
SQuAD to BioASQ: analysis of general to speciﬁc

As biomedical information in the form of publications and electronic health records (EHR) increases at an increasingly fast pace, there is clear utility in having systems that can automatically handle information extraction, summarization, and question answering tasks. While there have been signiﬁcant strides in improving language tasks for general language, addressing domain-speciﬁc contexts still remains challenging. In this project, I apply and ﬁne-tune models to the SQuAD dataset and further modify/adapt for biomedical domain-speciﬁc question answering. I evaluated and compared performance on the SQuAD dataset and BioASQ, a biomedical literature QA dataset, with the goal of analyzing and developing approaches to leverage unsupervised language models for domain-speciﬁc applications. Upon generating various ﬁne-tuned models, the best performance for general language SQuAD QA achieved an F1 score of 76.717, EM score of 73.379, and for biomedical-speciﬁc BioASQ QA achieved an F1 score of 70.348 and EM score of 49.902.
Understanding Spatial Semantics in Natural Language

In this MSc thesis we focus on the Spatial Role Labeling (SpRL) task, which emerged as a variation of Semantic Role Labeling (SRL). The approach we advocate for SpRL fully exploits its kinship with SRL. It is based on adopting the NLPNET system, that is built on a multi-layer convolutional neural network and has proved its efficiency in dealing with the SRL task. We demonstrate that SpRL can be performed by a system originally designed for SRL, as well as that by applying several relatively simple input format transformations, it is possible to achieve a gain in the system’s performance. We present the results for the distinct cases of static and dynamic spatial relations. Although being relatively modest, the performance demonstrated by the system is comparable with that of the known state-of-the-art systems.
Sub-Meter Accurate UAV Navigation and Cycle Slip Detection with LTE Carrier Phase Measurements

A method to obtain sub-meter level navigation accuracy with cellular long-term evolution (LTE) signals is developed. The proposed method utilizes an LTE software-defined receiver (SDR) mounted on an unmanned aerial vehicle (UAV), which can produce carrier and code phase and Doppler frequency measurements from received LTE signals. Single difference measurements are used to remove the effect of the receiver’s clock bias. LTE eNodeBs’ clock biases are initialized using the known initial position of the UAV. The measurements are fused via an extended Kalman filter (EKF) to estimate the UAV’s position and integer ambiguities of the carrier phase single difference measurements. It is shown that since LTE signals have different carrier frequencies, conventional algorithms cannot be used to estimate the integer ambiguities. An algorithm to detect cycle slip is proposed, where the carrier phase measurements from the LTE eNodeB’s multiple antenna ports are used to detect cycle slip. Experimental results show a two-dimensional (2-D) position root-mean-squared error (RMSE) of 81 cm between the LTE navigation solution and the one obtained by a global navigation satellite systems (GNSS) receiver with real-time kinematic (RTK) coupled with an inertial measurement unit (IMU). Finally, the sources of error that affect the navigation solution accuracy are discussed.
Ecological niche modelling for prioritizing areas for domestication of introduced bamboo species in India

Identifying potential areas for domestication of a species or reintroduction in the wild is an important step in conservation and utilization. The traditional method of conducting multi-locational species trials and evaluating growth performance to match species with site is rather time consuming and not very cost effective. An innovative approach to overcome this issue is through ecological niche modeling (ENM) which is considered to be a robust tool for predicting suitable sites for domestication. In the present study, potential geographic distributions of industrially important bamboo Guadua angustifolia Kunth.were projected onto a spatial scale to predict suitable habitats in India. Also the species fitness was evaluated in the predicted habitats based on the primary growth data in distinct locations in humid tropics, sub humid and semi-arid regions of southern India and compared with the secondary growth data in the native range of Columbia, South America. The growth parameters such as survival percent, clump height, culm numbers and culm diameter had positive correlation with the predicted habitat. The results indicate that the growth performance was significantly higher in humid tropics compared to semiarid locations. Also the secondary growth data in native range was on par with the growth performance in humid tropics, which was predicted to be highly suitable habitat. Based on the niche modeling predictions and the comparison of growth data in the present study, it may be surmised that the fitness with respect to the growth performance was higher in habitats predicted to be highly suitable compared to habitats that were less suitable. These results have significant relevance in prioritizing potential areas for large scale domestication or afforestation programmes.
Deep Clustering for single-channel ego-noise suppression

In the context of audio signal processing for microphone-equipped robots, the robot’s self-created movement noise, so-called ego-noise, is a crucial problem. It massively corrupts the microphone signal and degrades the robot’s capability to interact intuitively with its environment. Therefore, ego-noise suppression is a key processing step in robot audition, which is commonly addressed using learning-based dictionary or template approaches. In this contribution, we introduce a deep-learning framework called Deep Clustering (DC) for ego-noise suppression in a single microphone channel, which was initially introduced by Hershey et al. for the task of speech separation. In DC, a bi-directional recurrent neural network is trained to embed each time-frequency bin of a mixture, containing ego-noise and speech, to a higher dimensional domain under the constraint that embeddings of bins dominated by ego-noise have maximal distance to those dominated by speech. During testing, clustering is performed in the embedding domain to assign each time-frequency bin uniquely to one of the two signal components and thereby allowing the estimation of both. We demonstrate that DC allows a significant reduction of ego-noise in the reconstructed signal. Additionally, we investigate the influence of the embedding size and the hidden layer size on the suppression performance.
3 D Implemented SRAM / DRAM Hybrid Cache Architecture for High-Performance and Low Power Consumption

This paper introduces our research status focusing on 3D-implemented microprocessors. 3D-IC is one of the most interesting techniques to achieve high-performance, low-power VLSI systems. Stacking multiple dies makes it possible to implement microprocessor cores and large caches (or DRAM) into the same chip. Although this kind of integration has a great potential to bring a breakthrough in computer systems, its efficiency strongly depends on the characteristics of target application programs. Unfortunately, applying die stacking implementation causes performance degradation for some programs. To tackle this issue, we introduce a novel cache architecture consisting of a small but fast SRAM and a stacked large DRAM. The cache attempts to adapt to varying behavior of application programs in order to compensate for the negative impact of the die stacking approach.
11961 Government Transfers , Work and Wellbeing : Evidence from the Russian Old-Age Pension NOVEMBER 2018

IZA DP No. 11961 NOVEMBER 2018 Government Transfers, Work and Wellbeing: Evidence from the Russian Old-Age Pension* This paper examines the impacts of a large and anticipated government transfer, the Russian old-age pension, on labor supply, home production and subjective wellbeing. The discontinuity in eligibility at pension age is exploited for inference. The 2006-2011 Russian Longitudinal Monitoring Survey is employed. Causal impacts differ across the sexes. Women reduce market work and appear to increase home production. They report increased wellbeing. Men reduce labor supply without any apparent increase in wellbeing. Pension receipt does not impact household composition. JEL Classification: I31, J22, J26, Z13
Classifying Firms with Text Mining 1

Statistics on the births, deaths and survival rates of firms are crucial pieces of information, as they enter as an input in the computation of GDP, the identification of each sector’s contribution to the economy, and the assessment of gross job creation and destruction rates. Official statistics on firm demography are made available only several months after data collection and storage, however. Furthermore, unprocessed and untimely administrative data can lead to a misrepresentation of the life-cycle stage of a firm. In this paper we implement an automated version of Eurostat’s algorithm aimed at distinguishing true startup endeavors from the resurrection of pre-existing but apparently defunct firms. The potential gains from combining machine learning, natural language processing and econometric tools for preprocessing and analyzing granular data are exposed, and a machine learning method predicting reactivations of deceptively dead firms is proposed.
ANOMALY DETECTION IN MOBILE PLATFORM USING MACHINE LEARNING MODELS

The recent advances in hardware, software and telecommunication have enabled development and use of wide range of mobiles from low cost to high cost which is equipped with a variety of advanced technology using the modern sensors. Mobile application not only gives us expedience but also brings inevitable security problems.With that being said the most vital reason for the security threat in mobile is unauthorized employment of the mobile phone but there us little method to protect mobile from unauthorized usage. So, it is very requisite of the model of the security performance of mobile intelligent terminal. In this paper,a practical and effective framework for anomaly detection using machine learning algorithm is proposed. First we analyze the data and produce the features and run a wide range of machine learning algorithms on it (Eg: Neural Networks,SVM,Decision trees .etc) and evaluate their performance individually.
Parallel GraphBLAS with OpenMP

SuiteSparse:GraphBLAS is a complete implementation of the GraphBLAS standard. It provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written with the GraphBLAS achieve high performance with minimal development time. Multithreaded parallelism through OpenMP provides additional speedup, which we illustrate on a 20-core Intel ® Xeon ® E5-2698 CPU system when solving various problems (triangle counting, k -truss, breadth-ﬁrst search, Bellman-Ford, local clustering coeﬃcient, and a sparse deep neural network problem). This wide variety of algorithms illustrates the expressiveness of the GraphBLAS API to create new graph algorithms. We present performance results with these algorithms on a set of large real-world graphs, using the newly developed Suite-Sparse:GraphBLAS v3.0. 1.
Optimum Design of a SRM Using FEM & PSO

owadays the use of the Switched Reluctance Motors (SRMs) has been increased in various home and industrial applications considerably. Despite of many advantages of this type of motors such as simple structure, low cost, and high reliability, the main disadvantage of them is their high torque pulsation. This paper presents a novel method to optimize a typical SRM such that the torque ripple reaches its minimum value. Meanwhile, the torque average and the motor efficiency become maximum. It is shown that the pole width to the pole pitch ratio have a great impact on the torque ripple and torque average for both stator and rotor poles. Finite Element Method (FEM) was used to obtain the torque ripple, the torque average and the motor efficiency for a large number of ratios. A functional relationship was developed between the input and the output parameters. Then, the Particle Swarm Optimization (PSO) was used to find the optimum design.
On the well-posedness, equivalency and low-complexity translation techniques of discrete-time hybrid automaton and piecewise affine systems

The main contribution of this paper is to present the systematic and low-complexity translation techniques between a class of hybrid systems referred to as automaton-based DHA and piecewise affine (PWA) systems. As an starting point the general modeling framework of the automaton-based DHA is represented which models the controlled and uncontrolled switching phenomena between linear continuous dynamics including discrete and continuous states, inputs and outputs. The basic theoretical definitions on the state trajectories of the proposed DHA with forward and backward evolutions which yield forward and backward piecewise affine (FPWA and BPWA) systems are given. Next, the well-posedness and equivalency properties are proposed and the sufficient conditions under which the well-posedness property is achieved with the automaton-based DHA and PWA systems are given. It is shown that the graphical structure of the proposed automaton-based DHA makes it possible to obtain analytically the equivalent PWA system with a polynomial complexity in contrast to the existing numerical translation techniques via decomposed structure of the DHA with an exponential complexity. Examples are presented to confirm the effectiveness of the proposed translation techniques.
MG Parsing as a Model of Gradient Acceptability in Syntactic Islands

It is well-known that the acceptability judgments at the core of current syntactic theories are continuous. However, an open debate is whether the source of such gradience is situated in the grammar itself, or can be derived from extra-grammatical factors. In this paper, we propose the use of a top-down parser for Minimalist grammars (Stabler, 2013; Kobele et al., 2013; Graf et al., 2017), as a formal model of how gradient acceptability can arise from categorical grammars. As a test case, we target the acceptability judgments for island effects collected by Sprouse et al. (2012a).
Orchestra : An Ensemble Approach to SQuAD 2 . 0

The Stanford Question Answering Dataset (SQuAD) 2.0 task tests both a system’s ability to answer reading comprehension questions and to determine when a question cannot be answered given the provided passage. In this report, we outline improvements we have made to a Bidirectional Attention Flow (BiDAF) model baseline, which was provided to us as described in the original BiDAF paper (Seo et al. 2016 [1]), though with the character embedding layer omitted. We developed several distinct improvements that resulted in increased EM and F1 scores relative to the baseline. Our first improvement used word embedding vectors that concatenate a given word’s GLoVe and word2vec embeddings for use in the word embedding layer. Our second improvement added a character embedding layer to the provided baseline. Further improvement resulted from replacing Bidirectional LSTMs with Bidirectional GRUs in selected layers of the BiDAF model. Though experiments with variable learning rates proved inconclusive, an ensemble model we call ORCHESTRA consisting of four of our highest performing models yielded our best performance on the CS224N SQuAD 2.0 test set, with an F1 score of 67.00 and an EM score of 63.86. As of 3:45pm on Sunday, March 17, 2019, our model ranked second highest for both F1 and EM scores out of 42 submissions on the test non-PCE leaderboard.
EFFICIENT APPROACH OF DETECTION AND VISUALIZATION OF THE DAMAGED TABLETS

People affected by many diseases in their lives and this effect with the productivity of the individual in society and the lives of the entire person and most of these diseases can be cured by medicines. The problem in pharmaceutical industries, in the actual situation, existing a number of defects incorporated in tablets inadequate fines to granules ratio, inadequate moisture content and poor machine settings can be some of the reasons for those visual defects such as faults in a cover of tablet pill. In addition, the production of medicines and pharmaceutical factories are expanded so it is difficult to control the quality of the tablets after packaging. The aim of this paper is to upgrade this manual tablet-sorting machine into an automated system with the aim of improving the speed and accuracy of the sorting process. The hole in the cover plastic package is common defects that can be found in tablets. Therefore, this defect was considered for the purpose of this paper. Damage on the Cover plastic package can be produced during the production of the packaging and can be produced before packaging therefore; will design system inspection the cover plastic before packaging for less damage money as much as possible, also inspection the cover plastic after packaging because the damage on the cover plastic packaging causes damage in pills and capsules. The proposed system algorithm includes preprocessing and feature extraction using opponent local binary patterns &opponent coordinated clusters representation (its the contribution research) and finally the classification of tablets for damage or undamaged issue using an artificial neural network algorithm (ANN) and specifically feed forward back propagation learning.The neural network training with feature extraction from the data after it has been tested. The experimental results are acceptable, the performances of the check pill cover plastic system indicated the total accuracy of 94.4% for testing. Also, the approach using opponent features provides better recognition accuracy than other approaches. The system is evaluated using sensitivity, specificity, and accuracy. The programs are done using Matlab package.
Resveratrol protects acrylamide / glycidamide induced genotoxicity in hepatic cell line

Resveratrol has been regarded to exhibit antigenotoxic effects. Resveratrol was isolated from healthy grape berries and wine. The trans-resveratrol was purified, characterized by HPLC and LC-MS. In this study the protective effect of trans-resveratrol against genotoxicity induced by acrylamide (AA), a toxicant formed in carbohydrate-rich foods subjected to temperatures, above 140o C and its metabolite glycidamide (GA) resulting from cytochrome P450 2E1 (CYP2E1) was investigated in HepG2 cells. Pretreatment of HepG2 cells with resveratrol (RES) showed ameliorating effects on AA and GA induced cytotoxicity, decreased the reactive oxygen species (ROS), and prevented DNA damage, micronucleus induction. Also resveratrol exhibited decreased in the activity, mRNA levels of CYP2E1 and increased activity and gene expression of GST, thus preventing the accumulation of GA. Also, RES showed the upregulation of few of the DNA repair genes thus preventing mutation accumulation induced by AA and GA. In conclusion RES potential antioxidant property protects HepG2 cells from genotoxicity induced by
The molecular responses of human embryonic kidney cells to abnormal expression of RNA binding motif protein 10 : cues to molecular mechanism and function

This study was aimed to illustrate the role of RNA binding motif protein 10 (RBM10) in the human embryonic kidney (HEK) 293 cells. The RNA sequencing data following RBM10 knockdown (KD) or over-expression (OE) in HEK293 cells were downloaded and reanalyzed. Differentially expressed genes (DEGs) were screened and divided into four groups, including RBM10 KD induced upand down-regulated, RBM10 OE induced upand down-regulated groups. In addition, direct (differentially expressed in RBM10 OE and KD groups) and indirect DEGs (differentially expressed in one groups) were identified, and protein-protein interaction (PPI) network for them was constructed. In the end, pathway enrichment analysis of genes was performed. Total 268 DEGs (169 upand 99 down-regulated) in RBM10 KD group and 113 DEGs (38 up and 75 down-regulated) in RBM10 OE group were obtained, of which, 34 DEGs were direct genes. A direct-indirect PPI network with 339 nodes including 18 direct DEGs, 156 indirect DEGs and 165 intermediate genes was gained. SAT1 (spermidine/spermine N1-acetyltransferase 1), HMBOX1 (homeobox containing 1), CLOCK (clock circadian regulator) and CCND2 (cyclin D2) with high degree were direct genes. Genes in PPI network were closely associated with signaling pathways related to cell cycle and cancers. In conclusion, RBM10 may be involved in the regulation of gene expression of CLOCK, SAT1 and CCND2. RBM10 may interact with these genes to involve in gene expression and congenital disease occurrence through multiple signaling pathways including dopaminergic synapse and Wnt and Hippo signaling pathway.
Application of Rough Set as Knowledge in Transfer Learning for Medical Corpus Analysis

Supervised transfer learning working on natural language processing, we propose a new rough set embedding model. For a query about a given context paragraph from medical documents semantic analysis, requires modeling complex interactions between the context and the query. In our previous work, we proposed a rough set preprocessing work in diabetes medical records. For the standardized diabetes corpus analysis, the RNN with rough set works better. And we expect this model to work on transfer learning. In this paper, we introduce the Rough Set Attention Flow Network, we use the rough set as the knowledge work in an unstandardized diabetes corpus semantic analysis. Keyword Transfer learning, Rough set, Semantic analysis, Medical document.
Techniques in Extracorporeal Circulation 4E

Historical aspects physiology and pathophysiology of extracorporeal circulation design and principles of the extracorporeal circuit membrane and bubble oxygenators filtration in cardiopulmonary bypass priming fluids for cardiopulmonary bypass anaesthetic techniques for cardiopulmonary bypass intra-operative myocardial protection postoperative management following cardiopulmonary bypass haematological aspects of extracorporeal circulation the role of the complement system during cardiopulmonary bypass changes in renal function associated with cardiopulmonary bypass the neurological sequalae of cardiopulmonary bypass microbiological aspects of heart, heart-lung and lung transplant patients blood conservation following cardiovascular surgery pulsatile flow during cardiopulmonary bypass low flow and circulatory arrest intra-aortic balloon counterpulsation ventricular assist devices artificial hearts-present and future heart and heart-lung tranplantation extracorporeal respiratory support future developments and potential applications of extracorporeal circulation in medicine and surgery.