Miracle-dz/UniFAR-FTUs huggingface dataset:
The Black-White Recognition Gap in Award Nominations

There is evidence showing racial bias in firms’ hiring decisions, but less is known about bias in career recognition. We construct a dataset from the second-largest US police department to estimate the Black-white gap in award nominations. Leveraging institutional features, we find that white supervisors are less likely to nominate Black officers conditional on work performance. This appears to be driven by supervisor bias in advocacy decisions rather than statistical discrimination. Given the reliance on subjective evaluations for promotions in many organizations, our findings have important implications for the Black-white promotion gap and the lack of diversity in upper-management positions.
Exploration of Approaches to Arabic Named Entity Recognition

The Named Entity Recognition (NER) task has attracted signiﬁcant attention in Natural Language Processing (NLP) as it can enhance the performance of many NLP applications. In this paper, we compare English NER with Arabic NER in an experimental way to investigate the impact of using diﬀerent classiﬁers and sets of features including language-independent and language-speciﬁc features. We explore the features and classiﬁers on ﬁve diﬀerent datasets. We compare deep neural network architectures for NER with more traditional machine learning approaches to NER. We discover that most of the techniques and features used for English NER perform well on Arabic NER. Our results highlight the improvements achieved by using language-speciﬁc features in Arabic NER.
Nexus between Cost Leadership Strategy and Performance: Fact or Fallacy in Milk Processing Firms in Kenya

Milk processing firms as a constituent of the food processing sector play a crucial function both economically and nutritionally. However, performance in the industry continues to be impended by high costs leading to low profitability margins, decline in output and collapse of some firms while others show stunted growth. It is hypothesized that this situation can be remedied by pursuing cost leadership strategy through economies of scale, economies of scope and operational efficiency. Extant literature however is scanty on how this strategy is employed by milk processing firms in Kenya with studies done failing to focus on how the firms manage costs as a driver for better performance. This has made it difficult to determine whether the hypothesized effect is a fact or fallacy. This study thus was an investigation of the effect of cost leadership strategy on performance of milk processing firms in Kenya. It was anchored on the balanced scorecard model complemented by the resource based view and capability based view theories. The study empirically examined the relationship using data from milk processing firms in Kenya obtained from a sample of 168 key respondents. The findings showed that cost leadership strategy had a positive and significant effect on performance of milk processing firms in Kenya. The study recommends that milk processors improve their performance by cutting costs through measures to increase their scale of operations, expand into related business areas and improve operational processes. The government and other the regulatory bodies should implement corresponding supportive policies and reforms.
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World

Deep Reinforcement Learning (DRL) has been successfully used to solve different challenges, e.g. complex board and computer games, recently. However, solving real-world robotics tasks with DRL seems to be a more difficult challenge. The desired approach would be to train the agent in a simulator and transfer it to the real world. Still, models trained in a simulator tend to perform poorly in real-world environments due to the differences. In this paper, we present a DRL-based algorithm that is capable of performing autonomous robot control using Deep Q-Networks (DQN). In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment. The method is evaluated in the Duckietown environment, where the agent has to follow the lane based on a monocular camera input. The trained agent is able to run on limited hardware resources and its performance is comparable to state-of-the-art approaches.
Critical Factors that Impact Performance of Machine Learning Algorithms for Polarity Determination of Movie Reviews

Sentiment Analysis (SA) of movie reviews has been shown to be extremely sensitive to the inherent review specific factors. The researches mostly focus on feature selection options and algorithms but do not dive deeper into other factors that may affect the performance of algorithms in prediction of sentiment polarity. Machine learning based supervised methods can be affected by factors such as the rating cut off point selected as label marker, the impact of the ratio of positive to negative reviews in the sample set or the number of reviews taken for training. In particular, the impact of sub-domains with regard to country specific factors is also not well understood and it can be interesting to study its impact by comparing the SA performances for the two largest country specific sub-domains within the domain of movie reviews: Bollywood and Hollywood. The study shows that the machine learning models perform differently on these sub-domains due to inherent differences in the nature of reviews. Also the study highlights that the impact of the number of training samples, selection of rating cut-off point and the positive to negative skew within the sample set. The study also concludes that while SVM (Support Vector Machine) delivers best performance in most cases, there are certain situations in which other machine learning algorithms may prove more useful.
Testing the Pollution Haven Hypothesis: Evidence from European Union Emissions Trading Scheme

This paper investigates the possible pollution haven effect caused by European Union Emissions Trading Scheme, using panel bilateral trade flows data between 2000 -2011. I find significant evidence in favor of pollution haven effect within the European Union, while on the other hand, extra European Union trade flows show opposite effect that the member countries have higher exports and lower imports for regulated industries. Whether permissions are auctioned has no significant effect. A shortage of allocated permits pushes a country into a disadvantage with respect to its trading ability.
Learning Instance-Speciﬁc and Search-Space Speciﬁc Distance-to-Goal Heuristics Using Neural Networks

We propose and evaluate a system which learns a neural-network heuristic function for forward search-based, satisﬁcing classical planning. Our system learns distance-to-goal estimators from scratch, given a single PDDL training instance. Training data is generated by backward regression search or by backward search from given or guessed goal states. In domains such as the 24-puzzle where all instances share the same search space, such heuristics can also be reused across all instances in the domain. We show that this relatively simple system can perform surprisingly well, sometimes competitive with well-known domain-independent heuristics.
Anaphora Reasoning Question Generation Using Entity Coreference

We propose an approach for Anaphora Reasoning Question Generation from a plain text: generating a question which needs anaphora resolution to answer. This is one type of Multiple Sentence Reasoning Question, a paragraph-level question which needs more than one sentence in a context to answer. We apply our system to Wikipedia articles and, based on our evaluation, our system generates more Anaphora Reasoning Question compared to the current state-of-the-art neural question generation model which intends to generate a paragraph-level question by around 30%.
Robot Simultaneous Localisation and Mapping with Dynamic Objects

This honours project develops a framework to integrate dynamic motion information in a Simultaneous Localisation and Mapping (SLAM) algorithm. The purpose of the framework is to track moving objects in a robot’s environment and integrate their motion information in a SLAM algorithm. Our expectation is that integrating the motion information of objects in a robot’s environment increases the SLAM solution accuracy. The framework has a front end that can generate simulated data with motion information according to several types of motion models, and a back end that processes the data through our SLAM solver algorithm to produce the final estimate. I contributed to the development of the front-end, and used the framework to test the validity, accuracy, and improvement in the SLAM estimation of these motion models. The results show that integrating the motion estimation of objects in a robot’s environment improves the estimation accuracy of the constructed map and robot’s position within it, however only for cases where the motion model used by the solver is an accurate representation of the actual motion. The most robust and realistic motion estimation from the models tested is constant motion. Tests on the algorithm with the constant motion estimation showed that it consistently improves the SLAM final estimate of the robot’s localisation and map.
An Enhanced Framework for Storage of Packets Securely on Cloud using Channel Quality Measure

The data which is outsourced is done based on cloud computing and there can be security reasons, the data can be compromised by performing attacks by other user and then among sensing points in the cloud. This project implements Optimized Centrality method which is energy efficient and also has good throughput. In the approach the sensing points are chosen based on residual energy and high channel quality, the sensing points which are not used in the path are then used for replication. The entire file is divided into fragments and then stored in the sensing points. The distance between the sensing points is computed by making use of T-color methods by making use of combination of (x,y). x is x position for the sensing point and y is y position of the sensing point. The proposed optimized centrality method is then compared against E-Centrality, Closeness Centrality and Between Centrality and then proved that proposed method is better with respect to delay, hops, energy consumption, efficient sensing points, in-efficient sensing points, security bytes‟ usage. The proposed also stores the packets by making use of DES method which is more secure as compared to AES used by compared methods.
UncertaintyEvaluationofWater Inrush inKarstTunnelsBasedon Epistemic Uncertainty with Possibility Theory

Joint War College, National Defense University of PLA, Beijing 100091, China High-tech Institute, Qingzhou, Shandong 262500, China State Key Laboratory of Disaster Prevention and Mitigation of Explosion and Impact, Army Engineering University of PLA, Nanjing 210007, China Jiangsu Nanjing Geo-Engineering Investigation Institute, Nanjing 210000, China Rocket Army Engineering Design and Research Institute, Beijing 100091, China 7e No. 32395 Troop of PLA, Shenyang 110000, China
The shRNA-mediated silencing of VEGF-C illustrates its role in proliferation, chemosensitization, tumor colonization, and anchorage independence

† Both authors had contributed equally; ‡ Registered Ph.D. student of Nirma University Abstract Vascular endothelial growth factor C (VEGF-C) is known to stimulate growth of endothelial cells to form tumor-as- sociated lymphatics, which leads to the migration of tumor cells to distant places. However, the function of VEGF-C is not limited to lymph angiogenesis only. VEGF-C is produced by breast cancer cells and binds to the receptors present on them and to lymphatic endothelial cells. The binding of VEGF-C to its receptors on a cancer cell is referred to as autocrine signaling. This ligand-receptor binding, activates a cascade of events that are diffe- rent from angiogenesis. Moreover, autocrine transmission of signals may facilitate the growth, migration, and drug sensitivity of cancer cells, including the malignancy of the breast. In this study, we investigated the effect of inhibition of VEGF-C gene expression via RNAi. A vector-based siRNA was constructed and transfected into breast cancer cell line MDA-MB-231. VEGF-C gene expression was analyzed via RT-PCR. In vitro proliferation, soft agar colony forma- tion, and scratch healing assays were performed. A relative decrease in VEGF-C gene expression observed in trans- fected cells indicated the silencing effect of shRNA. Due to unavailability of VEGF-C protein, the replicative potential of cancer cells decreased. No colonies on soft agar formed by these tumor cells were observed, which explains their incapability of being anchorage independent. Similarly, scratch healing and 3-(4, 5- dimethylthiazol-2-yl)-2, 5-diphenyl- tetrazolium bromide (MTT) assay showed a non-migratory and drug-sensitive nature of the transfected cells. All our results indicate that abundance of VEGF-C in tumor microenvironment will affect various non-angiogenic cell functions such as proliferation, migration, colonization, and chemoresistance.
Extraction Method of Glacier Microorganism Total DNA

In this paper,glacier microbial DNA was extracted by the method of SDS lysis,The result showed that this method could extract DNA with more than 15kb in size from different areas.They were used as templates for PCR amplification with success,using primers of bacterial 16SrRNA gene.Therefore,method of SDS lysis is more suitable for DNA extraction directly from a small amount of glacier sample.
Assessing Individual Poverty Status Using Repeated Cross-sectional Surveys

Aim of the paper is to introduce a methodology able to estimate vulnerability to poverty and poverty dynamics in absence of longitudinal data. Considering repeated cross-sectional data, we propose a way to directly estimates the probability of being poor in the next period based on a large information set that includes individual characteristics and contextual variables. The joint inclusion of contextual variables along with individual covariates allows to separate idiosyncratic from aggregate shocks. We also allow the effects of the predictors of poverty status to vary over time, removing the traditional hypothesis of time-invariance of the predictors. Macroeconomic forecasts that can potentially influence, directly and indirectly, individual poverty status, are included in the model for better estimate and forecast the probability of each individual to be poor. Methodologically, we estimate a logistic hierarchical model with different levels of variation within a bayesian framework. We empirically illustrate our approach for Kyrgyz Republic using independent cross-sectional Kyrgyz Integrated households budget and labor force surveys (KIHS) available over the period 2013– 2017.
Crop yield predictions-high resolution statistical model for intra-season forecasts applied to soybeans in the United States Gro Intelligence, Inc

This paper describes the yield model we built for United States(US) soybeans, which inherited most of its framework from that of [1]. The biggest difference from the method of [1] was that we aggregated all of the temporally varying geospatial variables according to the stages of the soybean growing season. We trained the model on 2009-2016 US county-level yield. In ”leave-one-yearout” tests, it explained 76 percent of the variation in yield across counties and years.
Beating the Stock Market with a Deep Reinforcement Learning Day Trading System

In this study we investigate the potential of using Deep Reinforcement Learning (DRL) to day trade stocks, taking into account the constraints imposed by the stock market, such as liquidity, latency, slippage and transaction costs. More specifically, we use a Deep Deterministic Policy Gradient (DDPG) algorithm to solve a series of asset allocation problems in order to define the percentage of capital that must be invested in each asset at each period, executing exclusively day trade operations. DDPG is a model-free, off-policy actor-critic method that can learn policies in high-dimensional and continuous action and state spaces, like the ones normally found in financial market environments. The proposed day trading system was tested in B3 - Brazil Stock Exchange, an important and understudied market, especially considering the application of DRL techniques to alpha generation. A series of experiments were performed from the beginning of 2017 until the end of 2019 and compared with ten benchmarks, including Ibovespa, the most important Brazilian market index, and the stock portfolios suggested by the main Brazilian banks and brokers during these years. The results were evaluated considering return and risk metrics and showed that the proposed method outperformed the benchmarks by a huge margin. The best results obtained by the algorithm had a cumulative percentage return of 311% in three years, with an annual average maximum drawdown around 19%.
Improving Single and Multi-View Blockmodelling by Algebraic Simplification

Blockmodelling is an important technique in social network analysis for discovering the latent structures and groupings in graphs. State-of-the-art approaches approximate the graph using matrix factorisation, which can discover both the latent graph structures and vertex groupings. However, factorisation is a one-way approximation, in that it only approximates the graph with a lossy model that removes the background noise. Traditional Blockmodelling methods rely on an alternating 2-step optimization that involves iteratively updating the matrix representing membership while fixing the matrix representing the graph’s underlying structure, and then updating the structure matrix while keeping the membership matrix fixed. We propose a single step optimization method, which uses algebraic simplifi-cation to directly update the lower dimensional, latent structure representation. This helps improve both the convergence and accuracy of blockmodelling. We also show that this approach can solve multi-view blockmodelling problems, involving multiple graphs over the same vertices. We use real datasets to show that our approach has much higher accuracy and comparable running times to competing approaches.
GROWTH IN THE SOUTHERN AFRICAN DEVELOPMENT COMMUNITY (SADC): THE ROLE OF HUMAN CAPITAL

The relationship between foreign direct investment (FDI) and economic growth has received considerable empirical attention in the Southern African Development Community (SADC) but the role that human capital plays in this nexus is not well-understood. This is despite economic theory firmly suggesting that human capital plays a pivotal role in fostering the adoption of superior foreign technologies and enhancing the spillover benefits (learning-by-doing) that emanate from inward FDI in host countries. Against this background, therefore, the paper utilizes the Mankiw-Romer-Weil growth model in examining linkages between human capital (in form of education), FDI and economic growth in 15 SADC countries. After running cross-country regressions on data covering the period 1990-2015, human capital is found to positively influence the size and significance of the FDI-effect on economic growth in SADC. This suggests that SADC countries should continue upgrading the quality and quantity of their education in order to maximize FDI-induced development.
Design and analysis of amino acid supplementation in hepatocyte culture using in vitro experiment and mathematical modeling

OF THE DISSERTATION Design and Analysis of Amino Acid Supplementation in Hepatocyte Culture Using in vitro Experiment and Mathematical Modeling By HONG YANG Dissertation Directors: Dr. Marianthi G. Ierapetritou, Ph.D. Dr. Charles M. Roth, Ph.D. Extracorporeal bioartificial liver (BAL) devices, involving primary hepatocytes, represent a promising option to provide temporary support for patients with liver failure. Current use of BAL is primary challenged by development of techniques for long-term culture of hepatocytes during plasma exposure, as occurs during clinical application. Previous in vitro studies and mathematical modeling analysis have shown that supplementation of amino acids to the plasma enhances liver-specific functions and reduces lipid accumulation. However, further improvement would be enhanced greatly by development of a rational strategy to design the profile of amino acid supplementation and by better understanding of the metabolic objectives of hepatocytes, and how they vary as a function of amino acid supplementation.
Lingua Sinica 2020/Vol. 6

Collocation analysis can be used to extract meaningful linguistic information from large-scale corpus data. This paper reviews the methodological issues one may encounter when performing collocation analysis for discourse studies on Chinese. We propose four crucial aspects to consider in such analyses: (i) the definition of collocates according to various parameters; (ii) the choice of analysis and association measures; (iii) the definition of the search span; and (iv) the selection of corpora for analysis. To illustrate how these aspects can be addressed when applying a Chinese collocation analysis, we conducted a case study of two Chinese causal connectives: yushi ‘that is why’ and yin’er ‘as a result’. The distinctive collocation analysis shows how these two connectives differ in volitionality, an important dimension of discourse relations. The study also demonstrates that collocation analysis, as an explorative approach based on large-scale data, can provide valuable converging evidence for corpus-based studies that have been conducted with laborious manual analysis on limited datasets.
Contrast Enhanced Tone Mapping Operator for High Dynamic Range Image Based on Guided Image Filter

In this paper, we propose a contrast enhancement algorithm using guided image filter (GIF). The GIF is used to divide an HDR image into a base layer and a detail layer. The energy scale of base layer determinate the darkness and brightness of the image. However, the detail information in the base layer is difficult to be displayed because of the high brightness and clusters of low brightness. We propose a contrast enhancement method by adjusting the gray level of base layer by subtracting the mean value of itself. It is combined with the detail layer to preserve the detail information. Experiment results show that the proposed algorithm has better performance in detail preservation and contrast enhancement.