Miracle-dz/UniFAR-FTUs huggingface dataset:
Partner Favorability and Acquisition Premia in Cross-Border M&A’s

In this paper, we examined the linkage between a firm’s sentiment toward the national identity of its acquisition partner and the acquisition premium paid. Using a sample of 435 cross-border M&A’s in 38 countries, we found that the acquisition premium is negatively associated with: 1) the degree of favorability the acquiring firm holds toward its target firm’s national identity; and 2) the degree of favorability the target firm holds toward its acquiring firm’s national identity. In addition, information asymmetricity was shown to moderate the relationship between the acquirer's favorability of the target firm and the acquisition premium. Specifically, the acquiring firms tend to rely more on their subjective favorability in unrelated acquisitions in which they lack information on the target firm. In sum, our results shed new insight on the effects of subjective favorability of national identities on acquisition premia in the context of cross-border M&A’s.
Comparing Ref-Vectors and Word Embeddings in a Verb Semantic Similarity Task

This work introduces reference vectors (Ref-Vectors), a new kind of word vectors in which the semantics is determined by the property of words to refer to world entities (i.e. objects or events), rather than by contextual information retrieved in a corpus. Ref-Vectors are here compared with state-of-the-art word embeddings in a verb semantic similarity task. The SimVerb-3500 dataset has been used as a benchmark to verify the presence of a statistical correlation between the semantic similarity derived by human judgments and those measured with Ref-Vectors and verb embeddings. Results show that Ref-Vector similarities are closer to human judgments, proving that, within the action domain, these vectors capture verb semantics better than word embeddings.
SSN_NLP@IDAT-FIRE-2019: Irony Detection in Arabic Tweets using Deep Learning and Features-based Approaches

The disparity between the statement and its intended meaning is referred to as irony. Detecting this disparity in Arabic tweets is a challenging task. We presented three approaches namely deep learning using transformers, deep learning using Recurrent Neural Networks (RNN) and a features-based approach for detecting the irony in Arabic tweets. Among these approaches, the deep learning approach using trans-formers scores better F1-score of 0.816 than the deep learning approach using RNN which has 0.719 and the features-based approach which scores 0.709 on the IDAT@FIRE-2019 dataset.
Appraisal of Teaching and Learning Process in Inclusive Primary Schools in Tanzania. A Case Study of Temeke Municipality in Dar es salaam

The study appraised the teaching and learning process in Temeke Municipality inclusive primary schools in Dar es Salaam region. The research instruments were questionnaires, interview guides documentary review and observation schedules. The findings indicated that teachers had inadequate knowledge about inclusive education, were not modifying curriculum contents using IEP to fulfil education al needs for pupils with disability in the classroom. Teachers too were using schemes of work and lesson plans for teaching normal pupils, however they failed to employ appropriate participatory teaching and learning strategies that culminated to limited classroom interaction. The shortage of teaching materials to be used in inclusive classrooms such as books with large prints, glasses for low vision, inadequate pictures, real things, learning devices like hearing aids, Braille and Sign Language was hardly used. It is recommended that education for pupils with disabilities in primary inclusive schools has to be regarded as an investment for the society. The Ministry of Education and Vocational Training, in collaboration with the Tanzania Institute of Education has to review the current teacher education programme to integrate inclusive education and provide in service training for teachers of inclusive schools. Teaching and learning materials have to be provided in inclusive schools. Further, Sign Language, Tactile Sign Language, and Braille have to be learnt by all teachers in inclusive schools likewise ordinary ones. It is recommended that, the coming Education and Training policy must include the system of inclusive primary schools in Tanzania. It is recommended that, since the study was conducted in Municipality involving only three primary schools; there is a need of conducting similar studies in other districts in Tanzania.
Deterrence by risk of detection? An inquiry into how elite athletes perceive the deterrent effect of the doping testing regime in their sport

Abstract Aims: A central paradigm of global anti-doping policy is detection-based deterrence, i.e. the risk of testing and exclusion from sport are effective doping deterrents. This paper investigates how elite athletes perceive the deterrent effect of the testing strategy in their sport and explores whether and how specific factors such as the frequency of testing influence athletes’ perceptions of testing as a deterrent. Methods: 645 Danish elite athletes completed a web-based questionnaire about their perceptions of testing efforts in their sport. Findings: 75% of the athletes considered the likelihood that a test would prove positive to be a deterrent. By contrast, only 40% found the risk of being selected for testing to be a deterrent. Athletes tested frequently and athletes from doping-risk sports were more likely to perceive testing as a deterrent. In total, 24% regarded neither the likelihood of testing nor detection as deterrents. 8% did not consider the likelihood of testing and detection nor the ban from sport as deterrents. Conclusions: Testing programmes – as a strategy to detect and deter doping – are no great deterrent for many athletes. The results highlight the limitations of detection-based deterrence and emphasise a need to give higher priority to additional prevention-orientated strategies. Recommondations are outlined.
Receiver Initiated Handshaking MAC Based on Traffic Estimation for Underwater Acoustic Networks

In underwater acoustic networks (UWANs), the unique characteristics of acoustic channels have posed grand challenges for the medium access control (MAC) protocol design. The long propagation delay problem has been widely explored in the recent literature. However, the long preamble problem with acoustic modems revealed in real experiments brings new challenges to underwater MAC design. The overhead of control messages in handshaking based protocols becomes significant due to the long preambles. To address this problem, we advocate the receiver initiated (RI) handshaking method with parallel reservation to improve the handshaking efficiency. Despite some existing work along the same direction, the data polling problem is still an open issue. Frequent information exchange with the senders will cause extra overhead owing to the long preambles in acoustic communication. The receiver needs to address two issues for efficient data polling: when to poll data from the sender and how much data to request. In this paper, we propose a traffic estimation based receiver initiated MAC (TERI-MAC) to solve this problem with an adaptive approach. Data polling in TERI-MAC depends on an on-line approximation of traffic distribution. It estimates the energy efficiency and channel utilization and starts the data request only when preferred performance can be achieved. TERI-MAC can achieve a stable energy efficiency with arbitrary network traffic patterns. For traffic estimation, we employ a resampling technique to keep a small computation and memory overhead. Via simulation, we evaluate the performance of TERI-MAC, in terms of energy efficiency, channel utilization and one-hop communication delay. Our results show that, compared with existing RI based underwater MAC protocols, TERI-MAC can achieve higher energy efficiency while with some delay penalty. This confirms the strength of TERI-MAC for delay tolerant applications.
Self-Driving like a Human driver instead of a Robocar: Personalized comfortable driving experience for autonomous vehicles

This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.
Understanding Natural Language Parking Instructions and Grounding for Self-driving Cars

There has been a rapid development of practical applications of automatic driving, and interactive operation using natural language will soon be necessary to easily operate autonomous cars. In this study, we attempt to realize a correspondence relationship, (i.e., symbol grounding) between driving instructions expressed in natural language and objects in the real world recognized by the sensors in a car. We then propose a method to solve the grounding problem by means of belief propagation method and show an example of the solution with the observed real sensor data. In particular, we focus on the parking operation of a car in this study.
Learning Adaptive Driving Behavior Using Recurrent Deterministic Policy Gradients

In this work, we propose adaptive driving behaviors for simulated cars using continuous control deep reinforcement learning. Deep Deterministic Policy Gradient(DDPG) is known to give smooth driving maneuvers in simulated environments. Unfortunately, simple feedforward networks, lack the capability to contain temporal information, hence we have used its Recurrent variant called Recurrent Deterministic Policy Gradients. Our trained agent adapts itself to the velocity of the traffic. It is capable of slowing down in the presence of dense traffic, to prevent collisions as well to speed up and change lanes in order to overtake when the traffic is sparse. The reasons for the above behavior, as well as, our main contributions are: 1. Application of Recurrent Deterministic Policy Gradients. 2. Novel reward function formulation. 3. Modified Replay Buffer called Near and Far Replay Buffers, wherein we maintain two replay buffers and sample equally from both of them.
Lead isotope and trace element patterns of German and Polish Kupferschiefer – a provenance study of bronze artifacts

The outstanding importance of the Early Bronze Age “Skydisk of Nebra” gave reason to investigate Central European copper occurrences in order to find out which occurrence might have been the source for its copper. Lead isotope ratios (by MC-ICP-MS) and trace element contents (Ag, As, Sb, Co, Ni, Bi, by energy-dispersive XRF) of German and Polish Kupferschiefer are presented and compared with data from the “Skydisk” and other artifacts found at Nebra. Trace element ratios and lead isotope data show clear differences between Kupferschiefer ore and Nebra artifacts. As a consequence, the investigated Kupferschiefer occurrences can be excluded as a copper source for the Nebra finds.
Effect of Postural Changes on Complexity Measures of Heart Rhythm in Late Adolescents

Even trivial changing of body position from supine to sitting triggers complex cardiovascular adaptive response to prevent brain ischemiaThe study was performed to investigate heart rate asymmetry (HRA) and entropy-based heart rate variability (HRV) in stationary conditions in two body positions. Forty male students, aged 18-20 participated in this study. High resolution (4 kHz) ECG was recorded for 15 minutes; first in supine and after that in sitting position. 512 beat-to-beat interval (RRi) time-series were selected from each recording. Entropy-based parameters such as sample entropy (SampEn) and approximate entropy (ApEn) were computed. Heart cycles shorter (acceleration) or longer (deceleration) from the previous ones and sequential monotonic changes of RRi were also discriminated. Number of HR sequential runs significantly correlated with SampEn and ApEn both in sitting and supine position. Postural change from supine to sitting position resulted in a significant HR increase from 69 to 77 bpm. Both SampEn and ApEn were decreased after changing position (from 1.54 to 1.20 and from 1.23 to 1.08, respectively)Our data obtained from nonlinear HRV analysis indicate a reduction of cardiac control complexity following postural upright changing of body position.
Evaluating the insecticidal potential of aqueous plant extracts from Zanthoxylum zanthoxyloides and Anacardium occidentale against insect pest complexes of cabbage in an open field experiment

1 African Regional Postgraduate Programme in Insect Science (ARPPIS) University of Ghana, Legon PMB Ghana 2 Department of Crop Science, University of Ghana, Legon, P. O. Box LG 25, Legon, Ghana 3 Forest and Horticultural Crops Research centre, University of Ghana, Legon, P.O. Box LG 25, Legon, Ghana 4 Department of Animal Biology and Conservation Science, University of Ghana, Legon, P. O. Box LG 25, Legon, Ghana . Accepted 21 June 2018
STOCHASTIC PROCESSES AND SOME APPLICATIONS

In this paper, we discuss about some applications to the birth and death process in biology and in the theory of waiting. First, we introduce some theoretical notions about the stochastic processes and we define the Markov chain and the Poisson process. Afterwards, we consider two processes of birth: "The Poissonian tide of particles” and “ The Yule-Furry process”, that have multiple applications, the theory of waiting being one of them. Finally, by using the theoretical concept presented above, we solve two examples: the first is a problem from veterinary medicine, and the second one is an example of the model M/M/1 from agriculture, which means if the entrance, the time of serving and the number of stations are given, we'll calculate the number of the biological units in system, the medium number of the applicants in line and the medium time of waiting.
Validating Cattell’s Sixteen Personality Factor Model with Exploratory Factor Analysis

As one of the first uses of factor analysis, Raymond Cattell’s Sixteen Personality Factor model was a revolution in psychometrics, paving the way for contemporary personality measures such as the Big Five traits. However, more recent studies on Cattell’s conclusions have cast doubt on the validity of such a model due to its irreproducibility. Using 163 questionnaire answers from 35,376 individuals, we used exploratory factor analysis techniques (developed by Cattell himself) to retrace Cattell’s analysis and compared the results to validate his model. Within the analysis, we used maximum likelihood estimation to get factor loadings, chose 16 factors, and then used a promax rotation to differentiate the chosen factors. While many questions were categorized into the factors they were originally meant to measure, we noticed many of the same problems cited by previous researchers, namely the grouping of certain components into more general factors and the lack of significance to support others. Additionally, we also argue that many of the patterns we observed in our results might have arisen from participant bias in the data collection process itself, as test-takers may have responded based on what they thought the questions measured. Background and significance The Sixteen Personality Factor Questionnaire (16PF Questionnaire) is a popular selfreported personality test developed by Raymond B. Cattell, and has many practical important applications on working with human behaviors, ranging from clinical diagnosis to career counselor. In developing this test, Cattell used factor analysis to determine the underlying personality traits based on self-ratings on questions (Cattell & Mead, 2008). Even though Exploratory Factor Analysis (EFA) is a very popular technique for studying human behavior in general, it is often misused in psychological research (Fabrigar et al., 1999). In particular, replication of Cattell’s methodology is met with mixed results: while Cattell and Mead (2008) claimed that these traits “have been confirmed in a wide range of independent studies”, a number of other researchers have failed to verify the factors in numerous different studies (Fehringer, 2004). What makes replicating these analyses so difficult is the fact that the obtained results are easily influenced by necessary subjective decisions within the process of factor analysis. The main purpose of this paper is therefore to validate the 16PF Questionnaire using factor analysis on a new dataset.
Grey-Box Model: An ensemble approach for addressing semi-supervised classification problems

In this paper, we propose a novel and interpretable grey-box ensemble using a selflabeled approach for semi-supervised classification problems. The prospective greybox ensembles a more interpretable whitebox model with a black-box technique. This scheme could guide the comparatively data expensive white-box component with the results from the more accurate black-box part. We evaluate the proposal in an inductive learning setting showing good performance in partially labeled datasets.
CS 224N: Final Paper

With the rise of social media, the task of toxicity classification is increasing in importance day by day. It is important that toxicity in online platforms is reduced so that the internet is a safe place for people no matter their gender, age, race, nationality, likes, dislikes, religious beliefs, or political beliefs. Our goal for this project, is to use natural language and deep learning models to predict the toxicity class of a particular comment. We are using a dataset containing around 160,000 social media comments across 6 different toxicity classes. To address this problem we built a new deep convolutional neural network architectures that uses character to sentence information to perform toxicity classification on the short texts. The binary classifier model we built was more successfully able to distinguish between toxic and non-toxic comments, however, the multilabel classifier model that does fine grained classification performed averagely.
Predictors of Distributed School Leadership in Croatian Primary Schools

This paper discusses the features of distributed school leadership (DSL) in Croatian primary schools with regard to its three central theoretical and practical dimensions: the participation of stakeholders in the process of school leadership, desirable social relations between the participants in the process of school leadership, and stakeholders’ influence over the decision-making process. The paper also assesses the development level of three selected groups of DSL predictors: supporting attitudes and activities of school principals, supporting characteristics of stakeholders participating in the decision-making process and supporting organisational and material resources. The results indicate that, in Croatia, the number of primary schools with less developed DSL is approximately equal to the number of schools with more developed DSL. The statistically significant positive correlation between the development level of all three predictor groups and the development level of the DSL features was also confirmed. These results suggest that introducing certain policy mechanisms could contribute to the enforcement of the DSL features in Croatian primary schools, which could in turn be of great help to policymakers at all levels of the decision-making process within the education system.
In Silico Study of Gaseous Air Pollutants Effects on Human Atrial Tissue

Exposure to gaseous air pollutants such as carbon monoxide (CO), nitric oxide (NO) and sulfur dioxide (SO2) promotes the occurrence of cardiac diseases. Investigations have shown that CO and SO2 block the calcium channel (ICaL) of myocytes. The SO2 also increases the sodium channel (INa), the transient outward (Ito) and inward rectifying (IK1) potassium currents. The NO blocks INa and increases ICaL. We developed concentration dependent equations to simulate the gaseous pollutants effects on the ionic currents. They were incorporated in the Courtemanche model of human atrial cell and in a 2D tissue model. A train of 10 stimuli was applied. The action potential duration (APD) was measured. S1-S2 cross-field protocol was applied to initiate a rotor. The CO and SO2 concentrations from 0 to 1000 uM and NO concentration from 0 to 500 nM were implemented. Six concentration combinations were simulated (cases 1 to 6). The gaseous air pollutants caused an APD shortening and loss of plateau phase of the action potential in a fraction that increases as the pollutant concentration increases. When the highest concentration was applied, the APD decreased by 81%. In the 2D model, from case 4 conditions it was possible to generate rotor, propagating with high stability. These results show pro-arrhythmic effects of gaseous air pollutants.
Evaluation of the professional practice environment of nursing in health institutions

Universidade Estadual de Campinas, Campinas, SP, Brazil. Novartis, São Paulo, SP, Brazil. Confl icts of Interest: none to declare. Abstract Objective: To evaluate the hospital environment where nursing performs its practice comparing public and private hospitals and describing the characteristics that received unfavorable evaluation (≤ 2.5 points) in the professionals’ perception. Methods: A comparative and cross-sectional study was conducted in fi ve hospitals (two public hospitals A and B; and three private ones C, D and E) from a city in the countryside of the state of São Paulo, with a total of 1773 nursing professionals. The instruments used were: data sheet for characterization of the sample and the Brazilian version of the Practice Environment Scale. In the data analysis, we used descriptive and inferential statistics. To compare the hospitals, we used the Kruskall Wallis test, followed by the Dunn post-test and the multinomial regression. Results: In the comparison of hospitals, D and E hospitals reached mean above the others and signifi cant differences (p<0.0001) were obtained in relation to the fi ve subscales of the instrument used. In the multinomial regression, hospital D obtained 5.8; hospital E 5.2; hospital C 3.0 and hospital A 2.7 chances of having a more favorable environment when compared to hospital B. Items with a score lower than 2.5 were associated, especially, lack of opportunity for development, recognition, participatory management and proper sizing. Conclusion: Private hospitals showed better performance when compared to the public ones, and the characteristics that received the worst evaluation were related to the participation of nurses in hospital affairs based on quality, support of the managers to the team and adequacy of resources.
Applying Partial Learning to Convolutional Neural Networks

This paper will explore a method for training convolutional neural networks (CNNs) that allows for networks that are larger than the training system would normally be able to handle to be trained. It will analyze both the validation accuracy of each method and the time each method takes to train to determine the viability of this method of training.