Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring driving behavior as a latent variable in safety modeling. A preliminary analysis from a driving simulator study

Driver behavior, among other factors, plays an important role in road safety. To better understand it, driving simulator studies and field operational studies and/or naturalistic driving studies are often used for analyzing human factors in road safety. In an attempt to eliminate road fatalities and serious injuries on European roads, the EU is adoping a “Zero Vision” (European Commission, 2011), setting targets and investigating the factors affecting road crashes, including infrastructure, vehicle safety, driver behavior, and emergency response. The on-going EU project i-DREAMS (https://idreamsproject.eu/wp/) aims to develop a tool for monitoring driving safety and bringing in the appropriate interventions (real–time and post–trip gamification). The mathematical modeling of a safety tolerance zone as postulated in i– DREAMS is a complex dynamic problem, benefiting from advanced technologies including OBDII, smart phone application (OSeven), Mobileye (ADAS advanced features using a single camera mounted on the windshield), CardioWheel (physiological based on electrocardiogram (ECG)). The proposed methodology in this paper is a data–driven approach using discrete choice models with safety levels as a dependent variable and driving behavior as a latent explanatory variable in these models; this could be formulated as well as “normal”/“abnormal” driving, or in a broader sense aggressiveness indicator. The developed discrete choice models will be tested under different forms (multinomial, nested, but also ordered); the overall methodology also focuses on how to implement these models dynamically. A preliminary step for this is to cluster driving observations into safety levels, based on several clustering techniques, including k–means, hierarchical clustering, and Gaussian Mixture Models. An application of the proposed methods on a dataset from a previous Greek driving simulator study demonstrated that the optimal number of clusters would be four using k–means (elbow) method. In the hEART 2020 conference, the developed discrete choice models will be presented using the safety clusters showcased in this paper.
Multiple Communities of Ego in Social Networks

—Communities in social networks have the tendency to overlap. Their overlapping parts carry important knowledge about the studied system. For this reason overlapping community identiﬁcation arises as an important task in social network analysis. Up to now, several different methods have been proposed but suffer from being sensitive to not only the number of nodes in overlapping parts of communities, but also the number of communities which overlap. In this work, we propose a new algorithm merging multiple communities around each ego nodes. It is designed for detecting highly overlapping communities at small size. These types of communities correspond to small social groups that share members such as families, close friends or coworkers. Experiments are conducted on artiﬁcial and real-world networks. Performances are evaluated quantitatively and qualitatively by comparing foremost state-of-art algorithms. The results show that our algorithm overcomes previous issues of overlapping community detection.
Trade Negotiation for Doing Business in Asian Region: A Conceptual Framework

Developing countries face various complexities and challenges while negotiating trade particularly large-scale trade with other countries. A strong and good trade negotiation can play a vital role in achieving the destination by implementing trade agreements suitably. The objectives of this paper is to provide the concept of trade negotiation in details and to sketch some guidelines for constructing strong trade negotiation. For this reason, by using various secondary data sources, this study tries to explain all the basic knowledge for trade negotiations and suggests some negotiating trade fashions which should improve the trade relationship between Bangladesh and other countries of Asia region like ASEAN countries.
Spoken word recognition in a second language: The importance of phonetic details

Spoken word recognition depends on variations in fine-grained phonetics as listeners decode speech. However, many models of second language (L2) speech perception focus on units such as isolated syllables, and not on words. In two eye-tracking experiments, we investigated how fine-grained phonetic details (i.e. duration of nasalization on contrastive and coarticulatory nasalized vowels in Canadian French) influenced spoken word recognition in an L2, as compared to a group of native (L1) listeners. Results from L2 listeners (English-native speakers) indicated that fine-grained phonetics impacted the recognition of words, i.e. they were able to use nasalization duration variability in a way similar to L1-French listeners, providing evidence that lexical representations can be highly specified in an L2. Specifically, L2 listeners were able to distinguish minimal word pairs (differentiated by the presence of phonological vowel nasalization in French) and were able to use variability in a way approximating L1-French listeners. Furthermore, the robustness of the French “nasal vowel” category in L2 listeners depended on age of exposure. Early bilinguals displayed greater sensitivity to some ambiguity in the stimuli than late bilinguals, suggesting that early bilinguals had greater sensitivity to small variations in the signal and thus better knowledge of the phonetic cue associated with phonological vowel nasalization in French, similarly to L1 listeners.
Prediction of learning behavior characters of MOOC’s data based on time series analysis

In recent years, big data is used in finance, education, biotechnology, and other fields. In the field of education, thanks to the overall characteristics of big data, it can be applied to understand the objective learning rules of students, to provide effective decision-making suggestions for educators. With the development of electronic communication and media technology, Massive Open Online Courses (MOOC), which contains a large number of user behaviors and information, has gradually become one of the mainstream ways of education. However, MOOC have also been criticized for their high dropout rate. Through the analysis of big data contained in MOOC real data set, it can help teachers master students’ learning rules and learning behaviors in time, which is of great significance to improve the level of education and the dropout rate of MOOC. First, it is helpful for teachers to adjust the course content in time according to the learning status. Second, teachers can carry out benign interventions on students’ learning behavior. In this paper, based on the analysis of the real MOOC data of students’ learning behaviors, we find that different groups of students have the same learning behavior at the same learning period, and students’ learning situations can be divided into three stages. Then ARIMA model is used to predict the learning behaviors of students, to further analyze the factors causing the difference in prediction accuracy, we propose an error analysis method based on MSE. Through the experimental observation, the method proposed in this paper can effectively find and predict the characteristics of students’ learning behavior, to give suggestions for improving the quality of teaching.
An online weighted sequential extreme learning machine for class imbalanced data streams

When general online classification algorithms deal with imbalanced data streams, there are always some problems, such as over fitting phenomenon caused by insufficient simple learning and instability of training model. In this paper, we introduce online sequential extreme learning machine (OSELM) as the basic theory model, and combine with the cost-sensitive strategy, then propose a cost-sensitive learning based online sequential extreme learning machine algorithm (C-OSELM). Firstly, in order to solve the problem that minority classes are easily misclassified due to class imbalance, use cost-sensitive strategy, by assigning different penalty parameters to various samples, a weighting matrix is constructed to improve the misclassification cost, thereby effectively alleviating the excessive deviation of decision surface. On this basis, in order to solve the problem that the penalty parameter is too single and the algorithm is not universal, the cost adjustment function is introduced to optimize the weight parameters to select the appropriate weight. Finally, 16 class II imbalanced datasets are used for comparison and verification. The experimental results show that the classification performances of the proposed C-OSELM algorithm are better than other comparative algorithms.
Mobile edge computing scheduling algorithm based on minimum total delay-time

As an emerging architecture, mobile edge computing (MEC) extends cloud computing services to network edges close to users through MEC servers, satisfying the application needs of real-time control and real-time data analysis. However, due to the limited computing power of MEC server, the task has a long delay-time. In order to improve the current situation, this paper proposes a scheduling algorithm of minimum total delay-time (MTD), in which the server determines the optimal order of task computation to MTD. In addition, this paper proposes an incentive mechanism for users to submit tasks with reasonable computation and expected completion time, while reducing the number and computation of submitted tasks when server computing resources are insufficient. The results show that the performance of the algorithm is close to that of the traditional scheduling algorithm, and total delay-time and average delay-time have improved by 17% to 200%.
Strong influence of convective heat transfer efficiency on the cooling benefits of green roof irrigation

By enhancing evapotranspiration (ET), green roofs provide cooling benefits for the urban environment and are recognized as a promising heat mitigation strategy. The evaporative cooling effects of green roofs strongly depend on the soil moisture conditions and thus irrigation may be needed to sustain the cooling benefits. It has been shown that the magnitude of cooling benefits offered by green roof irrigation varies spatially, but its controlling factors remain elusive. In this study, we combine a surface energy balance (SEB) model with global simulations generated by an improved Earth System Model to illustrate the key factors controlling the cooling benefits of green roof irrigation. We employ a simple irrigation scheme, which is only active when there is no ice in soil layers and when the soil moisture is below field capacity. As a result, most of the irrigation water leaves the green roof system via ET. We find that the magnitude and also the spatial variability of the cooling benefits of green roof irrigation are controlled by the irrigation amount, and a surface energy redistribution factor that encodes the efficiencies of different SEB components in transferring heat. Further analysis indicates that the enhancement of latent heat flux due to irrigation is largely balanced by the reduction of sensible heat flux on green roofs. Therefore, the amount of irrigation needed per unit decrease of green roof surface temperature is mainly controlled by the convective heat transfer efficiency. A lower convective heat transfer efficiency (e.g. under a lower wind speed) helps reduce the amount of irrigation needed per unit decrease of green roof surface temperature. This study highlights the importance of SEB in constraining the cooling benefits of green roof irrigation and provides valuable guidance for urban planners and policy makers in terms of heat mitigation and sustainable water management.
A Deep Neural Network for Stock Price Prediction

The stock market is a large, complex system that consists of many human factors. Due to the human action’s randomness to the market environment, the market behavior is usually erratic. In this case, many previous works were trying to find out an automatic prediction model that can capture relationships between stock market prices and the surroundings of the stock markets. Recently, a simple LSTM-attention mechanism was proposed to advance the prediction accuracy of previous works. However, the simple LSTM with attention mechanism only demonstrates how the past data influence the next. This forward-only relationship does not reflect a backward relationship, which is how the future data is related to the past data. To enforce the backward relationship on the attention-LSTM model, a multilayer bidirectional LSTM model with an attention mechanism is proposed. A bidirectional LSTM layer that can encode the data relationship in both directions is leveraged. Then attention mechanism is applied to introduce the model with the ability to focus on critical content and ignore the disturbance. Experimental result shows that the attention-BiLSTM model has a coefficient of the determinant of 0.9940, which is better than the baselines.
Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm

With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.
Interdecadal variation of summer rainfall in the Greater Mekong Subregion and its possible causes

This paper investigates the interdecadal variation in summer rainfall over the Greater Mekong Subregion (GMS) during 1981–2020 and its possible causes, using Climate Hazards Group Infrared Precipitation with Station rainfall datasets, European Centre for Medium-range Weather Forecasts Reanalysis fifth generation data and the atmospheric general circulation model ECHAM6. The dominant mode of summer rainfall in the GMS features a seesaw pattern with an increase in rainfall over the central–southern GMS and a decrease in rainfall in northwestern Myanmar–Yunnan. The dominant mode of the GMS summer rainfall shows a change in regime around 2001/2002. Interdecadal variability in rainfall is largely related to sea surface temperature anomalies (SSTAs) over the Western Pacific Warm Pool (WPWP). Warmer SSTAs in the WPWP lead to interdecadal variation in summer rainfall in the GMS by exciting an anomalous cyclone in the lower troposphere over the southern GMS–South China Sea. This is accompanied by anomalous ascending motions in the central–southern GMS and anomalous descending motions in northern Myanmar–Yunnan. The Matsuno–Gill mechanism, which links SSTAs with interdecadal variations in the GMS summer rainfall, is further confirmed by numerical experiments.
The community structure of word co-occurrence networks: Experiments with languages from the Americas

We study a set of algorithms to discover the community structure of networks for languages from the Americas. Our experiments are based on a parallel corpus which allows us to represent each language as a co-occurrence network. Four methods to calculate network modularity, as a measure of the quality of community structure, were used. We studied several aspects of the community structure of co-occurrence networks. First, we were able to construct the map of modularity variations across languages from the Americas. With this, we separated large groups of languages into low- and high-modularity families. We suggested also a strong influence of functional words on low-modularity languages. Finally, we found a strong relationship between word entropy values and modularity. Our approach is thus a simple network-based contribution to face data scarcity of languages which are in danger of disappearing.
Time-reversal symmetry breaking in superconductors through loop supercurrent order

We propose a novel superconducting ground state where microscopic supercurrent loops form spontaneously within a unit cell at the superconducting transition temperature with only uniform, onsite and intra-orbital singlet pairing. As a result of the circulating currents time-reversal symmetry (TRS) is spontaneously broken in the superconducting state. Using Ginzburg–Landau theory we describe in detail how these currents emerge in a toy model. We discuss the crystallographic symmetry requirements more generally to realize such a state and show that they are met by the Re6 X (X = Zr, Hf, Ti) family of TRS-breaking, but otherwise seemingly conventional, superconductors. We estimate an upper bound for the resulting internal magnetic fields and find it to be consistent with recent muon-spin relaxation experiments.
Flexible Temporal Point Processes Modeling with Nonlinear Hawkes Processes with Gaussian Processes Excitations and Inhibitions

We propose an extended Hawkes process model where the self–effects are of both excitatory and inhibitory type and follow a Gaussian Process. Whereas previous work either relies on a less ﬂex-ible parameterization of the model, or requires a large amount of data, our formulation allows for both a ﬂexible model and learning when data are scarce. Efﬁcient approximate Bayesian inference is achieved via data augmentation, and we describe a mean–ﬁeld variational inference approach to learn the model parameters. To demonstrate the ﬂexibility of the model we apply our methodology on data from two different domains and compare it to previously reported results.
REM WORKING PAPER SERIES ON THE ROOTS OF UNDERDEVELOPMENT: “WRONG EQUILIBRIUM” OR “MISCOORDINATION”?

This paper examines the Big Push industrialization model due to [Murphy et al., 1989] by featuring a game where public and private agents must coordinate their complementary investment decisions and the outcome where all agents invest dominates in payoffs the no-investment alternative. Two different paths of analysis are pursued. If the coordination game has complete information, the selection of the “right” equilibrium appears to be easier if the initial level of total factor productivity (TFP) is not too low. The comparison of the “payoff dominance” and the “risk dominance” criteria due to [Harsanyi and Selten, 1988] shows that the ability to plan jointly different kinds of investment relaxes the constraint on initial TFP. Industrialization can be alternatively modelled as an incomplete information game. In this case, underdevelopment follows from a coordination break, where typically the Government supplies infrastructures which remain underused because the private sector fails to modernize. We find out that such a coordination break is likelier in economies where the starting level of TFP is low. Consequently, a low initial TFP level tends to create a “Poverty Trap”, which however can be overcome by enhancing the ability to coordinate different kinds of investment, namely public and private. JEL classification: O10, O14, C71, C72, C73.
Sentiment Analysis of Film Reviews Based on BI-GRU +Attention+Capsule Fusion

In this paper, a sentiment analysis model based on the bi-directional GRU, Attention and Capusle fusion of BI-GRU+Attention+Capsule was designed and implemented based on the sentiment analysis task of the open film review data set IMDB, and combined with the bi-directional GRU, Attention and Capsule. It is compared with six deep learning models, such as LSTM, CNN, GRU, BI-GRU, CNN+GRU and GRU+CNN. The experimental results show that the accuracy of the BI-GRU model combined with Attention and Capusule is higher than the other six models, and the accuracy of the GRU+CNN model is higher than that of the CNN+GRU model, and the accuracy of the CNN+GRU model is higher than that of the CNN model. The accuracy of CNN model was successively higher than that of LSTM, BI-GRU and GRU model. The fusion model of BI-GRU +Attention+Capsule adopted in this paper has the highest accuracy among all the models. In conclusion, the fusion model of BI-GRU+Attention+Capsule effectively improves the accuracy of text sentiment classification.
Global Manufacturing Production: An Analysis and Determinants for Mexico's Trade in Value-Added

Mexico's participation in the global manufacturing production began with the in-bond or maquiladora industry in the mid-60s. Since then, the manufacturing industry has positioned itself in the Global Value Chains (GVCs). However, the analysis of foreign trade in global manufacturing production requires a different measurement that accounts for trade in value-added. In this article, we examine the global manufacturing production as well as the determining factors of trade in value-added for the 2003-2018 period. This analysis becomes relevant for designing an industrial policy focused on improving Mexico's participation in the creation of added value linked to GVC. We corroborate the significance of the U.S. industrial activity as a determinant of the trade in value-added. The Mexican industrial activity becomes a significant determinant as well as the gross fixed capital formation. However, we found that traditional variables, such as foreign direct investment, are not significant. These results led us to believe that more efforts in constructing a theoretical framework are needed to explain the trade in value-added.
Subsurface investigation of the surface modification of polydimethylsiloxane by 172‐nm vacuum ultraviolet irradiation using ToF‐SIMS and VUV spectrometry

We investigate the mechanism of polydimethylsiloxane (PDMS) surface modification by 172‐nm vacuum ultraviolet (VUV) light. Time‐of‐flight secondary ion mass spectrometry and optical spectrometry are used to measure the chemical composition and VUV transmittance of the PDMS before and after surface modification, respectively. For modified samples of bulk PDMS, the VUV transmittance and the depth of the modified region increased with increasing VUV dose. This can be explained by the following self‐reinforcing cycle of (1) modification of PDMS by VUV light to a more silica‐like composition, (2) improvement of the VUV light transparency, and (3) deeper modification. For thin‐film samples of PDMS formed on sapphire substrates, the transmittance at 172 nm also increased with increasing VUV dose and exceeded that of sapphire in the region from 172 to 300 nm. Finally, thin‐film samples of PDMS formed on silicon substrates, which function as a VUV reflector, were also investigated. For these samples, the secondary ion depth profiles for several chemical species in the PDMS were oscillatory, probably due to the interference of the incident and reflected VUV light. These results strongly suggest that the photon energy of the VUV light plays an important role in modifying PDMS.