Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph Layout Algorithms and Libraries: Overview and Improvements

This paper focuses on exploring the possibilities of applying graph drawing algorithms to lay out custom diagrams, with emphasis put on UML class diagrams. Implementing even the simplest of layout algorithms that would lead to acceptable results requires excessive knowledge of graph theory. For this reason, many developers have to rely on existing solutions. There are several open source Java libraries for graph drawing and analysis, but most of them come with certain problems and limitations making their integration with a separately developed graphical editor overly complex. To deal with those issues, we are developing another graph drawing and analysis library, called Grad.
Research on Destination Prediction for Urban Taxi based on GPS Trajectory

Researching on destination prediction has a particularly important influence on the location-based services' popularization. The traditional destination prediction algorithm is to retrieve the historical trajectory data to find the same trajectory sequences as the query trajectory and then derive the most likely location to be the predicted result. However, due to the limitation of the historical trajectory data, this method has low efficiency and accuracy. Thus, in this paper, we propose the Prediction algorithm based on time (PBT algorithm), which considers the influence of the factor of time on destination prediction. Experiments based on real data show that in terms of destination prediction, the PBT algorithm not only alleviates the limitation of the historical data in the traditional algorithm to make the results more realistic, but also is more effective.
Research on the Solution of BP Neural Network Training Problem

BP neural network is a common kind of neural network for recognition and classification, but it is obvious that some difficulties are find during the training process. In this paper, we introduced the basic principle of BP algorithm and propose a parameter adjustment procedure for activation function to speed up convergence and avoid gradient diffusion, and the feasibility of this method is proved by a basic BP neural network with simple structure. We analyzed this parameter's available value through a group of experiments based on simple three-layer BP full connected network. In addition, this activation function parameter adjustment procedure is also used in is suitable for Multilayer BP neural networks. Keywords—neural network; gradient diffusion; activation function; convergence speed; classification accuracy
Trends in the research design and application of mobile language learning: a review of 2007–2016 publications in selected SSCI journals

ABSTRACT This study reviewed the mobile technology-assisted language learning studies published from 2007 to 2016 in selected journals. Several critical issues were investigated, such as research methods, research issues, language and learner types, and learning outcomes. It was found that English as a foreign/second language was the most common target language; few studies have been conducted on native language learning. Early studies mainly focused on fostering learners’ individual language skills, while in the recent 5 years, researchers started to consider the issue of delivering multiple language skills in authentic learning environments. In addition, an increasing number of studies have involved mixed methods and longer treatment periods. Moreover, the effectiveness of mobile learning in terms of speaking, writing, vocabulary, and pronunciation was generally identified by most of the studies. On the other hand, more evidence is needed to further confirm its impacts on listening, reading, grammar, and integrated/whole language learning. Finally, some suggestions are provided to researchers or practitioners to facilitate their future work.
Fahrerassistenz zur Kollisionsvermeidung

Es wird ein Assistenzsystem vorgestellt, das den Fahrer in gefahrlichen Verkehrssituationen durch akustische Warnungen und Bremseingriffe bis hin zur Vollbremsung unterstutzt. Zur Verfolgung ausgedehnter Objekte dienen vier Nahbereichsradare sowie eine monokularen Kamera mit optischem Fluss. Ein Schwerpunkt ist die echtzeitfahige Situationsbewertung und Systemauslegung unter Sensor- und Verhaltensunsicherheit. Der Systemnutzen wird in zahlreichen Langs- und Querverkehrssimulationen analysiert.
Data Aggregation for Road Functionality Detection Based on Machine Leaning and VANET

Vehicular Ad hoc Network (VANET) is superior to traditional Intelligent Transportation Systems (ITS) for its vehicle-to-x communication. VANET is established on the underlying information exchanges and broadcast by vehicles and infrastructures in the network. The driving status of vehicles on road can be got from the periodically broadcasts messages and the particular data can be stored to be exploited for special service. These obtained data are richer and more representative than those data collected by fixed sensors such as ground induction coil, floating car data or video analysis, which can be utilized to get the real-time road traffic knowledge in a way similar to big data technology. And the existing relevant approaches about traffic detection and prediction face the following disadvantages: (1) insufficient data utilized; (2) hand engineered in features; and (3) suboptimal learning method. As the big data about the real time traffic can be obtained through VANETs conveniently, this work thus proposed a road functionality detection model based on data aggregation in VANETs applying machine learning algorithm. The performance of the scheme is evaluated through extensive simulations. The simulation results show that the approach of fusing the information of vehicles on the road can accurately detect the dysfunctional road state and further locate on the specific malfunctioning position particularly for high dimensions.
Lattice-Based Coding Schemes for Wireless Relay Networks

Compute-and-forward is a novel relaying paradigm in wireless communications in which relays in a network directly compute or decode functions of signals transmitted from multiple transmitters and forward them to a central destination. In this dissertation, we study three problems related to compute-and-forward. In the first problem, we consider the use of lattice codes for implementing a compute-and-forward protocol in wireless networks when channel state information is not available at the transmitter. We propose the use of lattice codes over Eisenstein integers and we prove the existence of a sequence of lattices over Eisenstein integers which are good for quantization and achieve capacity over an additive white Gaussian noise (AWGN) channel. Using this, we show that the information rates achievable with nested lattice codebooks over Eisenstein integers are higher than those achievable with nested lattice codebooks over integers considered by Nazer and Gastpar in [6] in the average sense. We also propose a separation-based framework for compute-and-forward that is based on the concatenation of a non-binary linear code with a modulation scheme derived from the ring of Eisenstein integers, which enables the coding gain and shaping gain to be separated, resulting in significantly higher theoretically achievable computation rates. In the second problem, we construct lattices based on spatially-coupled lowdensity parity check (LDPC) codes and empirically show that such lattices can approach the Poltyrev limit very closely for the point-to-point unconstrained AWGN channel. We then employ these lattices to implement a compute-and-forward protocol and empirically show that these lattices can approach the theoretically achievable rates closely.
Education and skills mismatch in the South African labour market

While a number of chapters in this volume have explored the labour market outcomes associated with the completion of various types of training and education, this chapter offers a broader picture of the match between education and employment. Matching the skills of the workforce with the needs of the labour market is an imperative in all countries but arguably even more so in South Africa, where the official unemployment rate stands at approximately 26.7% (Statistics South Africa [Stats SA] 2018). In the context of such high structural unemployment alongside concerted government efforts to support human capital development across the life cycle, the extent to which there is alignment between available qualifications and skills possessed by the labour force and those demanded by employers is critical.
The Effects of Feedback in Computerized Adaptive and Self-Adapted Tests.

Computerized adaptive (CA) testing uses an algorithm to match examinee ability to item difficulty, while self-adapted (SA) testing allows the examinee to choose the difficulty of his or her items. Research comparing SA and CA testing has shown that examinees experience lower anxiety and improved performance with SA testing. All previous research concerning SA testing has presented item feedback to the examinee before asking the examinee to choose the next item difficulty level. Moreover, item feedback has typically not been presented to examinees in previous CA testing research. The effects of presenting, versus withholding, item feedback in SA tests were studied for 135 graduate and 228 undergraduate students (128 males and 235 females). The instrument was a computerized algebra test to assess skills needed for a statistics class. Examinees administered the SA tests tended to obtain significantly higher ability estimates than did those who were administered the CA tests. Also, those taking the SA tests reported significantly lower post-test state anxiety than did those taking the CA tests. Interaction between test type and feedback was not found, suggesting that examinees are able to use the implicit feedback they receive when answering items. Five tables present study findings. (SLD) *********************************************************************** Reproductions supplied by EDRS are the best that can be made * from the original document. *********************************************************************** U.S. DEPARTMENT OF EDUCATION Office of Educahonal Research and Improvement ED T1ONAL RESOURCES INFORMATION CENTER (ERIC) This document hat been reproduced as received from the parson Or organization originating it 0 Minor changes have been made to improve reproduction quality Points of view or opinions slated in this document do not necessarily represent Oficial OERI position or policy "PERMISSION TO REPRODUCE THIS MATERIAL HAS BEEN GRANTED BY
Neural Extended State Observer Based Command Filtered Back-stepping Hypersonic Vehicle Integrated Guidance and Autopilot

This paper focuses on the integrated guidance and autopilot design with control input saturation in the end-game phase of hypersonic flight. Firstly, uncertain nonlinear integrated guidance and autopilot model is developed with third actuator dynamics, where the control surface deflection has magnitude constraint. Secondly, neural network is implemented in extended state observer (ESO) design, which is used to estimate the complex model uncertainty, nonlinearity and state coupling. Thirdly, a command filtered back-stepping controller is designed with hybrid sliding surfaces to improve the terminal performance. In the process, different command filters are implemented to avoid the influences of disturbances and repetitive derivation, meanwhile solve the problem of unknown control direction caused by saturation. The stability of closed-loop system is proved by Lyapunov theory, and the principles abided by the controller parameters are concluded through the proof. Finally, series of 6-DOF numerical simulations are presented to show the feasibility and validity of the proposed controller.
Universal design of a microcontroller and IoT system to detect the heart rate

Heart rate analysis provides vital information of the present condition of the human body. It helps medical professionals in diagnosis of various malfunctions of the body. The limitation of vision impaired and blind people to access medical devices cause a considerable loss of life. In this paper, we intended to develop a heart rate detection system that is usable for people with normal and abnormal vision. The system is based on a non-invasive method of measuring the variation of the tissue blood flow rate by means of a photo transmitter and detector through fingertip known as photoplethysmography (PPG). The signal detected is firstly passed through active low pass filter and then amplified by a two stages high gain amplifier. The amplified signal is feed into the microcontroller to calculate the heart rate and displays the heart beat via sound systems and Liquid Crystal Display (LCD). To distinguish arrhythmia, normal heart rate and abnormal working conditions of the system, recognition is provided in different sounds, LCD readings and Light Emitting Diodes (LED).
Expression and Inheritance of GUS Gene in Transgenic Tobacco Plants

Agrobacterium tumefaciens-mediated transformation of tobacco leaf discs was performed with plant transformation vec- tors of pBI 121 and pGus-Int. Both carry the neomycin phosphotransferase II (npt II) gene and, unlike the pBI 121, pGus-Int car- ries a portable plant intron inside the beta-glucuronidase (GUS) gene. In addition to the integration of the marker genes into the genomes of primary transformants, R1 and R2 progenies were confirmed by polymerase chain reaction (PCR) and Southern blot analyses. Histochemical and fluorometric analyses were also performed to determine the activity of the GUS gene. Segregation of progenies on kanamycin-resistance trait was also shown as evidence for the transformation. The analyses showed that the trans- genes were transmitted to subsequent generations in a Mendelian manner and intron sequences enhanced the expression of the GUS gene by 25-30% in some transformants.
Gaussian processes for accurate quantification of uncertainty and large-scale learning

Gaussian Process (GP) models are extensively used in data analysis given their flexible modeling capabilities and interpretability. The fully Bayesian treatment of GP models is analytically intractable, and therefore it is necessary to resort to either deterministic or stochastic approximations. This paper focuses on stochastic-based inference techniques. After discussing the challenges associated with the fully Bayesian treatment of GP models, a number of inference strategies based on Markov chain Monte Carlo methods are presented and rigorously assessed. In particular, strategies based on efficient parameterizations and efficient proposal mechanisms are extensively compared on simulated and real data on the basis of convergence speed, sampling efficiency, and computational cost.
A Crowd-Sensing Framework for Allocation of Time-Constrained and Location-Based Tasks

Thanks to the capabilities of the built-in sensors of smart devices, mobile crowd-sensing (MCS) has become a promising technique for massive data collection. In this paradigm, the service provider recruits workers (i.e., common people with smart devices) to perform sensing tasks requested by the consumers. To efficiently handle workers’ recruitment and task allocation, several factors have to be considered such as the quality of the sensed data that the workers can deliver and the different tasks locations. This allocation becomes even more challenging when the MCS tries to efficiently allocate multiple tasks under limited budget, time constraints, and the uncertainty that selected workers will not be able to perform the tasks. In this paper, we propose a service computing framework for time constrained-task allocation in location based crowd-sensing systems. This framework relies on (1) a recruitment algorithm that implements a multi-objective task allocation algorithm based on Particle Swarm Optimization, (2) queuing schemes to handle efficiently the incoming sensing tasks in the server side and at the end-user side, (3) a task delegation mechanism to avoid delaying or declining the sensing requests due to unforeseen user context, and (4) a reputation management component to manage the reputation of users based on their sensing activities and task delegation. The platform goal is to efficiently determine the most appropriate set of workers to assign to each incoming task so that high quality results are returned within the requested response time. Simulations are conducted using real datasets from Foursquare1 and Enron email social network.2 Simulation results show that the proposed framework maximizes the aggregated quality of information, reduces the budget and response time to perform a task and increases the average recommenders’ reputation and their payment.
Gestural Interaction with Three-Dimensional Interfaces; Current Research and Recommendations

New methods of interacting with latest three-dimensional (3D) display technologies are rapidly being tested, however, it is not yet certain, which kinds of interaction design would be most suitable for users to manipulate and interact with 3D content. Physical gestures have been a natural and intuitive mode of interaction for human-human communication and ways to replicate this for human-machine communication is increasingly being explored. This proceeding attempts to review the current state of technology and the existing research on the use of gestural interaction with three-dimensional interfaces. The focus is on the use of hand-based gestures, the scope is to identify challenges and limitations of using gestures with 3D interfaces, and the goal is to find guidelines to select appropriate gestures to interact with 3D displays. The paper also highlights existing gaps and potential future research areas, in the technological and usability domains, that must be addressed before gestures can be used seamlessly with 3D interfaces.
Title GONE WITH THE WIND : THE EVOLVING INFLUENCE OF SOCIAL MOVEMENTS AND COUNTER MOVEMENTS ON ENTREPRENEURIAL ACTIVITY IN THE US WIND INDUSTRY Permalink

Social movements can disrupt existing industries and inspire the emergence of new markets by drawing attention to problems with the status quo and promoting alternatives. We examine how the influence of social movements on entrepreneurial activity evolves as the markets they foster mature. Theoretically, we argue that the success of social movements in furthering market expansion leads to three related outcomes. First, the movement-encouraged development of market infrastructure reduces the need for continued social movement support. Second, social movements’ efforts on behalf of new markets increase the importance of resource availability for market entry. Third, market growth motivates countermovement that reduce the beneficial impact of initiator movements on entrepreneurial activity. We test these arguments by analyzing evolving social movement dynamics and entrepreneurial activity in the US wind Sustainability, Stakeholder Governance, and Corporate Social Responsibility Advances in Strategic Management, Volume 38, 339 364 Copyright r 2018 by Emerald Publishing Limited All rights of reproduction in any form reserved ISSN: 0742-3322/doi:10.1108/S0742-332220180000038018 339 D ow nl oa de d by U ni ve rs ity o f C al if or ni a B er ke le y, H ea th er H av em an A t 0 9: 47 0 8 A ug us t 2 01 8 (P T ) power industry from 1992 to 2007. We discuss the implications of our findings for the study of social movements, stakeholder management, sustainability, and entrepreneurship.
Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

ABSTRACTRepresentation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing .
University entrepreneurship center identity factors prioritization using TOPSIS method

University Entrepreneurship Center Identity (UECI) is a set of meanings by which a UECI allows itself to be known, and through which it allows people to describe, remember and relate to it. There are many factors stated in many studies related to UECI, but their importance level is not mentioned. In this study, the UECI factors are ranked by using TOPSIS method. A quantitative study was conducted to obtain experts’ views on UECI factors. The UECI factors were prioritized and ranked by using the “Technique for order of Preference by Similarity to Ideal Solution” (TOPSIS) from Multi-Criteria Decision Making (MCDM) method. The result of performing TOPSIS is as a novelty which assists UECI presentation. The high priority factors were introduced as “Innovativeness”, “Industry Interactions”, “Producing Highly Qualiﬁed Graduates”, “Proactiveness”, “Attract Entrepreneurial Faculty”, “Teamwork”, “Consulting”, and “Risk Taking”. This study proposes the importance level of UECI factors. At the end, it is recommended to evaluate the effect of UECI perception in different studies which contain university entrepreneurship center identity perception.
Deep learning based beat event detection in action movie franchises

Automatic understanding and interpretation of movies can be used in a variety of ways to semantically manage the massive volumes of movies data. “Action Movie Franchises” dataset is a collection of twenty Hollywood action movies from five famous franchises with ground truth annotations at shot and beat level of each movie. In this dataset, the annotations are provided for eleven semantic beat categories. In this work, we propose a deep learning based method to classify shots and beat-events on this dataset. The training dataset for each of the eleven beat categories is developed and then a Convolution Neural Network is trained. After finding the shot boundaries, key frames are extracted for each shot and then three classification labels are assigned to each key frame. The classification labels for each of the key frames in a particular shot are then used to assign a unique label to each shot. A simple sliding window based method is then used to group adjacent shots having the same label in order to find a particular beat event. The results of beat event classification are presented based on criteria of precision, recall, and F-measure. The results are compared with the existing technique and significant improvements are recorded.