Miracle-dz/UniFAR-FTUs huggingface dataset:
Classroom strategies that impact disruptive behavior on primary students

The purpose of the current study is to implement and eventually analyze the impact of a set of behavior management strategies to prevent, reduce and react towards disruptive behavior in the classroom. Therefore this research project seeks to test several behavior management strategies that may contribute to a controlled and successful learning and teaching environment. This qualitative research was carried out with 28 fifth graders from a Colombian elementary public school in the city of Pereira applying interviews, teacher journal, and observations as data collection methods in order to gather strong evidence for each strategy implemented. This study is relevant as it addresses a problem that teachers face daily in the classroom specially novice teacher who based on their possible lack of teaching experience need to become aware of such strategies. In conclusion, the analysis of the data obtained shows that the effectiveness of the strategies used had different results in terms of reducing or preventing problematic behavior which involved different lengths of effectiveness leading the researcher to implement some strategies more often than others in order to maintain the control on students’ attention. As a result, effective classroom management does not only enhance teaching conditions, but improves learning outcomes regarding L2 and even more when this process is aided by the use of effective behavior management strategies.
Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network

This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.
A Comprehensive Survey of Spam Profile Detection Methods in Online Social Networks

Social networks have grown into a popular way for internet surfers to interact with friends in addition to family members, reading news, and also discuss events. Users spend more time on popular social platforms (e.g., Facebook, Twitter, etc.) storing and sharing their personal information. This fact together with the prospect of communicating thousands of users fascinates the concentration of malicious users. They exploit the implicit trust interactions concerning users with the purpose of accomplishing their malicious objectives, for instance, create malicious links inside the posts/tweets, spread fake news, send out unsolicited messages to genuine users and so on. In this paper, we reviewed various existing techniques on spam profile detection in online social networks.
Importance Sampling for Coded-Modulation Error Probability Estimation

This paper proposes an efficient simulation method based on importance sampling to estimate the random-coding error probability of coded modulation. The technique is valid for complex-valued modulations over Gaussian channels, channels with memory, and naturally extends to fading channels. The simulation method is built on two nested importance samplers to respectively estimate the pairwise error probability and generate the channel input and output. The effect of the respective number of samples on the overall bias and variance of the estimate of the error probability is characterized. For a memoryless channel, the estimator is shown to be consistent and with a small variance, growing with the square root of the code length, rather than the exponential growth of a standard Monte Carlo estimator.
A sentiment analysis approach to improve authorship identification

Writing style is considered the manner in which an author expresses his thoughts, influenced by language characteristics, period, school, or nation. Often, this writing style can identify the author. One of the most famous examples comes from 1914 in Portuguese literature. With Fernando Pessoa and his heteronyms Alberto Caeiro, Álvaro de Campos, and Ricardo Reis, who had completely different writing styles, led people to believe that they were different individuals. Currently, the discussion of authorship identification is more relevant because of the considerable amount of widespread fake news in social media, in which it is hard to identify who authored a text and even a simple quote can impact the public image of an author, especially if these texts or quotes are from politicians. This paper presents a process to analyse the emotion contained in social media messages such as Facebook to identify the author's emotional profile and use it to improve the ability to predict the author of the message. Using preprocessing techniques, lexicon‐based approaches, and machine learning, we achieved an authorship identification improvement of approximately 5% in the whole dataset and more than 50% in specific authors when considering the emotional profile on the writing style, thus increasing the ability to identify the author of a text by considering only the author's emotional profile, previously detected from prior texts.
Design a compact square ring patch antenna with AMC for SAR reduction in WBAN applications

In this paper presents a compact square ring patch antenna with miniaturized AMC structure at 5.8 GHz for WBAN applications. To minimize detuning, keeping its radiation efficiency high and acceptable gain while keeping the SAR levels low for safety is a challenging task. One of the critical issues in WBAN antenna design is the size of the antenna for portable devices, because the size affects the gain and bandwidth. The AMC configuration decreases the back radiation and the effect frequency detuning results from the high loss in the human body. Furthermore, the AMC also increases the front-to-back ratio (FBR) of 15.3 dB. The proposed antenna has dimensions of 15.27×15.27×2.2 mm3 and provides a 404 MHz impedance bandwidth, with a gain improvement of 8.69 dBi and a 93.7% reduction of the initial SAR value. For this reason, the antenna is suitable for WBAN application in various fields, particularly in medical technology.
Offshore Wind Plants with VSC-HVDC Connection and their Impact on the Power System Stability: : Modeling and Grid Code Compliance

This report is built within the content of the MS.c course EE4545 at TU Delft. It aims to provide an introduction to typical grid codes for DC connected wind plant. In addition, the report discusses the modeling approach used for wind plants and VSC-HVDC systems and provides insight on the grid code compliance strategies.
A New Way to Compute the Probability of Informed Trading

Volume-Synchronized Probability of Informed Trading (VPIN) is a tool designed to predict extreme events like flash crashes in high-frequency trading. Its aim is to estimate the Probability of Informed Trading (PIN), which was built from a probabilistic framework. Some concerns have been raised about its theoretical foundations and its reliability. More precisely, it has been shown that theoretically the VPIN does not approximate the PIN as the PIN has been built with a time-clock framework and the VPIN with a volume clock one. On a practical point of view, the VPIN has been found to be sensitive to the starting point of computation of a data set and to different parameters, such as the classification rule. In this paper, in order to improve the PIN theoretical framework, we firstly analyze the theoretical foundations of the PIN and the VPIN models to have a better view of all its different assumption subtleties. It secondly makes it possible to point out some approximation flaws in the formula used to approximate the PIN and to propose another exact way to compute the PIN. All different results are illustrated with simulations.
Live–virtual–constructive simulation for testing and evaluation of air combat tactics, techniques, and procedures, Part 2: demonstration of the framework

In this paper, the use of the live (L), virtual (V), and constructive (C) simulation framework introduced in Part 1 of this two-part study is demonstrated in the testing and evaluation of air combat tactics, techniques, and procedures (TTP). Each TTP consists of rules that describe how aircraft pilots coordinate their actions to achieve goals in air combat. In the demonstration, the initial rules are defined by subject matter experts (SMEs). These rules are refined iteratively in separate C-, V-, and L-simulation stages. In the C-stage, an operationally used C-simulation model is used to provide optimal rules with respect to the probabilities of survival (Ps) and kill (Pk) of aircraft without considering human–machine interaction (HMI). In the V-stage, fighter squadrons’ V-simulators and SMEs’ assessment are used to modify these rules by evaluating their applicability with Pk and Ps, as well as HMI measures regarding pilots’ situation awareness, mental workload, and TTP rule adherence. In the L-stage, qualified fighter pilots fly F/A-18C aircraft in a real-life environment. Based on SMEs’ assessment, the TTP rules refined in the C- and L-stages result in acceptable Pk, Ps, and HMI measures in the L-stage. As such, the demonstration highlights the utility of the LVC framework.
Cost-Effective Predictive Flux Control for a Sensorless Doubly Fed Induction Generator

The paper introduces a cost effective predictive flux control (PFC) approach for a sensorless doubly fed induction generator (DFIG). The base operation of the proposed PFC depends on controlling the rotor flux ( $\alpha $ - $\beta$ ) components using a cost function which is derived through analyzing the relationship between the developed torque and the angular slip frequency. To improve the rotor flux estimation and prediction, an effective rotor flux observer is proposed. A robust rotor position estimator is proposed to guarantee a precise co-ordinate transformation. In order to save the cost, only one rotor current sensor is utilized to evaluate the rotor currents. The finite control set (FCS) principle is utilized to select the voltage vectors which enables the elimination of the pulse width modulation (PWM). To validate the feasibility of the proposed sensorless PFC approach, a comprehensive comparison is carried out between the proposed sensorless PFC and the predictive torque control (PTC) for the DFIG. The obtained results confirm and emphasize the superiority of the proposed PFC in achieving the control objectives with lower ripples content and less computational burdens. Moreover, the effectiveness of the rotor position and rotor flux estimators has been confirmed through the obtained results.
De-Identification of Emails: Pseudonymizing Privacy-Sensitive Data in a German Email Corpus

We deal with the pseudonymization of those stretches of text in emails that might allow to identify real individual persons. This task is decomposed into two steps. First, named entities carrying privacy-sensitive information (e.g., names of persons, locations, phone numbers or dates) are identified, and, second, these privacy-bearing entities are replaced by synthetically generated surrogates (e.g., a person originally named ‘John Doe’ is renamed as ‘Bill Powers’). We describe a system architecture for surrogate generation and evaluate our approach on CodeAlltag, a German email corpus.
Pre-Processing for Neural Model Design in A Real Industrial Problem

- In the last years, the artificial neural networks have been effectively applied to several industrial problems in order to improve knowledge and get a deeper insight into correlations among different factors which affect production processes. In many applications Neural Networks are applied to predict the relationship between available input process variables and the target to be forecasted. Data pre-processing is an important step in developing a neural network application, which could affect the accuracy and the results of the developed models and applications. In the present paper an approach is proposed for data pre-processing, concerning a particular application related to the steel production. Such approach is tested on one row dataset coming from a real industrial context and the obtained results demonstrate the effectiveness of an accurate and appropriate pre-processing phase
A Novel Timing-based Network Covert Channel Detection Method

Network stealth events are endless, and covert timing channel is one of the most difficult means to prevent. In order to further improve the detection rate of covert timing channel, several typical network covert timing channel construction algorithms are analyzed. On the basis of the above analysis, a detection method based on IPDs multidimensional features was proposed in this paper. IPDs of covert timing channels from three dimensions: shape, change rule and data statistics are analyzed. Respectively, polarization feature, autocorrelation feature, clustering feature are proposed, and the three features are unified into a model. The threshold method is used to determine whether the channel to be detected is a normal channel. Experiments show that the method can detect the existing covert timing channel with less time cost and compared with the traditional detection method has a certain rate of improvement.
EFFICIENCY ESTIMATION OF METHODS FOR SENTIMENT ANALYSIS OF SOCIAL NETWORK MESSAGES

The results of effectiveness evaluating of machine learning methods for sentiment analysis of social network messages are presented in this paper. The importance of the sentiment analysis problem as one of the important tasks of natural language processing in general and textual information processing in particular is substantiated. A review of existing methods and software for sentiment analysis are made. The choice of classifiers for sentiment analysis of texts for this research is substantiated. The principles of functioning of a Naïve Bayesian Classifier and classifier based on a recurrent neural network are described. Classifiers were sequentially trained in two corpuses: first, in the RuTweetCorp corpus, the corpus of short messages from the social network Twitter, and then on the Slang corpus, the corpus of messages from social networks Facebook and Instagram and posts from the Pikabu website, second corpus have been marked up the tonality of slang words. Information about the tonality of slang words was taken from the youth slang dictionary obtained as a result of the survey of users. The separation of texts by tonality was carried out into three classes: positive, negative and neutral. The efficiency of these classifiers was evaluated. Efficiency evaluation was carried out according to standard metrics Recall, Precision, F-measure, Accuracy. For the naive Bayesian classifier, after training on the first corpus, the following metric values were obtained: Recall = 0,853; Precision = 0,869; F-measure = 0,861; Accuracy = 0,855; and after training on the second corpus such values were obtained: Recall = 0,948; Precision = 0,975; F-measure = 0,961; Accuracy = 0,960. For the classifier based on a recurrent neural network, after training on the first corpus, the following metric values were obtained: Recall = 0,870; Precision = 0,878; F-measure = 0,874; Accuracy = 0,861; and after training on the second corpus such values were obtained: Recall = 0,965; Precision = 0,982; F-measure = 0,973; Accuracy = 0,973. These results prove that additional training on the second corpus increased the efficiency of classifiers by 10–11%.
Life in space

For nearly half a century, inclusive fitness theory has formed a bedrock of wholeorganism biology. It has helped explain social behaviours across the tree of life, including, via the major transitions view of evolution, the history of life itself. However, some significant questions remain. In this thesis, I consider challenges at the frontiers of inclusive fitness, in time, theory, and space. Specifically: (i) I study the evolution of cooperation early in the history of life. I develop models of simple molecular replicators, resolving previously puzzling results and identifying important life-history features of replicators for cooperation. (ii) I use kin selection models to develop a framework for understanding RNA cooperation and guiding empirical work on the origins of life. (iii) I develop and test a new hypothesis for the origin of the genome, the first major transition in individuality. (iv) I address criticisms of inclusive fitness theory, developing conceptual arguments for why it is a useful tool despite its flaws. (v) I show that recent mathematical criticisms of inclusive fitness misapplied inclusive fitness, and extend their models, recovering inclusive fitness maximisation. I provide formal arguments for a broader range of the theory’s application than previously thought by some mathematical biologists. (vi) I show that a recent paper about the effects of divorce on honest signalling in birds miscalculated inclusive fitness, and develop a formal model that predicts an effect of inclusive fitness on signalling in birds, which is supported by the data. (vii) Finally, I consider the universality of natural selection and major transitions in individuality, demonstrating how evolutionary theory can be used as a powerful tool in astrobiology. As a whole, this body of work extends the range of inclusive fitness theory’s application earlier in evolutionary time, to a wider range of biological scenarios, and further in space.
An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.
Low Power Design Methodologies

: With the demand of addition of more and more system on chip, the number of transistors are getting added which leads to the increase in power dissipation in the devices. Recently, due to the continuous scaling of feature size, a massive development has been taken place in the field of portable devices. Two main features of low power design are battery lifeline (especially in portable devices) and reliability Present electronics market is full of battery operated highly complex gadgets and multifunctional portable devices. The power consumption in these devices has become the pivotal constraint. The main objective of this paper is to focus on various perspectives of low power design in VLSI circuits.
Media Environment Transformations as an Object of Study of the Theory of Mass Communications

Changes in the media environment always lead to changes in the media system, which reflects a different quality of communication. The nature of actors, channels, effects becomes an object of attention of researchers of mass communications. The article deals with the problem of the transformation of the mass communication system, which includes both cinematography and fiction, which can be freely broadcast through various channels, including digital channels. The article considers a new concept, i.e. "communication product". It is understood as a result of professional activity in the field of public and mass communications for the production of a media product, an event product, an advertising and PR product. The media environment is currently characterized by its own messages. They can be built both in traditional newsmaking models (both in journalism and public relations), and in storytelling, narrative (both in advertising and media communications). The article considers such trends in the media environment as the use of hype content, memes and various fakes. The reasons for the emergence of media communications are revealed. There are technological reasons, as modern communication technologies can open access to information to all comers. There are social ones, as new media allows Internet users to actively participate in communications and selectively treat messages they receive. The characteristics of the phenomenon of media communications are distinguished, separating it from journalism, advertising and public relations. The notion of media communications as a process of creation, processing and broadcasting, as well as information exchange in individual, group, mass format through various channels of mass communication (mainly online) is being introduced into scientific circulation through various communication tools - verbal / non-verbal; auditory, audiovisual, visual.