Miracle-dz/UniFAR-FTUs huggingface dataset:
Edinburgh Research Explorer It is more than what you Say!

Extended Abstract To what extent do online interactions reveal an individual’s viewpoint? Examining the implications of those interactions in detecting users’ stances provides a better understanding of one of the pressing challenges in predicting viewpoints using only the raw texts of a user’s posts. To address this challenge, we propose a new representation of stance detection that incorporates users’ interaction elements to better predict their stances on a topic. Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or against (Mohammad et al., 2016). Lately, this problem has attracted considerable attention due to its value in studying rumours and fake news in social media (Ma et al., 2018). To date, most of the studies in this area have focused on using the textual elements of a user’s posts independently from social factors, such as homophily and network structure. There has been some work on studying the integration of network and content with a limited focus on the ideological political views or speciﬁc event (Tsakalidis et al., 2018; Himel-boim et al., 2013; Lai et al., 2016; Magdy et al., 2016). For instance the study of (Himelboim et al., 2013) focused on the liberal and conservative on twitter. Unlike previous work, rather than studying the stance on single topic and using a domain speciﬁc data, we study the stance in any domain. In this work, we analyse the effect of online social interactions on stance detection and demonstrate its superiority in predicting stances toward different topics. In addition, we show that complementing text disclosing stance with
Measuring gambling knowledge in adolescents: The construction of a new short scale for research and practice

Several studies show that many adolescents gamble and a considerable proportion of them develop pathological gambling behavior. It has been shown that adolescents often have erroneous gambling knowledge, for example they are not aware of the technical definition of gambling activities or perceive gambling as a social and recreational activity. Nevertheless, nowadays there is a lack of measurement tools with adequate psychometric properties to assess gambling knowledge in adolescents. For this reason, the aim of the present study was to develop a new instrument able to evaluate this specific construct with a sample of Italian adolescents, the Gambling Related Knowledge Scale – For Adolescents (GRKS-A). In order to develop the scale and test its psychometric properties, 445 Italian adolescents participated in the study. The final version of the scale was composed of 8 items. Analyses confirmed the adequacy of the one-factor model and the reliability of this short scale. Support for the validity was also provided by obtaining significant and negative correlations with cognitive distortions, gambling economic perception, and gambling frequency. Moreover, the additional predictive power of GRKS-A on gambling frequency - with respect to the other variables - was demonstrated. Overall, findings support the suitability of the GRKS-A to measure gambling-related knowledge in research and practice involving adolescents.
The Influence of Hotels' Green Activities on Customers' Ethical Behavior: The Case of Five-star Hotels in Alexandria

Due to the rising ecological consciousness in the hospitality industry , green hotel seems to be growing niche in the competitive lodging industry and the number of consumers who are interested to stay at green hotel has also increased. In addition, the impact of the hotel industry on the environment is quite large because it is an economic sector undergoing rapid growth. The hotels consume lots of water, energy, and unrecyclable products that in the end endanger the environment. A typical hotel can emit daily or yearly between 160 and 200 kg/m2 per room per floor of carbon dioxide (co2). It is conceivable that ecological awareness can influence on consumer behavior. Lately, consumers have become more possible to express their environmental consciousness by purchasing environmentally friendly products and services. Therefore, This study examine to what extent the hotels apply green activities, particularly in Alexandria-Egypt; to identify the relationship between ethics conducts for consumers and hotel green activities; to explore the most effect demographics factors on customers'ethical behavior; to discover the degree up to which potential consumers intend to stay at green hotel, boycott or act discursively against un ethical hotels; and finally to look into the relation between customers’ attitude and their actual behaviors towards green hotel. In order to achieve these objectives, (10100) web-based, on-site questionnaires and manually forms were randomly distributed to five-star hotels’ consumers in Alexandria governorate –Egypt. The returned and valid questionnaires were (1080) questionnaires (98,2%). The checklist was another method applied to reveal the least applied green activities among hotels and to support the result of the questionnaire. Data were described using mean, standard deviation, Pearson coefficient correlation, Spearman coefficient linear regression. The study highlighted that there is a positive significant relationship between hotel green activities and ethical consumer behavior. In addition, hoteliers did not take the advantage of the ethical consumer behavior of growing opportunities. Although, hoteliers applied certain activities, they ignored the role of engaging customers in green activities and announcing the environmental efforts for the community, which appeared stagnation of ethical consumer behavior.
Object Detection in Low Illumination Environment

We propose a motion detection algorithm which works well in low illumination environments. By using the level set based bimodal motion segmentation, the algorithm obtains an automatic segmentation of the motion region and the spurious regions due to the large CCD noise in low illumination environment are removed effectively. Based on a noise analysis, we will show how to obtain the required parameters automatically. Experimental results verify the stableness of the proposed algorithm in low illumination conditions.
TRAFFIC DETECTION OF TRANSMISSION OF BOTNET THREAT USING BP NEURAL NETWORK

With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats while bringing convenience. The most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnets. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false alarm rate. The results showed that it had high detection rate and low false alarm rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.
Application of Agent-Based Modeling in Multi-Source Biomass Supply Chain Purchase Planning and Scheduling for a Power Plant

Renewable energies play a pivotal role in social, political and environmental affairs of every country. Numerous reports state that among all the renewable options, biomass is one of the most sustainable alternatives. However, biomass-fired energy plants face several unintended potential consequences. The cost and risk burden on facility managers is an issue given the lack of historical data on life-cycle operation and maintenance of technologies. With the advancement of computational capabilities, Agent-Based Modeling and Simulation (ABMS) is rapidly replacing conventional simulation techniques. Creating a successful simulation model can support the enhancement of logistical efficiency. The most common simulation technique used in biomass supply chain management is System Dynamics (SD). SD modeling in nature is highly abstract and therefore inadequate to consider the complete biomass supply chain structure and all related detail and information. Agent-Based Modeling and Simulation (ABMS) can act as an add-on to complement SD simulation method. In this thesis, the fundamentals of ABMS are combined with SD simulation technique in order to overcome many limitations of current modeling and simulation practices. ABMS is a bottom-up modeling techniques where actors and participants of the system are given attributes and their behavior is encoded as a set of rules. Currently the applications of ABMS in biomass supply chain management is limited and few in numbers, however, this technique is gaining the interest of many specially in the field related to supply chain management. A simulation model for the process of biomass purchase scheduling, planning and management for a power plant is developed. The model establishes a holistic approach in absence of knowledge of BSC system’s behavior and provides a reusable base that facilitates modeling various scenarios and measuring their performance through simulation. To address the challenges, four different scenarios have been designed and implemented. First scenario analyzes the outputs of the system in case of an increase in the scale of the operation. In the second scenario, the acceptable biomass types have been limited to only woody and high quality types. In the third scenario, storage method has been improved and combined with hot air treatment method. As for the forth scenario, ambient storage method as the cheapest method with high deterioration rate has been investigated. The results of these scenarios have been reported and individually evaluated. By conducting a comparison analysis between these scenarios and the base scenario, their advantages and disadvantages have been assessed. The third scenario is identified as the most favorable.
Applying Recurrent Neural Networks with Long Short- Term Memory in Clustered Stocks

Predicting the stock market is a widely studied field, either due to the curiosity in finding an explanation for the behavior of financial assets or for financial purposes. Among these studies the best techniques use neural networks as a prediction tool. More specifically, the best networks for this purpose are called recurrent neural networks (RNN), and provide an extra option when dealing with a sequence of values. However, a great part of the studies is intended to predict the result of few stocks, therefore, this work aims to predict the behavior of a large number of stocks. For this, similar stocks were grouped based on their correlation and later the algorithm K-means was applied so that similar groups were clustered. After this process, the Long Short-Term Memory (LSTM) - a type of RNN - was used in order to predict the price of a certain group of assets. Results showed that clustering stocks did not influence the effectiveness of the network and that investors and portfolio managers can use it to simply their daily tasks.
Technology acceptance model for computer assisted personal interviewing

Data collection plays an important role in census and survey for statistical work. Badan Pusat Statistik (BPS) Indonesia spent a lot of effort on human resources, budget, and time and complexity management in order to have valid data. Computer Assisted Personal Interviewing (CAPI) is a system which support data collection process more simple and efficient. It important to measure the user acceptance of CAPI in statistical work.
Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks

ABSTRACT Information systems such as social media strongly influence public opinion formation. Additionally, communication on the internet is shaped by individuals and organisations with various aims. This environment has given rise to phenomena such as manipulated content, fake news, and social bots. To examine the influence of manipulated opinions, we draw on the spiral of silence theory and complex adaptive systems. We translate empirical evidence of individual behaviour into an agent-based model and show that the model results in the emergence of a consensus on the collective level. In contrast to most previous approaches, this model explicitly represents interactions as a network. The most central actor in the network determines the final consensus 60–70% of the time. We then use the model to examine the influence of manipulative actors such as social bots on public opinion formation. The results indicate that, in a highly polarised setting, depending on their network position and the overall network density, bot participation by as little as 2–4% of a communication network can be sufficient to tip over the opinion climate in two out of three cases. These findings demonstrate a mechanism by which bots could shape the norms adopted by social media users.
Polynomial and moment conic optimizations: theory and applications

OF THE DISSERTATION Polynomial and Moment Conic Optimizations: Theory and Applications by: Mohammad Mehdi Ranjbar Dissertation Director: Farid Alizadeh We investigate the non-negative univariate polynomial conic optimization (uPCO) problem from the perspective of applications and the algorithms. We start by considering the applications of uPCO, specifically in: 1Time-variant network flow problems, and 2Non-parametric estimation under shape constraints with splines . Regarding algorithms, we use non-symmetric interior point methods (IPMs) to solve this conic optimization problem. It is well known that uPCO can be formulated as a semidefinite programming (SDP) problem, and therefore, it can be solved by available software for SDP. However, doing so will result in squaring the number of decision variables, and thus it is impractical even for moderate size problems. In addition, straightforward SDP formulation involves numerically unstable processes. Regarding the latter issue, we propose an orthogonal change of basis. Using the Chebyshev polynomials (which form an orthogonal basis), uPCO problems with significantly higher dimensions can be solved. As for the former issue, we propose two direct non-symmetric interior-point algorithms, by specializing the non-symmetric homogeneous self-dual predictor-corrector (HSD P-C) IPM (proposed by Skajaa-Ye 2015) and a Mehrotra version (i.e. HSD M-P-C IPM) of this algorithm (proposed by Akle-Ye 2015). We consider implementing these algorithms in two approaches. In the first approach, we develop these IPMs for the dual of the uPCO problem, i.e., the univariate
Design of spiral labyrinth microstrip antenna for DVB-T application

Digital television broadcasting is technologies that have been developed by any country in the world. The advantages implementations of digital television broadcasting include reception of picture and sound sharper and better. This paper proposes a new design of spiral labyrinth microstrip antenna feed by microstrip line with array two element for Digital Video Broadcasting Technology (DVB-T) application at work frequency of 586 MHz. The design of spiral labyrinth is used to minimize the dimensions of microstrip patch antenna while maintaining the working frequency at 586 MHz and array technique used to improve gain of antenna. The proposed antenna design was originally a rectangular patch that has been modified by the labyrinth spiral method. From the measurement result obtained return loss of -14.15 dB and VSWR of 1.54 at working frequency of 586 MHz. Bandwidth of proposed antenna is 117 MHz (547 MHz–664 MHz) while gain of antenna is 7.78 dBi. Beside that, using of the labyrinth spiral patch successfully reduced the dimensions of the microstrip antenna until 62.2% compared with the conventional rectangular patch of microstrip antenna. This study is usefull for DVB-T application in order to achieve the maximum signal quality and picture.
Asymmetric RF/FSO Relaying with HPA non-Linearities and Feedback Delay Constraints

In this work, we investigate the performance of a dual-hop multiple relays system consisting of mixed Radio-Frequency (RF)/Free Space Optical (FSO) channels. The RF channels are subject to Rayleigh fading while the optical links experience the Double Generalized Gamma including atmospheric turbulence, path loss and the misalignment between the transmitter and the receiver aperture (also known as the pointing error). The FSO model also takes into account the receiver detection technique which could be either heterodyne or intensity modulation and direct detection. Partial Relay Selection with outdated Channel State Information is assumed based on the RF channels to select a relay and we also consider fixed and variable Amplify-and-Forward relaying schemes. In addition, we assume that the relays are affected by the high power amplifier non-linearities and herein we discuss two power amplifiers called Soft Envelope Limiter and Traveling Wave Tube Amplifier. Furthermore, novel closed-forms and tight upper bounds of the outage probability, the bit error probability, and the ergodic capacity are derived. Capitalizing on these performance, we derive the high SNR asymptotic to get engineering insights about the system gains such as the diversity and the coding gains. Finally, the mathematical expressions are validated using the Monte Carlo simulation.
Using students’ learning to improve teaching: the case of the cream rising to the top

Teachers go to class to teach their students. One may question how often teachers use students’ visualisation of a concept to probe students’ understanding and to improve their own teaching. This paper focuses on the link between analogical reasoning and mental modelling in the teaching of electric circuits. A study conducted with 17-year-old physics students of different ability is described. From the results of a pre-test, post-test and semi-structured interviews using an ‘analogy prompt’, it will be shown how, when learning is meaningful, analogy generation can be developed and modified easily by students to relate the analogue to the target in question, thus providing evidence of the existence of scientific models of concepts in the minds of the students. It will be argued that the teaching of abstract concepts may be improved by using analogies indicated by high ability/gifted students who manage to develop a scientific view of concepts in question.
Determinants of female fecundity and outcome of pregnancy; epidemiological cohort studies to the effects of age, biometry and life-style habits

textabstractBecoming a mother is no more a matter of destiny only, but increasingly a matter of choice as wel! Until recently, having children was as natura! as eating, drinking and dying. Children just "happened" during the course of (married) life. However in modem societies life does not just "happen" any more; life is organized around the assumption that men and women should fee! free to choose the goals in their Jives and the ways to achieve such a goal, in other words: to plan their Jives ahead. With the general availability of education, it was possible for women also to achieve a professional goal through a career path. With the availability of contraception, especially since the introduetion of the pill in the sixties, it was possible to design a reproductive "career" as to the question: Do I want children at all; if yes how many and when? In order to abserve changes in societal trends, the disciplines of epidemiology and demography are more suitable than the medica! sciences. A medica! doctor has been educated for the "exception", the individual patient approach and not for the "rule". Demograpbic data show that of those Dutch females who are now fifty years of age or older, about 10% have remairred childless. Of those, now 40 years old, 15% has nat had a child yet. The Netherlands Central Bureau of Stalistics prediets a further increase in childlessness at 40 to about 20-25% for females bom after 1965. Because there are no indications that infertility among females, who are currently 40 years of age, is higher than in previous generations, it seems that voluntary childlessness has risen considerably among women1 Thus an increasing number of women appear to plan a career of non-reproduction. However the women who do plan a reproductive "career" do not only opt for fewer children than in the past, but also that the child should be perfect, in impeccable state. A realm of prenatal screening services are developed to distinguish during early pregnancy between the affected and unaffected fetus; leaving the subsequent decision to carry the pregnancy to term to the individual parents. In other words a desired pregnancy does not necessarily lead to a desireful pregnancy outcome
Prosumption of Discourse of a ‘Beautiful Policewoman’ By Netizens in Online News Portal

The emergence of young and stylish policewomen called “beautiful policewomen” as newscasters in the traffic information broadcasting program and various events contribute to mass media coverage of policewomen. In February-March 2016 two online mass media in Indonesia, Liputan6 and Tribunnews, intensively reported a beautiful policewoman who worked as a secretary of West Java Police chief and involved in the handling of bomb incident operation in Bandung, West Java. The news discursively figure a professional woman in the masculine work field. Most of the news about “beautiful policewomen” mainly focused on their physical attractiveness and explored private issues. Such news had gone viral due to the content distribution through Facebook and gained various comments from netizens. This research focuses on the netizens’ comments on the news of “beautiful policewomen” on Liputan6 and Tribunnews, which had gone viral on Facebook. It is aimed to examine the prosumption of discourse of a “beautiful policewoman” by netizens. Using Gunther Kress’ multimodal critical discourse analysis, this research analyzes the forms of discourse of the related news contents and the way in which netizens discursively construct a policewoman as a subject. The research reveals that netizens tend to associate beauty with capability and physical attractiveness. On the other hand, the studied comments figure the prevalent masculine dominance in Indonesian police institution. The roles of professional women remain being overlooked not merely in the workplace such as police institution but also publicly by media as well as netizens.
An Ensemble Feature Selection Method to Detect Web Spam

Feature selection is an important issue in data mining, and it is used to reduce dimensions of features set. Web spam detection is one of research fields of data mining. With regard to increasing available information in virtual space and the need of users to search, the role of search engines and used algorithms are important in terms of ranking. Web spam is an illegal method to increase mendacious rank of internet pages by deceiving the algorithms of search engines, so it is essential to use an efficient method. Up to now, many methods have been proposed to face with web spam. An ensemble feature selection method has been proposed in this paper to detect web spam. Content features of standard dataset of WEBSPAM-UK2007 are used for evaluation. Bayes network classifier is used along with 70-30% training-testing spilt of dataset. The presented results show that AUC of this method is higher than the other methods reported in this paper. Moreover, the best values of evaluation metrics in our proposed method are optimal in comparison to the other methods reported in this paper. In addition, it improves classification metrics in comparison to basic feature selection methods.