Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Radicalisation and Extremism Online: A Survey

Introduction: Due to the lack of regulation, the large volume of user-generated online content reflects more closely the offline world than official news sources. Therefore, social media platforms have become an attractive space for anyone seeking independent information. One of the main goals of this work is to clarify concepts such as Extremism and Collective Radicalisation, Social Media, Sentiments/Emotions/Opinions Analysis, as well as the combinations of all of them. Methods: The automatic identification of extremism and collective radicalisation requires sophisticated Natural Language Processing (NLP) methods and resources, especially those dealing with opinions, emotions or sentiment analysis. Text mining and knowledge extraction are also crucial, in particular, directed toward social media and micro-blogging. Results: The present document comprehends a study on theoretical material, focusing on the main concepts of the subject, including the main problems and challenges, from the different areas that compose online radicalisation research. Understanding and detecting extremism and collective radicalism online has a connection to sentiment analysis and opinion mining. There are many barriers to understanding extremism and collective radicalisation; one is to differentiate between who is really engaged in the process and who is just eventually talking about it. Conclusions: The other focus of this work is to find the best ways to identify extremism and collective radicalisation on the internet, using sentiment analysis and focusing on probabilistic methods to create an unsupervised and language-independent approach.
Crude Oil Futures Hedging Strategies During COVID-19 Using VaR-Garch Models

In 2020, under the sweeping impact of the COVID-19 pandemic and a series of chain reactions that followed, the crude oil industry was hit hard. The most influential event was the price collapse of WTI futures and the unprecedented huge loss of some investors holding crude oil related products in their personal account in Bank of China. How to avoid the risk of fluctuations in prices for crude oil futures when encompassing extreme event? US WTI spot price and the futures price were analyzed to obtain the optimal hedging strategy for futures. First, crude oil futures and spot closing price were set as benchmarks, the following tests are carried out on their returns respectively: Auto-correlation test, Descriptive statistical test, Stability test, Co-integration test and Granger causality test; next, historical simulation and delta-normal method were applied to calculate the return rate of crude oil spot and hedged combination respectively, and it was proved that the hedging has a good effect.
How to Reasonably Determine the Volume of Heat-Storage Water Tank Used in Electric Heat-Storage Heating

The paper deals with the calculation formulas for determining the volume of heat storage water tank used in electric heat storage heating, and the factors of these formulas have been fixed. The conditions for determining the highest and lowest design temperature of heat storage water tank are proposed and the calculation formulas for preventing gas corrosion of pump in heating or open cycle boiler heating system are given. Finally the estimated index range of heat storage water tank for building area is provided in this paper. [
Mice transgenic for human CTLA4-CD28 fusion gene show proliferation and transformation of ATLL-like and AITL-like T cells

ABSTRACT CTLA4-CD28 gene fusion has been reported to occur in diverse types of T cell lymphoma. The fusion event is expected to convert inhibitory signals to activating signals and promote proliferation and potentially transformation of T cells. To test the function of the CTLA4-CD28 fusion gene in vivo, we generated a murine model that expresses the gene in a T cell-specific manner. The transgenic mice have shorter life spans and display inflammatory responses including lymphadenopathy and splenomegaly. T cells in turn show higher levels of activation and infiltrate various organs including the lung and skin. T cells, in particular CD4+ helper T cells, were also readily transplantable to immunocompromised mice. Transcriptomic profiling revealed that the gene expression pattern in CD4 + T cells closely resembles that of adult T cell leukemia/lymphoma (ATLL) and that of angioimmunoblastic T cell lymphoma (AITL) tissues. Consistently, we detected supernumerary FOXP3+ cells and PD-1+ cells in transgenic and transplanted mice. This is the first report demonstrating the transforming activity of the CTLA4-CD28 fusion gene in vivo, and this murine model should be useful in dissecting the molecular events downstream to this mutation.
Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Prediction of time series is associated with nondeterministic pattern analysis for uncertain conditions. Therefore, it is necessary to develop high-quality prediction methods for real-world applications. Type-2 fuzzy systems can handle high-order uncertainties, such as sequential dependencies associated with time series. Precise and reliable prediction can help to develop reasonable strategies and assist specialists in planning the best policies for modeling events in uncertain time series. In this study, a hybrid model (dynamic type-2 fuzzy time warping [DT2FTW]) was proposed for handling high-order uncertainties in time-series prediction. A type-2 fuzzy intelligent system was developed alongside a dynamic time warping algorithm for predicting the patterns’ similarity in long-time series for time-series prediction. The results demonstrate that the proposed DT2FTW model yields more reliable predictions on global standard benchmarks such as the Mackey-Glass, Dow Jones, and NASDAQ time-series. The results also conﬁrm that the proposed DT2FTW model has lower error rates than its counterpart algorithms in terms of the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). In addition, the results conﬁrm the superiority of the proposed model with an average area under the ROC curve (AUC) of 94%, with the 95% conﬁdence interval (92% - 95%) .
Study of Electronic Properties of GaAs Semiconductor Using Density Functional Theory

The properties of GaAs material in zinc blende type was calculated using Hiroshima Linear Plane Wave program based on the Density Functional Theory. This calculation aims to determine electronic properties of GaAs material are based on Density of States and energy band structure. This simulation’s results are DOS shows that hybridization of s orbital of Ga with s orbital of As provides covalent properties. The simulation of energy band structure from GaAs material indicates that semiconductor properties of GaAs is direct band gap. The energy band gap results obtained for GaAs is 0.80 eV. The computational result of the energy band gap calculation form HiLAPW has better accuracy and prediction with good agreement within reasonable acceptable errors when compared to some other DFT programs and the results of the experimental obtained.
Retired Government Officials On The Board Of Commissioners And Tax Aggressiveness In Indonesia

This study aims to determine the effect of retired government officials on board of commissioners on corporate tax aggressiveness. The presence of retired government officials on board of commissioners is assessed by categorizing companies into three groups: the company without any retired government officials on board of commissioners, the c[1]ompany with one retired government official on board of commissioners, and the company with more than one retired government officials on board of commissioners. Corporate tax aggressiveness is measured using effective tax rate (ETR). The analysis is conducted on 441 observation data generated using purposive sampling for all the listed company on the Indonesia Stock Exchange in the period of 2014 to 2016. The results reveal that relative to there being one retired government official on the board of commissioners, greater than one retired government official presence on the board of commisioners does not reduces corporate tax aggressiveness
Review of Autonomous Campus and Tour Guiding Robots with Navigation Techniques

ABSTRACT Autonomous robots are designed to support humans in diverse ways including companionship and assistance, as technology evolves, the burden of physical tasks which were performed by humans in the past are getting reduced. Autonomous guidance and navigation are popular fields that use mobile robots to replace humans. These robots are widely used in space, defence, education, and industry and require navigating safely through different environments. A significant aspect of Autonomous Robot Navigation is an obstacle and pedestrian avoidance. Most of the outdoor guiding robots follow the concept of SLAM (Simultaneous Localisation and Mapping) for moving from the initial point to the goal point. Moreover, image processing plays a vital role in the detection and recognition between humans and objects. The existing methodology comprises implementing autonomous tour guide robots are based in embedded systems which has a high degree of recognition accuracy. This paper provides a comprehensive review of the existing autonomous campus and tour guiding robots. This paper is steered at providing a satellite view of these existing assemblies and the advances made with them.
Research on the Realization of Travel Recommendations for Different Users Through Deep Learning Under Global Information Management

This article is mainly to study the realization of travel recommendations for different users through deep learning under global information management. The personalized travel route recommendation is realized by establishing personalized travel dynamic interest (PTDR) algorithm and distributed lock manager (DLM) model. It is hoped that this model can provide more complete data information of tourist destinations on the basis of the past, and can also meet the needs of users. The innovation of this article is to compare and analyze with a large number of baseline algorithms, highlighting the superiority of this model in personalized travel recommendation. In addition, the model incorporates the topic factor features, geographic factor features, and user preference features to make the data more in line with user needs and improve the efficiency and applicability of the model. It is hoped that the plan proposed in this article can help users make choices of tourist destinations more conveniently.
A cognitive neuroscience perspective on insight as a memory process: Searching for the solution.

This is one of two chapters on "A cognitive neuroscience perspective on insight as a memory process" to be published in the "Routledge International Handbook of Creative Cognition" by L. J. Ball & F. Valleé-Tourangeau (Eds.). In this chapter, we will describe cognitive and brain processes that lead to an insight. Inspired by cognitive theories, we translate some of insight's main cognitive subprocesses (problem representation, search, representational change, solution) into related neurocognitive ones and summarize them in a descriptive framework. Those described processes focus primarily on verbal insight and are explained using the remote associates task. In this task, the solver is provided with several problem elements (e.g. drop, coat, summer) and needs to find the (remotely related) target that matches those cues (e.g., rain). In a nutshell, insight is the consequence of a problem-solving process where the target is encoded in long-term memory but cannot be retrieved at first because the relationship between the problem elements and the target is unknown, precluding a simple memory search. Upon problem display, the problem elements and a whole network of associated concepts are automatically activated in long-term memory in distinct areas of the brain representing those concepts (=problem representation). Insight is assumed to occur when automatic processes suddenly activate the target after control processes associated with inferior frontal gyrus and anterior cingulate cortex activation manage to overcome prior knowledge and/or perceptual constraints by revising the current activation pattern (=representational change). The next chapter (https://psyarxiv.com/bevjm) will focus on the role of insight problem solving for long-term memory formation.
Teachers’ perceptions of English language teaching process via online learning during Covid-19

During the Covid-19 pandemic, many teachers and students were doing the teaching and learning process from home. They experienced many problems during the remote instruction. Therefore, the objectives of this research were to investigate the teachers' perceptions and obstacles in the process of teaching and learning English by using online learning during the Covid-19 pandemic. The method used in this research was mixed method, combining quantitative and qualitative methods. The instruments used were a set of questionnaire and interview. The data collected from the questionnaire was analyzed using descriptive statistic. In addition, the interview result was analyzed qualitatively to describe the obstacles. The result in the questionnaire indicated that most of the teachers were against the implementation of online learning, i.e. 55.45% and 44.54% teachers were in favor of this method of instruction. The problems can be categorized into technical and situational problems. These problems can be resolved depending on teachers' teaching experience, condition, and situation. Teachers can give the learning module to the students, and the teachers must be creative in delivering the teaching material. In addition, parents' support is very crucial for maintaining students’ motivation, enthusiasm, and expression during online learning.
Investigating the Effective Instructional Activities as well as Advising Students to use the Effective Study Strategies related to Their Academic Performance in Less Commonly Taught Language, Particularly Japanese

The purpose of this study is to investigate the effective instructional activities as well as advising students to use the effective study strategies related to their academic performance in less commonly taught language, particularly Japanese. The sample was college students who studied Japanese from 2017 to 2018 at the beginning level. Factor analysis was used to group items into meaningful factors. Independent t tests were used to investigate correlations with Test Scores. The test scores were gathered from the classroom tests (listening and written). Instructions showed that Cooperative and Challenging Instructions revealed statistically positive relation with Test Scores. Communicate Instruction did not show statistically positive relation with Test Scores. Learning strategies revealed statistically positive relation with Cognitive Strategy, Memory Practice, Study Skills, and Resource Management. Romaji (romanization of the Japanese written language) Use showed negative correlation. The pedagogical implications were presented and further investigation will be conducted using online survey.
Suitability of milk lactate dehydrogenase and serum albumin for pathogen-specific mastitis detection in automatic milking systems.

In response to intramammary infection (IMI), blood-derived leukocytes are transferred into milk, which can be measured as an increase of somatic cell count (SCC). Additionally, pathogen-dependent IgG increases in milk following infection. The IgG transfer into milk is associated with the opening of the blood-milk barrier, which is much more pronounced during gram-negative than gram-positive IMI. Thus, milk IgG concentration may help to predict the pathogen type causing IMI. Likewise, lactate dehydrogenase (LDH) and serum albumin (SA) cross the blood-milk barrier with IgG if its integrity is reduced. Because exact IgG analysis is complicated and difficult to automate, LDH activity and SA concentration aid as markers to predict the IgG transfer into milk in automatic milking systems (AMS). This study was conducted to test the hypothesis that LDH and SA in milk correlate with the IgG transfer, and in combination with SCC these factors allow the differentiation between gram-positive and gram-negative IMI or even more precisely the infection-causing pathogen. Further, the expression of these parameters in foremilk before (BME) and after (AME) milk ejection was tested. In the AMS, quarter milk samples (n = 686) from 48 Holstein-Friesian cows were collected manually BME and AME, followed by an aseptic sample for bacteriological culture. Mixed models were used to (1) predict the concentration of IgG transmitted from blood into milk based on LDH and SA; (2) use principal component analysis to evaluate joint patterns of SCC (cells/mL), IgG (mg/mL), LDH (U/L), and SA (mg/mL) and use the principal component scores to compare gram-positive, gram-negative, and control IMI types and BME versus AME samples; and (3) predict gram-positive and gram-negative IMI by inclusion of combined SCC-LDH and SCC-SA as predictors in the model. Overall, the SA and LDH had similar ability to predict IgG transmission from blood into milk. Comparing the areas under the curve (AUC) of the receiver operator characteristic curves, the SCC-LDH versus SCC-SA had lower gram-positive (AUC = 0.984 vs. 0.986) but similar gram-negative (AUC = 0.995 vs. 0.998) IMI prediction ability. The SCC, IgG, LDH, and SA were greater in gram-negative than in gram-positive IMI (BME and AME) in early lactation. All measured factors had higher values in milk samples taken BME than AME. In conclusion, LDH and SA could be used as replacement markers to indicate the presence of IgG transfer from blood into milk; in combination with SCC, both SA and LDH are suitable for differentiating IMI type, and BME is better for mastitis detection in AMS.
Analysis of Debt Restructuring Methods for Negative Equity Firm

This paper aims to find the best debt restructuring methods for negative equity firms. This study uses four common debt restructuring methods, i.e. Debt-to-Equity swaps, debt-to-asset swaps, issuing convertible bonds, and repayment agreements. Furthermore, based on each debt structure, we compared their price-to-book ratio and debt-toequity ratio (DER) to find the best possible method for every firm and their industries. The sample for this study is firms listed on the Indonesia Stock Exchange (IDX). The ideal capital structure of the firms is determined by using an iterative algorithm. After finding their ideal capital structure, firms will be treated by debt restructuring to reach their ideal capital structure. Mostly, firms are better performances while using debt to an equity swap and issuing convertible bonds. This condition is proven by increasing their P/BV and their better capital structure by looking at their DER. Furthermore, debt to asset swap is better to use for firms with high DER. Whilst debt repayment agreements do not affect the firm’s capital structure and its value.
Application of a modified MOPSO algorithm and multi-layer artificial neural network in centrifugal pump optimization

Centrifugal pump optimization problems usually have strong nonlinear characteristics and are sometimes non-differentiable. The traditional multi-objective particle swarm optimization (MOPSO) algorithm was modified to solve this situation, and performed better with respect to both accuracy and search speed in validation experiments. Based on the modified algorithm and multi-layer artificial neural networks, the shape of the impeller blades of an industrial inline pump was optimized to improve the comprehensive performance under multiple operating conditions. The non-uniform rational B-spline was applied in the parametric design of the blade geometry, and 14 design variables of the spline were finally utilized in the iteration. With constraint of the computational head, the efficiencies of the part-load condition, the nominal condition, and the overload condition were selected as the objective functions. After optimization, a dramatic efficiency rise was obtained in all the three specified operating conditions, and correlation between the inflow conditions before the impeller and the performance of the inline pump was indicated.
A Cloud-Based Secure Emergency Message Dissemination Scheme in Vehicular Adhoc Networks

The Internet of vehicles and vehicular ad-hoc networks (VANET) offers numerous opportunities for managing the transportation problems effectively. The high mobility and wireless communication in VANET lead to adequate network topology modifications, resulting in network instability and insecure data communication. With an unsteady flow of traffic, vehicles are unevenly distributed in the geographical areas in practice. A new type 2 fuzzy logic-based secure clustering (T2FLSC) with cloud-based data dissemination scheme called the T2FLSC-CDD model for the VANET has been introduced for resolving this issue. The vehicles are dynamically clustered by the use of the T2FLSC technique, which elects the CHs (Cluster Head) by the use of different parameters, namely, travelling speed (TS), link quality (LQ), trust factor (TF), inter-vehicle distance (IVD) and neighboring node count (NCC). The inclusion of the trust factor helps to select the proper CHs for the secure data dissemination process. Once the CHs are selected, a cloud connection can broadcast the Emergency messages to every vehicle. To save the location details of the vehicles in the cloud securely, the Blowfish technique has also been implemented in this work. For validating the effectiveness of the T2FLSC-CDD model, an extensive results analysis has been performed with respect to various measures. The attained simulation outcomes have pointed out that the proposed model has achieved maximum packet delivery ratio (PDR) and throughput with minimum key computation time (KCT), routing control overhead (RCO), time delay (TD), and key recovery time (KRT).
Functionalized amphiphilic polyethylene via direct copolymerizations of ethylene with α-olefin containing amino functionalization

Abstract Transition metal-catalyzed copolymerization of olefins with polar monomers signifies a challenge because transition metal catalysts usually easily get poisoned by heteroatoms. Novel amino-containing polyethylene with high molecular weight, fluorescent functionality was synthesized by direct copolymerization of ethylene and amino-olefin containing shielding groups. The highly efficient non-metallocene catalyst system with high tolerance to polar monomers [N, P] complexes [silicon bridged diphenyl phosphorus-phenyl-amine multi chelated] can effectively promote the copolymerization of ethylene and amino-olefins containing considerable group protection, in which the catalysts very high catalytic activity (up to 6.56 × 104 g mol−1 h−1) was achieved. The functional copolymer exhibited high molecular weight (up to 2.87 × 105 g mol–1) and amino-olefin comonomer incorporation (up to 2.43 mol%). Remarkably, the copolymer showed a high melting point (T m around 138.7 °C) and thermal stability. The incorporated amino acid ester unit changes the surface characteristics of the polyolefin, making it have excellent hydrophilicity and fluorescence. Furthermore, as the content of functional copolymer increases, the properties both have significantly increased. Graphical Abstract