Search results for: neural perception.

Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

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Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

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Authors: Ruanzhenghao Shi

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Student movements have persisted in mainland China, especially in elite universities since the Tiananmen Prodemocracy Movement, contrary to the lack of studies on them. However, the participants' repertoire, mobilization and mode of interaction with authorities are vastly different from their predecessors in the 1980s as well as their western counterparts. In most of the cases, agents, cognizant of the high cost of action and their vulnerability to the authorities, consciously curtailed certain repertoire and themes of resistance. Thus these movements, without appreciable organized force, were self-interested, fragmentally mobilized, lowly integrated and limited within the campus. This study documents the 2014 protest against Yanching Academy program at Peking University, a top-tier Chinese university that played the leading role in the 1989 protest. The 2014 case is different from abovementioned trend of submissive resistance in the last twenty years, insofar as it is a value-oriented and emotion-driven collective action with the resurgence of some repertoire. The participants perceived the university's contemporary ineffectiveness and clumsiness in control and administration, higher Party authorities' indifference to less-political themes, and an increasing number of potential advocates, including students, intellectuals and social media. It shows that resisters' perception of their relative strength to their opponents - in this case, the university and its system for controlling students - under specific circumstances, not merely political opportunities or institutional changes, stimulates the participants and thus contributes to the mobilization and organization of a collective action, even under severe social control.

Keywords: collective action, China, university students, resistance

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Authors: M. Devaki, K. B. Jayanthi

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The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

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Authors: Sabitha Rahim

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In the context of teaching vocabulary of English as Foreign Language (EFL), the confluence of memory and retention is one of the most significant factors in students' language acquisition. The progress of students engaged in foreign language acquisition is often stymied by vocabulary attrition, which leads to learners' lack of confidence and motivation. However, among other factors, little research has investigated the importance of neuroplasticity in Foreign Language acquisition and how underused neural pathways lead to the loss of plasticity, thereby affecting the learners’ vocabulary retention and motivation. This research explored the effect of enhancing vocabulary acquisition of EFL students in the Foundation Year at King Abdulaziz University through various methods and neuroplasticity exercises that reinforced their attention, motivation, and engagement. It analyzed the results to determine if stimulating the brain of EFL learners by various physical and mental activities led to the improvement in short and long term memory in vocabulary retention. The main data collection methods were student surveys, assessment records of teachers, student achievement test results, and students' follow-up interviews. A key implication of this research is for the institutions to consider having multiple varieties of student activities promoting brain plasticity within the classrooms as an effective tool for foreign language acquisition. Building awareness among the faculty and adapting the curriculum to include activities that promote brain plasticity ensures an enhanced learning environment and effective language acquisition in EFL classrooms.

Keywords: language acquisition, neural paths, neuroplasticity, vocabulary attrition

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Authors: Folorunsho J. O., Umar M. A.

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Access to potable water supply in both the rural and urban regions of the world has been neglected, and this has severely affected man and the aesthetics of the natural environment of man. This has further worsened the issue of diseases prevalence. This study considered the effects of inadequate domestic water supply on human health in selected neighbourhoods of Lokoja. The study used descriptive statistics such as relative frequencies, percentages and inferential statistics to analyse the data obtained through the use of structured questionnaire. The results revealed that the females and male constituted 56% and 44% of the respondents respectively; 62% of the respondents married and 32% are unmarried; respondents between ages 31 and 40 years constitute majority of the study population, while respondents with tertiary education constituted 35%, and those with secondary education were 32% of the total respondents. Furthermore, civil servants constituted 40% and unemployed 16% of the total respondents. In terms of monthly income, 40% of the respondents was found to earn between ₦31,000 - 40,000 monthly. On the perception of households on the availability and adequacy of domestic water supply, the study revealed that 64.7% of the respondents have pipe-borne water as their main source of water supply, with only 28.5% out of the 64.7% have pipe-borne water supply daily. On the relationship between water supply characteristics and health status among households, the result shows that 76% of the respondents perceived a strong relationship between water supply and health status. Cumulatively, 67% of the respondents confirm that both the quality and quantity of water supplied play a critical role in determining health status of residents of the study area. The respondents also reported skin diseases (96%), diarrhoea (96%), malaria (91%), cholera (67%), dysentery (67%), and respiratory diseases (67%) as the most perceived and experienced in the area, the disease rate in the prevalence order of malaria (81%), diarrhoea (61%), skin diseases (58%), cholera (34%), dysentery (31%) and respiratory disease (14%) respectively. Finally, the results further showed how households cope with inadequate water supply with 52% of the respondents confirm that they regularly treat their water before it was deployed for domestic uses, while 35%, 26%, 25%, 10% and 4% of the 52% respectively, adopted boiling, addition of alums, filtering with fabrics, chlorination and bleaching as the preferred treatment methods. The study thus recommended policy options that will aggressively launch adequate potable water supply infrastructure in the study area.Keywords: Potable Water, Supply, Human Health, Perception, Chlorination

Keywords: potable water, human health, perception, chlorination

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: Hany Elsaid Fawaz Abdallah

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This study presents a new artificial neural network(ANN) model to predict the Nusselt Number and pressure drop for the turbulent flow in a triangular corrugated plate heat exchanger for forced air and turbulent water flow. An experimental investigation was performed to create a new dataset for the Nusselt Number and pressure drop values in the following range of dimensionless parameters: The plate corrugation angles (from 0° to 60°), the Reynolds number (from 10000 to 40000), pitch to height ratio (from 1 to 4), and Prandtl number (from 0.7 to 200). Based on the ANN performance graph, the three-layer structure with {12-8-6} hidden neurons has been chosen. The training procedure includes back-propagation with the biases and weight adjustment, the evaluation of the loss function for the training and validation dataset and feed-forward propagation of the input parameters. The linear function was used at the output layer as the activation function, while for the hidden layers, the rectified linear unit activation function was utilized. In order to accelerate the ANN training, the loss function minimization may be achieved by the adaptive moment estimation algorithm (ADAM). The ‘‘MinMax’’ normalization approach was utilized to avoid the increase in the training time due to drastic differences in the loss function gradients with respect to the values of weights. Since the test dataset is not being used for the ANN training, a cross-validation technique is applied to the ANN network using the new data. Such procedure was repeated until loss function convergence was achieved or for 4000 epochs with a batch size of 200 points. The program code was written in Python 3.0 using open-source ANN libraries such as Scikit learn, TensorFlow and Keras libraries. The mean average percent error values of 9.4% for the Nusselt number and 8.2% for pressure drop for the ANN model have been achieved. Therefore, higher accuracy compared to the generalized correlations was achieved. The performance validation of the obtained model was based on a comparison of predicted data with the experimental results yielding excellent accuracy.

Keywords: artificial neural networks, corrugated channel, heat transfer enhancement, Nusselt number, pressure drop, generalized correlations

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Authors: Qinglu Wu, Anna Wai-Man Choi, Peilian Chi

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Gratitude is one individual strength that not only facilitates the mental health, but also fosters the relationship satisfaction in the romantic relationship. In terms of moral effect theory and stress-and-coping theory of forgiveness, present study not only investigated the association between grateful disposition and relationship satisfaction, but also explored the mechanism by comprehensively examining the potential mediating roles of three profiles of forgiveness (trait forgivingness, decisional forgiveness, emotional forgiveness), another character strength that highly related to the gratitude and relationship satisfaction. Structural equation modeling was used to conduct the multiple mediator model with a sample of 103 Chinese college students in dating relationship (39 male students and 64 female students, Mage = 19.41, SD = 1.34). Findings displayed that both gratitude and relationship satisfaction positively correlated with decisional forgiveness and emotional forgiveness. Emotional forgiveness was the only mediator, and it completely mediated the relationship between gratitude and relationship satisfaction. Gratitude was helpful in enhancing individuals’ perception of satisfaction in romantic relationship through replacing negative emotions toward partners with positive ones after transgression in daily life. It highlighted the function of emotional forgiveness in personal healing and peaceful state, which is important to the perception of satisfaction in relationship. Findings not only suggested gratitude could provide a stability for forgiveness, but also the mechanism of prosocial responses or positive psychological processes on relationship satisfaction. The significant roles of gratitude and emotional forgiveness could be emphasized in the intervention working on the romantic relationship development or reconciliation.

Keywords: decisional forgiveness, emotional forgiveness, gratitude, relationship satisfaction, trait forgivingness

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Authors: Leonard D. Agana, Wendell Ace Dela Cruz, Arjan C. Lingaya, Bonifacio T. Doma Jr.

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The purpose of this paper is to study the predict the fuel performance parameters, which include drivability index (DI), vapor lock index (VLI), and vapor lock potential using distillation curve and Reid vapor pressure (RVP) of dual alcohol-gasoline fuel blends. Distillation curve and Reid vapor pressure were predicted using artificial neural networks (ANN) with macroscopic properties such as boiling points, RVP, and molecular weights as the input layers. The ANN consists of 5 hidden layers and was trained using Bayesian regularization. The training mean square error (MSE) and R-value for the ANN of RVP are 91.4113 and 0.9151, respectively, while the training MSE and R-value for the distillation curve are 33.4867 and 0.9927. Fuel performance analysis of the dual alcohol–gasoline blends indicated that highly volatile gasoline blended with dual alcohols results in non-compliant fuel blends with D4814 standard. Mixtures of low-volatile gasoline and 10% methanol or 10% ethanol can still be blended with up to 10% C3 and C4 alcohols. Intermediate volatile gasoline containing 10% methanol or 10% ethanol can still be blended with C3 and C4 alcohols that have low RVPs, such as 1-propanol, 1-butanol, 2-butanol, and i-butanol. Biography: Graduate School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Muralla St., Intramuros, Manila, 1002, Philippines

Keywords: dual alcohol-gasoline blends, distillation curve, machine learning, reid vapor pressure

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Authors: Muhammad Khubaib

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The Holy Quran purport is to establish a democratic system in which Allah has the right to special authority and He who has the supreme power or sovereignty. The supreme leader, Allah ceded the right to govern to his prophet and whoever would ever rule he would have to govern as a deputy of Prophet of Allah and he will not have the right to deviate from the basic rules of law and constitution. Centuries before the birth of prevailing democracy, Muslim scholars and researchers continuously keep using the term of “Jamhür” (majority) in their books. Islam gives the basic importance to the public opinion to establish a government and make the public confidence necessary for the government. The most effective way to gain the trust of the people in the present to build national institutions is through the vote. Vote testifies in favor of the candidate and majority tells us who is more honest and talented. Each voter stands at the position of trustworthy. To vote a cruel person would be tantamount to treason and even not to vote would be considered as a national offence. After transparent process, the selected member of government would be seemed a fine example of the saying of Muhammad (S.A.W) in which he said; the majority of my people will never be agreed at misleading. In short in this article, there would be discussed democracy in the Islamic perception, while elaborating the western democracy so that it can be cleared that in which way the Holy Quran supported the democracy and what gestures Muhammad (S.A.W) made to spread the democracy and on the basis of those gestures, and how come those gestures are being followed to choose the sacred caliphate. It's hoped that this research would be helpful to refine the democratic system and support to meet the challenges Muslim world are facing.

Keywords: democracy, modern democratic system, respect of majority opinion, vote casting

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Authors: Nader Khalafian, Mohsen Ghaderi

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Field observations have revealed many examples of structures which were damaged due to ground surface deformation caused by the faulting phenomena. In this paper some efforts were made in order to estimate the length and location of the ground surface where large displacements were created due to the reverse faulting. This research has conducted in two steps; (1) in the first step, a 2D explicit finite element model were developed using ABAQUS software. A subroutine for Mohr-Coulomb failure criterion with strain softening model was developed by the authors in order to properly model the stress strain behavior of the soil in the fault rapture zone. The results of the numerical analysis were verified with the results of available centrifuge experiments. Reasonable coincidence was found between the numerical and experimental data. (2) In the second step, the effects of the fault dip angle (δ), depth of soil layer (H), dilation and friction angle of sand (ψ and φ) and the amount of fault offset (d) on the soil surface displacement and fault rupture path were investigated. An artificial neural network-based model (ANN), as a powerful prediction tool, was developed to generate a general model for predicting faulting characteristics. A properly sized database was created to train and test network. It was found that the length and location of the zone of displaced ground surface can be accurately estimated using the proposed model.

Keywords: reverse faulting, surface deformation, numerical, neural network

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Authors: A. M. John-Otumu, M. M. Rahman, M. C. Onuoha, E. P. Ojonugwa

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A Convolutional Neural Network (CNN) model for predicting Lassa fever was built using Python 3.8.0 programming language, alongside Keras 2.2.4 and TensorFlow 2.6.1 libraries as the development environment in order to reduce the current high risk of Lassa fever in West Africa, particularly in Nigeria. The study was prompted by some major flaws in existing conventional laboratory equipment for diagnosing Lassa fever (RT-PCR), as well as flaws in AI-based techniques that have been used for probing and prognosis of Lassa fever based on literature. There were 15,679 blood smear microscopic image datasets collected in total. The proposed model was trained on 70% of the dataset and tested on 30% of the microscopic images in avoid overfitting. A 3x3x3 convolution filter was also used in the proposed system to extract features from microscopic images. The proposed CNN-based model had a recall value of 96%, a precision value of 93%, an F1 score of 95%, and an accuracy of 94% in predicting and accurately classifying the images into clean or infected samples. Based on empirical evidence from the results of the literature consulted, the proposed model outperformed other existing AI-based techniques evaluated. If properly deployed, the model will assist physicians, medical laboratory scientists, and patients in making accurate diagnoses for Lassa fever cases, allowing the mortality rate due to the Lassa fever virus to be reduced through sound decision-making.

Keywords: artificial intelligence, ANN, blood smear, CNN, deep learning, Lassa fever

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Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

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In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

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Authors: Juanita Morris

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The purpose of this study will examine the mediating relationships between the theories of intelligence, mathematics anxiety, gender stereotype threat, meta-cognition and math performance through the use of eye tracking technology, affecting student perception and problem-solving abilities. The participants will consist of (N=80) female graduate students. Test administered were the Abbreviated Math Anxiety Scale, Tobii Eye Tracking software, gender stereotype threat through Google images, and they will be asked to describe their problem-solving approach allowed to measure metacognition. Participants will be administered mathematics problems while having gender stereotype threat shown to them through online images while being directed to look at the eye tracking software Tobii. We will explore this by asking ‘Is mathematics anxiety associated with the theories of intelligence and gender stereotype threat and how does metacognition and math performance place a role in mediating those perspectives?’. It is hypothesized that math-anxious students are more likely affected by the gender stereotype threat and that may play a role in their performance? Furthermore, we also want to explore whether math anxious students are more likely to be an entity theorist than incremental theorist and whether those who are math anxious will be more likely to be fixated on variables associated with coefficients? Path analysis and independent samples t-test will be used to generate results for this study. We hope to conclude that both the theories of intelligence and metacognition mediate the relationship between mathematics anxiety and gender stereotype threat.

Keywords: math anxiety, emotions, affective domains fo learning, cognitive underlinings

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Authors: Aisyiyah Fauziah Rahmah

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Generally, an urban city has a rapid growth, it has frequent a variety of problems, especially of convenience in public space usage. The density of population in urban areas and the high activity is also indicated as a cause of urban resident lifestyle for the worse in social relationships and allow for the stress. Streets and green space (parks) are the only public space in a residential area which is used as a place to build social activity, to meet and interact with the other housing dweller. The high level of activity and social interaction that occurs will affect the spatial arrangement. It can be effected the space structures in housing more complex. Ease in access to public space is the reason many dweller prefer doing social activities there. Hillier in Carmona et al (2003) explains that the pattern and intensity of movement of individuals is influenced by the configuration of space, even the space structure can be regarded as the single most influential determinant of movements in the space. Whyte in Zhang and Lawson (2009) also suggest some factors such as seats, trees, water and legibility of space encourage people to stay in public outdoor space. Furthermore this activities can attract more activities. Villa Bukit Tidar is a housing in Lowokwaru District which highest number of people in Malang City, so social activity is also high there. It has natural and recreational concept and provided with view of Malang City from heights. This potential is able to attract the people who live there to stay in public outdoor space and doing activities there. From this study we can find whether the ease of access to public space and visual satisfaction of Villa Bukit Tidar housing affect the usage of public space. This study was carried out by observing the streets pattern and plot pattern to know the configuration space of Villa Bukit Tidar housing through values of connectivity and integrity by resulting from space sintax analysis. Distributing questionnaires also carried out to determine the level of satisfaction and importance perception of visual condition in the public space in Villa Bukit Tidar housing through Important Performance Analysis (IPA). Results of this research indicated that the public spaces in Villa Bukit Tidar housing who has high connectivity and integrity is considered to be visually satisfied and it has a higher public space usage than has low connectivity and integrity are considered to be visually dissatisfied.

Keywords: configuration space, visual perception, social activities, public space usage

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Authors: Ummi Sholihah Pertiwi Abidin, Eva Fadhilah

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Social changes are more and more growing and having many different forms as the time passed and thought methods in the society. One of many forms of that social changes is the emancipation of women that is flourishing by the inception of gender equality perception between men and women in all aspects including education. It’s not anymore found the distinction between genders in learning and the education achieving right at this globalized era. But, it is still many perceptions which are against that equality of education achieving right, either come from the women’s selves or many external factors. They assumed that they are going to be a mother in the future, and a wife, someone with responsible for taking care of the household and everything inside, while the husband is the one who has the responsible for looking for the living. So comes from this kind of assumption, the perception against the education equality between genders, which means there is no need for them –women- to achieve the high education because they will still end up as housewives. Except those working or career women that need high education to support their works. These women are not aware that even a mother needs the high and capable education. Because, as the 'mother to be,' they surely need broad knowledge from the education to educate their children in the future. It is such a big fault to say the kind of thing, 'It is no matter that I am not educated, in case I’m just a housewife. The important thing is my children get a great education'. Unfortunately, it is still often found, saying 'A housewife job is not a big deal to do with high education.' This qualitative method paper raises a theme about the importance of education for women, no matter what will they be in the future. Because however, and whatever is the woman’s career outside the house, or even not working outside, she’s still a mother for her children, and 'educational provision' is a great need. And so forth, this educational provision is a big deal to do with future generation’s foredoom, regarding the first source of children’s knowledge and the first school for them is their mother.

Keywords: women education, mother to be, educational provision, first school, future generation’s foredoom

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Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Naziru Suleiman

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The ever increasing cases of financial frauds and corporate accounting scandals in recent years have raised more concern on the operation of internal control mechanisms and performance of the internal audit departments in organizations. In most cases the seeming presence of both the internal control system and internal audit in organizations do not prove useful as frauds errors and irregularities are being perpetuated. The aim of this study, therefore, is to assess the role of internal audit in achieving the objectives of internal control system of federal hospitals in Kano State from the perception of the respondents. The study used survey research design and generated data from primary source by means of questionnaire. A total number of 100 copies of questionnaire were administered out of which 68 were duly completed and returned. Cronbach’s alpha was used to test the internal validity of the various items in the constructs. Descriptive statistics, chi-square test, Mann Whitney U test and Kruskal Wallis ANOVA were employed for the analysis of data. The study finds that from the perception of the respondents, internal audit departments in Federal Hospitals in Kano State are effective and that they contribute positively to the overall attainment of the objectives of internal control system of these hospitals. There is no significant difference found on the views of the respondents from the three hospitals. Hence, the study concludes that strong and functional internal audit department is a basic requirement for effectiveness of operations of the internal control system. In the light of the findings, it is recommended that internal audit should continue to ensure that the objectives of internal control system of these hospitals are achieved through proper and adequate evaluation and review of the system.

Keywords: internal audit, internal control, federal hospitals, financial frauds

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Authors: Carmel Sofer, Dan Vilenchik, Ron Dotsch, Galia Avidan

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Studies investigating emotional facial expressions typically reveal consensus among observes regarding the meaning of basic expressions, whose number ranges between 6 to 15 emotional states. Given this limited number of discrete expressions, how is it that the human vocabulary of emotional states is so rich? The present study argues that perceivers use sequences of these discrete expressions as the basis for a much richer vocabulary of emotional states. Such mechanisms, in which a relatively small number of basic components is expanded to a much larger number of possible combinations of meanings, exist in other human communications modalities, such as spoken language and music. In these modalities, letters and notes, which serve as basic components of spoken language and music respectively, are temporally linked, resulting in the richness of expressions. In the current study, in each trial participants were presented with sequences of two images containing facial expression in different combinations sampled out of the eight static basic expressions (total 64; 8X8). In each trial, using single word participants were required to judge the 'state of mind' portrayed by the person whose face was presented. Utilizing word embedding methods (Global Vectors for Word Representation), employed in the field of Natural Language Processing, and relying on machine learning computational methods, it was found that the perceived meanings of the sequences of facial expressions were a weighted average of the single expressions comprising them, resulting in 22 new emotional states, in addition to the eight, classic basic expressions. An interaction between the first and the second expression in each sequence indicated that every single facial expression modulated the effect of the other facial expression thus leading to a different interpretation ascribed to the sequence as a whole. These findings suggest that the vocabulary of emotional states conveyed by facial expressions is not restricted to the (small) number of discrete facial expressions. Rather, the vocabulary is rich, as it results from combinations of these expressions. In addition, present research suggests that using word embedding in social perception studies, can be a powerful, accurate and efficient tool, to capture explicit and implicit perceptions and intentions. Acknowledgment: The study was supported by a grant from the Ministry of Defense in Israel to GA and CS. CS is also supported by the ABC initiative in Ben-Gurion University of the Negev.

Keywords: Glove, face perception, facial expression perception. , facial expression production, machine learning, word embedding, word2vec

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Authors: Mohammad Khazaei, Reza Rostami, Hasan Gharayagh Zandi, Rouhollah Basatnia, Mahbubeh Ghayour Najafabadi

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Objective: Methylphenidate has been demonstrated to enhance attention and cognitive processes, and placebo treatments have also been found to improve attention and cognitive processes. Additionally, methylphenidate may have positive effects on motion perception and sports performance. Nevertheless, additional research is needed to fully comprehend the neural mechanisms underlying the effects of methylphenidate and placebo on cognitive and motor functions. Methods: In this randomized controlled trial, 18 young semi-professional basketball players aged 18-23 years were randomly and equally assigned to either a Ritalin or Placebo group. The participants performed 20 consecutive free throws; their scores were recorded on a 0-3 scale. The participants’ brain activity was recorded using electroencephalography (EEG) for 5 minutes seated with their eyes closed. The Ritalin group received a 10 mg dose of methylphenidate, while the Placebo group received a 10mg dose of placebo. The EEG was obtained 90 minutes after the drug was administere Results: There was no significant difference in the absolute power of brain waves between the pre-test and post-tests in the Placebo group. However, in the Ritalin group, a significant difference in the absolute power of brain waves was observed in the Theta band (5-6 Hz) and Beta band (21-30 Hz) between pre- and post-tests in Fp2, F8, and Fp1. In these areas, the absolute power of Beta waves was higher during the post-test than during the pre-test. The Placebo group showed a more significant difference in free throw scores than the Ritalin group. Conclusions: In conclusion, these results suggest that Ritalin effect on brain activity in areas associated with attention and cognitive processes, as well as improve basketball free throws. However, there was no significant placebo effect on brain activity performance, but it significantly affected the improvement of free throws. Further research is needed to fully understand the effects of methylphenidate and placebo on cognitive and motor functions.

Keywords: methylphenidate, placebo effect, electroencephalography, basketball free throw

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Authors: Helen Pushkarskaya, David Tolin, Lital Ruderman, Ariel Kirshenbaum, J. MacLaren Kelly, Christopher Pittenger, Ifat Levy

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Obsessive-Compulsive Disorder (OCD) produces profound morbidity. Difficulties with decision making and intolerance of uncertainty are prominent clinical features of OCD. The nature and etiology of these deficits are poorly understood. We used a well-validated choice task, grounded in behavioral economic theory, to investigate differences in valuation and value-based choice during decision making under uncertainty in 20 unmedicated participants with OCD and 20 matched healthy controls. Participants’ choices were used to assess individual decision-making characteristics. Compared to controls, individuals with OCD were less consistent in their choices and less able to identify options that were unambiguously preferable. These differences correlated with symptom severity. OCD participants did not differ from controls in how they valued uncertain options when outcome probabilities were known (risk) but were more likely than controls to avoid uncertain options when these probabilities were imprecisely specified (ambiguity). These results suggest that the underlying neural mechanisms of valuation and value-based choices during decision-making are abnormal in OCD. Individuals with OCD show elevated intolerance of uncertainty, but only when outcome probabilities are themselves uncertain. Future research focused on the neural valuation network, which is implicated in value-based computations, may provide new neurocognitive insights into the pathophysiology of OCD. Deficits in decision-making processes may represent a target for therapeutic intervention.

Keywords: obsessive compulsive disorder, decision-making, uncertainty intolerance, risk aversion, ambiguity aversion, valuation

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Authors: Chijioke Oliver Nwodoh, Jonah Ikechukwu Eze, Loretta Chika Ukwuaba, Ifeoma Ndubuisi, Ada Carol Nwaneri, Ijeoma Lewechi Okoronkwo

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Presence of nursing workforce with unequal qualification and status in Nigeria has undermined the growth of nursing profession in the country. Upgrading of the existing basic and post-basic nursing schools to degree-awarding institutions in Nigeria is a way-forward to solving this inequality problem and Nigeria teaching hospitals are in vantage position for this project due to the already existing supportive structure and manpower in those hospitals. What the nurse trainers and the stakeholders of the teaching hospitals may hold for or against the upgrading is a determining factor for the upgrading project, but that is not clear and has not been investigated in Nigeria. The study investigated the perception of nurse trainers and stakeholders of teaching hospitals in Enugu State of Nigeria on the obstacles and ways-forward to upgrading nursing schools to degree-awarding institutions in Nigeria. The study specifically elicited what the subjects may view as obstacles to upgrading basic and post-basic nursing schools to degree-awarding institutions in Nigeria and ascertained their suggestions on the possible ways of overcoming the obstacles. By utilizing cross-sectional descriptive design and a purposive sampling procedure, 78 accessible subjects out of a total population of 87 were used for the study. The generated data from the subjects were analyzed using frequencies, percentages and mean for the research questions and Pearson’s chi-square for the hypotheses, with the aid of Statistical Package for Social Sciences Version 20.0. The result showed that lack of extant policy, fund, and disunity among policy makers and stakeholders of nursing profession are the main obstacles to the upgrading. However, the respondents did not see items like: stakeholders and nurse trainers of basic and post-basic schools of nursing; fear of admitting and producing poor quality nurses; and so forth, as obstacles to the upgrading project. Institution of the upgrading policy by Nursing and Midwifery Council of Nigeria, funding, awareness creation for the upgrading and unison among policy makers and stakeholders of nursing profession are the major possible ways to overcome the obstacles. The difference in the subjects’ perceptions between the two hospitals was found to be statistically insignificant (p > 0.05). It is recommended that the policy makers and stakeholders of nursing in Nigeria should unite and liaise with Federal Ministries of Health and Education for modalities and actualization of upgrading nursing schools to degree-awarding institutions in Nigeria.

Keywords: nurse trainers, obstacles, perception, stakeholders, teaching hospital, upgrading basic nursing schools, ways-forward

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Authors: Tatjana Kochetkova

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This paper explores the pivotal role of environmental education, specifically outdoor education, in facilitating a psychological connection to nature among young adults. This research aims to contribute to building an empirical and conceptual basis of ecopsychology by providing a picture of psyche-nature interaction. It presents the results of the four-day connection-to-nature workshop. It intends to find out the effects of the awareness of nature on subjective well-being and perception of the meaning of life. This led to finding a battery-recharging effect of nature and the influence of nature at four levels of awareness: external physical perception, internal (bodily) sensation, emotions, and existential meaning. The research on the psychological bond of humans with the natural environment, the subject of ecopsychology, is still in its infancy. However, despite several courageous and fruitful attempts, there are still no direct answers to the fundamental questions about the way in which the natural environment influences humans and the specific role of nature in the human psyche. The urge to address this question was the primary reason for the current experiment. The methodology of this study was taken from the study of Patterson, and from White and Hendee. The methodology included a series of assignments on the perception of nature (the exercises are described in the attachment). Experiences were noted in a personal diary, which we used later for analysis. There are many trustworthy claims that contact with nature has positive effects on human subjective well-being and that it is of essential psychological and spiritual value. But, there is a need for more support and theoretical explanation for this phenomenon. As a contribution to filling these gaps, this qualitative study was conducted. The aim of this study is to explore the psychological effects of short-term awareness of wilderness on one’s subjective well-being and on one’s sense of the meaning of life. This specific study is based on the more general hypothesis that there are positive relationships between the experience of wilderness and the development of the self, feelings of community, and spiritual development. It restricted the study of the psychological effects of short term stay in nature to two variables (subjective well-being and the sense of meaning of life). The study aimed at (i) testing the hypothesis that there are positive effects of the awareness of wilderness on the subjective sense of well-being and meaning in life, (ii) understanding the nature of the psychological need for wilderness. Although there is a substantial amount of data on the psychological benefits of nature, we still lack a theory that explains the findings. The present research aims to contribute to such a theory. This is an experiment aimed specifically at the effects of nature on the sense of well-being and meaning in life.

Keywords: environmental education, psychological connection to nature, subjective well-being, symbolic meaning of nature, emotional reaction to nature, meaning of life

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Authors: Olga Guseynova

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Being an individual activity, the memorizing process is affected to a greater degree by the individual variables; therefore, one of the decisive factors influencing the memorization is students’ individual characteristics. Based on an extensive literature study in the domains of piano education, psychology, and neuroscience, this comprehensive research was designed in order to develop three memorizing strategies that are reflective of individual students’ learning modalities (visual, kinesthetic and auditory) applied to the piano education. The design of the study required an interdisciplinary approach which incorporated the outcome of neuropsychological and pedagogic experiments. The objectives were to determine the interaction between the process of perception and the process of memorizing music; to systematize the methods of memorizing piano sheet music in accordance with the specifics of perception types; to develop Piano Memorization Inventory (PMI) and the Three Memorizing Strategies (TMS). The following research methods were applied: a method of interdisciplinary analysis and synthesis, a method of non-participant observation. As a result of literature analysis, the following conclusions were made: the majority of piano teachers and piano students participated in the surveys, had not used and usually had not known any memorizing strategy regarding learning styles. As a result, they had used drilling as the main strategy of memorizing. The Piano Memorization Inventory and Three Memorizing Strategies developed by the author of the research were based on the observation and findings of the previous researches and considered the experience of pedagogical and neuropsychological studies.

Keywords: interdisciplinary approach, memorizing strategies, perceptual learning styles, piano memorization inventory

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Authors: Mahjabeen Akbar

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Dental aesthetics and its associated psychosocial influence have a significant impact on individuals. Correcting malocclusions is a key motivating factor for majority patients; however, psychosocial factors have been rarely incorporated in evaluating malocclusions. Therefore, it is necessary to study the psychosocial influence of malocclusion in patients. The study aimed to determine the psychosocial influence of dental aesthetics in dental students by the ‘Psychosocial Impact of Dental Aesthetics Questionnaire’ and self-rated Aesthetic Component of the Index of Orthodontic Treatment Need (IOTN). This was a quantitative study using a cross-sectional study design. One hundred twenty dental students (71 females and 49 males; mean age 24.5) were selected via purposive sampling from July to August 2019. Dental students with no former orthodontic treatment were requested to fill out the ‘Psychosocial Impact of Dental Aesthetics Questionnaire.’ Variables including; self-confidence/insecurity, social influence, psychological influence and self-perception of the need of an orthodontic treatment were evaluated by a sequence of statements, while dental aesthetics were evaluated by using the IOTN Aesthetic Component. To determine the significance, the Kruskal-Wallis test was utilized. The results show that all four variables measuring psychosocial impact indicated significant correlations with the perceived malocclusions with a p-value of less than 0.01. The results conclude there is a strong psychological and social influence of altered dental aesthetics on an individual. Moreover, the relationship between the IOTN-AC grading with the psychosocial wellbeing of an individual stands proven, indicating that the perception of altered dental aesthetics is as important as a factor in treatment need as the amount of malocclusion.

Keywords: dental aesthetics, malocclusion, psychosocial influence, dental students

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Authors: Shirley S. Ho, Alisius X. L. D. Leong, Jiemin Looi, Agnes S. F. Chuah

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Nuclear energy is a contentious technology that has attracted much public debate over the years. The prominence of nuclear energy in Southeast Asia (SEA) has burgeoned due to the surge of interest and plans for nuclear development in the region. Understanding public perceptions of nuclear energy in SEA is pertinent given the limited number of studies conducted. In particular, five SEA nations – Singapore, Malaysia, Indonesia, Thailand, and Vietnam are of immediate interest as that they are amongst the most economically developed or developing nations in the SEA region. High energy demands from economic development in these nations have led to considerations of adopting nuclear energy as an alternative source of energy. This study aims to explore whether differences in the nuclear developmental stage in each country affects public perceptions of nuclear energy. In addition, this study seeks to find out about the type and importance of communication credibility as a judgement heuristic in facilitating message acceptance across these five countries. Credibility of a communication channel is a crucial component influencing public perception, acceptance, and attitudes towards nuclear energy. Aside from simply identifying the frequently used communication channels, it is of greater significance to understand public perception of source and media credibility. Given the lack of studies conducted in SEA, this exploratory study adopts a qualitative approach to elicit a spectrum of opinions and insights regarding the key communication aspects influencing public perceptions of nuclear energy. Specifically, the capitals of each of the abovementioned countries - Kuala Lumpur, Bangkok, and Hanoi - were selected, with the exception of Singapore, an island city-state, and Yogyakarta, the most populous island of Indonesia to better understand public perception towards nuclear energy. Focus group discussions were utilized as the mode of data collection to elicit a wide variety of viewpoints held by the participants, which is well-suited for exploratory research. In total, 156 participants took part in the 13 focus group discussions. The participants were either local citizens or permanent residents aged between 18 and 69 years old. Each of the focus groups consists of 8-10 participants, including both male and female participants. The transcripts from each focus group were analysed using NVivo 10, and the text was organised according to the emerging themes or categories. The general public in all the countries was familiar but had no in-depth knowledge with nuclear energy. Four dimensions of nuclear energy communication were identified based on the focus group discussions: communication channels, perceived credibility of sources, circumstances for discussion, and discussion style. The first dimension, communication channels refers to the medium through which participants receive information about nuclear energy. Four types of media emerged from the discussions. They included online and social media, broadcast media, print media, and word-of- mouth (WOM). Collectively, across all five countries, participants were found to engage in different types of knowledge acquisition and information seeking behavior depending on the communication channels used.

Keywords: nuclear energy, public perception, communication, Southeast Asia, source credibility

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Authors: Şenol Yanar, Sinan Çeli̇kbi̇lek, Mehmet Bayansalduz, Yusuf Can

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Managerial competence defines as the skills that managers in managerial positions have in relation to managerial responsibilities and managerial duties. Today's organizations, which are in a competitive environment, have the desire to work with effective managers in order to be more advantageous position than the other organizations they are competing with. In today's organizations, self-efficacy and collective competence belief that determine managerial competencies of managers to assume managerial responsibility are of special importance. In this framework, the aim of this study is to examine the effects of sports managers' perceptions of self-efficacy and collective competence in managerial competence perceptions. In the study, it has also been analyzed if there is a significant difference between self-efficacy, collective competence and managerial competence levels of sports managers in terms of their gender, age, duty status, year of service and level of education. 248 sports managers, who work at the department of sports service’s central and field organization at least as a chief in the manager position, have been chosen with random sampling method and they have voluntarily participated in the study. In the study, the self-efficacy scale which was developed by Schwarzer, R. & Jerusalem, M. (1995), collective competence scale developed by Goddard, Hoy and Woolfolk-Hoy (2000) and managerial competence scale developed by Cetinkaya (2009) have been used as a data collection tool. The questionnaire form used as a data collection tool in the study includes a personal information form consisting of 5 questions; questioning gender, age, duty status, years of service and level of education. In the study, Pearson Correlation Analysis has been used for defining the correlation among self-efficacy, collective competence belief, and managerial competence levels in sports managers and regression analysis have been used to define the affect of self-efficacy and collective competence belief on the perception of managerial competence. T-test for binary grouping and ANOVA analysis have been used for more than binary groups in order to determine if there is any significant difference in the level of self-efficacy, collective and managerial competence in terms of the participants’ duty status, year of service and level of education. According to the research results, it has been found that there is a positive correlation between sports managers' self-efficacy, collective competence beliefs, and managerial competence levels. According to the results of the regression analysis, it is understood that the managers’ perception of self-efficacy and collective competence belief significantly defines the perception of managerial competence. Also, the results show that there is no significant difference in self-efficacy, collective competence, and level of managerial competence of sports managers in terms of duty status, year of service and level of education.

Keywords: sports manager, self-efficacy, collective competence, managerial competence

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Authors: Jamunarani Appalasamy, Tha Kyi Kyi, Quek Kia Fatt, Joyce Pauline Joseph, Anuar Zaini M. Zain

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Background: The Health Belief Theory had always been associated with chronic disease management. Various health behaviour concepts and perception branching from this Health Belief Theory had involved with medication understanding, use, and self-efficacy which directly link to medication adherence. In a previous quantitative and qualitative study, stroke patients in Malaysia were found to be strongly believing information obtained by various sources such as the internet and social communication. This action leads to lower perception of their stroke preventative medication benefit which in long-term creates non-adherence. Hence, this study intends to pilot an intervention which uses audio-visual concept incorporated with mHealth service to enhance learning and self-reflection among stroke patients to manage their disease. Methods/Design: Twenty patients will be allocated to a proposed intervention whereas another twenty patients are allocated to the usual treatment. The intervention involves a series of developed audio-visual videos sent via mobile phone which later await for responses and feedback from the receiver (patient) via SMS or recorded calls. The primary outcome would be the medication understanding, use and self-efficacy measured over two months pre and post intervention. Secondary outcome is measured from changes of blood parameters and other self-reported questionnaires. Discussion: This study shall also assess uptake/attrition, feasibility, and acceptability of this intervention. Trial Registration: NMRR-15-851-24737 (IIR)

Keywords: health belief, medication understanding, medication use, self-efficacy

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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