Search results for: cognitive behavioral training

Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

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Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram signals (PCG) can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded Phonocardiogram (PCG) signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded ElectroCardioGrams (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show a segmentation testing performance average of 76 % sensitivity and 94 % specificity.

Keywords: heart sounds, PCG segmentation, event detection, recurrent neural networks, PCG curve length

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Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

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Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

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Authors: Siriporn Poolsuwan, Kanyarat Bussaban

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This research aims to investigate and analyze user’s behaviour towards the utilization of the online news clipping database at Suan Sunandha Rajabhat University, Thailand. Data is gathered from 214 lecturers and 380 undergraduate students by using questionnaires. Findings show that most users knew the online news clipping service from their friends, library’s website and their teachers. The users learned how to use it by themselves and others learned by training of SSRU library. Most users used the online news clipping database one time per month at home and always used the service for general knowledge, up-to-date academic knowledge and assignment reference. Moreover, the results of using the online news clipping service problems include the users themselves, service management, service device- computer and tools – and the network, service provider, and publicity. This research would be benefit for librarians and teachers for planning and designing library services in their works and organization.

Keywords: online database, user behavior, news clipping, library services

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Authors: Hediye Saglam

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This research has been carried out to bring in the 6 acquisitions in the 2014 Preschool Teaching Programme of the Turkish Ministry of Education through the method of analogy. This research is practiced based on the experimental pattern with pre-test and final test controlling groups. The working group of the study covers the group between 5-6 ages. The study takes 5 weeks including the 2 weeks spent for pre-test and the final test. It is conducted with the preschool teacher who gives the lesson along with the researcher in the in-class and out-of-class rainbow experiments of the students for 5 weeks. 'One Sample T Test' is used for the evaluation of the pre-test and final test. SPSS 17 programme is applied for the analysis of the data. Results: As an outcome of the study it is observed that analogy method affects children’s learning of the rainbow. For this very reason teachers should receive inservice training for different methods and techniques like analogy. This method should be included in preschool education programme and should be applied by teachers more often.

Keywords: acquisitions of preschool education programme, analogy method, pre-test/final test, rainbow experiments

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Authors: Maryam Alimardani, Kazuo Hiraki

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This study presents a framework for development of a new generation of therapy robots that can interact with users by monitoring their physiological and mental states. Here, we focused on one of the controversial methods of therapy, hypnotherapy. Hypnosis has shown to be useful in treatment of many clinical conditions. But, even for healthy people, it can be used as an effective technique for relaxation or enhancement of memory and concentration. Our aim is to develop a robot that collects information about user’s mental and physical states using electroencephalogram (EEG) and electromyography (EMG) signals and performs costeffective hypnosis at the comfort of user’s house. The presented framework consists of three main steps: (1) Find the EEG-correlates of mind state before, during, and after hypnosis and establish a cognitive model for state changes, (2) Develop a system that can track the changes in EEG and EMG activities in real time and determines if the user is ready for suggestion, and (3) Implement our system in a humanoid robot that will talk and conduct hypnosis on users based on their mental states. This paper presents a pilot study in regard to the first stage, detection of EEG and EMG features during hypnosis.

Keywords: hypnosis, EEG, robotherapy, brain-computer interface (BCI)

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Authors: Muhammad Ajmal, Ahmad Shahrul Nizam Bin Isha, Shahrina Md. Nordin, Paras Behrani, Al-Baraa Abdulrahman Al-Mekhlafi

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This study aims to investigate the impact of safety management practices via safety compliance on occupational injuries in the context of downstream the oil and gas industry of Malaysia. However, it is still challenging for researchers and academicians to control occupational injuries in high-safety-sensitive organizations. In this study response rate was 62%, and 280 valid responses were used for analysis through SmartPLS. The study results revealed that safety management practices (management commitment, safety training, safety promotion policies, workers’ involvement) play a significant role in lowering the rate of accidents in downstream the oil and gas industry via safety compliance. Furthermore, the study results also revealed that safety management practices also reduce safety management costs of organizations, e.g., lost work days and employee absenteeism. Moreover, this study is helpful for safety leaders and managers to understand the importance of safety management practices to lower the ratio of occupational injuries.

Keywords: safety management, safety compliance, occupational injuries, oil and gas, Malaysia

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Authors: Tanit Pruktara

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The research aimed to study the students’ learning achievement and awareness level on electrical energy consumption and conservation and also to investigate the students’ attitude on the developed multimedia supplemented instructional unit for learning household electrical energy consumption and conservation in grade 10 Thailand student. This study used a quantitative method using MCQ for pre and post-achievement tests and Likert scales for awareness and attitude survey questionnaires. The results from this were employed to improve the multimedia to be appropriate for the classroom and with real life situations in the second phase, the main study. The experimental results showed that the developed learning unit significantly improved the students’ learning achievement as well as their awareness of electric energy conservation. Additional we found the student will enjoy participating in class activities when the lessons are taught using multimedia and helps them to develop the relevance between the course and real world situations.

Keywords: lesson plan, media and animations, training course, vocational school in Thailand

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Yolina A. Petrova, Georgi I. Petkov

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The paper presents a symbolic model for category learning and categorization (called RoleMap). Unlike the other models which implement learning in a separate working mode, role-governed category learning and categorization emerge in RoleMap while it does its usual reasoning. The model is based on several basic mechanisms known as reflecting the sub-processes of analogy-making. It steps on the assumption that in their everyday life people constantly compare what they experience and what they know. Various commonalities between the incoming information (current experience) and the stored one (long-term memory) emerge from those comparisons. Some of those commonalities are considered to be highly important, and they are transformed into concepts for further use. This process denotes the category learning. When there is missing knowledge in the incoming information (i.e. the perceived object is still not recognized), the model makes anticipations about what is missing, based on the similar episodes from its long-term memory. Various such anticipations may emerge for different reasons. However, with time only one of them wins and is transformed into a category member. This process denotes the act of categorization.

Keywords: analogy-making, categorization, category learning, cognitive modeling, role-governed categories

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Authors: Mohamad Kassab, Amal El Fallah Seghrouchni, Frederic Barbaresco, Raed Abu Zitar

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Detecting objects such as drones is a challenging task as their relative size and maneuvering capabilities deceive machine learning models and cause them to misclassify drones as birds or other objects. In this work, we investigate applying several deep learning techniques to benchmark real data sets of flying drones. A deep learning paradigm is proposed for the purpose of mitigating the complexity of those systems. The proposed paradigm consists of a hybrid between the AdderNet deep learning paradigm and the Single Shot Detector (SSD) paradigm. The goal was to minimize multiplication operations numbers in the filtering layers within the proposed system and, hence, reduce complexity. Some standard machine learning technique, such as SVM, is also tested and compared to other deep learning systems. The data sets used for training and testing were either complete or filtered in order to remove the images with mall objects. The types of data were RGB or IR data. Comparisons were made between all these types, and conclusions were presented.

Keywords: drones detection, deep learning, birds versus drones, precision of detection, AdderNet

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Authors: Azeddine El-Hassouny, Chandrashekhar Meshram, Geraldin Nanfack

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In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32.

Keywords: label noise, deep learning, discrete latent variable, variational inference, MNIST, CIFAR32

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Authors: Mohammad Anwar, Shah Waliullah

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The development of science and technology in the present era has an urgent demand for the training of thinking of undergraduates. This requirement actively promotes research and teaching of basic theories, beneficial to the career development of students. This study clarified the dialectical relation between the thermodynamic equilibrium constant and reaction rate constant through the contrast thinking method. Findings reveal that both the isobaric Van't Hoff equation and the Arrhenius equation had four similar forms, and the change in the trend of both constants showed a similar law. By the derivation of the formation rate constant of the product (KY) and the consumption rate constant of the reactant (KA), the ratio of both constants at the end state indicated the nature of the equilibrium state in agreement with that of the thermodynamic equilibrium constant (K^θ (T)). This study has thus presented that the thermodynamic equilibrium constant contained the characteristics of microscopic dynamics based on the analysis of the reaction mechanism, and both constants are organically connected and unified. The reaction enthalpy and activation energy are closely related to each other with the same connotation.

Keywords: thermodynamic equilibrium constant, reaction rate constant, PBL teaching, dialectical relation, innovative thinking

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Authors: Vine Petzer, Mirna Nel

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An explanatory mixed method design was used to investigate accounting classroom conditions in the Further Education and Training (FET) Phase in South Africa. A descriptive survey research study with a heterogeneous group of learners and teachers was conducted in the first phase. In the qualitative phase, semi-structured individual interviews with learners and teachers, as well as observations in the accounting classroom, were employed to gain more in depth understanding of the learning conditions in the accounting classroom. The findings of the empirical research informed the development of a model for teachers in accounting, supporting them to use more effective teaching methods and create positive learning conditions for all learners to experience successful learning. A model towards creating positive Accounting classroom conditions that support successful learning was developed and recommended for education policy and decision-makers for use as a classroom intervention capacity building tool. The model identifies and delineates classroom practices that exert significant effect on learner attainment of quality education.

Keywords: accounting classroom conditions, positive education, successful learning, teaching accounting

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Authors: Tadesse Abera

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This study aimed to investigate the contribution of self-advocacy and engagement in the inclusion of SWDs in HEIs. A convergent parallel mixed methods design was employed. This article reports the quantitative strand. A total of 246 SWDs were selected through stratified proportionate random sampling technique from five public HEIs in Ethiopia. Data were collected through Self-advocacy questionnaire, student engagement scale, and college student experience questionnaire and analyzed through frequency, percentage, mean, standard deviation, correlation, one sample t-test and multiple regression. Both self-advocacy and engagement were found to have a predictive power on inclusion of respondents in the HEIs, where engagement was found to be more predictor. From the components of self-advocacy, knowledge of self and leadership and from engagement dimensions sense of belonging, cognitive, and valuing in their respective orders were found to have a stronger predictive power on the inclusion of respondents in the institutions. Based on the findings it was concluded that, if students with disabilities work hard to be self-determined, strive for realizing social justice, exert quality effort and seek active involvement, their inclusion in the institutions would be ensured.

Keywords: self-advocacy, engagement, inclusion, students with disabilities, higher education institution

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Authors: Mohaned G. Abed

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Attention Deficit Hyperactivity Disorder (ADHD) is considered one of the most frequently diagnosed psychiatric childhood disorders. It has an effect on 3–5% of school-aged children, and brings about difficulties in academic and social interaction. This study explored the knowledge and beliefs of parents in Saudi Arabia about children with ADHD. The Knowledge about Attention Deficit Disorder Questionnaire (KADD-Q) was administered to a sample of parents, followed by interviews with a subset of the total respondents. The results indicated that the parents knew more about the characteristics of ADHD than they knew about its related causes and treatment. Overall, the findings indicated that these parents had some knowledge about general characteristics of ADHD, but they had little understanding of causes and possible interventions. These results suggest an important need for more formal parents training regarding all aspects of ADHD in school age children.

Keywords: attention deficit hyperactivity disorder, childhood disorders, school-aged children, difficulties in academic, social interaction

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Authors: Tipu Sultan

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The proposed research intends to explore the gender role attitudes and work-life balance among dual-earner couples in Pakistan. With the increase of female labor force participation in Pakistan, the trend of dual-earner couples has been increased than ever before. This new trend of dual-earner families has significantly affected the personal life of dual-earner couples. Due to major change in household structures, the traditions and the routine activities are in continuous transition. Balancing work and family life is more complex in the patriarchal society of Pakistan because of the social expectations of gender roles. A dichotomous behavioral reflection is being observed in Pakistani society. The one group of people having an egalitarian attitude are supporting the new gender roles of females, whereas the other group of people having a traditional mindset is still in the favor of patriarchy. Therefore, gender roles are re-evaluated, and it would be more interesting to raise questions on the interplay of new gender roles and work-life balance among dual-earners. The semi-structured interview guide will be utilized to explore gender role attitudes, ideal and in-practice gender roles, experiences of work-life imbalances/balances, possible strategies to create a balance between work and family life among dual-earner couples.

Keywords: dual-earner couples, gender role attitudes, Pakistan, work-life balance

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Authors: Haodong Huang, Zida Zhao, Shilong Sun, Chiyao Li, Wenfu Xu

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The control methods for legged robots based on deep reinforcement learning have seen widespread application; however, the inherent black-box nature of neural networks presents challenges in understanding the decision-making motives of the robots. To address this issue, we propose a fully interpretable deep reinforcement learning training method to elucidate the underlying principles of legged robot motion. We incorporate the dynamics of legged robots into the policy, where observations serve as inputs and actions as outputs of the dynamics model. By embedding the dynamics equations within the multi-layer perceptron (MLP) computation process and making the parameters trainable, we enhance interpretability. Additionally, Bayesian optimization is introduced to train these parameters. We validate the proposed fully interpretable motion control algorithm on a legged robot, opening new research avenues for motion control and learning algorithms for legged robots within the deep learning framework.

Keywords: deep reinforcement learning, interpretation, motion control, legged robots

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Nihad Alem

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Nowadays, distance learning (DL) is widely perceived as a reformed type of education that takes advantage of technology to give more appealing opportunities especially for learners whose life conditions impede their attendance to regular classrooms however, creating interactional environment for students to expand their learning community and alleviate the feeling of loneliness and isolation should receive more attention when designing a distance learning course. This research aims to explore whether the audio/video peer learning can offer pedagogical add-ons to the Algerian distance learners and what are the pros and cons of its application as an educational experience in a synchronous environment mediated by Skype. Data were collected using video recordings of six sessions, reflective logs, and in-depth semi-structured interviews and will be analyzed by qualitatively identifying and measuring the three constitutional elements of the educational experience of peer learning namely the social presence, the cognitive presence, and the facilitation presence using a modified community of inquiry coding template. The findings from this study will provide recommendations for effective peer learning educational experience using the facilitation presence concept.

Keywords: audio/visual peer learning, community of inquiry, distance learning, facilitation presence

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Authors: Hamed Rahmani, Wim Groot

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The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Centre of Iran and the Ministry of Cooperatives Labour and Social Welfare that was taken from the labour force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of six in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education and years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment

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Authors: Lenell D. Walton

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African American families have consistently contended that their child’s special education team does not provide the services necessary to meet their child’s academic goals. Special education teams must guide and mentor African American students and their families through the special education process. This qualitative study examined African American female caregivers' perceptions, experiences, and expectations regarding the special education process. Data collection methods utilized in the study included a survey, semi-structured interviews, and three focus groups. Data were analyzed and compared to identify themes. Three themes emerged from the survey: education and training, participation, and challenges. Six major themes emerged: (a) differences in treatment and cultural disconnect, (b) lack of support and resources, (c) participants’ experiences of the special education process, (d) parent participation, (e) barriers and concerns, and (f) expectations. Implications for policy and practice to improve the special education process are discussed.

Keywords: African American, caregivers, critical race theory, special education

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Giselle Maggie Fer Castañeda, Diego Iván González

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The objective of this work was to study the autoshaped choice behavior in the Donahoe, Burgos and Palmer (DBP) neural network model and analyze it under the matching law. Autoshaped choice can be viewed as a form of economic behavior defined as the preference between alternatives according to their relative outcomes. The Donahoe, Burgos and Palmer (DBP) model is a connectionist proposal that unifies operant and Pavlovian conditioning. This model has been used for more than three decades as a neurobehavioral explanation of conditioning phenomena, as well as a generator of predictions suitable for experimental testing with non-human animals and humans. The study consisted of different simulations in which, in each one, a ratio of reinforcement was established for two alternatives, and the responses (i.e., activations) in each of them were measured. Choice studies with animals have demonstrated that the data generally conform closely to the generalized matching law equation, which states that the response ratio equals proportionally to the reinforcement ratio; therefore, it was expected to find similar results with the neural networks of the Donahoe, Burgos and Palmer (DBP) model since these networks have simulated and predicted various conditioning phenomena. The results were analyzed by the generalized matching law equation, and it was observed that under some contingencies, the data from the networks adjusted approximately to what was established by the equation. Implications and limitations are discussed.

Keywords: matching law, neural networks, computational models, behavioral sciences

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Authors: Francisco Julio Batle Lorente

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The present paper aims at presenting a new category of role for academia: proactive creator/promoter of communities of practice in emerging areas of innovation. It is based in research among practitioners in three different areas: social entrepreneurship, alumni engaged in entrepreneurship and innovation, and digital nomads. The concept of CoP is related to an intentionally created space to share experiences and collectively reflect on the cases arising from practice. Such an endeavour is not contemplated in the literature on academic roles in an explicit way. The goal of the paper is providing a framework for this function and throw some light on the perception and priorities of members of emerging communities (78 alumni, 154 social entrepreneurs, and 231 digital nomads) regarding community, learning, engagement, and networking, areas in which the university can help and, by doing so, contributing to signal the emerging area and creating new opportunities for the academia. The research methodology was based in Survey research. It is a specific type of field study that involves the collection of data from a sample of elements drawn from a well-defined population through the use of a questionnaire. It was considered that survey research might be valuable to the present project and help outline the utility of various study designs and future projects with the emerging communities that are the object of the investigation. Open questions were used for different topics, as well as critical incident technique. It was used a standard technique for survey sampling and questionnaire design. Finally, it was defined a procedure for pretesting questionnaires and for data collection. The questionnaire was channelled by means of google forms. The results indicate that the members of emerging, innovative CoPs and learning such the ones that were selected for this investigation lack cohesion, inspiration, networking, opportunities for creation of social capital, opportunities for collaboration beyond their existing and close network. The opportunity that arises for the academia from proactively helping articulate CoP (and Communities of learning) are related to key elements of any CoP/ CoL: community construction approaches, technological infrastructure, benefits, participation issues and urgent challenges, trust, networking, technical ability/training/development and collaboration. Beyond training, other three areas (networking, collaboration and urgent challenges) were the ones in which the contribution of universities to the communities were considered more interesting and workable to practitioners. The analysis of the responses for the open questions related to perception of the universities offer options for terra incognita to be explored for universities (signalling new areas, establishing broader collaborations with research, government, media and corporations, attracting investment). Based on the findings from this research, there is some evidence that CoPs can offer a formal and informal method of professional and interprofessional development for member of any emerging and innovative community and can decrease social and professional isolation. The opportunity that it offers to academia can increase the entrepreneurial and engaged university identity. It also moves to academia into a realm of civic confrontation of present and future challenges in a more proactive way.

Keywords: social innovation, new roles of academia, community of learning, community of practice

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Authors: Ahmad Al Aboudi

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The Department of Archaeology at King Saud University conduct a long Term excavations since 2004 at the archaeological site of (Qurh) in Al-Ula area. The history of the site goes back to the eighth century AD. The main aim of the excavations is the training of the students on the archaeological field work associated with the scientific skills of exploring, surveying, classifying, documentations and other necessary in the field archaeology. During the 12th Season of Excavations, an area of 20 × 40 m2 of the site was excavated. The depth of the excavating the site was reached to 2-3 m. Many of the architectural features of a residential area in the northern part of the site were excavated this season. Circular walls made of mud-brick and a brick column drums and tiles made of clay were revealed this season. Additionally, lots of findings such as Gemstones, jars, ceramic plates, metal, glass, and fabric, as well as some jewelers and coins were discovered. This paper will deal with the main results of this field project including the architectural features and phenomena and their interpretations, the classification of excavated material culture remains and stratigraphy.

Keywords: Islamic architecture, Islamic art, excavations, early Islamic city

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Authors: Sanja Seljan, Ivan Dunđer

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The paper presents combined automatic speech recognition (ASR) for English and machine translation (MT) for English and Croatian in the domain of business correspondence. The first part presents results of training the ASR commercial system on two English data sets, enriched by error analysis. The second part presents results of machine translation performed by online tool Google Translate for English and Croatian and Croatian-English language pairs. Human evaluation in terms of usability is conducted and internal consistency calculated by Cronbach's alpha coefficient, enriched by error analysis. Automatic evaluation is performed by WER (Word Error Rate) and PER (Position-independent word Error Rate) metrics, followed by investigation of Pearson’s correlation with human evaluation.

Keywords: automatic machine translation, integrated language technologies, quality evaluation, speech recognition

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Authors: Ibrahim Shaker, Amr El Hossany, Moustafa Osman, Mohamed El Raey

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This paper will explore integration model between GIS–SCADA system and enclosure quantification model to approach the impact of failure-safe event. There are real demands to identify spatial objects and improve control system performance. Nevertheless, the employed methodology is predicting electro-mechanic operations and corresponding time to environmental incident variations. Open processing, as object systems technology, is presented for integration enclosure database with minimal memory size and computation time via connectivity drivers such as ODBC:JDBC during main stages of GIS–SCADA connection. The function of Geographic Information System is manipulating power distribution in contrast to developing issues. In other ward, GIS-SCADA systems integration will require numerical objects of process to enable system model calibration and estimation demands, determine of past events for analysis and prediction of emergency situations for response training.

Keywords: air dispersion model, environmental management, SCADA systems, GIS system, integration power system

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

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Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: bioassay, machine learning, preprocessing, virtual screen

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Authors: Shawnrece Campbell

Abstract:

Emotional intelligence as an essential job skill is growing in popularity among human resource professionals and hiring managers. Companies value those who have high emotional intelligence because of their personal competences (self-awareness, self-regulation, motivation) and social competences (empathy, social skills). In implementing any training system to teach emotional intelligence, the best methodologies for acquiring and/or improving these competences should be taken into consideration. This study focuses on how students perceived the art of writing as a tool for self-improvement. During this session, participants will engage in a brief activity designed to help students develop emotional intelligence. As a part of the discussion, participants will learn the results of a junior-level literary seminar conducted to better understand students’ thoughts and views about the effectiveness of writing as a tool for emotional healing. An analysis of qualitative textual data is presented. The outcomes indicated that students found using writing as a tool for emotional intelligence development as highly effective. The findings also revealed that students have positive perceptions of using writing as a self-healing art that leads to increased emotional intelligence and believe that writing courses of this nature enhance students’ appreciation of the value of the liberal arts.

Keywords: emotional intelligence quotient, healing, soft skills, writing

Procedia PDF Downloads 210

Authors: C. Pearcy, C. Rees

Abstract:

Cognitive behavioural therapy (CBT) and exposure and response prevention (ERP) are currently the most efficacious treatments for Obsessive-compulsive disorder (OCD). Many clients, however, remain symptomatic following treatment. As a result, refusal of treatment, withdrawal from treatment, and partial adherence to treatment are common amongst ERP. Such limitations have caused few professionals to actually engage in ERP therapy, which has warranted the exploration of alternative treatments. This study evaluated an online self-help treatment program for OCD (the OCD Doctor Online); a 4-week Metacognitive Therapy (MCT) program which has implemented strategies from Wells’ Metacognitive model of OCD. The aim of the present study was to investigate whether an online self-help treatment using MCT would reduce symptoms of OCD, reduce unhelpful metacognitions and improve quality of life. Treatment effectiveness was assessed using a case series methodology in 3 consecutively referred individuals. At post-treatment, all participants showed reductions in unhelpful metacognitive beliefs (MCQ-30) and improvements in quality of life (Q-LES-Q), which were maintained through to 4 week follow-up. Two of the three participants showed reductions in OCD symptomology (OCI-R), which were further reduced at 4-week follow-up. The present study suggests that internet-based self-help treatment may be an effective means of delivering MCT to adults with OCD.

Keywords: internet-based, metacognitive therapy, obsessive-compulsive disorder, self-help

Procedia PDF Downloads 439