Search results for: learning motion patterns

Authors: Fiona Wahr, Sitalakshmi Venkatraman

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Industry employers require new graduates to bring with them a range of knowledge, skills and abilities which mean these new employees can immediately make valuable work contributions. These will be a combination of discipline and professional knowledge, skills and abilities which give graduates the technical capabilities to solve practical problems whilst interacting with a range of stakeholders. Underpinning the development of these disciplines and professional knowledge, skills and abilities, are “enabling” knowledge, skills and abilities which assist students to engage in learning. These are academic and learning skills which are essential to common starting points for both the learning process of students entering the course as well as forming the foundation for the fully developed graduate knowledge, skills and abilities. This paper reports on a project created to introduce and strengthen these enabling skills into the first semester of a Bachelor of Information Technology degree in an Australian polytechnic. The project uses an action research approach in the context of ongoing continuous improvement for the course to enhance the overall learning experience, learning sequencing, graduate outcomes, and most importantly, in the first semester, student engagement and retention. The focus of this is implementing the new curriculum in first semester subjects of the course with the aim of developing the “enabling” learning skills, such as literacy, research and numeracy based knowledge, skills and abilities (KSAs). The approach used for the introduction and embedding of these KSAs, (as both enablers of learning and to underpin graduate attribute development), is presented. Building on previous publications which reported different aspects of this longitudinal study, this paper recaps on the rationale for the curriculum redevelopment and then presents the quantitative findings of entering students’ reading literacy and numeracy knowledge and skills degree as well as their perceived research ability. The paper presents the methodology and findings for this stage of the research. Overall, the cohort exhibits mixed KSA levels in these areas, with a relatively low aggregated score. In addition, the paper describes the considerations for adjusting the design and delivery of the new subjects with a targeted learning experience, in response to the feedback gained through continuous monitoring. Such a strategy is aimed at accommodating the changing learning needs of the students and serves to support them towards achieving the enabling learning goals starting from day one of their higher education studies.

Keywords: enabling skills, student retention, embedded learning support, continuous improvement

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Authors: Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

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Time series data consist of successive data points collected over a period of time. Accurate prediction of future values is essential for informed decision-making in several real-world applications, including electricity load demand forecasting, lifetime estimation of industrial machinery, traffic planning, weather prediction, and the stock market. Due to their critical relevance and wide application, there has been considerable interest in time series forecasting in recent years. However, the proliferation of sensors and IoT devices, real-time monitoring systems, and high-frequency trading data introduce significant intricate temporal variations, rapid changes, noise, and non-linearities, making time series forecasting more challenging. Classical methods such as Autoregressive integrated moving average (ARIMA) and Exponential Smoothing aim to extract pre-defined temporal variations, such as trends and seasonality. While these methods are effective for capturing well-defined seasonal patterns and trends, they often struggle with more complex, non-linear patterns present in real-world time series data. In recent years, deep learning has made significant contributions to time series forecasting. Recurrent Neural Networks (RNNs) and their variants, such as Long short-term memory (LSTMs) and Gated Recurrent Units (GRUs), have been widely adopted for modeling sequential data. However, they often suffer from the locality, making it difficult to capture local trends and rapid fluctuations. Convolutional Neural Networks (CNNs), particularly Temporal Convolutional Networks (TCNs), leverage convolutional layers to capture temporal dependencies by applying convolutional filters along the temporal dimension. Despite their advantages, TCNs struggle with capturing relationships between distant time points due to the locality of one-dimensional convolution kernels. Transformers have revolutionized time series forecasting with their powerful attention mechanisms, effectively capturing long-term dependencies and relationships between distant time points. However, the attention mechanism may struggle to discern dependencies directly from scattered time points due to intricate temporal patterns. Lastly, Multi-Layer Perceptrons (MLPs) have also been employed, with models like N-BEATS and LightTS demonstrating success. Despite this, MLPs often face high volatility and computational complexity challenges in long-horizon forecasting. To address intricate temporal variations in time series data, this study introduces Times2D, a novel framework that parallelly integrates 2D spectrogram and derivative heatmap techniques. The spectrogram focuses on the frequency domain, capturing periodicity, while the derivative patterns emphasize the time domain, highlighting sharp fluctuations and turning points. This 2D transformation enables the utilization of powerful computer vision techniques to capture various intricate temporal variations. To evaluate the performance of Times2D, extensive experiments were conducted on standard time series datasets and compared with various state-of-the-art algorithms, including DLinear (2023), TimesNet (2023), Non-stationary Transformer (2022), PatchTST (2023), N-HiTS (2023), Crossformer (2023), MICN (2023), LightTS (2022), FEDformer (2022), FiLM (2022), SCINet (2022a), Autoformer (2021), and Informer (2021) under the same modeling conditions. The initial results demonstrated that Times2D achieves consistent state-of-the-art performance in both short-term and long-term forecasting tasks. Furthermore, the generality of the Times2D framework allows it to be applied to various tasks such as time series imputation, clustering, classification, and anomaly detection, offering potential benefits in any domain that involves sequential data analysis.

Keywords: derivative patterns, spectrogram, time series forecasting, times2D, 2D representation

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Authors: Amit Kumar

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Accurate identification of deteriorated air quality regions is very helpful in devising better environmental practices and mitigation efforts. In the present study, an attempt has been made to identify the air pollutant dispersion patterns especially NOX due to vehicular and industrial sources over a rapidly developing urban city, Visakhapatnam (17°42’ N, 83°20’ E), India, during April 2009. Using the emission factors of different vehicles as well as the industry, a high resolution 1 km x 1 km gridded emission inventory has been developed for Visakhapatnam city. A dispersion model AERMOD with explicit representation of planetary boundary layer (PBL) dynamics and offline coupled through a developed coupler mechanism with a high resolution mesoscale model WRF-ARW resolution for simulating the dispersion patterns of NOX is used in the work. The meteorological as well as PBL parameters obtained by employing two PBL schemes viz., non-local Yonsei University (YSU) and local Mellor-Yamada-Janjic (MYJ) of WRF-ARW model, which are reasonably representing the boundary layer parameters are considered for integrating AERMOD. Significantly different dispersion patterns of NOX have been noticed between summer and winter months. The simulated NOX concentration is validated with available six monitoring stations of Central Pollution Control Board, India. Statistical analysis of model evaluated concentrations with the observations reveals that WRF-ARW of YSU scheme with AERMOD has shown better performance. The deteriorated air quality locations are identified over Visakhapatnam based on the validated model simulations of NOX concentrations. The present study advocates the utility of tNumerical Simulation of Air Pollutant Using Coupled AERMOD-WRF Modeling System over Visakhapatnam: A Case Studyhe developed gridded emission inventory of NOX with coupled WRF-AERMOD modeling system for air quality assessment over the study region.

Keywords: WRF-ARW, AERMOD, planetary boundary layer, air quality

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Authors: Hashem Dadashpoor, Sadegh Saeidi Shirvan

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While with the evolution of the economy towards a knowledge-based economy, innovation is a requirement for metropolitan regions, the adoption of an open innovation strategy is an option and a requirement for many industrial firms in these regions. Studies show that investing in research and development units cannot alone increase innovation. Within the framework of the theory of learning regions, this gap, which scholars call it the ‘innovation gap’, is filled with regional features of firms. This paper attempts to investigate the factors affecting the open innovation of firms in metropolitan regions, and it searches for these in territorial innovation models and, in particular, the theory of learning regions. In the next step, the effect of identified factors which is considered as regional learning factors in this research is analyzed on the innovation of sample firms by SPSS software using multiple linear regression. The case study of this research is constituted of industrial enterprises from two groups of food industry and auto parts in Toos industrial town in Mashhad metropolitan region. For data gathering of this research, interviews were conducted with managers of industrial firms using structured questionnaires. Based on this study, the effect of factors such as size of firms, inter-firm competition, the use of local labor force and institutional infrastructures were significant in the innovation of the firms studied, and 44% of the changes in the firms’ innovation occurred as a result of the change in these factors.

Keywords: regional knowledge networks, learning regions, interactive learning, innovation

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Authors: Ksenia Meshkova

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With increasing amount of multimedia data, it is very important to automate and speed up the process of obtaining meta. This process means not just recognition of some object or its movement, but recognition of the entire scene versus separate frames and having timeline segmentation as a final result. Labeling datasets is time consuming, besides, attributing characteristics to particular scenes is clearly difficult due to their nature. In this article, we will consider autoencoders application to unsupervised scene recognition and clusterization based on interpretable features. Further, we will focus on particular types of auto encoders that relevant to our study. We will take a look at the specificity of deep learning related to information theory and rate-distortion theory and describe the solutions empowering poor interpretability of deep learning in media content processing. As a conclusion, we will present the results of the work of custom framework, based on autoencoders, capable of scene recognition as was deeply studied above, with highlights generation resulted out of this recognition. We will not describe in detail the mathematical description of neural networks work but will clarify the necessary concepts and pay attention to important nuances.

Keywords: neural networks, computer vision, representation learning, autoencoders

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Uduak Umoh, Imo Eyoh, Emmauel Nyoho

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This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753.

Keywords: Machine Learning Algorithms , Interval Type-2 Fuzzy Logic, Fire Outbreak, Support Vector Machine, K-Nearest Neighbour, Principal Component Analysis

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Authors: Charléa M. Smith, Raiden L. Schodowski, Arletty Pinel

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Due to the COVID-19 pandemic, the Faculty of Medical Sciences of The University of the West Indies (UWI) Mona in Kingston, Jamaica, had to rapidly migrate to digital and blended learning. Students in the preclinical stage of the program transitioned to full-time online learning, while students in the clinical stage experienced decreased daily patient contact and the implementation of a blend of online lectures and virtual clinical practice. Such sudden changes were coupled with the institutional pressure of the need to introduce a novel approach to education without much time for preparation, as well as additional strain endured by the faculty, who were overwhelmed by serving as frontline workers. During the period July 20 to August 23, 2021, this study surveyed preclinical and clinical students to capture their experiences with these changes and their recommendations for future use of digital modalities of learning to enhance medical education. It was conducted with a fellow student of the 2021 cohort of the MultiPod mentoring program. A questionnaire was developed and distributed digitally via WhatsApp to all medical students of the UWI Mona campus to assess students’ experiences and perceptions of the advantages, challenges, and impact on individual knowledge proficiencies brought about by the transition to predominantly digital learning environments. 108 students replied, 53.7% preclinical and 46.3% clinical. 67.6% of the total were female and 30.6 % were male; 1.8% did not identify themselves by gender. 67.2% of preclinical students preferred blended learning and 60.3% considered that the content presented did not prepare them for clinical work. Only 31% considered that the online classes were interactive and encouraged student participation. 84.5% missed socialization with classmates and friends and 79.3% missed a focused environment for learning. 80% of the clinical students felt that they had not learned all that they expected and only 34% had virtual interaction with patients, mostly by telephone and video calls. Observing direct consultations was considered the most useful, yet this was the least-used modality. 96% of the preclinical students and 100% of the clinical ones supplemented their learning with additional online tools. The main recommendations from the survey are the use of interactive teaching strategies, more discussion time with lecturers, and increased virtual interactions with patients. Universities are returning to face-to-face learning, yet it is unlikely that blended education will disappear. This study demonstrates that students’ perceptions of their experience during mobility restrictions must be taken into consideration in creating more effective, inclusive, and efficient blended learning opportunities.

Keywords: blended learning, digital learning, medical education, student perceptions

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Authors: Yu-Mei Tseng, Chih-Chun Wu

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As information and communication technology (ICT) has become a part of people’s lives, most teenagers born after the 1980s and grew up in internet generation are called digital natives. Meanwhile, those teenagers’ parents are called digital immigrants. They need to keep learning new skills of ICT. This study investigated that high school students helped their parents set up social network services (SNS) and taught them how to use ICT. This study applied paper and pencil anonymous questionnaires that asked the ICT learning and ICT products using in high school students’ parents. The sample size was 2,621 high school students, including 1,360 (51.9%) males and 1,261 (48.1%) females. The sample was from 12 high school and vocational high school in central Taiwan. Results from paired sample t-tests demonstrated regardless genders, both male and female high school students help mothers set up Facebook and LINE more often than fathers. In addition, both male and female high school students taught mothers to use ICT more often than fathers. Meanwhile, both male and female high school students teach mothers to use SNS more often than fathers. The results showed that intergenerational ICT teaching occurred more often between mothers and her children than fathers. It could imply that mothers play a more important role in family ICT learning than fathers, or it could be that mothers need more help regarding ICT than fathers. As for gender differences, results from the independent t-tests showed that female high school students were more likely than male ones to help their parents setup Facebook and LINE. In addition, compared to male high school students, female ones were more likely to teach their parents to use smartphone, Facebook and LINE. However, no gender differences were detected in teaching mothers. The gender differences results suggested that female teenagers offer more helps to their parents regarding ICT learning than their male counterparts. As for area differences, results from the independent t-tests showed that the high school in remote area students were more likely than metropolitan ones to teach parents to use computer, search engine and download files of audio and video. The area differences results might indicate that remote area students were more likely to teach their parents how to use ICT. The results from this study encourage children to help and teach their parents with ICT products.

Keywords: adult ICT learning, family ICT learning, ICT learning, urban-rural gap

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Authors: Chun-Yao Lee, Nando Purba, Benny Iskandar

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This paper proposed a phase resolve partial discharge (PRPD) shape method to classify types of defect stator coil generator by using off-line PD measurement instrument. The recorded PRPD, by using the instruments MPD600, can illustrate the PRPD patterns of partial discharge of unit’s defects. In the paper, two of large units, No.2 and No.3, in Inalum hydropower plant, North Sumatera, Indonesia is adopted in the experimental measurement. The proposed PRPD shape method is to mark auxiliary lines on the PRPD patterns. The shapes of PRPD from two units are marked with the proposed method. Then, four types of defects in IEC 60034-27 standard is adopted to classify the defect types of the two units, which types are microvoids (S1), delamination tape layer (S2), slot defect (S3) and internal delamination (S4). Finally, the two units are actually inspected to validate the availability of the proposed PRPD shape method.

Keywords: partial discharge (PD), stator coil, defect, phase resolve pd (PRPD)

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Authors: Dina L. DiSantis

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Development and implementation of a place-based watershed science course for high school students will prove to be a valuable experience for both student and teacher. By having students study and assess the watershed dynamics of a local stream, they will better understand how human activities affect this valuable resource. It is important that students gain tangible skills that will help them to have an understanding of water quality analysis and the importance of preserving our Earth's water systems. Having students participate in real world practices is the optimal learning environment and can offer students a genuine learning experience, by cultivating a knowledge of place, while promoting education for sustainability. Additionally, developing a watershed science course for high school students will give them a hands-on approach to studying science; which is both beneficial and more satisfying to students. When students conduct their own research, collect and analyze data, they will be intimately involved in addressing water quality issues and solving critical water quality problems. By providing students with activities that take place outside the confines of the indoor classroom, you give them the opportunity to gain an appreciation of the natural world. Placed-based learning provides students with problem-solving skills in everyday situations while enhancing skills of inquiry. An overview of a place-based watershed science course and its impact on student learning will be presented.

Keywords: education for sustainability, place-based learning, watershed science, water quality

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Authors: Cosmas Maphosa, Ndileleni P. Mudzielwana, Lufuno Netshifhefhe

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Core business in a university centers on a curriculum. Teaching, learning, assessment and university products all have a bearing on the curriculum. In this discussion paper, the researchers engage in theoretical underpinnings of curriculum development in universities in South Africa. The paper is hinged on the realization that meaningful curriculum development is only possible if academic staff member has a thorough understanding of curriculum, curriculum design principles, and processes. Such understanding should be informed by theory. In this paper, the researchers consider curriculum, curriculum orientations, and the role of learning outcomes in curriculum development. Important and key considerations in module/course design are discussed and relevant examples given. The issue of alignment, as an important aspect of module/course design, is also explained and exemplified. Conclusions and recommendations are made.

Keywords: curriculum, curriculum development, knowledge, graduate attributes, competencies, teaching and learning

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

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Authors: M. V. Belubekyan, S. R. Martirosyan

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On the example of an elastic rectangular plate streamlined by a supersonic gas flow, we have investigated the phenomenon of divergence and of panel flatter of the overrunning of the gas flow at a free edge under assumption of the presence of concentrated inertial masses and moments at the free edge. We applied a new approach of finding of solution of these problems, which was developed based on the algorithm for an analytical solution finding. This algorithm is easy to use for theoretical studies for the wides circle of nonconservative problems of linear elastic stability. We have established the relation between the characteristics of natural vibrations of the plate and velocity of the streamlining gas flow, which enables one to draw some conclusions on the stability of disturbed motion of the plate depending on the parameters of the system plate-flow. Its solution shows that either the divergence or the localized divergence and the flutter instability are possible. The regions of the stability and instability in space of parameters of the problem are identified. We have investigated the dynamic behavior of the disturbed motion of the panel near the boundaries of region of the stability. The safe and dangerous boundaries of region of the stability are found. The transition through safe boundary of the region of the stability leads to the divergence or localized divergence arising in the vicinity of free edge of the rectangular plate. The transition through dangerous boundary of the region of the stability leads to the panel flutter. The deformations arising at the flutter are more dangerous to the skin of the modern aircrafts and rockets resulting to the loss of the strength and appearance of the fatigue cracks.

Keywords: stability, elastic plate, divergence, localized divergence, supersonic panels flutter

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Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

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Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

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Authors: Barenten Suciu

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Classical matrix calculus and Routh-Hurwitz stability conditions, applied to the snake-like motion of the conical wheel axle, lead to the conclusion that the hunting mode is inherently unstable, and its natural frequency is a complex number. In order to analytically solve such a complicated vibration model, either the inertia terms were neglected, in the model designated as geometrical, or restrictions on the creep coefficients and yawing diameter were imposed, in the so-called dynamical model. Here, an alternative solution is proposed to solve the hunting mode, based on the observation that the bullet train wheel axle is equipped with cylindrical wheels. One argues that for such wheel treads, the geometrical hunting is irrelevant, since its natural frequency becomes nil, but the dynamical hunting is significant since its natural frequency reduces to a real number. Moreover, one illustrates that the geometrical simplification of the wheel causes the stabilization of the hunting mode, since the characteristic quartic equation, derived for conical wheels, reduces to a quadratic equation of positive coefficients, for cylindrical wheels. Quite simple analytical expressions for the damping ratio and natural frequency are obtained, without applying restrictions into the model of contact. Graphs of the time-depending hunting lateral perturbation, including the maximal and inflexion points, are presented both for the critically-damped and the over-damped wheel axles.

Keywords: bullet train, creep, cylindrical wheels, damping, dynamical hunting, stability, vibration analysis

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Authors: Engi E. I. Sarhan, Usama M. Rashad, Ibrahim M. I. Hamoda, Mohammed K. Mohamed

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Background: This study aimed to know the effect of nitrogen-based cryotherapy on the spasticity of calf muscle in stroke patients. Patients were selected from the outpatient clinic of Neurology, Al-Mansoura general hospital, Al-Mansoura University. Subjects and methods: Thirty Stroke Patients of both sexes ranged from 45 to 60 years old were divided randomly into two equal groups, a study group (A) received a nitrogen-based cryotherapy, a selective physical therapy program and ankle foot orthosis (AFO), while as patients in control group (B) received the same program and AFO only. The treatment duration was three times per week for four weeks for both groups. We assessed spasticity of calf muscle before and after treatment subjectively using modified Ashworth scale (MAS) and objectively via measuring H / M ratio on electromyography machine. We also assessed ankle dorsiflexion ROM objectively using two dimensions motion analysis (2D). Results: After treatment, there was a highly significant improvement in the study group compared to the control group regarding the score of MAS, no significant difference in the study group compared to the control group regarding the readings of H / M ratio, highly significant improvement in the study group compared to the control group regarding the 2D motion analysis findings. Conclusion: This modality considers effective in reducing spasticity in the calf muscle and improving ankle dorsiflexion of the affected limb.

Keywords: ankle foot orthosis, nitrogen-based cryotherapy, stroke, spasticity

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Authors: Kyria Rebeca Finardi

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The study discusses the role of foreign languages in general and of English in particular in the process of internationalization of higher education (IHE), defined as the intentional integration of an international, intercultural or global dimension in the purpose, function or offer of higher education. The study is bibliographical and offers a brief outline of the current political, economic and educational scenarios in Brazil, before discussing some possibilities and challenges for the development of multilingualism and IHE there. The theoretical background includes a review of Brazilian language and internationalization policies. The review and discussion concludes that the use of the Content and Language Integrated Learning (CLIL) approach and the Intercomprehension approach to foreign language teaching/learning are relevant alternatives to foster multilingualism in that context.

Keywords: Brazil, higher education, internationalization, multilingualism

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Authors: Costrie Ganes Widayanti

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Learning Disabilities (LDs) are less understood by the Indonesia’s educational practitioners. As a result, students with LDs are at risk of being outcast from the learning process that requires participation, which potentially disconnects them academically and socially. Its objective is to raise the voice of students with LDs regarding their engagement in the classroom. This research is conducted in two urban regular public primary schools in Indonesia. The study uses an ethnographic case study research design, which explores the views and experiences of four (4) students with LDs. The data were collected using participant observations and interviews. The preliminary findings highlighted two areas: 1) the stigmatization about LDs; and 2) perceived membership. Having LDs was a barrier to fully engage in the academic and social life. Interestingly, they were more likely dependent on each other for support as limited assistance was offered by teachers and peers. Their peers did not take a keen interest in helping them when they found difficulties with the assignments. Furthermore, due to their low academic performance, they were not in favor of being nominated as a group member. In a situation that required them to do a group assignment, they were not expected to give a contribution, positioning themselves as incompatible. These findings indicated that such practices legitimate the hegemony of the superior over those who are powerless and left behind.

Keywords: engagement, experiences, learning disability, qualitative design

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Authors: Petchpong Mayukhachot

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The purposes of this research were (1) to develop a distance training package on topic of Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission; (2) to study the effects of using the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission. and (3) to study the samples’ opinion on the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission Research and Development was used in this research. The purposive sampling group of this research was 39 Vocational Teachers of Office of The Vocational Education Commission. Instruments were; (1) the distance training package, (2) achievement tests on understanding of Production of Handbook and Report writing for innovative learning and teaching and learning activities to develop practical skills, and (3) a questionnaire for sample’s opinion on the distance training package. Percent, Mean, Standard Deviation, the E1/E2 efficiency index and t-test were used for data analysis. The findings of the research were as follows: (1) The efficiency of the distance training package was established as 80.90 / 81.90. The distance training package composed of the distance training package document and a manual for the distance training package. The distance training package document consisted of the name of the distance training package, direction for studying the distance training package, content’s structure, concepts, objectives, and activities after studying the distance training package. The manual for the distance training package consisted of the explanation of the distance training package and objectives, direction for using the distance training package, training schedule, documents as a manual of speech, and evaluations. (2) The effects of using the distance training package on topic Production of Handbook and Report writing for innovative learning and teaching for Vocational Teachers of Office of The Vocational Education Commission were the posttest average scores of achievement on understanding of Technology and Occupations teaching for development of critical thinking of the sample group were higher than the pretest average scores. (3) The most appropriate of trainees’ opinion were contents of the distance training package is beneficial to performance. That can be utilized in Teaching or operations. Due to the content of the two units is consistent and activities assigned to the appropriate content.

Keywords: distance training package, handbook writing for innovative learning, teaching report writing for innovative learning, teaching

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Authors: Anduela Lile

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The purpose of this cross sectional study was to investigate the effectiveness of teaching and learning Statistics from a student centered perspective in higher education institutions. Statistics education has emphasized the application of tangible and interesting examples in order to motivate students learning about statistical concepts. Participants in this study were 112 bachelor students enrolled in the ‘Applied Statistics’ course in Sports University of Tirana. Experimental group students received a student-centered teaching approach; Control group students received an instructor-centered teaching approach. This study found student-centered approach student group had statistically significantly higher assessments scores (52.1 ± 18.9) at the end of the evaluation compared to instructor-centered approach student group (61.8 ± 16.4), (t (108) = 2.848, p = 0.005). Results concluded that student-centered perspective can improve student positive attitude to statistical methods and to motivate project work. Therefore, findings of this study may be very useful to the higher education institutions to establish their learning strategies especially for courses related to Statistics.

Keywords: student-centered, instructor-centered, course assessment, learning outcomes, applied statistics

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Authors: Gabriela Chmelikova, Ludmila Hurajova, Pavol Bozek

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Current era has brought hand in hand with the vast and fast development of technologies enormous pressure on individuals to keep being well - oriented in their professional fields. Almost all projects in the real world require an interdisciplinary perspective. These days we notice some cases when students face that real requirements for jobs are in contrast to the knowledge and competences they gained at universities. Interlacing labor market and university programs is a big issue these days. Sometimes it seems that higher education only “chases” reality. Simulation-based learning can support students’ touch with real demand on competences and knowledge of job world. The contribution provided a descriptive study of some cases of simulation-based teaching environment in different courses at STU MTF in Trnava and discussed how students and teachers perceive this model of teaching-learning approach. Finally, some recommendations are proposed how to enhance closer relationship between academic world and labor market.

Keywords: interdisciplinary approach, simulation-based learning, students' job readiness, teaching environment in higher education

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Authors: Shruti Malviya, Chris Bowler

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Analysis of microbial eukaryotic diversity is fundamental to understanding ecosystems’ structure, biology, and ecology. Diatoms (Stramenopiles, Bacillariophyceae) are one of the most diverse and ecologically prominent groups of phytoplankton. This study was performed to enhance the understanding of global biodiversity patterns and structure of planktonic diatom communities across the world's oceans. We used the metabarcoding data set generated from the biological samples and associated environmental data collected during the Tara Oceans (2009-2013) global circumnavigation covering all major oceanic provinces. A total of ~18 million diatom V9-18S rDNA tags from 126 sampling stations, constituting 631 size-fractionated plankton communities were generated. Using ~250,000 unique diatom metabarcodes, the global diatom distribution and diversity across size classes, genus and ecological niches was assessed. Notably, our analysis revealed: (i) a new estimate of the total number of planktonic diatom species, (ii) a considerable unknown diversity and exceptionally high diversity in the open ocean, and (iii) complex diversity patterns across oceanic provinces. Also, co-occurrence of several ribotypes in locations separated by great geographic distances (equatorial stations) demonstrated a widespread but not ubiquitous distribution. This work provides a comprehensive perspective on diatom distribution and diversity in the world’s oceans and elaborates interconnections between associated theories and underlying drivers. It shows how meta-barcoding approaches can provide a framework to investigate environmental diversity at a global scale, which is deemed as an essential step in answering various ecological research questions. Consequently, this work also provides a reference point to explore how microbial communities will respond to environmental conditions.

Keywords: diatoms, Tara Oceans, biodiversity, metabarcoding

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Authors: Adrian Millea

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In hierarchical reinforcement learning, one of the main issues encountered is the discovery of subgoal states or options (which are policies reaching subgoal states) by partitioning the environment in a meaningful way. This partitioning usually requires an expensive global clustering operation or eigendecomposition of the Laplacian of the states graph. We propose a local solution to this issue, much more efficient than algorithms using global information, which successfully discovers subgoal states by computing a simple function, which we call heterogeneity for each state as a function of its neighbors. Moreover, we construct a value function using the difference in heterogeneity from one step to the next, as reward, such that we are able to explore the state space much more efficiently than say epsilon-greedy. The same principle can then be applied to higher level of the hierarchy, where now states are subgoals discovered at the level below.

Keywords: exploration, hierarchical reinforcement learning, locality, options, value functions

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Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa

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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.

Keywords: ICT, e-health, machine learning, ICU, healthcare

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Authors: Paola Utreras, Yazmina Olmos, Loreto Sanhueza

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This work has the purpose of study and incorporate Problem Based Learning (PBL) for engineering students, through the analysis of several thermal images of dwellings located in different geographical points of the Region de los Ríos, Chile. The students analyze how heat is transferred in and out of the houses and how is the relation between heat transfer and climatic conditions that affect each zone. As a result of this activity students are able to acquire significant learning in the unit of heat and temperature, and manage to reverse previous conceptual errors related with energy, temperature and heat. In addition, student are able to generate prototype solutions to increase thermal efficiency using low cost materials. Students make public their results in a report using scientific writing standards and in a science fair open to the entire university community. The methodology used to measure previous Conceptual Errors has been applying diagnostic tests with everyday questions that involve concepts of heat, temperature, work and energy, before the unit. After the unit the same evaluation is done in order that themselves are able to evidence the evolution in the construction of knowledge. As a result, we found that in the initial test, 90% of the students showed deficiencies in the concepts previously mentioned, and in the subsequent test 47% showed deficiencies, these percent ages differ between students who carry out the course for the first time and those who have performed this course previously in a traditional way. The methodology used to measure Significant Learning has been by comparing results in subsequent courses of thermodynamics among students who have received problem based learning and those who have received traditional training. We have observe that learning becomes meaningful when applied to the daily lives of students promoting internalization of knowledge and understanding through critical thinking.

Keywords: engineering students, heat flow, problem-based learning, thermal images

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Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

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Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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Authors: Chialing Chang

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Language and culture are critical foundations of international mobility. However, the students who are limited to the local environment may affect their learning outcome and global perspective. Video Conferencing has been proven an economical way for students as a medium to communicate with international students around the world. In Taiwan, the National Development Commission advocated the development of bilingual national policies in 2030 to enhance national competitiveness and foster English proficiency and fully launched bilingual activation of the education system. Globalization is closely related to the development of Taiwan's education. Therefore, the teacher conducted an integrated lesson through interdisciplinary learning. This study aims to investigate how the teacher helps develop students' global and language core competencies in the international education class. The methodology comprises four stages, which are lesson planning, class observation, learning data collection, and speech analysis. The Grice's Conversational Maxims are adopted to analyze the students' conversation in the video conferencing course. It is the action research from the teacher's reflection on approaches to developing students' language learning skills. The study lays the foundation for mastering the teacher's international education professional development and improving teachers' teaching quality and teaching effectiveness as a reference for teachers' future instruction.

Keywords: international education, language learning, Grice's conversational maxims, video conferencing course

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Authors: Maureen Lilian Klos

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In this paper, the notion of autonomy in language teaching and learning is explored with a view to designing particular subject-specific academic literacy at higher education level, for mostly English second or third language learners at the Nelson Mandela University, Port Elizabeth, South Africa. These courses that are contextualized in subject-specific fields studied by students in Arts, Education and Social Science Faculties aim to facilitate learners in the manipulation of cognitively demanding academic texts. However, classroom contact time for these courses is limited to one ninety sessions per week. Thus, learners need to be autonomously responsible for developing their own skills when manipulating and negotiating appropriate academic textual conventions. Thus, a model was designed to allow for gradual learner independence in language learning skills. Learners experience of the model was investigated using the Phenomenological Research Approach. Data in the form of individual written reflections and transcripts of unstructured group interviews were analyzed for themes and sub-themes. These findings are discussed in the article with a view to addressing the practical concerns of the learners in this case study.

Keywords: academic literacies, autonomy, language learning and teaching, subject-specific language

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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