Search results for: professional learning communities (PLCs)

Authors: Maho Komura

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The purpose of this study was to determine from a questionnaire survey of teachers the status of implementation of individualized support for the pupils with suspected developmental disabilities in regular elementary school classes in Japan. In inclusive education, the goal is for all pupils to learn in the same place as much as possible by receiving the individualized support they need. However, in the Japanese school culture, strong "homogeneity" sometimes surfaces, and it is pointed out that it is difficult to provide individualized support from the viewpoint of formal equality. Therefore, we decided to conduct this study in order to examine whether there is a difference in the frequency of implementation depending on the content of individualized support and to consider the direction of future individualized support. The subjects of the survey were 196 public elementary school teachers who had been in charge of regular classes within the past five years. In the survey, individualized support was defined as individualized consideration including rational consideration, and did not include support for the entire class or all pupils enrolled in the class (e.g., reducing the amount of homework for pupils who have trouble learning, changing classroom rules, etc.). (e.g., reducing the amount of homework for pupils with learning difficulties, allowing pupils with behavioral concerns to use the library or infirmary when they are unstable). The respondents were asked to choose one answer from four options, ranging from "very much" to "not at all," regarding the degree to which they implemented the nine individual support items that were set up with reference to previous studies. As a result, it became clear that the majority of teachers had pupils with developmental disabilities or pupils who require consideration in terms of learning and behavior, and that the majority of teachers had experience in providing individualized support to these pupils. Investigating the content of the individualized support that had been implemented, it became clear that the frequency with which it was implemented varied depending on the individualized support. Individualized support that allowed pupils to perform the same learning tasks was implemented more frequently, but individualized support that allowed different learning tasks or use of places other than the classroom was implemented less frequently. It was suggested that flexible support methods tailored to each pupil may not have been considered.

Keywords: inclusive education, ndividualized support, regular class, elementary school

Procedia PDF Downloads 136

Authors: Tris Kee

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This research investigates social sustainability factors in transitional housing projects and their impact on fostering healthy living environments that promote physical activity and social interaction for residents. Social sustainability is integral to individual health and well-being, as emphasized by Goal 11 of the 2030 Agenda for Sustainable Development, which highlights the importance of safe, affordable, and accessible transport systems, green spaces, and public spaces catering to vulnerable populations' needs. Communal spaces in urban environments are essential for fostering social sustainability, as they serve as settings for physical activities and social interactions among diverse socio-economic groups. Factors such as neighborhood social atmosphere, historical context, social disparity, and mobility can influence the relationship between existing and transitional communities. Mental health effects can be measured through housing segregation, mobility and accessibility, and housing tenure. A significant research gap exists in understanding the living environment of transitional housing in Hong Kong and the social sustainability factors affecting residents' mental and physical health. To address this gap, our study employs a mixed-methods approach combining survey questionnaires and interviews to gather both quantitative and qualitative data. This methodology will provide comprehensive insights into residents' experiences and perceptions. Our research's main contribution is identifying key social sustainability factors in transitional housing and their impact on residents' well-being, informing policy-making and the creation of inclusive, healthy living environments. By addressing this research gap, we aim to provide valuable insights for future housing projects, ultimately promoting the development of socially sustainable transitional communities.

Keywords: social sustainablity, affordable housing, transitional housing, high density housing

Procedia PDF Downloads 95

Authors: Boonrat Plangsorn

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Using ebooks can make not only a meaningful learning and achievement for students, but also can help teacher effectively enhance and improve their teaching. Nowadays, teachers try to develop ebooks for their class but it does not success in some cases because they do not have clear understanding on how to design, develop, and using ebooks that align with their teaching and learning objectives. Thus, the processes of using, designing, and producing ebooks have become one of important competency for teacher because it will enhance teacher’s knowledge for ebooks production. The purposes of this research were: (1) to develop the competency of producing and using ebooks of teachers in Chachengsao and (2) to promote the using ebooks of teachers in Chachengsao. The research procedures were divided into four phases. Phase I (study components and process of the designing and development of ebooks) was an interview in which the qualitative data were collected from five experts in instructional media. Phase II (develop teachers’ competency of producing ebooks) was a workshop for 28 teachers in Chachengsao. Phase III (study teachers’ using ebooks) was an interview in which the qualitative data were collected from seven teachers. Phase IV (study teachers’ using ebooks) was an interview in which the qualitative data were collected from six teachers. The research findings were as follows: 1. The components of ebooks comprised three components: ebooks structure, multimedia, and hyperlink. The eleven processes of design ebooks for education included (1) analyze the ebooks objective, (2) analyze learner characteristics, (3) set objective, (4) set learning content, (5) learner’s motivation, (6) design and construct activity, (7) design hyperlink, (8) produce script and storyboard, (9) confirm storyboard by expert, (10) develop ebooks, and (11) evaluate ebooks. 2. The evaluation of designing and development of ebooks for teacher workshop revealed the participants’ highest satisfaction (M = 4.65). 3. The teachers’ application of ebooks were found that obstacles of producing an ebooks: Time period of producing ebooks, a readiness of school resources, and small teacher network of producing and using ebooks. The result of using ebooks was students’ motivation. 4. The teachers’ ebooks utilization through educational research network of teacher in Chachengsao revealed that the characteristic of ebooks consist of picture, multimedia, voice, music, video, and hyperlink. The application of ebooks caused students interested in the contents; enjoy learning, and enthusiastic learning.

Keywords: ebooks, producing ebooks competency, using ebooks competency, educational research network

Procedia PDF Downloads 356

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 185

Authors: Melissa Hachem, Maxime Loizeau, Nadine Saleh, Isabelle Momas, Lynda Bensefa-Colas

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Professional drivers are exposed inside their vehicles to high levels of air pollutants due to the considerable time they spend close to motor vehicle emissions. Little is known about ultrafine particles (UFP) or black carbon (BC) adverse respiratory health effects compared to the regulated pollutants. We aimed to study the short-term associations between UFP and BC concentrations inside vehicles and (1) the onset of mucosal irritation and (2) the acute changes in lung function of Parisian taxi drivers during a working day. An epidemiological study was carried out on 50 taxi drivers in Paris. UFP and BC were measured inside their vehicles with DiSCmini® and microAeth®, respectively. On the same day, the frequency and the severity of nose, eye, and throat irritations were self-reported by each participant and a spirometry test was performed before and after the work shift. Multivariate analysis was used to evaluate the associations between in-taxis UFP and BC concentrations and mucosal irritation and lung function, after adjustment for potential confounders. In-taxis UFP concentrations ranged from 17.9 to 37.9 × 103 particles/cm³ and BC concentrations from 2.2 to 3.9 μg/m³, during a mean of 9 ± 2 working hours. Significant dose-response relationships were observed between in-taxis UFP concentrations and both nasal irritation and lung function. The increase of in-taxis UFP (for an interquartile range of 20 × 103 particles/cm3) was associated to an increase in nasal irritation (adjusted OR = 6.27 [95% CI: 1.02 to 38.62]) and to a reduction in forced expiratory flow at 25–75% by −7.44% [95% CI: −12.63 to −2.24], forced expiratory volume in one second by −4.46% [95% CI: −6.99 to −1.93] and forced vital capacity by −3.31% [95% CI: −5.82 to −0.80]. Such associations were not found with BC. Incident throat and eye irritations were not related to in-vehicle particles exposure; however, they were associated with outdoor air quality (estimated by the Atmo index) and in-vehicle humidity, respectively. This study is the first to show a significant association, within a short-period of time, between in-vehicle UFP exposure and acute respiratory effects in professional drivers.

Keywords: black carbon, lung function, mucosal irritation, taxi drivers, ultrafine particles

Procedia PDF Downloads 183

Authors: Tong He, Long Chen, Irag Mantegh, Wen-Fang Xie

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This paper proposes an image-based obstacle avoidance and tracking target identification strategy in GPS-degraded or GPS-denied environment for an Unmanned Aerial Vehicle (UAV). The traditional force algorithm for obstacle avoidance could produce local minima area, in which UAV cannot get away obstacle effectively. In order to eliminate it, an artificial potential approach based on harmonic potential is proposed to guide the UAV to avoid the obstacle by using the vision system. And image-based visual servoing scheme (IBVS) has been adopted to implement the proposed obstacle avoidance approach. In IBVS, the pixel accuracy is a key factor to realize the obstacle avoidance. In this paper, the deep reinforcement learning framework has been applied by reducing pixel errors through constant interaction between the environment and the agent. In addition, the combination of OpenTLD and Tensorflow based on neural network is used to identify the type of tracking target. Numerical simulation in Matlab and ROS GAZEBO show the satisfactory result in target identification and obstacle avoidance.

Keywords: image-based visual servoing, obstacle avoidance, tracking target identification, deep reinforcement learning, artificial potential approach, neural network

Procedia PDF Downloads 148

Authors: Mardianto

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Education and learning are now grown rapidly. Synergy of techonology especially instructional technology in the learning activities are very big influence on the effectiveness of learning and creativity to achieve optimal results. But on the other hand there is a education value that is difficult to be articulated through character-forming technology such as honesty, discipline, hard work, heroism, and so forth. Learning strategy and storytelling from the past until today is still an option for teachers to convey the message of character values. With the material was loaded from the local culture (stories folklore), the combination of learning objectives (build character child) strategy, and traditional methods (storytelling and story), and the preservation of local culture (dig tale folklore) is critical to maintaining the nation's culture. In the context of maintaining the nation's culture, then since the age of the child at the level of government elementary school a necessity. Globalization, the internet and technology sometimes feel can displace the role of the teacher in the learning activities. To the oral tradition is a mainstay of storytelling should be maintained and preserved. This research was conducted at the elementary school in the city of Medan, North Sumatra Indonesia, with a random sampling technique, the 27 class teachers were respondents who were randomly assigned to the Madrasah Ibtdaiyah (Islamic Elementary School) both public and private. Research conducted at the beginning of 2014 refers to a curriculum that is being transformed in the environment ministry Republic Religion Indonesia. The results of this study indicate that; the declining skills of teachers to develop storytelling this can be seen from; 74.07% of teachers have never attended a special training storytelling, 85.19% no longer nasakah new stories, only 22.22% are teachers who incorporate methods of stories in the learning plan. Most teachers are no longer concerned with storytelling, among those experiencing difficulty in developing methods because the story; 66.67% of children are more interested in children's cartoons like Bobo boy, Angrybirds and others, 59.26 children prefer other activities than listening to a story. The teachers hope, folklore books should be preserved, storytelling training should be provided by the government through the ministry of religion, race or competition of storytelling should be scheduled, writing a new script-based populist storytelling should be provided immediately. The teachers’ hope certainly not excessive, by realizing the story method becomes articulation as the efforts of child character development based populist, therefore the local knowledge can be a strong fortress facing society in the era of progress as at present, and future.

Keywords: story telling, local wisdom, education, technology development

Procedia PDF Downloads 280

Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

Procedia PDF Downloads 126

Authors: Robert Caulk

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A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI.

Keywords: machine learning, market trend detection, open-source, adaptive learning, parameter space exploration

Procedia PDF Downloads 96

Authors: Elif D. Oguz Erkal, David Krackhardt, Erica Cochran-Hameen

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Even though social network analysis (SNA) is an abundantly studied concept for many organizations and industries, a clear SNA approach to the project teams has not yet been adopted by the construction industry. The main challenges for performing SNA in construction and the apparent reason for this gap is the unique and complex structure of each construction project, the comparatively high circulation of project team members/contributing parties and the variety of authentic problems for each project. Additionally, there are stakeholders from a variety of professional backgrounds collaborating in a high-stress environment fueled by time and cost constraints. Within this case study on Project RE, a design & build project performed at the Urban Design Build Studio of Carnegie Mellon University, social network analysis of the project design team will be performed with the main goal of applying social network theory to construction project environments. The research objective is to determine a correlation between the network of how individuals relate to each other on one’s perception of their own professional strengths and weaknesses and the communication patterns within the team and the group dynamics. Data is collected through a survey performed over four rounds conducted monthly, detailed follow-up interviews and constant observations to assess the natural alteration in the network with the effect of time. The data collected is processed by the means of network analytics and in the light of the qualitative data collected with observations and individual interviews. This paper presents the full ethnography of this construction design team of fourteen architecture students based on an elaborate social network data analysis over time. This study is expected to be used as an initial step to perform a refined, targeted and large-scale social network data collection in construction projects in order to deduce the impacts of social networks on project performance and suggest better collaboration structures for construction project teams henceforth.

Keywords: construction design teams, construction project management, social network analysis, team collaboration, network analytics

Procedia PDF Downloads 203

Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 70

Authors: Simon Oga Egboja

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In Africa, particularly in Nigeria, unemployment is a serious issue of concern to every citizen. Hence, this paper focuses on the employment situation and trends in Nigeria. It also investigated the causes why unemployment persists in the country. Prominent among them is the population explosion and rapid expansion of education opportunities all over the country without a corresponding increase in industrial establishment. The paper also discusses the way of reducing the rate of unemployment by encouraging graduates of tertiary institutions in Nigeria to read professional courses and also to indulge in the habit of establishing small-scale enterprises so that after them school they can be self-employed rather than relying solely on government for employment.

Keywords: causes, population, remedy, unemployment

Procedia PDF Downloads 276

Authors: Nick Woodier, Iain Moppett

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Background: It is estimated that one in ten patients admitted to hospital will suffer an adverse event in their care. While the majority of these will result in low harm, patients are being significantly harmed by the processes meant to help them. Healthcare, therefore, seeks to make improvements in patient safety by taking learning from other industries that are perceived to be more mature in their management of safety events. Of particular interest to healthcare are ‘near misses,’ those events that almost happened but for an intervention. Healthcare does not have any guidance as to how best to manage and learn from near misses to reduce the chances of harm to patients. The authors, as part of a larger study of near-miss management in healthcare, sought to learn from the UK nuclear sector to develop principles for how healthcare can identify, report, and learn from near misses to improve patient safety. The nuclear sector was chosen as an exemplar due to its status as an ultra-safe industry. Methods: A Grounded Theory (GT) methodology, augmented by a scoping review, was used. Data collection included interviews, scenario discussion, field notes, and the literature. The review protocol is accessible online. The GT aimed to develop theories about how nuclear manages near misses with a focus on defining them and clarifying how best to support reporting and analysis to extract learning. Near misses related to radiation release or exposure were focused on. Results: Eightnuclear interviews contributed to the GT across nuclear power, decommissioning, weapons, and propulsion. The scoping review identified 83 articles across a range of safety-critical industries, with only six focused on nuclear. The GT identified that nuclear has a particular focus on precursors and low-level events, with regulation supporting their management. Exploration of definitions led to the recognition of the importance of several interventions in a sequence of events, but that do not solely rely on humans as these cannot be assumed to be robust barriers. Regarding reporting and analysis, no consistent methods were identified, but for learning, the role of operating experience learning groups was identified as an exemplar. The safety culture across nuclear, however, was heard to vary, which undermined reporting of near misses and other safety events. Some parts of the industry described that their focus on near misses is new and that despite potential risks existing, progress to mitigate hazards is slow. Conclusions: Healthcare often sees ‘nuclear,’ as well as other ultra-safe industries such as ‘aviation,’ as homogenous. However, the findings here suggest significant differences in safety culture and maturity across various parts of the nuclear sector. Healthcare can take learning from some aspects of management of near misses in nuclear, such as how they are defined and how learning is shared through operating experience networks. However, healthcare also needs to recognise that variability exists across industries, and comparably, it may be more mature in some areas of safety.

Keywords: culture, definitions, near miss, nuclear safety, patient safety

Procedia PDF Downloads 109

Authors: Camille Lacan, Pierre Desmet

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In the crowdfunding model, a campaign succeeds by collecting the funds required over a predefined duration. The success of a CF campaign depends both on the capacity to attract members of the online communities concerned, and on the community members’ involvement in online word-of-mouth recommendations. To maximize the campaign's success probability, project creators (i.e., an organization appealing for financial resources) send messages to contributors to ask them to issue word of mouth. Internet users relay information about projects through Word of Mouth which is defined as “a critical tool for facilitating information diffusion throughout online communities”. The effectiveness of these messages depends on the message framing and the time at which they are sent to contributors (i.e., at the start of the campaign or close to the deadline). This article addresses the following question: What are the effect of message framing and temporal distance on the willingness to share word of mouth? Drawing on Perspectives Theory and Construal Level Theory, this study examines the interplay between message framing (Gains vs. Losses) and temporal distance (message while the deadline is coming vs. far) on intention to share word of mouth. A between-subject experimental design is conducted to test the research model. Results show significant differences between a loss-framed message (lack of benefits if the campaign fails) associated with a short deadline (ending tomorrow) compared to a gain-framed message (benefits if the campaign succeeds) associated with a distant deadline (ending in three months). However, this effect is moderated by the anticipated regret of a campaign failure and the temporal orientation. These moderating effects contribute to specifying the boundary condition of the framing effect. Handling the message framing and the temporal distance are thus the key decisions to influence the willingness to share word of mouth.

Keywords: construal levels, crowdfunding, message framing, word of mouth

Procedia PDF Downloads 256

Authors: Eleanor F. Willard

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The attrition rate on distance learning courses can be high. This research examines how online students often react when faced with poor results. Using q methodology, it was found that the emotional response level and the type of social support sought by students were key influences on their attitude to failure. As educational and psychological researchers, we are adept at measuring learning and achievement, but examining attitudes towards barriers to learning are not so well researched. The distance learning student has differing needs from onsite learners and, as the attrition rate is notoriously high in the online student population, examining learners’ attitude towards adversity and barriers is important. Self-report measures such as questionnaires are useful in terms of ascertaining levels of constructs such as resilience and academic confidence. Interviewing, too, can gain in depth detail of the opinions of such a population, but only in individuals. The aim of this research was to ascertain what the feelings and attitudes of online students were when faced with a setback. This was achieved using q methodology due to its use of both quantitative and qualitative methodology and its suitability for exploratory research. The emphasis with this methodology is the attitudes, not the individuals. The work was focused upon a population of distance learning students who attended a school on site for one week as part of their studies. They were engaged in a psychology masters conversion course and, as such, were graduate students. The Q sort had 30 items taken from the Academic Resilience Scale (ARS-30). The scale items represent three constructs; perseverance, reflecting (including adaptive help-seeking) and negative affect. These are widely acknowledged as being relevant concepts underpinning psychological resilience. The q sort was conducted with 19 students in total. This is done by participants arranging statement cards regarding how similar to themselves they believe each statement to be. This was done after reading a vignette describing an experience of academic failure. Commonalities and differences between the sorts from all participants are then analyzed in terms of correlations and response patterns. Following data collection, the participants' responses were initially analyzed and the key perspectives (factors) to emerge were labelled ‘persevering individuals’ and ‘emotional networkers’. The differences between the two perspectives centre around the level of emotion felt when faced with barriers and the extent that students enlist the help of others inside and outside of the university. The dominant factor to emerge from the sorts of ‘persevering individuals’ demonstrated that many distance learners are tenacious. However, for other students, the level of emotional and social support is pivotal in helping them complete their studies when facing adversity. This was demonstrated by the ‘emotional networkers’ perspective. This research forms a starting point for further work on engaging and retaining online students at university and can potentially provide insight into how universities can lower attrition rates on distance learning courses.

Keywords: academic resilience, distance learning, online learning, q methodology

Procedia PDF Downloads 131

Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

Procedia PDF Downloads 119

Authors: Osman Okumuş

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The pandemic has significantly affected every aspect of life. Especially in recenttimes, as a result of this effect, we have come closer to technology. Distance education has taken the place of formal education rather than supporting formal education. Thiscreatednewexperiencesforbothteachersandstudents. This research focused on revealing the experiences of the same students in distance and formal education, especially in history lessons. In the study, which was designed as a case study, 20 students were interviewed through a semi-structured interview form prepared by the researcher. The results show that both learning environments provide students with important experiences. However, despite the fact that the students developed their digital competencies and experienced different learning environments, they focused on formal education in the name of socialization.

Keywords: history lessons, distance education, pandemic., formal education

Procedia PDF Downloads 106

Authors: Elaine Crable, Amit Sen

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Short-term study abroad programs have become a mainstay of MBA programs. The benefits of international business experiences, along with its exposure to global cultures, are well documented. However, developing a rewarding study, abroad program at the graduate level can be challenging for Faculty, especially when devising such a program for a group of part-time MBA students who come with a wide range of experiences and demographic characteristics. Each student has individual expectations for the study abroad experience. This study provides suggestions and considerations for Faculty that are planning to design a short-term study abroad program, especially for part-time MBA students. Insights are based on a recent experience leading a group of twenty-one students on a ten-day program to Morocco. The trip was designed and facilitated by two faculty members and a local Moroccan facilitator. This experience led to a number of insights and recommendations. First, the choice of location is critical. The choice of Morocco was very deliberate, owing to its multi-faceted cultural landscape and international business interest. It is an Islamic State with close ties to Europe both culturally and geographically and Morocco is a multi-lingual country with some combination of three languages spoken by most – English, Arabic, and French. Second, collaboration with a local ‘academic’ partner allowed the level of instruction to be both rigorous and significantly more engaging. Third, allowing students to participate in the planning of the trip enabled the trip participants to collaborate, negotiate, and share their own experiences and strengths. The pre-trip engagement was structured by creating four sub-groups, each responsible for an assigned city. Each student sub-group had to provide a historical background of the assigned city, plan the itinerary including sites to visit, cuisine to experience, industries to explore, markets to visit, plus provide a budget for that city’s expenses. The pre-planning segment of the course was critical for the success of the program as students were able to contribute to the design of the program through collaboration and negotiation with their peers. Fourth, each student sub-group was assigned industry to study within Morocco. The student sub-group prepared a presentation and a group paper with their analysis of the chosen industries. The pre-planning activities created strong bonds among the trip participants, which was evident when faced with on-ground challenges, especially when it was necessary to quickly evacuate due to a surprise USA COVID evacuation notice. The entire group supported each other when quickly making their way back to the United States. Unfortunately, the trip was cut short by two days due to this emergency exit, but the feedback regarding the program was very positive all around. While the program design put pressure on the Faculty leads regarding planning and coordination upfront, the outcome in terms of student engagement, student learning, collaboration and negotiation were all favorable and worth the effort. Finally, an added value, the cost of the program for the student was significantly lower compared to running a program with a professional provider.

Keywords: business education, experiential learning, international education, study abroad

Procedia PDF Downloads 172

Authors: Rahul Paul, Kedar Nath Das

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The paradigm of Reinforcement Learning (RL) has become prominent in training intelligent agents to make decisions in environments that are both dynamic and uncertain. The primary objective of RL is to optimize the policy of an agent in order to maximize the cumulative reward it receives throughout a given period. Nevertheless, the process of optimization presents notable difficulties as a result of the inherent trade-off between exploration and exploitation, the presence of extensive state-action spaces, and the intricate nature of the dynamics involved. Evolutionary Optimization Methods (EOMs) have garnered considerable attention as a supplementary approach to tackle these challenges, providing distinct capabilities for optimizing RL policies and value functions. The ongoing advancement of research in both RL and EOMs presents an opportunity for significant advancements in autonomous decision-making systems. The convergence of these two fields has the potential to have a transformative impact on various domains of artificial intelligence (AI) applications. This article highlights the considerable influence of EOMs in enhancing the capabilities of RL. Taking advantage of evolutionary principles enables RL algorithms to effectively traverse extensive action spaces and discover optimal solutions within intricate environments. Moreover, this paper emphasizes the practical implementations of EOMs in the field of RL, specifically in areas such as robotic control, autonomous systems, inventory problems, and multi-agent scenarios. The article highlights the utilization of EOMs in facilitating RL agents to effectively adapt, evolve, and uncover proficient strategies for complex tasks that may pose challenges for conventional RL approaches.

Keywords: machine learning, reinforcement learning, loss function, optimization techniques, evolutionary optimization methods

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Authors: R. Ajith, Kamal Bijlani

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Over the past few years, tremendous evolutions have happened in the educational discipline. Serious game, which is regarded as one of the most important inventions is being widely for learning purposes. Serious games can be used to negate the various drawbacks that the current evaluation and assessment methods have, like examination anxiety and the lack of proper feedback given to the learners. This paper proposes serious game as a tool for conducting evaluations and assessments. The examination anxiety faced by learners can be reduced, as they are provided with a game as an examination. The serious game also tracks learner’s actions, records them and provide feedback based on the predefined set of actions according to the course objectives. The appropriate feedback given to the learner will help in developmental activities in the learning process.

Keywords: serious games, evaluation, performance assessment, examination anxiety, performance feedback

Procedia PDF Downloads 596

Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Kinda Alsamara

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Foreign language education is a complex endeavor that often presents educators with a range of challenges and difficulties. This study shed light on the specific challenges encountered in the context of teaching Arabic as a foreign language at the Australian National University (ANU). Drawing from real-world experiences and insights, we explore the multifaceted nature of these challenges and discuss strategies that educators have employed to address them. The challenges in teaching the Arabic language encompass various dimensions, including linguistic intricacies, cultural nuances, and diverse learner backgrounds. The complex Arabic script, grammatical structures, and pronunciation patterns pose unique obstacles for learners. Moreover, the cultural context embedded within the language demands a nuanced understanding of cultural norms and practices. The diverse backgrounds of learners further contribute to the challenge of tailoring instruction to meet individual needs and proficiency levels. This study also underscores the importance of technology in tackling these challenges. Technological tools and platforms offer innovative solutions to enhance language acquisition and engagement. Online resources, interactive applications, and multimedia content can provide learners with immersive experiences, aiding in overcoming barriers posed by traditional teaching methods. Furthermore, this study addresses the role of instructors in mitigating challenges. Educators often find themselves adapting teaching approaches to accommodate different learning styles, abilities, and motivations. Establishing a supportive learning environment and fostering a sense of community can contribute significantly to overcoming challenges related to learner diversity. In conclusion, this study provides a comprehensive overview of the challenges faced in teaching Arabic as a foreign language at ANU. By recognizing these challenges and embracing technological and pedagogical advancements, educators can create more effective and engaging learning experiences for students pursuing Arabic language proficiency.

Keywords: Arabic, Arabic online, blended learning, teaching and learning, Arabic language, educational aids, technology

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Setyarini Sri, Nursalim Agus

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Integrating critical thinking and digital based learning is one of demands in teaching English in 21st century. Child-friendly digital storytelling (CFDS) is an innovative learning model to promote young learners’ critical thinking. Therefore, this study aims to (1) investigate how child-friendly digital storytelling is implemented to promote young learners’ critical thinking in speaking English; (2) find out the benefits gained by the students in their learning based on CFDS. Classroom Action Research (CAR) took place in two cycles in which each of the cycle covered four phases namely: Planning, Acting, Observing, and Evaluating. Three classes of seventh graders were selected as the subjects of this study. Data were collected through observation, interview with some selected students as respondents, and document analysis in the form individual recorded storytelling. Sentences, phrases, words found in the transcribed data were identified and categorized based on Bloom taxonomy. The findings from the first cycle showed that the students seemed to speak critically that can be seen from the way they understood the story and related the story to their real life. Meanwhile, the result investigated from the second cycle likely indicated their higher level of critical thinking since the students spoke in English critically through comparing, questioning, analyzing, and evaluating the story by giving arguments, opinions, and comments. Such higher levels of critical thinking were also found in the students’ final project of individual recorded digital story. It is elaborated from the students’ statements in the interview who claimed CFDS offered opportunity to the students to promote their critical thinking because they comprehended the story deeply as they experienced in their real life. This learning model created good learning atmosphere and engaged the students directly so that they looked confident to retell the story in various perspectives. In term of the benefits of child-friendly digital storytelling, the students found it beneficial for some enjoyable classroom activities through watching beautiful and colorful pictures, listening to clear and good sounds, appealing moving motion and emotionally they were involved in that story. In the interview, the students also stated that child-friendly digital storytelling eased them to understand the meaning of the story as they were motivated and enthusiastic to speak in English critically.

Keywords: critical thinking, child-friendly digital storytelling, English speaking, promoting, young learners

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Authors: Wu Hong, Pan Cuilian, Wu Panzifan

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The student’s attention during the class is one of the most important indicators for the learning evaluation; the level of attention is directly related to the results of primary education. In recent years, extensive research has been conducted across China on improving primary school students’ attention during class. During the specific teaching activities in primary school, students have the characteristics of short concentration periods, high probability of distraction, and difficulty in long-term immersive learning. In physical education teaching, where there are mostly outdoor activities, this characteristic is particularly prominent due to the large changes in the environment and the strong sense of freshness among students. It is imperative to overcome this characteristic in a targeted manner, improve the student’s experience in the course, and raise the degree of systematization. There are many ways to improve the systematization of teaching and learning, but most of them lack quantitative indicators, which makes it difficult to evaluate the improvements before and after changing the teaching methods. Based on the situation above, we use the case analysis method, combined with a literature review, to study the negative impact of low systematization levels in primary school physical education teaching, put forward targeted improvement suggestions, and make a quantitative evaluation of the method change.

Keywords: attention, adolescent, evaluation, systematism, training-method

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Authors: Sami M. Alshareef

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The rapid growth of smart grid technology has brought significant advancements to the power industry. However, with the increasing interconnectivity and reliance on information and communication technologies, smart grids have become vulnerable to cyber-attacks, posing significant threats to the reliable operation of power systems. Among the critical components of smart grids, the Automatic Generation Control (AGC) system plays a vital role in maintaining the balance between generation and load demand. Therefore, protecting the AGC system from cyber threats is of paramount importance to maintain grid stability and prevent disruptions. Traditional security measures often fall short in addressing sophisticated and evolving cyber threats, necessitating the exploration of innovative approaches. Machine learning, with its ability to analyze vast amounts of data and learn patterns, has emerged as a promising solution to enhance AGC system security. Therefore, this research proposal aims to address the challenges associated with detecting and mitigating cyber-attacks on AGC in smart grids by leveraging machine learning techniques on automatic generation control of two-area power systems. By utilizing historical data, the proposed system will learn the normal behavior patterns of AGC and identify deviations caused by cyber-attacks. Once an attack is detected, appropriate mitigation strategies will be employed to safeguard the AGC system. The outcomes of this research will provide power system operators and administrators with valuable insights into the vulnerabilities of AGC systems in smart grids and offer practical solutions to enhance their cyber resilience.

Keywords: machine learning, cyber-attacks, automatic generation control, smart grid

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Authors: Nguyen Thi Thu Trang

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Climate change and sea level rise is one of the greatest challenges facing human beings in the 21st century. Because of sea level rise, several low-lying coastal areas around the globe are at risk of being completely submerged, disappearing under water. Particularly in Viet Nam, the rise in sea level is predicted to result in more frequent and even permanently inundated coastal plains. As a result, land reserving fund of coastal cities is going to be narrowed in near future, while construction ground is becoming increasingly limited due to a rapid growth in population. Faced with this reality, the solutions are being discussed not only in tradition view such as accommodation is raised or moved to higher areas, or “living with the water”, but also forwards to “living on the water”. Therefore, the concept of a sustainable floating community with floating houses based on the precious value of long term historical tradition of water dwellings in Viet Nam would be a sustainable solution for adaptation of climate change and sea level rise in the coastal areas. The sustainable floating community is comprised of sustainability in four components: architecture, environment, socio-economic and living quality. This research paper is focused on sustainability in architectural component of floating community. Through detailed architectural analysis of current floating houses and floating communities in Viet Nam, this research not only accumulates precious values of traditional architecture that need to be preserved and developed in the proposed concept, but also illustrates its weaknesses that need to address for optimal design of the future sustainable floating communities. Based on these studies the research would provide guidelines with appropriate architectural solutions for the concept of sustainable floating community with floating housing units that are adapted to climate change and sea level rise in Viet Nam.

Keywords: guidelines, sustainable floating community, floating houses, Vietnam

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Authors: Mangala. Sadasivan, Mary Hughes, Bryan Kelly

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Medical Education must focus on bridging the gap between teaching and learning when training pre-clinical year students in skills needed to keep up with medical knowledge and to meet the demands of health care in the future. The authors were interested in showing that a 3-S Model (building strength, developing stamina, and increasing speed) using a bridged curriculum design helps connect teaching and learning and improves students’ retention of basic science and clinical knowledge. The authors designed three learning modules using the 3-S Model within a systems course in a pre-clerkship medical curriculum. Each module focused on a bridge (concept map) designed by the instructor for specific content delivered to students in the course. This with-in-subjects design study included 304 registered MSU osteopathic medical students (3 campuses) ranked by quintile based on previous coursework. The instructors used the bridge to create self-directed learning exercises (building strength) to help students master basic science content. Students were video coached on how to complete assignments, and given pre-tests and post-tests designed to give them control to assess and identify gaps in learning and strengthen connections. The instructor who designed the modules also used video lectures to help students master clinical concepts and link them (building stamina) to previously learned material connected to the bridge. Boardstyle practice questions relevant to the modules were used to help students improve access (increasing speed) to stored content. Unit Examinations covering the content within modules and materials covered by other instructors teaching within the units served as outcome measures in this study. This data was then compared to each student’s performance on a final comprehensive exam and their COMLEX medical board examinations taken some time after the course. The authors used mean comparisons to evaluate students’ performances on module items (using 3-S Model) to non-module items on unit exams, final course exam and COMLEX medical board examination. The data shows that on average, students performed significantly better on module items compared to non-module items on exams 1 and 2. The module 3 exam was canceled due to a university shut down. The difference in mean scores (module verses non-module) items disappeared on the final comprehensive exam which was rescheduled once the university resumed session. Based on Quintile designation, the mean scores were higher for module items than non-module items and the difference in scores between items for Quintiles 1 and 2 were significantly better on exam 1 and the gap widened for all Quintile groups on exam 2 and disappeared in exam 3. Based on COMLEX performance, all students on average as a group, whether they Passed or Failed, performed better on Module items than non-module items in all three exams. The gap between scores of module items for students who passed COMLEX to those who failed was greater on Exam 1 (14.3) than on Exam 2 (7.5) and Exam 3 (10.2). Data shows the 3-S Model using a bridge effectively connects teaching and learning

Keywords: bridging gap, medical education, teaching and learning, model of learning

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Authors: Jeffrey M. Byford

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The conceptualized theory of decision making on historical events often does not conform to uniform beliefs among students. When presented the opportunity, many students have differing opinions and rationales associated with historical events and outcomes. The intent of this paper was to provide students with the economic, social and political dilemmas associated with the autonomy of East Berlin. Students ranked seven possible actions from the most to least acceptable. In addition, students were required to provide both positive and negative factors for each decision and relative ranking. Results from this activity suggested that while most students chose a financial action towards West Berlin, some students had trouble justifying their actions.

Keywords: content-centered learning, cold war, Berlin, decision-making

Procedia PDF Downloads 460