Search results for: online learning management system

Authors: Yiwei Li, Mingyu Gao

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Integrating hybrid memories with heterogeneous processors could leverage heterogeneity in both compute and memory domains for better system efficiency. To ensure performance isolation, we introduce Hydrogen, a hardware architecture to optimize the allocation of hybrid memory resources to heterogeneous CPU-GPU systems. Hydrogen supports efficient capacity and bandwidth partitioning between CPUs and GPUs in both memory tiers. We propose decoupled memory channel mapping and token-based data migration throttling to enable flexible partitioning. We also support epoch-based online search for optimized configurations and lightweight reconfiguration with reduced data movements. Hydrogen significantly outperforms existing designs by 1.21x on average and up to 1.31x.

Keywords: hybrid memory, heterogeneous systems, dram cache, graphics processing units

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Authors: Lidia Lombardi

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Waste management in Europe and North America is evolving towards sustainable materials management, intended as a systemic approach to using and reusing materials more productively over their entire life cycles. Various waste management strategies are prioritized and ranked from the most to the least environmentally preferred, placing emphasis on reducing, reusing, and recycling as key to sustainable materials management. However, non-recyclable materials must also be appropriately addressed, and waste-to-energy (WtE) offers a solution to manage them, especially when a WtE plant is integrated within a complex system of waste and wastewater treatment plants and potential users of the output flows. To evaluate the environmental effects of such system integration, Life Cycle Assessment (LCA) is a helpful and powerful tool. LCA has been largely applied to the waste management sector, dating back to the late 1990s, producing a large number of theoretical studies and applications to the real world as support to waste management planning. However, LCA still has a fundamental role in helping the development of waste management systems supporting decisions. Thus, LCA was applied to evaluate the environmental performances of a Municipal Solid Waste (MSW) management system, with improved separate material collection and recycling and an integrated network of treatment plants including WtE, anaerobic digestion (AD) and also wastewater treatment plant (WWTP), for a reference study case area. The proposed system was compared to the actual situation, characterized by poor recycling, large landfilling and absence of WtE. The LCA results showed that the increased recycling significantly increases the environmental performances, but there is still room for improvement through the introduction of energy recovery (especially by WtE) and through its use within the system, for instance, by feeding the heat to the AD, to sludge recovery processes and supporting the water reuse practice. WtE offers a solution to manage non-recyclable MSW and allows saving important resources (such as landfill volumes and non-renewable energy), reducing the contribution to global warming, and providing an essential contribution to fulfill the goals of really sustainable waste management.

Keywords: anaerobic digestion, life cycle assessment, waste-to-energy, municipal solid waste

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Lily Chimuanya, Esther Ajiboye, Emmanuel Uba

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One of the consequences of online communication is the birth of new orthography genres characterised by novel conventions of abbreviation and acronyms usually referred to as Netspeak. Netspeak, also known as internet slang, is a style of writing mainly used in online communication to limit the length of text characters and to save time. The aim of this study is to evaluate how second language users of the English language have internalised this new convention of writing; identify the recurrent patterns of Netspeak; and assess the consistency of the use of the identified patterns in relation to their meanings. The study is corpus-based, and data drawn from WhatsApp chart pages of selected groups of Nigerian English speakers show a large occurrence of inconsistencies in the patterns of Netspeak and their meanings. The study argues that rather than emphasise the negative impact of Netspeak on the communicative competence of second language users, studies should focus on suggesting models as yardsticks for standardising the usage of Netspeak and indeed all other emerging language conventions resulting from online communication. This stance stems from the inevitable global language transformation that is eminent with the coming of age of information technology.

Keywords: abbreviation, acronyms, Netspeak, online communication, standardisation

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Authors: Mark Williams

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The digital revolution has radically transformed our social environment creating vast opportunities for interconnectivity and social interaction. This revolution, however, has also changed the reach and impact of hate crime, with social media providing a new platform to victimize and harass users in their homes. In this way, developments in the information and communication technologies have exacerbated and facilitated the commission of hate crime, increasing its prevalence and impact. Unfortunately, legislators, policymakers and criminal justice professionals have struggled to keep pace with these technological developments, reducing their ability to intervene in, regulate and govern the commission of hate crimes online. This work is further complicated by the global nature of this crime due to the tendency for offenders and victims to reside in multiple different jurisdictions, as well as the need for criminal justice professionals to obtain the cooperation of private companies to access information required for prosecution. Drawing on in-depth interviews with key criminal justice professionals and policymakers with detailed knowledge in this area, this paper examines the specific challenges the police and prosecution services face as they attempt to intervene in and prosecute the commission of hate crimes online. It is argued that any attempt to reduce online othering, such as the commission of hate crimes online, must be multifaceted, collaborative and involve both innovative technological solutions as well as internationally agreed ethical and legal frameworks.

Keywords: cybercrime, digital policing, hate crime, social media

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Authors: Reda Moussa Massoud Embark

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Tourism and hospitality graduate development is key to the future state of the tourism and hospitality industry. Meanwhile, Information and Communication Technology is increasingly becoming the engine for improving productivity and business opportunities in the travel and hospitality industry. Given the challenges and fierce global competition that have arisen in today's hospitality industry, it was important to shed light on strategic management. In addition, five-star hotels play a key role in supporting the tourism industry and investments in Egypt. Therefore, this study aims to examine the extent to which strategic management practices are implemented in five-star hotels in Egypt and to examine the differences between resort and inner-city hotels in terms of the implementation of strategic management processes. The influence of different hotel types on the implementation of the strategic management process is examined. A simple random sampling technique is used to select a sample of the target population, including hotels in the cities of Sharm el-Sheikh, Cairo and Hurghada. The data collection tool used in this study is an interviewer-administered questionnaire. Finally, combining the study results with the literature review allowed to present a set of recommendations to be addressed to hoteliers in the area of strategic management practices. Education and training in tourism and hospitality must take these changes into account in order to improve the ability of future managers to use a variety of tools and strategies to make their organizations more efficient and competitive. Therefore, this study aims to examine the types and effectiveness of training courses offered by tourism and hospitality departments in Egypt and to assess the importance of these training courses from the perspective of the graduate. The survey is aimed at graduates who have completed three different majors in the past decade: tourism and hospitality. Findings discussed the nature, level and effectiveness of the training provided at these faculties and the extent to which the training programs were valued by graduates working in different fields, and finally recommended specific practices to improve learning effectiveness increase and increase perceived employee benefits in the tourism and hospitality industry.

Keywords: marketing channels, crisis, hotel, international, tour, operators, online travel agencies, e-tourism, hotel websites, tourism, web-tourism, strategic-management, strategic tools, five-star hotels, resorts, downtown hotels, Egyptian Markets.

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Authors: Khlaid Albishi

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This paper participates in giving new vision and explains the learning and acquisition processes of English language by analyzing a certain context. Five important factors in English language acquisition and learning are discussed and suitable solutions are provided. The factors are compared with the learners' linguistic background at Bisha College of Technology BCT attempting to link the issues faced by students and the research done on similar situations. These factors are phonology, age of acquisition, motivation, psychology and courses of English. These factors are very important; because they interfere and affect specific learning processes at BCT context and general English learning situations.

Keywords: language acquisition, language learning, factors, Bisha college

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Authors: Reni Francis

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Learning has transcended the classroom boundaries to create a learner centric, interactive, and integrative teaching learning environment. This study analysed the impact of iLearn on the teaching, learning, and evaluation among 100 teacher trainees. The objectives were to cater to the different learning styles of the teacher trainees, to incorporate innovative teaching learning activities, to assist in peer tutoring, to implement different evaluation processes. i: Identifying the learning styles among the teacher trainees through VARK Learning style checklist was followed by planning the teaching-learning process to meet the learning styles of the teacher trainees. L: Leveraging innovations in teaching- learning by planning and creating modules incorporating innovative teaching learning and hence the concept based year plan was prepared. E: Engage learning through constructivism using different teaching methodology to engage the teacher trainees in the learning process through Workshop, Round Robin, Gallery walk, Co-Operative learning, Think-Pair-Share, EDMODO, Course Networking, Concept Map, Brainstorming Sessions, Video Clippings. A: Assessing the learning through an Open Book assignment, Closed book assignment, and Multiple Choice Questions and Seminar presentation. R: Remediation through peer tutoring through Mentor-mentee approach in the tutorial groups, Group work, Library Hours. N: Norming new standards. This was done in the form of extended remediation and tutorials to understand the need of the teacher trainee and support them for further achievements in learning through Face to face interaction, Supervised Study Circle, Mobile (Device) learning. The findings of the study revealed the positive impact of iLearn towards student achievement and enhanced social skills.

Keywords: academic achievement, innovative strategy, learning styles, social skills

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: Sang-Keun Moon, Hong-Rok Lim, Jin-O Kim

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This paper presents a Power Quality (PQ) modeling and filtering processes for the distribution system disturbances using recognition learning methods. Typical PQ waveforms with mathematical applications and gathered field data are applied to the proposed models. The objective of this paper is analyzing PQ data with respect to monitoring, discriminating, and evaluating the waveform of power disturbances to ensure the system preventative system failure protections and complex system problem estimations. Examined signal filtering techniques are used for the field waveform noises and feature extractions. Using extraction and learning classification techniques, the efficiency was verified for the recognition of the PQ disturbances with focusing on interactive modeling methods in this paper. The waveform of selected 8 disturbances is modeled with randomized parameters of IEEE 1159 PQ ranges. The range, parameters, and weights are updated regarding field waveform obtained. Along with voltages, currents have same process to obtain the waveform features as the voltage apart from some of ratings and filters. Changing loads are causing the distortion in the voltage waveform due to the drawing of the different patterns of current variation. In the conclusion, PQ disturbances in the voltage and current waveforms indicate different types of patterns of variations and disturbance, and a modified technique based on the symmetrical components in time domain was proposed in this paper for the PQ disturbances detection and then classification. Our method is based on the fact that obtained waveforms from suggested trigger conditions contain potential information for abnormality detections. The extracted features are sequentially applied to estimation and recognition learning modules for further studies.

Keywords: power quality recognition, PQ modeling, waveform feature extraction, disturbance trigger condition, PQ signal filtering

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: A. Anis, W. Bekheet, A. El Hakim

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Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

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Authors: B. Alajmi, L. Marouf, A. S. Chaudhry

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In recent years, increasing emphasis has been placed upon issues concerning the evaluation of health care. In this regard, knowledge management has also been considered an important component of the evaluation process. KM facilitates the transfer of existing knowledge or the development of new knowledge among healthcare staff and patients. This research aimed to examine how hospitals in Kuwait employ knowledge management practices, including capturing, sharing, and generating, and the perceived impact of KM practices on performance of hospitals in Kuwait. Through adopting a quantitative survey method with 277 sample of doctors, the study found that in terms of the three major knowledge management practices – knowledge capturing, sharing, and generating – the adoption of KM practices were rated very low in the sampled hospitals in Kuwait. Hospitals paid little attention to the main activities that support the transfer of expertise among doctors in hospitals. However, as predicted by previous studies, knowledge management practices were perceived to have an impact on hospitals’ performance. Through knowledge capturing, sharing, and generating, hospitals could improve the services they provide through documenting best practices, transforming their hospitals into learning organizations in which lessons learned are captured, stored, and made available for others to learn from.

Keywords: knowledge management, hospitals, knowledge management practices, knowledge management tools, performance

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Authors: Shieh Shiow-fang

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Superstitious thoughts or actions often occur during athletic competitions. Often "superstitious rituals" have a positive impact on the performance of competitive athletes. Athletes affirm the many psychological benefits of religious beliefs mostly in a positive way. Method: By snowball sampling, we recruited 10 experienced competitive athletes as participants. We used in-person and online one-to-one in-depth interview to collect their experiences about sport superstition. The total interview time was 795 minutes. We analyzed the raw data with the grounded theory processes suggested by Strauss and Corbin (1990). Results: The factors affecting athlete performance are ritual beliefs, taboo awareness, learning norms, and spontaneous attribution behaviors. Conclusion: We concluded that sports superstition reflects several psychological implications. The analysis results of this paper can provide another research perspective for the future study of sports superstition behavior.

Keywords: superstition, taboo awareness, learning norms, competitive athlete

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Authors: Jawaher A. Alsultan, Allen Feldman

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With the rapid switch to remote learning due to the COVID-19 pandemic, science teachers were suddenly required to teach their classes online. This breakneck shift to eLearning raised the question of how teacher educators could support science teachers who wanted to use reform-based methods of instruction while using virtual technologies. In this retrospective self-study, we, two science teacher educators, examined our practice as we worked with science teachers to implement inquiry, discussion, and argumentation [IDA] through eLearning. Ten high school science teachers from a large school district in the southeastern US participated virtually in the COVID-19 Community of Practice [COVID-19 CoP]. The CoP met six times from the end of April through May 2020 via Zoom. Its structure was based on a model of action research called enhanced normal practice [ENP], which includes exchanging stories, trying out ideas, and systematic inquiry. Data sources included teacher educators' meeting notes and reflective conversations, audio recordings of the CoP meetings, teachers' products, and post-interviews of the teachers. Findings included a new understanding of the role of existing relationships, shared goals, and similarities in the participants' situations, which helped build trust in the CoP, and the effects of our paying attention to the science teachers’ needs led to a well-functioning CoP. In addition, we became aware of the gaps in our knowledge of how the teachers already used apps in their practice, which they then shared with all of us about how they could be used for online teaching using IDA. We also identified the need to pay attention to feelings about tensions between the teachers and us around the expectations for final products and the project's primary goals. We found that if we are to establish relationships between us as facilitators and teachers that are honest, fair, and kind, we must express those feelings within the collective, dialogical processes that can lead to learning by all members of the CoP, whether virtual or face-to-face.

Keywords: community of practice, facilitators, self-study, action research

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

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Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfill requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: single classifier, ensemble learning, multi-target tracking, multiple classifiers

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Authors: Zhongde Wang, Fanwei Meng, Ling Yang

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Today, social media deeply engages in urban spatial production in a ‘Disembedding’ form, allowing the public to ‘foresight’ physical spaces through online platforms and subsequently engage in corresponding ‘sight’ and visits, thus leading to the emergence of ‘Internet Celebrity Spots’. This paper delves into the laws of action of online media, focusing on experiences. From the perspectives of the public, space, and media, it thoroughly analyzes the experiential design strategies of Chongqing's Liziba Light Rail Station, including the construction of the experiential mainline capturing the matrix of public behavior, the creation of experiential sidelines leveraging spatial advantages, and the deepening of experiential touchpoints to promote media resonance. This analysis aims to provide insights and references for similar urban spaces to transition from ‘internet-famous’ to ‘real-famous’ attractions.

Keywords: online media, urban space, disembedding, internet celebrity spots, experience design

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Authors: Zhuoqun Sun, Francisco Sierra, A. Booth

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With BIM rapidly implemented in the design and construction stage of a project, researchers begin to focus on improving the operation and maintenance stage with the aid of BIM. Since the increasing amount of electronic equipment installed in the building, building management system becomes mainstream for controlling a building, especially in higher education institutions that can play an important role in terms of reducing carbon emission and improving energy efficiency. Currently, an approach to integrate BIM and BMS to improve HEIs facility management has not been established yet. Thus, this paper aims to analyse the benefits, issues, and trends of BIM and BMS integration and their application in HEIs. A systematic literature review was carried out on SCOPUS by applying the PRISMA methodology. 73 articles have been chosen based on keywords, abstracts, and the full content of the articles. The benefit and existed issues from the articles are analysed. The review shows the need to develop a tool to improve facility management through BIM BMS integration.

Keywords: BIM, BMS, HEIs, review

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Authors: Sunantha Teyarachakul

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We consider the worker-learning Newsvendor Model, under the case of lost-sales for unmet demand, with the research objective of proposing the cost-minimization order policy and lot size, scheduled to arrive at the beginning of the selling-period. In general, the New Vendor Model is used to find the optimal order quantity for the perishable items such as fashionable products or those with seasonal demand or short-life cycles. Technically, it is used when the product demand is stochastic and available for the single selling-season, and when there is only a one time opportunity for the vendor to purchase, with possibly of long ordering lead-times. Our work differs from the classical Newsvendor Model in that we incorporate the human factor (specifically worker learning) and its influence over the costs of processing units into the model. We describe this by using the well-known Wright’s Learning Curve. Most of the assumptions of the classical New Vendor Model are still maintained in our work, such as the constant per-unit cost of leftover and shortage, the zero initial inventory, as well as the continuous time. Our problem is challenging in the way that the best order quantity in the classical model, which is balancing the over-stocking and under-stocking costs, is no longer optimal. Specifically, when adding the cost-saving from worker learning to such expected total cost, the convexity of the cost function will likely not be maintained. This has called for a new way in determining the optimal order policy. In response to such challenges, we found a number of characteristics related to the expected cost function and its derivatives, which we then used in formulating the optimal ordering policy. Examples of such characteristics are; the optimal order quantity exists and is unique if the demand follows a Uniform Distribution; if the demand follows the Beta Distribution with some specific properties of its parameters, the second derivative of the expected cost function has at most two roots; and there exists the specific level of lot size that satisfies the first order condition. Our research results could be helpful for analysis of supply chain coordination and of the periodic review system for similar problems.

Keywords: inventory management, Newsvendor model, order policy, worker learning

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Authors: Veepaul Kaur Mann Balwinder Singh

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The COVID-19 had an intense impact on several countries across the world. National governments have imposed widespread restrictions to prevent the growth of this pandemic. The new health competitive scenario induced by the COVID-19 crisis raised many issues on how business activities should be reorganized due to the difficulties of physical interactions with distributors, suppliers and customers. The pandemic has particularly affected the whole selling process because of the relevant issues that emerged in managing physical sale channels and interactions with one another, both in the Business-to-Consumer and in the Business-to-Business markets. Recent research about the appropriate actions and strategies that could help firms overcome the crisis has highlighted the key role of digital expertise that may ensure connections and, thus, help business activities run smoothly. This could be true, especially with the occurrence of strong limitations on physical interactions during the COVID-19 pandemic. The catastrophe changes life publically and economically. People are living alone for following the social distancing norms. In that set-up, Digital Marketing is playing an important role in civilization. Anyone can buy any item, pay bills, transfer money and compare items through Digital Marketing without physical interactions. After COVID-19, people will be more aware of health safety and trust. So, through Digital Marketing, organizations can approach customers and provide good service environments. In such a situation, the online network becomes the most important encouraging for online customers to get in contact with the firm and carry out online selling and purchasing activities around the world.

Keywords: COVID-19, business, digital marketing, online customer

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Authors: Isaac Kofi Mensah

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The manual system of voting has been the most widely used system of electing representatives around the globe, particularly in Africa. Due to the known numerous problems and challenges associated with the manual system of voting, many countries are migrating to the electronic voting system as a suitable and credible means of electing representatives over the manual paper-based system. This research paper therefore investigated the factors influencing adoption and use of an electronic voting system in Ghana. A total of 400 Questionnaire Instruments (QI) were administered to potential respondents in Ghana, of which 387 responded representing a response rate of 96.75%. The Technology Acceptance Model was used as the theoretical framework for the study. The research model was tested using a simple linear regression analysis with SPSS. A little of over 71.1% of the respondents recommended the Electoral Commission (EC) of Ghana to adopt an electronic voting system in the conduct of public elections in Ghana. The results indicated that all the six predictors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived free and fair elections (PFFF), perceived credible elections (PCE), perceived system integrity (PSI) and citizens trust in the election management body (CTEM) were all positively significant in predicting the readiness of citizens to adopt and use an electronic voting system in Ghana. However, jointly, the hypotheses tested revealed that apart from Perceived Free and Fair Elections and Perceived Credible and Transparent Elections, all the other factors such as PU, Perceived System Integrity and Security and Citizen Trust in the Election Management Body were found to be significant predictors of the Willingness of Ghanaians to use an electronic voting system. All the six factors considered in this study jointly account for about 53.1% of the reasons determining the readiness to adopt and use an electronic voting system in Ghana. The implications of this research finding on elections in Ghana are discussed.

Keywords: credible elections, Election Management Body (EMB), electronic voting, Ghana, Technology Acceptance Model (TAM)

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Authors: Yen-Mei Lee

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The concept of a knowledge-behavior gap has been discussed for several years. It is addressed that an individual’s knowledge does not sufficiently transfer to his or her actual actions. This concept is mostly focused on fields related to medicine or applied to health care issues to explain how people or patients connect their personal knowledge to actual health care behaviors. To our knowledge, seldomly has this research been applied to discuss people’s online information seeking behavior. In the current study, the main purpose is to investigate the relationship between web users’ personal values and their actual performances when seeking information on the Internet. The total number of twenty-eight participants, divided into one experienced group (n=14) and one novice group (n=14), were recruited and asked to complete a self-report questionnaire of fifty items related to information seeking actions and behaviors. During the execution, participants needed to rate the importance level (how important each item is) and the performance level (how often they actually do each item) from 1 to 10 points on each item. In this paper, the mean scores of the importance and the performance level are analyzed and discussed. The results show that there is a gap between web user’s knowledge and their actual online seeking behaviors. Both experienced group and novice group have higher average scores of the importance level (experienced group = 7.57, novice group = 6.01) than the actual performance level (experienced group = 6.89, novice group = 5.00) in terms of the fifty online information seeking actions. On the other hand, the experienced group perceives more importance of the fifty online seeking actions and performs actual behaviors better than the novice group. Moreover, experienced participants express a consistent result between their concept knowledge and actual behaviors. For instance, they feel extending a seeking strategy is important and frequently perform this action when seeking online. However, novice participants do not have a consistency between their knowledge and behaviors. For example, though they perceive browsing and judging information are less important than they get lost in the online information seeking process. However, in the actual behavior rating, the scores show that novices do browsing and judge information more often than they get lost when seeking information online. These results, therefore, help scholars and educators have a better understanding of the difference between experienced and novice web users regarding their concept knowledge and actual behaviors. In future study, figuring out how to narrow down the knowledge-behavior gap and create practical guidance for novice users to increase their online seeking efficiency is crucial. Not only could it help experienced users be aware of their actual information seeking behaviors, but also help the novice become mastery to concisely obtain information on the Internet.

Keywords: experienced web user, information seeking behavior, knowledge-behavior gap, novice, online seeking efficiency

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Authors: Kumiko Inagaki

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Qualitative changes to the undergraduate education have recently become the focus of attention in Japan. This is occurring against the backdrop of declining birthrate and increasing university enrollment, as well as drastic societal changes of advance toward globalization and a knowledge-based society. This paper describes the cases of Japanese universities that promoted various forms of experiential learning around the theme of social participation. The opportunity of learning through practical experience, where students turn their attention to social problems and take pains to consider means of resolving them, creates opportunities to demonstrate “human power” applicable to all sorts of activities the following graduation, thereby guaranteeing students’ continuous growth throughout their careers.

Keywords: career education, experiential learning, subjective learning, university education

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Naz Najma, Grace T. M. Dal Sasso, Maria de Lourdes de Souza

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The development of information and communication technologies and the accessibility of mobile devices has increased the possibilities of the teaching and learning process anywhere and anytime. Mobile and web application allows the production of constructive teaching and learning models in various educational settings, showing the potential for active learning in nursing. The objective of this study was to present the development of an educational technology (Savinglife®, an app) for learning cardiopulmonary resuscitation and advanced cardiovascular life support training. Savinglife® is a technological production, based on the concept of virtual learning and problem-based learning approach. The study was developed from January 2016 to November 2016, using five phases (analyze, design, develop, implement, evaluate) of the instructional systems development process. The technology presented 10 scenarios and 12 simulations, covering different aspects of basic and advanced cardiac life support. The contents can be accessed in a non-linear way leaving the students free to build their knowledge based on their previous experience. Each scenario is presented through interactive tools such as scenario description, assessment, diagnose, intervention and reevaluation. Animated ECG rhythms, text documents, images and videos are provided to support procedural and active learning considering real life situation. Accessible equally on small to large devices with or without an internet connection, Savinglife® offers a dynamic, interactive and flexible tool, placing students at the center of the learning process. Savinglife® can contribute to the student’s learning in the assessment and management of basic and advanced cardiac life support in a safe and ethical way.

Keywords: problem-based learning, cardiopulmonary resuscitation, nursing education, advanced cardiac life support, educational technology

Procedia PDF Downloads 305

Authors: Ma Yuzhe, Burra Venkata Durga Kumar

Abstract:

The computer is a great invention. As people use computers more and more frequently, the demand for PCs is growing, and the performance of computer hardware is also rising to face more complex processing and operation. However, the operating system, which provides the soul for computers, has stopped developing at a stage. In the face of the high price of UNIX (Uniplexed Information and Computering System), batch after batch of personal computer owners can only give up. Disk Operating System is too simple and difficult to bring innovation into play, which is not a good choice. And MacOS is a special operating system for Apple computers, and it can not be widely used on personal computers. In this environment, Linux, based on the UNIX system, was born. Linux combines the advantages of the operating system and is composed of many microkernels, which is relatively powerful in the core architecture. Linux system supports all Internet protocols, so it has very good network functions. Linux supports multiple users. Each user has no influence on their own files. Linux can also multitask and run different programs independently at the same time. Linux is a completely open source operating system. Users can obtain and modify the source code for free. Because of these advantages of Linux, it has also attracted a large number of users and programmers. The Linux system is also constantly upgraded and improved. It has also issued many different versions, which are suitable for community use and commercial use. Linux system has good security because it relies on a file partition system. However, due to the constant updating of vulnerabilities and hazards, the using security of the operating system also needs to be paid more attention to. This article will focus on the analysis and discussion of Linux security issues.

Keywords: Linux, operating system, system management, security

Procedia PDF Downloads 108

Authors: Rasha Alshaye

Abstract:

Blended learning has become an innovative model that combines face-to-face with e-learning approaches. The Saudi Electronic University (SEU) has adopted blended learning as a flexible approach that provides instructors and learners with a motivating learning environment to stimulate the teaching and learning process. This study investigates the perceptions of English language instructors, teaching the four English language skills at Saudi Electronic University. Four main domains were examined in this study; challenges that the instructors encounter while implementing the blended learning approach, enhancing student-instructor interaction, flexibility in teaching, and the lack of technical skills. Furthermore, the study identifies and represents the instructors’ aspirations and plans to utilize this approach in enhancing the teaching and learning experience. Main findings indicate that instructors at Saudi Electronic University experience some challenges while teaching the four language skills. However, they find the blended learning approach motivating and flexible for them and their students. This study offers some important understandings into how instructors are applying the blended learning approach and how this process can be enriched.

Keywords: blended learning, English language skills, English teaching, instructors' perceptions

Procedia PDF Downloads 139

Authors: Suriya

Abstract:

Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

Procedia PDF Downloads 48

Authors: Ziwei Huang

Abstract:

The standard unit of Shenzhen land spatial planning has the characteristics of vertical conduction, horizontal evaluation, internal balance and supervision of implementation. It mainly assumes the role of geospatial unit, assists in promoting the complex development of the business in Shenzhen and undertakes the management and transmission of upper and lower levels of planning as well as the Urban management functions such as gap analysis of public facilities, planning evaluation and dynamic monitoring of planning information. Combining with the application examples of the analysis of gaps in public facilities in Longgang District, it can be found that the standard unit of land spatial planning in Shenzhen as a small-scale geographic basic unit, has a stronger urban spatial coupling effect. However, the universality of the application of the system is still lacking and it is necessary to propose more scientific and powerful standard unit delineation standards and planning function evaluation indicators to guide the implementation of the system's popularization and application.

Keywords: Shenzhen city, land spatial planning, standard unit system, urban delicacy management

Procedia PDF Downloads 129