Search results for: language learning strategy instruction

Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Uzma Ahmad

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hyber Pakhtunkhwa (KPK) is the most turbulent province of Pakistan, sharing a longborder with Afghanistan. For about four decades, KPK is facing a series of international events. The peak was reached after 9/11when region was labelled as posing a major theatre of militancy and terrorism which was intensified when Tehrik Taliban Pakistan (TTP) began attempts to seize the authority of state. One of the main focus of TTP was to damage and uprooting of female education system and infrastructure in KPK which later became the site of a massacre of school children of Army Public School Peshawar on 16 December 2014.It resulted to the launching of Zarb-e-Azb against the TTP insurgency,casualty and crime rates in the KPKas a whole dropped by 40.0% as compared to 2011–13. All this has badly hampered the female education both in terms of quantity and quality. Malala Yousafzai who is now an advocate of female education has been a victim of Talibans brutality in that area. And thelanguage in which she managed to express herself to the International community is English.Keeping in view the situation, the present project was designed with a sole aim to focus on female students of the area which are few in numbers and to investigate some specific area, where they have been confronting problems in the use of grammar, vocabulary,tenses and organization of ideas in writings. The reasons might be the careless attitude, insufficient reading habits, lack of interest and poor knowledge of English language. The methodology was a descriptive one as it shows the effects of the internal efficiency(independent variables) on an intermediate college’s progress(dependent variables). It was a case study since data was collected from a focused group of 60 female students of arts and humanities at Swabi college at Intermediate level. The ultimate focus was to explore the possibilities of creating a Gender friendly environment for female students. This research has proved how the correct use of English language has given them confidence to move ahead side by side with men and to acknowledge their right of self-determination.

Keywords: capacity building, female education, gender friendly, internal efficiency

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Authors: Shafait Hussain Ali

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Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

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Authors: G. Petruzzelli, F. Pedron, M. Grifoni, M. Barbafieri, I. Rosellini, B. Pezzarossa

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As a result of diverse industrial activities, pollution from numerous contaminant affects both groundwater and soils. Many contaminated sites have been discovered in industrialized countries and their remediation is a priority in environmental legislations. The aim of this paper is to provide the evolution of remediation from consolidated invasive technologies to environmental friendly green strategies. Many clean-up technologies have been used. Nowadays the technologies selection is no longer exclusively based on eliminating the source of pollution, but the aim of remediation includes also the recovery of soil quality. “Green remediation”, a strategy based on “soft technologies”, appears the key to tackle the issue of remediation of contaminated sites with the greatest attention to environmental quality, including the preservation of soil functionality.

Keywords: bioremediation, Green Remediation, phytoremediation, remediation technologies, soil

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Authors: Zelikha Labbani

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The In Vitro isolated micro spore culture is the most powerful androgenic pathway to produce doubled haploid plants in the short time. To deviate a micro spore toward embryogenesis, a number of factors, different for each species, must concur at the same time and place. Once induced, the micro spore undergoes numerous changes at different levels, from overall morphology to gene expression. Induction of micro spore embryogenesis not only implies the expression of an embryogenic program, but also a stress-related cellular response and a repression of the gametophytic program to revert the microspore to a totipotent status. As haploid single cells, micro spore became a strategy to achieve various objectives particularly in genetic engineering. In this study we would show the most recent advances in the producing haploid embryos via In Vitro isolated micro spore culture.

Keywords: haploid cells, In Vitro isolated microspore culture, success

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Authors: Chun-Chuan Yang, Jeng-Yueng Chen, Yi-Ting Mai, Chen-Ming Yang

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In this paper, the design of integrated sleep scheduling for relay nodes and user equipments under a Donor eNB (DeNB) in the mode of Time Division Duplex (TDD) in LTE-A is presented. The idea of virtual time is proposed to deal with the discontinuous pattern of the available radio resource in TDD, and based on the estimation of the traffic load, three power saving schemes in the top-down strategy are presented. Associated mechanisms in each scheme including calculation of the virtual subframe capacity, the algorithm of integrated sleep scheduling, and the mapping mechanisms for the backhaul link and the access link are presented in the paper. Simulation study shows the advantage of the proposed schemes in energy saving over the standard DRX scheme.

Keywords: LTE-A, relay, TDD, power saving

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Authors: Abdulmjeid Aljerawi

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This study aimed to investigate the students' attitudes towards the use of social networking in education. Due to the nature of the study, and on the basis of its problem, objectives, and questions, the researcher used the descriptive approach. An appropriate questionnaire was prepared and validity and reliability were ensured. The questionnaire was then applied to the study sample of 434 students from King Saud University.

Keywords: social networks, education, learning, students

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Authors: Doru Anastasiu Popescu, Dan Rădulescu

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In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language.

Keywords: Tag, HTML, web page, genetic algorithm, similarity value, binary tree

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Authors: Diego Mauricio Murillo Gomez, Enney Leon Gonzalez Ramirez, Hugo Piedrahita, Jairo Yate

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This paper proposes a methodology for the management of noise in open mining industries based on an integral concept, which takes into consideration occupational and environmental noise as a whole. The approach relies on the characterization of sources, the combination of several measurements’ techniques and the use of acoustic prediction software. A discussion about the difference between frequently used acoustic indicators such as Leq and LAV is carried out, aiming to establish common ground for homologation. The results show that the correct integration of this data not only allows for a more robust technical analysis but also for a more strategic route of intervention as several departments of the company are working together. Noise control measurements can be designed to provide a healthy acoustic surrounding in which the exposure workers but also the outdoor community is benefited.

Keywords: environmental noise, noise control, occupational noise, open mining

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Authors: Riin Seema

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Guidance and counseling as a whole-school responsibility is a global trend. Counseling is a specific competence, that consist of cognitive, emotional, attitudinal, and behavioral components. To authors best knowledge, there are no self-assessment scales for teachers in the whole world to measure teachers’ counseling competency. In 2016 an Estonian scale on teachers counseling competence was developed during an Interdisciplinary Project at Tallinn University. The team consisted of 10 interdisciplinary students (psychology, nursery school, special and adult education) and their supervisor. In 2017 another international Interdisciplinary Project was carried out for adapting the scale in English for international students. Firstly, the Estonian scale was translated by 2 professional translators, and then a group of international Erasmus students (again from psychology, nursery school, special and adult education) selected the most suitable translation for the scale. The developed ‘Teacher’s Counselling Competence Self-Efficacy Scale’ measures teacher’s self-efficacy beliefs in their own competence to perform different counseling tasks (creating a counseling relationship, using different reflection techniques, etc.). The scale consists of 47 questions in a 5-point numeric scale. The scale is created based on counseling theory and scale development and validation theory. The scale has been used as a teaching and learning material for counseling courses by 174 Estonian and 10 international student teachers. After filling out the scale, the students also reflected on the scale and their own counseling competencies. The study showed that the scale is unidimensional and has an excellent Cronbach alpha coefficient. Student’s qualitative feedback on the scale has been very positive, as the scale supports their self-reflection. In conclusion, the developed ‘Teacher’s Counselling Competence Self-Efficacy Scale’ is a useful tool for supporting student teachers’ learning.

Keywords: competency, counseling, self-efficacy, teacher students

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Authors: Adel Bouchakhchoukha, Hakim Akeb

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In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained.

Keywords: team orienteering problem, vehicle routing, beam search, local search

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Authors: Wang Zhenzhen, Wang Xu, Hong Liangping

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The construction of thematic commercial streets has been on the hotspot with the rapid development of cities. In order to improve the image and competitiveness of cities, many cities are building or rebuilding thematic commercial streets. However, many contradictions and problems have emerged during this process. Therefore, it is significant, for both the practice and the research, to analyse the development of thematic commercial streets and provide some useful suggestions. Through the deep research and comparative study of the eight typical thematic commercial streets in Harbin, this paper summarize the current situations, laws and influencing factors of the development of these streets, and then put forward some suggestions about the plan, constructions and developments of the thematic commercial streets.

Keywords: thematic commercial streets, laws of the development, influence factors, the constructions and developments, degrees of aggregation

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Authors: Mohamed El Fares Djellatou, Fracoise Filion

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The Ashukin program that means bridge in Naskapi or Atikamekw language, has been designed to offer a partnership between nursing students and an indigenous community. The students design a health promotion project tailored to the needs of the community. The issues of intimidation in primary school and cyber-dependence in high school were some concerns in a rural Atikamekw community. The goal of the project was to have a conversation with indigenous youths, aged 10-16 years old, on the challenges presented by intimidation and cyber dependence as well as promoting healthy relationships online and within the community. Methods: Multiple progressive inquiry questions (PIQs) were used to assess the feasibility and importance of this project for the Atikamekw nation, and to determine a plan to follow. The theoretical foundations to guide the conception of the project were the Population Health Promotion Model (PHPM), the First Nations Holistic Lifelong Learning Model, and the Medicine Wheel. A broad array of social determinants of health were addressed, including healthy childhood development, personal health practices, and coping skills, and education. The youths were encouraged to participate in interactive educational sessions, using PowerPoint presentations and pamphlets as the main effective strategies. Additional tools such as cultural artworks and physical activities were introduced to strengthen the inter-relational and team spirit within the Indigenous population. A quality assurance tool (QAT) was developed specifically to determine the appropriateness of these health promotion tools. Improvements were guided by the feedback issued by the indigenous schools’ teachers and social workers who filled the QATs. Post educational sessions, quantitative results have shown that 93.48% of primary school students were able to identify the different types of intimidation, 72.65% recognized more than two strategies, and 52.1% were able to list at least four resources to diffuse intimidation. On the other hand, around 75% of the adolescents were able to name at least three negative effects, and 50% listed three strategies to reduce cyber-dependence. This project was meant to create a bridge with the First Nation through health promotion, a population that is known to be disadvantaged due to systemic health inequity and disparities. Culturally safe care was proposed to deal with the two identified priority issues, and an educational toolkit was given to both schools to ensure the sustainability of the project. The project was self-financed through fundraising activities, and it yielded better results than expected.

Keywords: indigenous, first nation, bullying, cyber-dependence, internet addiction, intimidation, youth, adolescents, school, community nursing, health promotion

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Authors: Sun-Hee Lee, Amanda Kraley

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The “MeToo” movement is a social movement against sexual abuse and harassment. Though the hashtag went viral in 2017 following different cultural flashpoints in different countries, the initial response was quiet in South Korea. This radically changed in January 2018, when a high-ranking senior prosecutor, Seo Ji-hyun, gave a televised interview discussing being sexually assaulted by a colleague. Acknowledging public anger, particularly among women, on the long-existing problems of sexual harassment and abuse, the South Korean media have focused on several high-profile cases. Analyzing the media representation of these cases is a window into the evolving South Korean discourse around “MeToo.” This study presents a linguistic analysis of “MeToo” discourse in South Korea by utilizing a corpus-based approach. The term corpus (pl. corpora) is used to refer to electronic language data, that is, any collection of recorded instances of spoken or written language. A “MeToo” corpus has been collected by extracting newspaper articles containing the keyword “MeToo” from BIGKinds, big data analysis, and service and Nexis Uni, an online academic database search engine, to conduct this language analysis. The corpus analysis explores how Korean media represent accusers and the accused, victims and perpetrators. The extracted data includes 5,885 articles from four broadsheet newspapers (Chosun, JoongAng, Hangyore, and Kyunghyang) and 88 articles from two Korea-based English newspapers (Korea Times and Korea Herald) between January 2017 and November 2020. The information includes basic data analysis with respect to keyword frequency and network analysis and adds refined examinations of select corpus samples through naming strategies, semantic relations, and pragmatic properties. Along with the exponential increase of the number of articles containing the keyword “MeToo” from 104 articles in 2017 to 3,546 articles in 2018, the network and keyword analysis highlights ‘US,’ ‘Harvey Weinstein’, and ‘Hollywood,’ as keywords for 2017, with articles in 2018 highlighting ‘Seo Ji-Hyun, ‘politics,’ ‘President Moon,’ ‘An Ui-Jeong, ‘Lee Yoon-taek’ (the names of perpetrators), and ‘(Korean) society.’ This outcome demonstrates the shift of media focus from international affairs to domestic cases. Another crucial finding is that word ‘defamation’ is widely distributed in the “MeToo” corpus. This relates to the South Korean legal system, in which a person who defames another by publicly alleging information detrimental to their reputation—factual or fabricated—is punishable by law (Article 307 of the Criminal Act of Korea). If the defamation occurs on the internet, it is subject to aggravated punishment under the Act on Promotion of Information and Communications Network Utilization and Information Protection. These laws, in particular, have been used against accusers who have publicly come forward in the wake of “MeToo” in South Korea, adding an extra dimension of risk. This corpus analysis of “MeToo” newspaper articles contributes to the analysis of the media representation of the “MeToo” movement and sheds light on the shifting landscape of gender relations in the public sphere in South Korea.

Keywords: corpus linguistics, MeToo, newspapers, South Korea

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Authors: Majid Mokhtarnia, Alireza Amini

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Mobile operators deal with increasing in the data traffic as a critical issue. As a result, a vital responsibility of the operators is to deal with such a trend in order to create added values. This paper addresses a bundling method in a Canvas business model in a WiFi Data Offloading (WDO) strategy by which some elements of the model may be affected. In the proposed method, it is supposed to sell a number of data packages for subscribers in which there are some packages with a free given volume of data-offloaded WiFi complimentary. The paper on hands analyses this method in the views of attractiveness and profitability. The results demonstrate that the quality of implementation of the WDO strongly affects the final result and helps the decision maker to make the best one.

Keywords: bundling, canvas business model, telecommunication, WiFi data offloading

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Authors: Thomas Trouton, Sebastian Tanner, Manvir Sandher

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The use of simulation in undergraduate and postgraduate medical curricula is ever-growing, is a useful addition to the traditional apprenticeship model of learning within medical education, and better prepares graduates for the team-based approach to healthcare seen in real-life clinical practice. As a learning tool, however, undergraduate medical students often have little understanding of the theory behind the use of medical simulation and have little experience in planning and delivering their own simulated teaching sessions. We designed and implemented a student-selected component (SSC) as part of the undergraduate medical curriculum at the University of Buckingham Medical School to introduce students to the concepts behind the use of medical simulation in education and allow them to plan and deliver their own simulated medical scenario to their peers. The SSC took place over a 2-week period in the 3rd year of the undergraduate course. There was a mix of lectures, seminars and interactive group work sessions, as well as hands-on experience in the simulation suite, to introduce key concepts related to medical simulation, including technical considerations in simulation, human factors, debriefing and troubleshooting scenarios. We evaluated the success of our SSC using “Net Promotor Scores” (NPS) to assess students’ confidence in planning and facilitating a simulation-based teaching session, as well as leading a debrief session. In all three domains, we showed an increase in the confidence of the students. We also showed an increase in confidence in the management of common medical emergencies as a result of the SSC. Overall, the students who chose our SSC had the opportunity to learn new skills in medical education, with a particular focus on the use of simulation-based teaching, and feedback highlighted that a number of students would take these skills forward in their own practice. We demonstrated an increase in confidence in several domains related to the use of medical simulation in education and have hopefully inspired a new generation of medical educators.

Keywords: simulation, SSC, teaching, medical students

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Authors: Joy Chadwick

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There is no doubt that classrooms of today are more complex and diverse than ever before. Preparing teacher candidates to meet these challenges is essential to ensure the retention of teachers within the profession and to ensure that graduates begin their teaching careers with the knowledge and understanding of how to effectively meet the diversity of students they will encounter. Creating inclusive classrooms requires teachers to have a repertoire of effective instructional skills and strategies. Teachers must also have the mindset to embrace diversity and value the uniqueness of individual students in their care. This qualitative study analyzed teacher candidates' experiences as they completed a fourteen-week teaching practicum while simultaneously completing a university course focused on inclusive pedagogy. The research investigated the challenges and successes teacher candidates had in navigating the translation of theory related to inclusive pedagogy into their teaching practice. Applying threshold concept theory as a framework, the research explored the troublesome concepts, liminal spaces, and transformative experiences as connected to inclusive practices. Threshold concept theory suggests that within all disciplinary fields, there exists particular threshold concepts that serve as gateways or portals into previously inaccessible ways of thinking and practicing. It is in these liminal spaces that conceptual shifts in thinking and understanding and deep learning can occur. The threshold concept framework provided a lens to examine teacher candidate struggles and successes with the inclusive education course content and the application of this content to their practicum experiences. A qualitative research approach was used, which included analyzing twenty-nine course reflective journals and six follow up one-to-one semi structured interviews. The journals and interview transcripts were coded and themed using NVivo software. Threshold concept theory was then applied to the data to uncover the liminal or stuck spaces of learning and the ways in which the teacher candidates navigated those challenging places of teaching. The research also sought to uncover potential transformative shifts in teacher candidate understanding as connected to teaching in an inclusive classroom. The findings suggested that teacher candidates experienced difficulties when they did not feel they had the knowledge, skill, or time to meet the needs of the students in the way they envisioned they should. To navigate the frustration of this thwarted vision, they relied on present and previous course content and experiences, collaborative work with other teacher candidates and their mentor teachers, and a proactive approach to planning for students. Transformational shifts were most evident in their ability to reframe their perceptions of children from a deficit or disability lens to a strength-based belief in the potential of students. It was evident that through their course work and practicum experiences, their beliefs regarding struggling students shifted as they saw the value of embracing neurodiversity, the importance of relationships, and planning for and teaching through a strength-based approach. Research findings have implications for teacher education programs and for understanding threshold concepts theory as connected to practice-based learning experiences.

Keywords: inclusion, inclusive education, liminal space, teacher education, threshold concepts, troublesome knowledge

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Authors: Shaima Almalhan, Lubna Shukri, Miriam Talal, Safaa Teskieh

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Since artificial intelligence is a rapidly evolving topic that has had a significant impact on technical growth and innovation, this paper examines people's awareness, use, and engagement with the Google Translate application. To see how familiar aware users are with the app and its features, quantitative and qualitative research was conducted. The findings revealed that consumers have a high level of confidence in the application and how far people they benefit from this sort of innovation and how convenient it makes communication.

Keywords: artificial intelligence, google translate, speech recognition, language translation, camera translation, speech to text, text to speech

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Authors: Hsiao-Ting Lin

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This paper traces the origin of Taiwan’s semiconductor industry by arguing that the present-day popular concept of “Silicon Shield” was the result of inadvertent and accidental decision-making rather than a thoughtful, carefully devised strategy by the Taiwanese government. In the 1970s, a small group of techno-bureaucrats boldly suggested the investment of making the island a hub for semiconductor manufacturing. A fierce internal debate led to a reluctant approval by Chiang Ching-kuo, then premier of Taiwan and the de facto ruler of the island state. With the support of an American outfit, Taiwan gradually transformed itself into an island of IC manufacturing. In retrospect, the creation of Taiwan as the top maker of semiconductors globally involved both luck and severe policy debates, as well as bold decision-making at the top level, which will be demonstrated in my presentation.

Keywords: semicondoctor, Taiwan, silicon shield, Chiang ching-kuo

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Authors: Shari S. C. Shang, Lynn S. L. Chiu

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In the digital payment market, some consumers use only one payment wallet while many others play multi-homing with a variety of payment services. With the diffusion of new payment systems, we examined the determinants of the adoption of multi-homing behavior. This study aims to understand how a digital payment provider dynamically expands business touch points with cross-business strategies to enrich the digital ecosystem and avoid the trap of multi-homing in customers. By synthesizing platform ecosystem literature, we constructed a two-dimensional research framework with one determinant of user digital behavior from offline to online intentions and the other determinant of digital payment touch points from convenient accessibility to cross-business platforms. To explore on a broader scale, we selected 12 digital payments from 5 countries of UK, US, Japan, Korea, and Taiwan. With the interplays of user digital behaviors and payment touch points, we group the study cases into four types: (1) Channel Initiated: users originated from retailers with high access to in-store shopping with face-to-face guidance for payment adoption. Providers offer rewards for customer loyalty and secure the retailer’s efficient cash flow management. (2) Social Media Dependent: users usually are digital natives with high access to social media or the internet who shop and pay digitally. Providers might not own physical or online shops but are licensed to aggregate money flows through virtual ecosystems. (3) Early Life Engagement: digital banks race to capture the next generation from popularity to profitability. This type of payment aimed to give children a taste of financial freedom while letting parents track their spending. Providers are to capitalize on the digital payment and e-commerce boom and hold on to new customers into adulthood. (4) Traditional Banking: plastic credit cards are purposely designed as a control group to track the evolvement of business strategies in digital payments. Traditional credit card users may follow the bank’s digital strategy to land on different types of digital wallets or mostly keep using plastic credit cards. This research analyzed business growth models and inter-firms’ coopetition strategies of the selected cases. Results of the multiple case analysis reveal that channel initiated payments bundled rewards with retailer’s business discount for recurring purchases. They also extended other financial services, such as insurance, to fulfill customers’ new demands. Contrastively, social media dependent payments developed new usages and new value creation, such as P2P money transfer through network effects among the virtual social ties, while early life engagements offer virtual banking products to children who are digital natives but overlooked by incumbents. It has disrupted the banking business domains in preparation for the metaverse economy. Lastly, the control group of traditional plastic credit cards has gradually converted to a BaaS (banking as a service) model depending on customers’ preferences. The multi-homing behavior is not avoidable in digital payment competitions. Payment providers may encounter multiple waves of a multi-homing threat after a short period of success. A dynamic cross-business collaboration strategy should be explored to continuously evolve the digital ecosystems and allow users for a broader shopping experience and continual usage.

Keywords: digital payment, digital ecosystems, multihoming users, cross business strategy, user digital behavior intentions

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Yan Zhang

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

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

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Meng Su

Abstract:

High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Utkrash Sarkar, Vineet Tiwari, Shailendra Singh

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The development of social media has brought about a tremendous change in the marketing scenario for every industry. It has become a new hybrid element of the promotional mix in the marketing segment. This paper tries to show some light on the fact that in today’s scenario social media is a platform that everyone should take in consideration for any type of marketing campaign. In this paper, we have formulated a questionnaire, and through it, we have tried to gather information from the respondents that how social media is influencing their decision when they choose their travel destinations for tourism purpose, does it help in creating any awareness about places which they don’t have an idea? As a result, guiding the state government and providing them with a marketing strategy that how they can use social media in a better manner so that they could help increase their revenue and can make people aware about the places of the state which the target audience can plan to go for their next vacation.

Keywords: social media, marketing, information, decision making

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Mark Darius Juszczak

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While epistemology, as a sub-field of philosophy, is generally concerned with theoretical questions about the nature of knowledge, the explosion in digital media technologies has resulted in an exponential increase in the storage and transmission of human information. That increase has resulted in a particular non-linear dynamic – digital epistemological functions are radically altering how and what we know. Neither the rate of that change nor the consequences of it have been well studied or taken into account in developing state-level strategies for epistemological functions. At the current time, US Federal policy, like that of virtually all other countries, maintains, at the national state level, clearly defined boundaries between various epistemological agencies - agencies that, in one way or another, mediate the functional use of knowledge. These agencies can take the form of patent and trademark offices, national library and archive systems, departments of education, departments such as the FTC, university systems and regulations, military research systems such as DARPA, federal scientific research agencies, medical and pharmaceutical accreditation agencies, federal funding for scientific research and legislative committees and subcommittees that attempt to alter the laws that govern epistemological functions. All of these agencies are in the constant process of creating, analyzing, and regulating knowledge. Those processes are, at the most general level, epistemological functions – they act upon and define what knowledge is. At the same time, however, there are no high-level strategic epistemological directives or frameworks that define those functions. The only time in US history where a proxy state-level epistemological strategy existed was between 1961 and 1969 when the Kennedy Administration committed the United States to the Apollo program. While that program had a singular technical objective as its outcome, that objective was so technologically advanced for its day and so complex so that it required a massive redirection of state-level epistemological functions – in essence, a broad and diverse set of state-level agencies suddenly found themselves working together towards a common epistemological goal. This paper does not call for a repeat of the Apollo program. Rather, its purpose is to investigate the minimum structural requirements for a national state-level epistemological strategy in the United States. In addition, this paper also seeks to analyze how the epistemological work of the multitude of national agencies within the United States would be affected by such a high-level framework. This paper is an exploratory study of this type of framework. The primary hypothesis of the author is that such a function is possible but would require extensive re-framing and reclassification of traditional epistemological functions at the respective agency level. In much the same way that, for example, DHS (Department of Homeland Security) evolved to respond to a new type of security threat in the world for the United States, it is theorized that a lack of coordination and alignment in epistemological functions will equally result in a strategic threat to the United States.

Keywords: strategic security, epistemological functions, epistemological agencies, Apollo program

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: Lingling Li, Peng Zhao, Runze Guan, Alice Jones, Tao Yu

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Background: Shoulder pain and weakness are associated with complex pathologies and often precludes weightlifters from participation in training. The role and mode of exercise training in weightlifters with shoulder pathology remains unclear. Objectives: This case report described an exercise program in management of an elite weightlifter with primary complaint of right shoulder pain and weakness. Methods: A 22-year-old weightlifter presented with 2-year duration of right shoulder pain and weakness which was worsened by routine weightlifting training, and symptoms were not relieved with steroid injection, manual therapy nor usual physiotherapy. There was a limitation in all active range of motion especially horizontal extension (13ᵒ) and external rotation (41ᵒ) with pain intensity at 4/10 and 10/10 (numeric pain rating score) respectively. Muscle weakness was most significant at supraspinatus and teres minor, 38% and 27% respectively compared to his left shoulder (hand-held dynamometry, Micro FET2). An exercise training program focusing on improving mobility was designed for this athlete following a comprehensive physical assessment. Exercises included specific stretching, muscle activating and scapular stability training; once per day, and for 60 minutes each session. All exercises were completed under instruction as pain allowed. Quantitative assessment was conducted at the end of each week for 3 weeks. Outcomes: After the program, the athlete was pain-free in all movements except the O’Brien active compression internal rotation test, the pain was however reduced from 10/10 to 3/10. The horizontal extension and external rotation range increased to 79ᵒ to 120ᵒ respectively, and strength of all rotator cuff muscles returned to normal. At 1-month follow up, the athlete was totally pain-free and had returned to normal function and weightlifting training activities. The outcomes sustained through 6-month and one year. Conclusion: This case report supports the use of a mobility-focused exercise program for management of shoulder pain and weakness in an elite weightlifter athlete.

Keywords: exercise training, mobility, rehabilitation, shoulder pain, weightlifting

Procedia PDF Downloads 182