Search results for: students’ learning achievements

Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Muhammad Atif, Cang Yan

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The enhancement of low-light images is a significant area of study aimed at enhancing the quality of captured images in challenging lighting environments. Recently, methods based on convolutional neural networks (CNN) have gained prominence as they offer state-of-the-art performance. However, many approaches based on CNN rely on increasing the size and complexity of the neural network. In this study, we propose an alternative method for improving low-light images using an autoencoder-based multiscale knowledge transfer model. Our method leverages the power of three autoencoders, where the encoders of the first two autoencoders are directly connected to the decoder of the third autoencoder. Additionally, the decoder of the first two autoencoders is connected to the encoder of the third autoencoder. This architecture enables effective knowledge transfer, allowing the third autoencoder to learn and benefit from the enhanced knowledge extracted by the first two autoencoders. We further integrate the proposed model into the PIX to PIX GAN framework. By integrating our proposed model as the generator in the GAN framework, we aim to produce enhanced images that not only exhibit improved visual quality but also possess a more authentic and realistic appearance. These experimental results, both qualitative and quantitative, show that our method is better than the state-of-the-art methodologies.

Keywords: low light image enhancement, deep learning, convolutional neural network, image processing

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Authors: David Ruiz, Carlos Nava, Roberto Carbajal

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The psychological profile has become one of the most important sources of information when it comes to individual selection and the hiring process in any organization. Psychological instruments are used to collect data about variables that are considered critically important for performance in work. However, because of conceptual chaos in organizational psychology, most of the information provided by psychological testing is not directly useful for Mexican human resources professionals to take hiring decisions. The aims of this paper are 1) to underline the lack of conceptual precision in theoretical testing foundations in Mexico and 2) presenting a reliability and validity analysis of a frustration tolerance instrument created as an alternative to a heuristically conduct individual assessment in organizations. First, a description of assessment conditions in Mexico is made. Second, an instrument and a theoretical framework is presented as an alternative to the assessment practices in the country. A total of 65 Psychology Iztacala Superior Studies Faculty students were assessed. Cronbach´s alpha coefficient was calculated and an exploratory factor analysis was carried out to prove the scale unidimensionality. Reliability analysis revealed good internal consistency of the scale (Cronbach’s α = 0.825). Factor analysis produced 4 factors for the scale. However, factor loadings and explained variation give proof to the scale unidimensionality. It is concluded that the instrument has good psychometric properties that will allow human resources professionals to collect useful data. Different possibilities to conduct psychological assessment are suggested for future development.

Keywords: psychological assessment, frustration tolerance, human resources, organizational psychology

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

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Authors: Jenna Barriault, Reeshma Haji

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Online consumer behaviourand reliance on online reviews may be more pervasive than ever, andthis necessitates a better scientific understanding of the widespread phenomenon of online deception. The present research focuses on the understudied topic of deceiver’s distrust, where those who engage in deception later have less trust in others in the context of online restaurant reviews. The purpose was to examine deception and valence in online restaurant reviews and the effects they had on deceiver’s distrust. Undergraduate university students (N = 76) completed an online study where valence was uniquely manipulated by telling participants that either positive (or negative reviews) were influential and asking them to write a correspondingly valenced review. Deception was manipulated in the same task. Participants in the deception condition were asked to write an online restaurant review that was counter to their actual experience of the restaurant (negative review of a restaurant they liked, positive review of the restaurant they did not like). In the no deception condition, participants were asked to write a review that they actually liked or didn’t like (based on the valence condition to which they were randomly assigned). Participants’ trust was then assessed through various measures, includingfuture reliance on online reviews. There was a main effect of deception on reliance on online reviews. Consistent with deceiver’s distrust, those who deceived reported that they would rely less on online reviews. This study demonstrates that even when participants are induced to write a deceptive review, it can result in deceiver’s distrust, thereby lowering their trust in online reviews. If trust or reliance can be altered through deception in online reviews, people may start questioning the objectivity or true representation of a company based on such reviews. A primary implication is that people may reduce theirreliance upon online reviews if they know they are easily subject to manipulation. The findings of this study also contribute to the limited research regarding deceiver’s distrust in an online context, and further research is clarifying the specific conditions in which it is most likely to occur.

Keywords: deceiver’s distrust, deception, online reviews, trust, valence

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Authors: Ioannis Andrianakis, Panagiotis Panagiotopoulos, Kyriakos Chatzidimitriou, Dimitrios Tampakis, Manolis Falelakis

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Live music, whether performed in organized venues, restaurants, hotels or any other spots, creates value chains that support and develop local economies and tourism development. In this paper, we describe PerformAnts, a platform that increases the mobility of musicians and their accessibility to remotely located venues by rationalizing the cost of live acts. By analyzing the event history and taking into account their potential availability, the platform provides bespoke recommendations to both bands and venues while also facilitating the organization of tours and helping rationalize transportation expenses by realizing an innovative mechanism called “chain booking”. Moreover, the platform provides an environment where complicated tasks such as technical and financial negotiations, concert promotion or copyrights are easily manipulated by users using best practices. The proposed solution provides important benefits to the whole spectrum of small/medium size concert organizers, as the complexity and the cost of the production are rationalized. The environment is also very beneficial for local talent, musicians that are very mobile, venues located away from large urban areas or in touristic destinations, and managers who will be in a position to coordinate a larger number of musicians without extra effort.

Keywords: machine learning, music industry, creative industries, web applications

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Authors: Chrysovalanto Sofia Karatosidi, Dimitra Iordanoglou

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The overlap of leadership skills with personality traits, beliefs, values, and the integration of cognitive abilities, analytical and critical thinking skills into leadership competencies raises the need to segregate further and investigate them. Hence, the domains of cognitive functions that contribute to leadership effectiveness should also be identified. Organizational cognitive neuroscience and neuroleadership can shed light on the study of these critical leadership skills. As the first part of our research, this pilot study aims to explore the relationships between higher-order cognitive functions (executive functions), trait emotional intelligence (EI), personality, and general cognitive ability in leadership. Twenty-six graduate and postgraduate students were assessed on neuropsychological tests that measure important aspects of executive functions (EF) and completed self-reported questionnaires about trait EI, personality, leadership styles, and leadership effectiveness. Specifically, we examined four core EF—fluency (phonemic and semantic), information updating and monitoring, working memory, and inhibition of prepotent responses. Leadership effectiveness was positively associated with phonemic fluency (PF), which involves mental flexibility, in turn, an increasingly important ability for future leaders in this rapidly changing world. Transformational leadership was positively associated with trait EI, extraversion, and openness to experience, a result that is following previous findings. The relationship between specific EF constructs and leadership effectiveness emphasizes the role of higher-order cognitive functions in the field of leadership as an individual difference. EF brings a new perspective into leadership literature by providing a direct, non-invasive, scientifically-valid connection between brain function and leadership behavior.

Keywords: cognitive neuroscience, emotional intelligence, executive functions, leadership

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Authors: Somoshree Datta, Chithra A. V., Sandeep Nithyanandan, Smitha K. K.

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Space exploration has always been at the forefront of technological innovation, pushing the boundaries of human knowledge and capabilities. In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field, offering unprecedented opportunities to enhance the efficiency, autonomy and intelligence of space missions. This survey paper aims to provide a comprehensive overview of the multifaceted applications of AI in space exploration, exploring the evolution of this synergy and its impact on mission success, scientific discovery, and the future of space endeavors. Indian Space Research Organization (ISRO) has achieved great feats in the recent moon mission (Chandrayaan-3) and sun mission (Aditya L1) by using artificial intelligence to enhance moon navigation as well as help young scientists to study the Sun even before the launch by creating AI-generated image visualizations. Throughout this survey, we will review key advancements, challenges and prospects in the intersection of AI and space exploration. As humanity continues its quest to explore the cosmos, the integration of AI promises to unlock new frontiers, reshape mission architectures, and redefine our understanding of the universe. This survey aims to serve as a comprehensive resource for researchers, engineers and enthusiasts interested in the dynamic and evolving landscape of AI applications in space exploration.

Keywords: artificial intelligence, space exploration, space missions, deep learning

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Authors: Kitda Praraththajariya

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The main purpose of this research was to study how to communicate the identity of the Mueang district, SamutSongkram province for ecotourism. The qualitative data was collected through studying related materials, exploring the area, in-depth interviews with three groups of people: three directly responsible officers who were key informants of the district, twenty foreign tourists and five Thai tourist guides. A content analysis was used to analyze the qualitative data. The two main findings of the study were as follows: (1) The identity of Amphur (District) Mueang, SamutSongkram province. This establishment was near the Mouth of Maekong River for normal people and tourists, consisting of rest accommodations. There are restaurants where food and drinks are served, rich mangrove forests, Hoy Lod (Razor Clam) and mangrove trees. Don Hoy Lod, is characterized by muddy beaches, is a coastal wetland for Ramsar Site. It is at 1099th ranging where the greatest number of Hoy Lod (Razor Clam) can be seen from March to May each year. (2) The communication of the identity of AmphurMueang, SamutSongkram province which the researcher could find and design to present in English materials can be summed up in 4 items: 1) The history of AmphurMueang, SamutSongkram province 2) WatPhetSamutWorrawihan 3) The Learning source of Ecotourism: Don Hoy Lod and Mangrove forest 4) How to keep AmphurMueang, SamutSongkram province for ecotourism.

Keywords: foreigner tourists, signified, semiotics, ecotourism

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Beverly Komen

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Online crimes have been on the rise in the recent days, especially with the hit of the covid-19 pandemic. The coronavirus pandemic has led to an unprecedented rise in screen time, this means more families are relying on technology and digital solutions to keep children learning, spending more time on the virtual platforms can leave children vulnerable to online abuse and exploitation. With ease access of affordable phones, internet, and increased online activities, all children are at risk of being abused online hence making the digital space unsafe for children. With these increased use of technology and its accessibility, children are at risk of facing challenges such as access to inappropriate content, online grooming, identity theft, cyber bullying, among other risks. The big question is; as we enjoy the benefits brought in by technology, how do we ensure that our children are save in this digital space? With the analysis of the current trends, there is a gap in knowledge on people’s understanding on child online protection and safety measures when using the digital space. A survey conducted among 50 parents in Nairobi in Kenya indicated that there is a gap in knowledge on online protection of children and over 50 % of the participants shared that for sure they have no idea on how to protect children online. This paper seeks to address the concept of child protection in the digital space and come up with viable solutions in protecting children from online vices.

Keywords: child protection, digital space, online risks, online grooming, cyber bulying, online child sexual exploitation, and abuse

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Authors: Cheryl M. Bowen

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Understanding that children thrive in a multi-systems approach to mental health development, a growing number of schools often promote school-based positive youth development however, there is scant empirical evidence investigating effective school-based “wraparound” mental health services for low income, Latinx children and their families. This 10-month case study utilizes a sample of 281 low-income, Latinx parents and their children, and 23 K-5th grade teachers living in northern California to test the hypothesis that a school-based mental health program can strengthen students’ developmental asset attainment and positively impact the school environment. The study utilized triangulated data to ascertain the effects of two program levels - (a) mental health and (b) positive child development services. All services were site-based and meant to target a wide variety of families. Findings from the study report that the universal mental health program increased the developmental asset attainment in 5 out of 8 thriving indicators thus transforming the child within his/her environment. Data collected from the administrative referral report demonstrate that the project also positively impacted the school climate. Parents and teachers felt more connected to the school, and referrals were down for discipline (35%), academics (66%), and suspensions (51%). The study concludes that a transformational ecology model of positive child development is the most effective means to nurture connections to all socializing agencies in a child’s ecosystem.

Keywords: case study, child development, positive youth development, developmental assets, ecological systems theory

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Authors: Hanna Faron, Wojciech Marcinkowski, Daniel Prusak

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The aim of this article is to familiarize with the activity of AGH Racing scientific circle, pertaining to the international project -Formula Student, giving the opportunity to young engineers from all around the world to validate their talent and knowledge in the real world conditions, under the pressure of time, and the design requirements. Every year, the team begins the process of building a race car from the formation of human resources. In case of the public sector, to which public universities can be included, the scientific circles represent the structure uniting students with the common interests and level of determination. Due to the scientific nature of the project which simulates the market conditions, they have a chance to verify previously acquired knowledge in practice. High level of the innovation and competitiveness of participating in the project Formula Student teams, requires an intelligent organizational system, which is characterized by a high dynamics. It is connected with the necessity of separation of duties, setting priorities, selecting optimal solutions which is often a compromise between the available technology and a limited budget. Proper selection of the adequate guidelines in the design phase allows an efficient transition to the implementation stage, which is process-oriented implementation of the project. Four dynamic and three static competitions are the main verification and evaluation of year-round work and effort put into the process of building a race car. Acquired feedback flowing during the race is a very important part while monitoring the effectiveness of AGH Racing scientific circle, as well as the main criterion while determining long-term goals and all the necessary improvements in the team.

Keywords: SAE, formula student, race car, public sector, automotive industry

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Authors: Nkosingiphile Mbusozayo Zungu

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The purpose of the current study is to adopt informetrics methods to analyse the research on phishing from 2012 to 2021 in three selected databases in order to contribute to global cybersecurity through impactful research. The study follows a quantitative research methodology. We opted for the positivist epistemology and objectivist ontology. The analysis focuses on: (i) the productivity of individual authors, institutions, and countries; (ii) the research contributions, using co-authorship as a measure of collaboration; (iii) the altmetrics of selected research contributions; (iv) the citation patterns and research impact of research on phishing; and (v) research contributions by keywords, to discover the concepts that are related to phishing. The preliminary findings favour developed countries in terms of quantity and quality of research in the domain. There are unique research trends and patterns in the developing countries, including those in Africa, that provide opportunities for research development in the domain in the region. This study explores an important research domain by using unexplored method in the region. The study supports the SDG Agenda 2030, such as ending abuse, exploitation, trafficking, and all other forms of violence and torture of children through the use of cyberspace (SDG 16). Further, the results from this study can inform research, teaching, and learning largely in Africa. Invariably, the study contributes to cybersecurity awareness that will mitigate cybersecurity threats against vulnerable communities.

Keywords: phishing, cybersecurity, informetrics, information security

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Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

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Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

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Authors: Sarah Rietze, Hannes Zacher

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Daily stand-up meetings are the most commonly used method in agile teams. In daily stand-ups, team members gather to coordinate and align their efforts, typically for a predefined period of no more than 15 minutes. The primary purpose is to ask and answer the following three questions: What was accomplished yesterday? What will be done today? What obstacles are impeding my progress? Daily stand-ups aim to enhance communication, mutual understanding, and support within the team, as well as promote collective learning from mistakes through daily synchronization and transparency. The use of daily stand-ups is intended to positively influence psychological safety within teams, which is the belief that it is safe to show oneself and take personal risks. Two studies will be presented, which explore the relationships between daily stand-ups, psychological safety, and psychological well-being. In a first study, based on survey results (n = 318), we demonstrated that daily stand-ups have a positive indirect effect on job satisfaction and a negative indirect effect on turnover intention through their impact on psychological safety. In a second study, we investigate, using an experimental design, how the use of daily stand-ups in teams enhances psychological safety and well-being compared to a control group that does not use daily stand-ups. Psychological safety is considered one of the most crucial cultural factors for a sustainable, agile organization. Agile approaches, such as daily stand-ups, are a critical part of the evolving work environment and offer a proactive means to shape and foster psychological safety within teams.

Keywords: occupational wellbeing, agile work practices, psychological safety, daily stand-ups

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Authors: Jason Dew

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Podcasts are increasingly sources of intellectual content for many who desire to broaden their worldview. Topics range from sports to folklore, entertainment to spirituality. The list from which to choose is large, demonstrating the public’s interest in this medium. While traditional classrooms continue to serve the curious and upward bound, podcasts also satisfy intellectual cravings, especially for those on the go. The paper will explore how the podcast, The Professor’s Bayonet, attempts to scratch these itches by offering 4-5 minute commentaries on literary works, both classic and contemporary, through the dual lenses of current trends in society and theology. The reason for this approach is borne out of the direction many students take in exchanges of ideas. They have a sincere interest in how the books that are covered are relevant to their lives, and their questions are probing to the extent that dips into theology are helpful. Cursory examinations of whatever topic just won’t suffice. Those in Generation Z, especially, are parched for real and true answers. The paper, therefore, will share some excerpts from a selection of episodes, explaining the reasons behind why certain works were showcased. In an episode entitled “The Possibility of Evil,” for example, Shirley Jackson’s 1965 short story of the same name is explored, focusing on why the protagonist, Adela Strangeworth, leaves nasty little notes in the mailboxes of those in her small community she deems deserving of a good tongue-lashing. There is a negative result and the opportunity to make the connection to social media and how millions of individuals are guilty of the very same thing Adela Strangeworth is guilty of, making Jackson’s work somewhat prophetic. Reasons for this behavior are explored, namely what it says about how we as a society have evolved both interpersonally and spiritually.

Keywords: podcast, social commentary, theology, literary

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Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Hao Yuan, Yongjian Shen, Xiangjun He, Yuheng Li, Zhouzhou Zhang, Pengyu Zhang, Minkang Cai

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Remote sensing data can provide support for decision-making in disaster assessment or disaster relief. The traditional processing methods of sensitive targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Methods based on deep learning for sensitive target hiding are faster and more flexible. But these methods have disadvantages in training time and cost of calculation. This paper proposed a target hiding model Self Attention (SA) Deepfill, which used self-attention modules to replace part of gated convolution layers in image inpainting. By this operation, the calculation amount of the model becomes smaller, and the performance is improved. And this paper adds free-form masks to the model’s training to enhance the model’s universal. The experiment on an open remote sensing dataset proved the efficiency of our method. Moreover, through experimental comparison, the proposed method can train for a longer time without over-fitting. Finally, compared with the existing methods, the proposed model has lower computational weight and better performance.

Keywords: remote sensing mapping, image inpainting, self-attention mechanism, target hiding

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Authors: Tejas Dhadphale

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The user-centered design (UCD) approach has led to an increased interest in empathy within the product development process. Designers have explored several empathetic methods and tools such as personas, empathy maps, journey maps, user needs statements and user scenarios to capture and visualize users’ needs. The goal of these tools is not only to generate a deeper and shared understanding of user needs but also to become a point of reference for subsequent decision making, brainstorming and concept evaluation tasks. The purpose of this study is to measure the effect of empathy priming on divergent brainstorming tasks. This study compares the effects of three empathy tools, personas, empathy maps and user needs statements, on ideation fluency and originality of ideas during brainstorming tasks. In a three-between-subjects experimental design study, sixty product design students were randomly assigned to one of three conditions: persona, empathy maps and user needs statements. A one-way, between-subjects analysis of variance (ANOVA) revealed a a statistically significant difference in empathy priming on fluency and originality of ideas. Participants in the persona group showed higher ideation fluency and generated a greater number of original ideas compared to the other groups. The results show that participants in the user need statement group to generate a greater number of feasible and relevant ideas. The study also aims to understand how formatting and visualization of empathy tools impact divergent brainstorming tasks. Participants were interviewed to understand how different visualizations of users’ needs (personas, empathy maps and user needs statements) facilitated idea generation during brainstorming tasks. Implications for design education are discussed.

Keywords: empathy, persona, priming, Design research

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Authors: Kwame Amoah

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Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: T. Abdulkadir

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Most often, marking and assessment of multiple choice kinds of examinations have been opined by many as a cumbersome and herculean task to accomplished manually in Nigeria. Usually this may be in obvious nexus to the fact that mass numbers of candidates were known to take the same examination simultaneously. Eventually, marking such a mammoth number of booklets dared and dread even the fastest paid examiners who often undertake the job with the resulting consequences of stress and boredom. This paper explores the evolution, as well as the set aim to envision and transcend marking the Multiple Choice Objectives- type examination into a thing of creative recreation, or perhaps a more relaxing activity via the use of the mobile phone. A more “pragmatic” dimension method was employed to achieve this work, rather than the formal “in-depth research” based approach due to the “novelty” of the mobile-smartphone e-Marking Scheme discovery. Moreover, being an evolutionary scheme, no recent academic work shares a direct same topic concept with the ‘use of cell phone as an e-marking technique’ was found online; thus, the dearth of even miscellaneous citations in this work. Additional future advancements are what steered the anticipatory motive of this paper which laid the fundamental proposition. However, the paper introduces for the first time the concept of mobile-smart phone e-marking, the steps to achieve it, as well as the merits and demerits of the technique all spelt out in the subsequent pages.

Keywords: cell phone, e-marking scheme (eMS), mobile phone, mobile-smart phone, multiple choice objectives (MCO), smartphone

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Authors: H. Haryati, A. Nor Azura

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Over the turn of the century, the advertising landscape has evolved significantly, from print media to digital media. In line with the shift to the advanced science and technology dramatically shake the framework of societies Fifth Industrial Revolution (IR5.0), technological endeavors have increased exponentially, which influenced user interaction more inspiring through online advertising that intentionally leads to buying behavior. Users are more accustomed to interactive content that responds to their actions. Thus, immersive experience has transformed into a new engagement experience To centennials. The purpose of this paper is to investigate pleasure and arousal as the fundamental elements of consumer emotions and affective responses to marketing stimuli. A quasi-experiment procedure will be adopted in the research involving 40 undergraduate students in Nilai, Malaysia. This study employed a 2 (celebrity endorser vs. Social media influencer) X 2 (high and low visual complexity) factorial between-subjects design. Participants will be exposed to a printed version depicting a fashion product endorsed by a celebrity and social media influencers, presented in high and low levels of visual complexity. While the questionnaire will be Distributing during the lab test session is used to control their honesty, real feedback, and responses through the latest Instagram design and engagement. Therefore, the research aims to define the immersive experience on Instagram and the interaction between pleasure and arousal. An advertisement that evokes pleasure and arousal will be likely getting more attention from the target audience. This is one of the few studies comparing the endorses in Instagram advertising. Also, this research extends the existing knowledge about the immersive visual complexity in the context of social media advertising.

Keywords: immersive visual experience, instagram, pleasure, arousal

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Authors: Wafaa Salah, Mostafa A. Salama, Jane Doe

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Recently, the corporate social performance (CSP) is not less important than the corporate financial performance (CFP). Debate still exists about the nature of the relationship between the CSP and CFP, whether it is a positive, negative or a neutral correlation. The objective of this study is to explore the relationship between corporate social responsibility (CSR) reports and CFP. The study uses the accounting-based and market-based quantitative measures to quantify the financial performance of seven organizations listed on the Egyptian Stock Exchange in 2007-2014. Then uses the information retrieval technologies to quantify the contribution of each of the three dimensions of the corporate social responsibility report (environmental, social and economic). Finally, the correlation between these two sets of variables is viewed together in a model to detect the correlations between them. This model is applied on seven firms that generate social responsibility reports. The results show a positive correlation between the Earnings per share (market based measure) and the economical dimension in the CSR report. On the other hand, total assets and property, plant and equipment (accounting-based measure) are positively correlated to the environmental and social dimensions of the CSR reports. While there is not any significant relationship between ROA, ROE, Operating income and corporate social responsibility. This study contributes to the literature by providing more clarification of the relationship between CFP and the isolated CSR activities in a developing country.

Keywords: financial, social, machine learning, corporate social performance, corporate social responsibility

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Authors: Rafi Ullah, Shah Muhammad Emaduddin, Taha Jilani

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Vehicle Adhoc Networks (VANET) belongs to a special class of Mobile Adhoc Network (MANET) with high mobility. Network is created by road side vehicles equipped with communication devices like GPS and Wifi etc. Since the environment is highly dynamic due to difference in speed and high mobility of vehicles and weak stability of the network connection, it is a challenging task to design an efficient routing protocol for such an unstable environment. Our proposed algorithm uses heuristic for the calculation of optimal path for routing the packet efficiently in collaboration with several other parameters like geographical location, speed, priority, the distance among the vehicles, communication range, and networks congestion. We have incorporated probabilistic, heuristic and machine learning based approach inconsistency with the relay function of the memory buffer to keep the packet moving towards the destination. These parameters when used in collaboration provide us a very strong and admissible heuristics. We have mathematically proved that the proposed technique is efficient for the routing of packets, especially in a medical emergency situation. These networks can be used for medical emergency, security, entertainment and routing purposes.

Keywords: heuristics routing, intelligent routing, VANET, route optimization

Procedia PDF Downloads 160

Authors: Mumtaz Ali Baloch

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This article aims at to review the policies and gaps including the socioeconomic and institutional factors that affected the enrollment of disabled children and caused drop-outs. It provides insights to scrutinize the gaps in policies, socioeconomic, and institutional factors with the specific concern in enrollment and drop out of disabled children in Pakistan, and Balochistan in particular. The findings of this study revealed that the old-age centralized policies and a number of socio-economic and institutional factors seemed to have significantly affected the enrollment and quality education in the case of physically disabled children. There were only a few schools functional in entire Balochistan. For example, an entire province (Balochistan) there are only two schools for disabled children, established in Quetta city. In the other 31 districts, an estimated population of 300,000 people of each district there were no schools for the disabled children. The findings of this study revealed that there is a great distinction between the policy and practice in the case of physically disabled children in Quetta, Balochistan. Consequently, such children seemed to have been out of schools. Dropout after the class eighth grade is almost 100%, as there are no high schools available for physically/disabled children, in Balochistan. The concerned organizations and authorities need to develop and ratify specific policies, provide required) facilities to the schools including sufficient budget, streamline the academic planning, and an effective monitoring and evaluation system. Only awareness and motivation could not help in improving the enrollment rate and decreasing the dropout in the case of physically disabled children. There is an urgent need to provide the required facilities to the schools. Almost all students needed assistive equipment, effective physical therapy as well as regular medical facilities. Such measures can improve the enrolment and rehabilitation of children.

Keywords: education policy, practices, physically disabled children, challenges, Balochistan, Pakistan

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Authors: Vimala Rani P, Narasimha Malikarjunan, Mercy Shalinie S

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Data Networking was brought forth as an instantiation of information-centric networking. The attackers can send a colossal number of spoofs to take hold of the Pending Interest Table (PIT) named an Interest Flooding attack (IFA) since the in- interests are recorded in the PITs of the intermediate routers until they receive corresponding Data Packets are go beyond the time limit. These attacks can be detrimental to network performance. PIT expiration rate or the Interest satisfaction rate, which cannot differentiate the IFA from attacks, is the criterion Traditional IFA detection techniques are concerned with. Threshold values can casually affect Threshold-based traditional methods. This article proposes an accurate IFA detection mechanism based on a Multiple Feature-based Extreme Learning Machine (MF-ELM). Accuracy of the attack detection can be increased by presenting the entropy of Internet names, Interest satisfaction rate and PIT usage as features extracted in the MF-ELM classifier. Furthermore, we deploy a queue-based hostile Interest prefix mitigation mechanism. The inference of this real-time test bed is that the mechanism can help the network to resist IFA with higher accuracy and efficiency.

Keywords: information-centric network, pending interest table, interest flooding attack, MF-ELM classifier, queue-based mitigation strategy

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Authors: Pranjali Avinash Yadav-Deshmukh

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Remarkable development of the Internet along with the new technological innovation, such as high-speed systems and affordable large storage space have led to a tremendous increase in the amount and accessibility to digital records. For any person, studying of all these data is tremendously time intensive, so there is a great need to access effective multi-document summarization (MDS) systems, which can successfully reduce details found in several records into a short, understandable summary or conclusion. For semantic representation of textual details in ontology area, as a theoretical design, our system provides a significant structure. The stability of using the ontology in fixing multi-document summarization problems in the sector of catastrophe control is finding its recommended design. Saliency ranking is usually allocated to each phrase and phrases are rated according to the ranking, then the top rated phrases are chosen as the conclusion. With regards to the conclusion quality, wide tests on a selection of media announcements are appropriate for “Jammu Kashmir Overflow in 2014” records. Ontology centered multi-document summarization methods using “NLP centered extraction” outshine other baselines. Our participation in recommended component is to implement the details removal methods (NLP) to enhance the results.

Keywords: disaster management, extraction technique, k-means, multi-document summarization, NLP, ontology, sentence extraction

Procedia PDF Downloads 365