Aim/Purpose: This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background: The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology: The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman’s (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution: This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings: Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations for Practitioners: Decision makers and top management are encouraged to focus on the identified highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendation for Researchers: This research can be considered a stepping stone to investigating the impact of COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society: Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research: Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results.

Aim/Purpose: This research aims to develop a smart agricultural knowledge management framework to empower emergent farmers and extension officers (advisors to farmers) in developing countries as part of a smart farming lab (SFL). The framework utilizes knowledge objects (KOs) to capture information and knowledge of different forms, including indigenous knowledge. It builds upon a foundation of established agricultural knowledge management (AKM) models and serves as the cornerstone for an envisioned SFL. This framework facilitates optimal decision support by fostering linkages between these KOs and relevant organizations, knowledge holders, and knowledge seekers within the SFL environment. Background: Emergent farmers and extension officers encounter numerous obstacles in their knowledge operations and decision-making. This includes limited access to agricultural information and difficulties in applying it effectively. Many lack reliable sources of support, and even when information is available, understanding and applying it to specific situations can be challenging. Additionally, extension offices struggle with operational decisions and knowledge management due to agricultural organizations operating isolated in silos, hindering their access to necessary knowledge. This research introduces an SFL with a proposed AKM process model aimed at transforming emergent farmers into smart, innovative entities by addressing these challenges. Methodology: This study is presented as a theory-concept paper and utilizes a literature review to evaluate and synthesize three distinct AKM models using several approaches. The results of the analysis are used to design a new AKM process model. Contribution: This research culminates in a new AKM process framework that incorporates the strengths of various existing AKM models and supports emergent farmers and extension officers to become smart, innovative entities. One main difference between the three models analyzed, and the one proposed in this research, is the deployment and use of knowledge assets in the form of KOs. The proposed framework also incorporates metadata and annotations to enhance knowledge discoverability and enable AI-powered applications to leverage captured knowledge effectively. In practical terms, it contributes by further motivating the use of KOs to enable the transfer and the capturing of organizational knowledge. Findings: A model for an SFL that incorporates the proposed agricultural knowledge management framework is presented. This model is part of a larger knowledge factory (KF). It includes feedback loops, KOs, and mechanisms to facilitate intelligent decision-making. The significance of fostering interconnected communities is emphasized through the creation of linkages. These communities consist of knowledge seekers and bearers, with information disseminated through social media and other communication integration platforms. Recommendations for Practitioners: Practitioners and other scholars should consider implementing the proposed AKM process model as part of a larger SFL to support emergent farmers and extension officers in making operational decisions and applying knowledge management strategies. Recommendation for Researchers: The AKM process model is only presented in conceptual form. Therefore, researchers can practically test and assess the new framework in an agricultural setting. They can also further explore the potential of social media integration platforms to connect knowledge seekers with knowledge holders. Impact on Society: The proposed AKM process model has the potential to support emergent farmers and extension officers in becoming smart, innovative entities, leading to improved agricultural practices and potentially contributing to food security. Future Research: This paper discusses the AKM process model in an agrarian setting, but it can also be applied in other domains, such as education and the healthcare sector. Future research can evaluate the model’s effectiveness and explore and further investigate the semantic web and social media integration.

Aim/Purpose: This research aims to evaluate models from meta-learning techniques, such as Riemannian Model Agnostic Meta-Learning (RMAML), Model-Agnostic Meta-Learning (MAML), and Reptile meta-learning, to obtain high-quality metadata. The goal is to utilize this metadata to increase accuracy and efficiency in identifying chili varieties in smart agriculture. Background: The identification of chili varieties in smart agriculture is a complex process that requires a multi-faceted approach. One challenge in chili variety identification is the lack of a large and diverse dataset. This can be addressed using meta-learning techniques, which allow the model to leverage knowledge learned from other related tasks or artificially expand the dataset by applying transformations to existing data. Another challenge is the variation in growing conditions, which can affect the appearance of chili varieties. Meta-learning techniques can help address this challenge by allowing the model to adapt to variations in growing conditions with task-specific embeddings and optimizations. With the help of meta-learning techniques, such as data augmentation, data characterization, selection of datasets, and performance estimation, quality metadata for accurate identification of chili varieties can be achieved even in the presence of limited data and variations in growing conditions. Furthermore, the use of meta-learning techniques in chili variety identification can also assist in addressing challenges related to the computational complexity of the task. Methodology: The research approach employed is quantitative, specifically comparing three models from meta-learning techniques to determine which model is most suitable for our dataset. Data was collected from the variety assembly garden in the form of images of chili leaves using a mobile device. The research successfully gathered 1,974 images of chili leaves, with 697 images of large red chilies, 649 images of curly red chilies, and 628 images of cayenne peppers. These chili leaf images were then processed using augmentation techniques. The results of image data augmentation were categorized based on leaf characteristics (such as oval, lancet, elliptical, serrated leaf edges, and flat leaf edges). Subsequently, training and validation utilized three models from meta-learning techniques. The final stage involved model evaluation using 2-way and 3-way classification, as well as 5-shot and 10-shot learning scenarios to select the dataset with the best performance. Contribution: Improving classification accuracy, with a focus on ensuring high-quality data, allows for more precise identification and classification of chili varieties. Enhancing model training through an emphasis on data quality ensures that the models receive reliable and representative input, leading to improved generalization and performance in identifying chili varieties. Findings: With small collections of datasets, the authors have used data augmentation and meta-learning techniques to overcome the challenges of limited data and variations in growing conditions. Recommendations for Practitioners: By leveraging the knowledge and adaptability gained from meta-learning, accurate identification of chili varieties can be achieved even with limited data and variations in growing conditions. The use of meta-learning techniques in chili variety identification can greatly improve the accuracy and reliability of the identification process. Recommendation for Researchers: Using meta-learning techniques, such as transfer learning and parameter optimization, researchers can overcome challenges related to limited data and variations in growing conditions in chili variety identification. Impact on Society: The findings from this research can help identify superior chili seeds, thereby motivating farmers to cultivate high-quality chilies and achieve bountiful harvests. Future Research: We intend to verify our approach on a more extensive array of datasets and explore the implementation of more resilient regularization techniques, going beyond image augmentation, within the meta-learning techniques. Furthermore, our goal is to expand our research to encompass the automatic learning of parameters during training and tackle issues associated with noisy labels. Building on the insights gained from our observed outcomes, a future objective is to enhance the refinement of model-agnostic meta-learning techniques that can effectively adapt to intricate task distributions with substantial domain gaps between tasks. To realize this aim, our proposal involves devising model-agnostic meta-learning techniques specifically designed for multi-modal scenarios.

Breast cancer is leading cause of mortality among women compared to other types of cancers. Hence, early breast cancer diagnosis is crucial to the success of treatment. Various pathological and imaging tests are available for the diagnosis of breast cancer. However, it may introduce errors during detection and interpretation, leading to false-negative and false-positive results due to lack of pre-processing of it. To overcome this issue, we proposed a effective image pre-processing technique-based on Otsu's thresholding and single-seeded region growing (SSRG) to remove artefacts and segment the pectoral muscle from breast mammograms. To validate the proposed method, a publicly available MIAS dataset was utilised. The experimental finding showed that proposed technique improved 18% breast cancer detection accuracy compared to existing methods. The proposed methodology works efficiently for artefact removal and pectoral segmentation at different shapes and nonlinear patterns.

Nowadays, we are surrounded by artifacts that are capable of adaptive behavior, such as electric pots, boiler timers, automatic doors, and robots. The literature concerning human beings’ conceptions of “traditional” artifacts is vast, however, little is known about our conceptions of behaving artifacts, nor of the influence of the interaction with such artifacts on cognitive development, especially among children. Since these artifacts are provided with an artificial “mind,” it is of interest to assess whether and how children develop a Theory of Artificial Mind (ToAM) which is distinct from their Theory of Mind (ToM). The study examined a new theoretical scheme named ToAM (Theory of Artificial Mind) by means of qualitative and quantitative methodology among twenty four 5-7 year old children from central Israel. It also examined the effects of interacting with behaving artifacts (constructing versus observing the robot’s behavior) using the “RoboGan” interface on children’s development of ToAM and their ToM and looked for conceptions that evolve among children while interacting with behaving artifacts which are indicative of the acquisition of ToAM. In the quantitative analysis it was found that the interaction with behaving artifacts, whether as observers or constructors and for both age groups, brought into awareness children’s ToM as well as influenced their ability to understand that robots can behave independently and based on external and environmental conditions. In the qualitative analysis it was found that participating in the intervention influenced the children’s ToAM for both constructors and for the younger observer. Engaging in building the robot’s behavior influenced the children’s ability to explain several of the robots’ behaviors, their understanding of the robot’s script-based behavior and rule-based behavior and the children’s metacognitive development. The theoretical and practical importance of the study is discussed.

Can questions sent to Open-Educational-Resource (OER) websites such as Ask-An-Expert serve as indicators for students’ interest in science? This issue was examined using an online questionnaire which included an equal number of questions about the topics “space” and “nutrition” randomly selected from three different sources: a 5th-grade science textbook, the “Ask-An-Expert” website, and questions collected from other students in the same age group. A sample of 113 5th-graders from two elementary schools were asked to rate their interest level in finding out the answer to these questions without knowledge of their source. Significant differences in students’ interest level were found between questions: textbook questions were ranked lowest for both subjects, and questions from the open-resource were ranked high. This finding suggests that questions sent to an open-resource could be used as an indicator of students’ interest in science. In addition, the high correlation of interests expressed by students from the two schools may point to a potential generalization of the findings. This study contributes by highlighting OER as a new and promising indicator of student interest, which may help bring “student voices” into mainstream science teaching to increase student interest in science.

This study seeks to explore what factors influence students’ ICT use and web technology competence. The objectives of this study are the following: (a) To what extent do certain elements of Rogers’ (2003) Diffusion of Innovations Theory (DOI) explain students’ ICT use, (b) To what extent do personality characteristics derived from the Big Five approach explain students’ ICT use, and (c) To what extent does motivation explain students’ ICT use. The research was conducted in Israel during the second semester of the academic year 2013-14, and included two groups of participants: a group of Educational Technology students (ET) and a group of Library and Information Science students (LIS). Findings add another dimension to the importance of Rogers’ DOI theory in the fields of Educational Technology and Library and Information Science. Further, findings confirm that personality characteristics as well as motivation affect ICT use. If instructors would like to enhance students’ ICT use, they should be aware of individual differences between students, and they should present to students the advantages and usefulness of ICT, thus increasing their motivation to use ICT, in the hopes that they will become innovators or early adopters.

Aim/Purpose: This study examines the influence of Performance Expectancy (PE), Effort Expectancy (EE), and Facilitating Conditions (FC) on the use of smart phones for mobile learning by postgraduate students in University of Ibadan, Nigeria. Background: Due to the low level of mobile learning adoption by students in Nigeria, three base constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) model were used as factors to determine smart phone use for mobile learning by the postgraduate students in the University of Ibadan. Methodology: The study adopted a descriptive survey research design of the correlational type, the two-stage random sampling technique was used to select a sample size of 217 respondents, and a questionnaire was used to collect data. Descriptive statistics (frequency counts, percentages, mean, and standard deviation), test of norm, and inferential statistics (correlation and regression analysis) were used to analyze the data collected. Contribution: The study empirically validated the UTAUT model as a model useful in predicting smart phone use for mobile learning by postgraduate students in developing countries. Findings: The study revealed that a significant number of postgraduate students used their smart phones for mobile learning on a weekly basis. Findings also revealed a moderate level of Performance Expectancy (???? =16.97), Effort Expectancy (???? =12.57) and Facilitating Conditions (???? =15.39) towards the use of smart phones for mobile learning. Results showed a significant positive relationship between all the independent variables and use of smart phones for mobile learning (PE, r=.527*; EE, r=.724*; and FCs, r=.514*). Out of the independent variables, PE was the strongest predictor of smart phone use for mobile learning (β =.189). Recommendations for Practitioners: Librarians in the university library should organize periodic workshops for postgraduate students in order to expose them to the various ways of using their smart phones to access electronic databases. Recommendation for Researchers: There is a need for extensive studies on the factors influencing mobile technologies adoption and use in learning in developing countries. Impact on Society: Nowadays, mobile learning is increasingly being adopted over conventional learning systems due to its numerous benefits. Thus, this study provides an insight into the issues influencing the use of smart phones for mobile learning by postgraduate students from developing countries. Future Research: This study utilized the base constructs of the UTAUT model to determine smart phone use for mobile learning by postgraduate students in a Nigerian university. Subsequent research should focus on other theories to ascertain factors influencing Information Technology adoption and usage by students in developing countries.

Contribution: This study contributes to scientific literature by detailing the impact of specific factors on the privacy concerns of citizens living in an African city Findings: The findings reveal that the more that impersonal data is collected by the Smart City of Cape Town, the lower the privacy concerns of the digital natives. The findings also show that the digital natives have higher privacy concerns when they express a strong need to be aware of the security measure put in place by the city. Recommendations for Practitioners: Practitioners (i.e., policy makers) should ensure that it is a legal requirement to have security measures in place to protect the privacy of the citizens while collecting data within the smart city of Cape Town. These regulations should be made public to appease any apprehensions from its citizens towards smart city implementations. Less personal data should also be collected on the citizens. Recommendation for Researchers: Researchers should further investigate issues related to privacy concerns in the context of African developing countries. Such is the case since the population of these countries might have unique cultural and philosophical perspectives that might influence how they perceive privacy. Impact on Society: Cities are becoming “smarter” and in developing world context like Africa, privacy issues might not have as a strong influence as is the case in the developing world. Future Research: Further qualitative studies should be conducted to better understand issues related to perceived benefits, perceived control, awareness of how data is collected, and level of privacy concerns of digital natives in developing countries.

Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student’s grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence learning processes, with and without a general overview of the problem that it is under examination. Applying the idea of the general perspective that the network gets on the sentences and deriving recommendations from this for teaching processes. Background: We looked for common patterns of computer errors and human grammar mistakes. Also deducing the neural network’s learning process, deriving conclusions, and applying concepts from this process to the process of human learning. Methodology: We used DL technologies and research methods. After analysis, we built models from three types of complex neuronal networks – LSTM, Bi-LSTM, and GRU – with sequence-to-sequence architecture. After this, we combined the sequence-to- sequence architecture model with the attention mechanism that gives a general overview of the input that the network receives. Contribution: The cost of computer applications is cheaper than that of manual human effort, and the availability of a computer program is much greater than that of humans to perform the same task. Thus, using computer applications, we can get many desired examples of mistakes without having to pay humans to perform the same task. Understanding the mistakes of the machine can help us to under-stand the human mistakes, because the human brain is the model of the artificial neural network. This way, we can facilitate the student learning process by teaching students not to make mistakes that we have seen made by the artificial neural network. We hope that with the method we have developed, it will be easier for teachers to discover common mistakes in students’ work before starting to teach them. In addition, we show that a “general explanation” of the issue under study can help the teaching and learning process. Findings: We performed the test case on the Hebrew language. From the mistakes we received from the computerized neuronal networks model we built, we were able to classify common human errors. That is, we were able to find a correspondence between machine mistakes and student mistakes. Recommendations for Practitioners: Use an artificial neural network to discover mistakes, and teach students not to make those mistakes. We recommend that before the teacher begins teaching a new topic, he or she gives a general explanation of the problems this topic deals with, and how to solve them. Recommendations for Researchers: To use machines that simulate the learning processes of the human brain, and study if we can thus learn about human learning processes. Impact on Society: When the computer makes the same mistakes as a human would, it is very easy to learn from those mistakes and improve the study process. The fact that ma-chine and humans make similar mistakes is a valuable insight, especially in the field of education, Since we can generate and analyze computer system errors instead of doing a survey of humans (who make mistakes similar to those of the machine); the teaching process becomes cheaper and more efficient. Future Research: We plan to create an automatic grammar-mistakes maker (for instance, by giving the artificial neural network only a tiny data-set to learn from) and ask the students to correct the errors made. In this way, the students will practice on the material in a focused manner. We plan to apply these techniques to other education subfields and, also, to non-educational fields. As far as we know, this is the first study to go in this direction ‒ instead of looking at organisms and building machines, to look at machines and learn about organisms.

This paper identifies areas in the design science research (DSR) subfield of the information systems (IS) discipline where a more detailed consideration of practitioner audiences of socio-technical design artifacts could improve current IS DSR research practice and proposes an initial conceptualization of these audiences. The consequences of not considering artifact audiences are identified through a critical appraisal of the current informing science lenses in the IS DSR literature. There are specific shortcomings in four areas: 1) treating practice stakeholders as a too homogeneous group, 2) not explicitly distinguishing between social and technical parts of socio-technical artifacts, 3) neglecting implications of the artifact abstraction level, and 4) a lack of explicit consideration of a dynamic or evolutionary fitness perspective of socio-technical artifacts. The findings not only pave the way for future research to further improve the conceptualization of artifact audiences, in order to improve the informing power – and thus, impact on practice and research relevance – of IS DSR projects; they can also help to bridge the theory-practice gap in other disciplines (e.g. computer science, engineering, or policy-oriented sociology) that seek to produce social and/or technical artifacts of practical relevance.

In the fast changing global economic landscape, the cultivation of sustainable entrepreneurial ventures is seen as a vital mechanism that will enable businesses to introduce new innovative products to the market faster and more effectively than their competitors. This research paper investigated phenomena that may play a significant role when entrepreneurs implement creative ideas resulting in successful technology enabled start-ups within the South African market place. Constant and significant changes in technology provide several challenges for entrepreneurship. Various themes such as innovation, work experience, idea generation, education and partnership formation have been explored to assess their impact on entrepreneurship. Reflection and a design thinking approach underpinned a rigorous analysis process to distill themes from the data gathered through semi structured interviews. From the findings it was evident that the primary success influencers include the formation of partnership, iterative cycles, and certain types of education. The secondary influencers included the origination of an idea, the use of innovation. and organizational culture as well as work experience. This research illustrates how Informing Science as a transdisicpline can provide a philosophical underpinning to communicate and synthesise ideas from constituent disciplines in an attempt to create a more cohesive whole. This diverse environment, comprising people, technology, and business, requires blending different elements from across diverse fields to yield better science. With this backdrop, this preliminary study provides an important foundation for further research in the context of a developing country where entrepreneurial ventures may have a socio-economical impact. The themes that emerged through this study could provide avenues for further research.

Aim/Purpose: This paper aims to investigate how Kuwaiti Academic Libraries (KALs) have responded to the rapidly evolving Smartphone-Apps (SP-Apps) environment, as well as exploring the level of electronic services provided in these libraries. Background: This study can illustrate whether the governmental, academic libraries in the State of Kuwait have already benefited from the mobile services provided by smart phones or not. Methodology: In this study, the researchers use both qualitative and quantitative methods. Therefore, questionnaires and interviews are used in order to collect in-depth data in this field. The questionnaire sample was 400 respondents. They divided in two KALs: Kuwait University Library (KUL) and Public Authority of Applied Education Training Library (PAAETL), while eight individual interviews were conducted one-to-one in this research. Contribution: This paper may be important for academic libraries to identify shortcomings in the smartphones’ content and services they provide and in highlighting efforts by libraries to address their users’ needs in this area. Findings: The findings show that most participants expressed the need to introduce an SP-App to their library. They also confirmed that there are many difficulties in creating an SP-App including lack of budget, lack of awareness of library management, lack of clarity about library management strategic objectives, and vision for an SP-App. Recommendations for Practitioners: Designing SP-Apps that have reliable content and user interface that is easy to use is a considerable challenge. For this reason, the study highly recommends introducing SP-Apps for KALs as soon as possible. Future Research: The recommendations proposed are relevant to Kuwait. Further research may be useful in this field in other developing countries, in order to test or develop the suggested strategy.

Aim/Purpose: Collaborative, interdisciplinary research is growing rapidly, but we still have limited and fragmented understanding of what is arguably the heart of such research—collaborative, interdisciplinary reasoning (CIR). Background: This article integrates neo-Pragmatist theories of reasoning with insights from literature on interdisciplinary research to develop a working definition of collaborative, interdisciplinary reasoning. The article then applies this definition to an empirical example to demonstrate its utility. Methodology: The empirical example is an excerpt from a Toolbox workshop transcript. The article reconstructs a cogent, inductive, interdisciplinary argument from the excerpt to show how CIR can proceed in an actual team. Contribution: The study contributes operational definitions of ‘reasoning together’ and ‘collaborative, interdisciplinary reasoning’ to existing literature. It also demonstrates empirical methods for operationalizing these definitions, with the argument reconstruction providing a brief case study in how teams reason together. Findings: 1. Collaborative, interdisciplinary reasoning is the attempted integration of disciplinary contributions to exchange, evaluate, and assert claims that enable shared understanding and eventually action in a local context. 2. Pragma-dialectic argument reconstruction with conversation analysis is a method for observing such reasoning from a transcript. 3. The example team developed a strong inductive argument to integrate their disciplinary contributions about modeling. Recommendations for Practitioners: 1. Interdisciplinary work requires agreeing with teammates about what is assertible and why. 2. To assert something together legitimately requires making a cogent, integrated argument. Recommendation for Researchers: 1. An argument is the basic unit of analysis for interdisciplinary integration. 2. To assess the argument’s cogency, it is helpful to reconstruct it using pragma-dialectic principles and conversation analysis tools. 3. To assess the argument’s interdisciplinary integration and participant roles in the integration, it is helpful to graph the flow of words as a Sankey chart from participant-disciplines to the argument conclusion. Future Research: How does this definition of CIR relate to other interdisciplinary ‘cognition’ or ‘learning’ type theories? How can practitioners and theorists tell the difference between true intersubjectivity and superficial agreeableness in these dialogues? What makes an instance of CIR ‘good’ or ‘bad’? How does collaborative, transdisciplinary reasoning differ from CIR, if at all?

Aim/Purpose: The aim of this paper is to propose an ensemble learners based classification model for classification clickbaits from genuine article headlines. Background: Clickbaits are online articles with deliberately designed misleading titles for luring more and more readers to open the intended web page. Clickbaits are used to tempted visitors to click on a particular link either to monetize the landing page or to spread the false news for sensationalization. The presence of clickbaits on any news aggregator portal may lead to an unpleasant experience for readers. Therefore, it is essential to distinguish clickbaits from authentic headlines to mitigate their impact on readers’ perception. Methodology: A total of one hundred thousand article headlines are collected from news aggregator sites consists of clickbaits and authentic news headlines. The collected data samples are divided into five training sets of balanced and unbalanced data. The natural language processing techniques are used to extract 19 manually selected features from article headlines. Contribution: Three ensemble learning techniques including bagging, boosting, and random forests are used to design a classifier model for classifying a given headline into the clickbait or non-clickbait. The performances of learners are evaluated using accuracy, precision, recall, and F-measures. Findings: It is observed that the random forest classifier detects clickbaits better than the other classifiers with an accuracy of 91.16 %, a total precision, recall, and f-measure of 91 %.

Aim/Purpose: To discuss the Design Science Research approach by comparing some of its canons with observed practices in projects in which it is applied, in order to understand and structure it better. Background: Recent criticisms of the application of the Design Science Research (DSR) approach have pointed out the need to make it more approachable and less confusing to overcome deficiencies such as the unrealistic evaluation. Methodology: We identified and analyzed 92 articles that presented artifacts developed from DSR projects and another 60 articles with preceding or subsequent actions associated with these 92 projects. We applied the content analysis technique to these 152 articles, enabling the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of activities performed and the types of artifacts involved. Contribution: The content analysis of these 152 articles enabled the preparation of network diagrams and an analysis of the longitudinal evolution of these projects in terms of the activities and types of artifacts involved. Evidence was found of a precedence hierarchy among different types of artifacts, as well as nine new opportunities for entry points for the continuity of DSR studies. Only 14% of the DSR artifacts underwent an evaluation by typical end users, characterizing a tenth type of entry point. Regarding the evaluation process, four aspects were identified, which demonstrated that 86% of DSR artifact evaluations are unrealistic. Findings: We identified and defined a set of attributes that allows a better characterization and structuring of the artifact evaluation process. Analyzing the field data, we inferred a precedence hierarchy for different artifacts types, as well as nine new opportunities for entry points for the continuity of DSR studies. Recommendation for Researchers: The four attributes identified for analyzing evaluation processes serve as guidelines for practitioners and researchers to achieve a realistic evaluation of artifacts. Future Research: The nine new entry points identified serve as an inspiration for researchers to give continuity to DSR projects.

Aim/Purpose: Cyberbullying (CB) is an ongoing phenomenon that affects youth in negative ways. Using online news articles to provide information to schools can help with the development of comprehensive cyberbullying prevention campaigns, and in restoring faith in news reporting. The inclusion of online news also allows for increased awareness of cybersafety issues for youth. Background: CB is an inherent problem of information delivery and security. Textual analysis provides input into prevention and training efforts to combat the issue. Methodology: Text extraction and text analysis methods of term and concept extraction; text link analysis and sentiment analysis are performed on a body of news articles. Contribution: News articles are determined to be a major source of information for comprehensive cyberbullying prevention campaigns. Findings: Online news articles are relatively neutral in their sentiment; terms and topic extraction provide fertile ground for information presentation and context. Recommendation for Researchers: Researchers should seek support for research projects that extract timely information from online news articles. Future Research: Refinement of the terms and topics analytic model, as well as a system development approach for information extraction of online CB news.

Aim/Purpose: In this article, the subjective perspectives of young people in Ghana on the use of digital media are elaborated. The aim is to make the positions of young people visible in the often adult-dominated discourse on digital media and to overcome adult-centered considerations in academic and public debates. In addition, the focus on young people from the Global South is intended to help make their underrepresented voices present in this discourse. Background: Digital media devices and Internet access are conditional on people’s social, economic, and educational participation. Many people in the Global South in particular are not yet granted such access. For children and young people worldwide, the educational opportunities offered by digital media are associated with potential threats to mental health and well-being. However, young people’s views on digital media are rarely addressed, especially in the Global South. Methodology: Based on a qualitative thematic analysis of responses to open-ended questionnaire questions, young Ghanaians’ views on smartphone use and how it affects academic success are examined. Contribution: By focusing on the subjective perspectives of young people, especially from the Global South, voices that have hardly been heard in the discourse on digital media are made audible. This should help overcome the dominant adult-centered perspectives in this discourse. Findings: For young people in Ghana, digital media are part of their everyday lives and often necessary to succeed at school. At the same time, they are concerned about the dangers, e.g., from overuse or cybercrime, for which they have few strategies to deal with. In their answers, they refer to socio-culturally specific discourses and values as well as to generational hierarchies that they perceive and deal with, which go far beyond the topic of digital media use. This makes clear the social tensions in which the debate about digitalization is embedded. Recommendation for Researchers: Young people’s knowledge of and perspectives on digital media is an important resource for learning to use them in an emancipated way. Future Research: Future research should recognize young people as experts in their own right on the issue, explore ways to include their perspectives in the discourse on digital media use and work with them to harness the future potential of the technology and avoid risks.

Aim/Purpose: This paper examines the transformative impact of Artificial Intelligence (AI) on professional skills in organizations and explores strategies to address the resulting challenges. Background: The rapid integration of AI across various sectors is automating tasks and reducing cognitive workload, leading to increased productivity but also raising concerns about job displacement. Successfully adapting to this transformation requires organizations to implement new working models and develop strategies for upskilling and reskilling their workforce. Methodology: This review analyzes recent research and practice on AI's impact on human skills in organizations. We identify key trends in how AI is reshaping professional competencies and highlight the crucial role of transversal skills in this evolving landscape. The paper also discusses effective strategies to support organizations and guide workers through upskilling and reskilling processes. Contribution: The paper contributes to the existing body of knowledge by examining recent trends in AI's impact on professional skills and workplaces. It emphasizes the importance of transversal skills and identifies strategies to support organizations and workers in meeting upskilling and reskilling challenges. Our findings suggest that investing in workforce development is crucial for ensuring that the benefits of AI are equitably distributed among all stakeholders. Findings: Our findings indicate that organizations must employ a proactive approach to navigate the AI-driven transformation of the workplace. This approach involves mapping the transversal skills needed to address current skill gaps, helping workers identify and develop skills required for effective AI adoption, and implementing processes to support workers through targeted training and development opportunities. These strategies are essential for ensuring that workers' attitudes and mental models towards AI are adaptable and prepared for the changing labor market. Recommendation for Researchers: We emphasize the need for researchers to adopt a transdisciplinary approach when studying AI's impact on the workplace. Given AI's complexity and its far-reaching implications across various fields including computer science, mathematics, engineering, and behavioral and social sciences, integrating diverse perspectives is crucial for a holistic understanding of AI's applications and consequences. Future Research: Looking ahead, further research is needed to deepen our understanding of AI's impact on human skills, particularly the role of soft skills in AI adoption within organizations. Future studies should also address the challenges posed by Industry 5.0, which is expected to bring about even more extensive integration of new technologies and automation.

This paper presents a novel deep learning technique called quantum dilated convolutional neural network-DenseNet (Q-DenseNet) for prediction of heart disease in spark framework. At first, the input data taken from the database is allowed for data partitioning using fast fuzzy C-means clustering (FFCM). The partitioned data is fed into spark framework, where pre-processed by missing data imputation and quantile normalisation. The pre-processed data is further allowed for selection of suitable features. Then, the selected features from the slave nodes are merged and fed into master node. The Q-DenseNet is used in master node for the prediction of heart disease. The performance improvement of the designed Q-DenseNet model is validated by comparing with traditional prediction models. Here, the Q-DenseNet method achieved superior performance with maximum of 92.65% specificity, 91.74% sensitivity, and 90.15% accuracy.

The PALS system captures physical context from sensed data, reasons about the context and associated context-driven policies to make access-control decisions and detect intrusions into smart home systems based on both network and behavioral data

The post paper: Context Sensitive Access Control in Smart Home Environments appeared first on UMBC ebiquity.

If partnering with local firms is an intuitive strategy with which to mitigate uncertainty in foreign ventures, then why don't organizations always partner with local firms, especially in uncertain settings? We address this question by unbundling the effects of uncertainty in foreign ventures at the venture and country levels. We contend that, while both levels increase the need for partnering with local firms in foreign ventures, country-level uncertainty increases the difficulty of partnering with local firms and decreases the likelihood of such partnerships. We also posit that experiential learning helps firms manage the two types of uncertainty, and thereby reduces the need for partnering—yet, experience in the host country makes partnering more feasible and increases the likelihood of such partnerships. To test our hypotheses, we conceptualize the decision to partner with a local firm in a foreign venture as a multilayered decision, and model it accordingly. Using a global sample of venture capital investments made between 1984 and 2011, we find support for the distinct effects of venture- and country-level uncertainty as well as for corresponding levels of experiential learning. These findings have implications for the literature on cross-border venture capital investment and international business in general.

Literature on partnerships has grown rapidly in the past decade across different disciplines. However, despite conceptual attention to the value of strategic multi-stakeholder collaboration to promote peace and reconciliation, challenges posed by (post-)conflict, fragile contexts have barely been considered in empirical studies. In this article we contribute by bringing together debates from different partnership literatures and providing an overview of existing, relatively limited research insights on partnerships for peace in fragile states. We present a typology of different levels (local, national, international) at which collaboration takes place and different types of partnerships (philanthropic, transactional, engagement, transformative). This is exemplified with specific attention to Africa, where most fragile states are found, and to partnerships with transformative potential. The analysis suggests that the lowest-level (local) partnerships tend to exclude the national government, while the most recent international, multilateral-driven collaboration has not included business; national cases are most transformative but incidental and not yet leveraged internationally. Despite the interconnected nature of conflict and fragility issues, linkages between partnerships and partners at different levels are largely missing, offering potential for further development by a broad spectrum of scholars and thought leaders. Insights from 'extreme' unconventional contexts thus have relevance for management research more generally.

"Best Places to Work" (BPTW) and similar competitions are a proliferating form of third party employment branding. Little is known, however, about how single or repeated third party employment branding occurrences relate to key human capital outcomes. Extending signaling theory by considering signal credibility and comparability, we use archival and survey data from 624 BPTW participants in sixteen competitions across a three-year period to develop and test hypotheses linking BPTW certifications to collective turnover rates and key informant perceptions of applicant pool quality. We find that certifications are associated with lower turnover rates, and in addition, propose competing crystallization and celebrity hypotheses that model turnover trajectories with repeated certifications, finding diminishing marginal turnover reductions across multiple certifications. We also examine company size and industry job opening moderators, finding that as certifications increase, applicant pool quality is (1) higher in smaller companies and (2) higher when job openings are scarcer. Finally, beyond being certified or not, we find supplemental evidence for effects of the specific certification level achieved (e.g., 2nd versus 15th). This investigation advances theory related to collective turnover, applicant pool quality, and employment branding, and is relevant to company decisions about seeking or re-seeking third party certifications.

We study the effects of actors' audience-specific reputations on their levels of success with different audiences in the same field. Extending recent work that has emphasized the presence of multiple audiences with different concerns, we demonstrate that considering audience specificity leads to an improved understanding of reputation effects. Using data on emerging artists in the field of contemporary art from 2001 to 2010, we investigate the manner in which artists' audience-specific reputations affect their subsequent success with two distinct audiences: museums and galleries. Our findings suggest that audience-specific reputations have systematically different effects with respect to success with museums and galleries. Our findings also illuminate the extent to which audience-specific reputations are relevant for emerging research on the contingent effects of reputation. In particular, our findings support our predictions that audiences differ from one another in terms of the extent to which other signals (specifically, status and interaction with other audiences) enhance or reduce the value of audience-specific reputations. Our study thus advances theory by providing empirical evidence for the value of incorporating audience-specific reputations into the general study of reputation.

Female corporate leadership has drawn increasing attention from academia and practitioners. We contribute to the literature by examining the impact of CEO succession with gender change—i.e., a male CEO succeeded by a female or vice versa. We propose that due to gender differences in executive leadership positions, CEO succession with gender change may amplify the disruption of the CEO succession process and thus adversely affect post-succession firm performance and increase the likelihood of successor early departure. Using data from 3,320 CEO successions in companies listed in China's Shanghai and Shenzhen Stock Exchanges from 1997 to 2010, we find evidence to support this argument. We also find that the negative (positive) impact of male-to-female succession on firm performance (the likelihood of successor early departure) may be weakened by positive organizational attitudes toward female leadership as indicated by the presence of other female leaders on the firm's board of directors and/or top management team, and the successor's inside origin.

Management scholars have noted that leader departures often foreshadow higher turnover intentions (or lower organizational attachment) by subordinates left behind, especially when relationships between the departing leader and the subordinates, or leader-member exchanges (LMX), had been of high quality. In this paper, we posit that the quality of subordinates' relationships with all members of their relational system, not only their leader, must be considered to better understand how leader departures affect subordinates' organizational attachment. Our proposed relationships are illustrated in a theoretical model that includes phenomena at the individual-level (i.e., a subordinate's identification with the departing leader and with his/her organization), at the group-level (i.e., turnover contagion), and at the organizational level (i.e., organization-wide developmental climate). As such, we propose that elucidating how leader-departures affect organizational attachment requires multi-level theorizing and constructs. Theoretical and practical implications of such a 360-degree relational perspective on leader-departure effects are discussed.

Carlo Lipizzi’s Societal impacts of artificial intelligence and machine learning offers a critical and comprehensive analysis of artificial intelligence (AI) and machine learning’s effects on society. This book provides a balanced perspective, cutting through the

The heart and the chip: our bright future with robots, by Daniela Rus and Gregory Mone, is an insightful exploration of the future of robotics and artificial intelligence (AI), focusing on how these technologies will transform every aspect of our lives. Rus, a

The study shows that artificial intelligence (AI) has become highly important in contemporary computing because of its capacity to efficiently tackle intricate jobs that were typically carried out by people. The authors provide scientific literature that analyzes and

US Cartridge 9mm 147 Grain JHP LE Contract Overrun ammunition, 200 rounds for $67.44 or $0.34 each with a coupon code. There is FREE shipping for orders over $149.

President Donald Trump laid out a bold vision for America’s future—one where freedom of speech is non-negotiable, and censorship from both government and big tech is crushed.

WITH working from home being the new norm, the hours tend to blur and we often find ourselves looking for a pick-me-up to get through the day.

Now, working responsibly while enjoying a cold one is made possible with Heineken 0.0 – a beer minus the guilt and the alcohol.

In line with the brand’s advocacy of responsible consumption, Heineken is giving away 10,000 Heineken 0.0 four-can packs to refresh your next long day of work.

To kick-start a “work smart, drink smart” routine, simply sign up at www.heineken00drinksmart.com.

As you drag the Heineken 0.0 can into the calendar within the site, a redemption form will pop up and a free four-can Heineken pack 0.0 will be making its way to your doorstep.

“Our days are often filled with back-to-back meetings and long checklists to complete,” said Heineken Malaysia Bhd marketing director Pablo Chabot.

“There are times we cannot even pause to take a break.

“We hear you, hence we are giving away 10,000 free four-can packs of Heineken 0.0 for you to enjoy while you work.

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The free four-can packs are available on a first come, first served basis and subject to eligibility.

Only one redemption per person is allowed, while stocks last.

Those who miss out on this giveaway can purchase the packs via the Drinkies app (available on both iOS and Android) or www.drinkies.my.

For more information, visit www.heineken00drinksmart.com.

(Heineken 0.0 and all promotions and giveaways are for non-Muslims aged 21 and above only.)

U.S. President-elect Donald Trump on Tuesday named Elon Musk and former Republican presidential candidate Vivek Ramaswamy to lead a newly created Department of Government Efficiency, rewarding two of Trump’s well known supporters from the private sector.

Musk and Ramaswamy “will pave the way for my Administration to dismantle Government Bureaucracy, slash excess regulations, cut wasteful expenditures, and restructure Federal Agencies,“ Trump said in a statement.

Trump said the new department “will provide advice and guidance from outside of government,“ signaling the entity would operate outside the confines of government.

However, it would work with the White House and Office of Management & Budget to “drive large scale structural reform, and create an entrepreneurial approach” to government never seen before.

Trump said their work would conclude by July 4, 2026, making it a “gift” to the country on the 250th anniversary of the signing of the Declaration of Independence.

Musk, ranked by Forbes as the richest person in the world, already stood to benefit from Trump’s victory, with the billionaire entrepreneur expected to wield extraordinary influence to help his companies and secure favorable government treatment.

Musk gave millions of dollars to support Trump’s presidential campaign and made public appearances with him. Trump had said he would offer Musk a role in his administration promoting government efficiency.

He has many links to Washington, opens new tab and his lineup of companies includes electric car company Tesla (TSLA.O), opens new tab, social media platform X and rocket company SpaceX.

“This will send shockwaves through the system, and anyone involved in government waste, which is a lot of people!” Musk said, according to Trump’s statement, which called the new government initiative “potentially ‘The Manhattan Project’ of our time,“ referring to the U.S. plan to build the atomic bomb that helped end World War Two.

Ramaswamy is the founder of a pharmaceutical company who ran for the Republican presidential nomination against Trump and then threw his support behind the former president after dropping out.

“We will not go gently, @elonmusk,“ Ramaswamy said on X.

Musk reposted the announcement from Trump on his X account and added comments such as that, “The merch will be (fire),“ using three fire emojis, and, “People have no idea how much this will move the needle!”

He also posted: “Threat to democracy? Nope, threat to BUREAUCRACY!!!”

The acronym of the new department - DOGE - coincides with the name of the cryptocurrency dogecoin that Musk promotes.

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NEW DELHI: A toxic haze enveloped India’s national capital on Wednesday morning as temperatures dropped and pollution surged, reducing visibility in some parts and prompting a warning from airport authorities that flights may be affected.

Delhi overtook Pakistan’s Lahore as the world’s most polluted city in Swiss group IQAir’s live rankings, with an air quality index (AQI) score of more than 1,000, considered “hazardous”, but India’s pollution authority said the AQI was around 350.

Officials were not immediately available to explain the variation.

The India Meteorological Department (IMD) said the pollution had reduced visibility to 100 metres (328 feet) in some places by around 8 a.m. (0230 GMT).

“Low visibility procedures” were initiated at the city’s Indira Gandhi International Airport, operator Delhi International Airport Limited said in a post on social media platform X.

“While landing and takeoffs continue at Delhi Airport, flights that are not CAT III compliant may get affected,“ the authority said.

CAT III is a navigation system that enables aircraft to land even when visibility is low.

The IMD said the city’s temperature dropped to 17 degrees Celsius (63 degrees Fahrenheit) on Wednesday morning from 17.9C on Tuesday, and may fall further as sunlight remains cut off due to the smog.

Delhi battles severe pollution every winter as cold, heavy air traps dust, emissions, and smoke from farm fires set off illegally in the adjoining, farming states of Punjab and Haryana.

Previously, authorities have closed schools, placed restrictions on private vehicles, and stopped some building work to curb the problem.

The city’s environment minister said last week that the government was keen to use artificial rain to cut the smog.

Pakistan’s Punjab province, which shares a border with India, has also banned outdoor activities, closed schools, and ordered shops, markets and malls to close early in some parts in an effort to protect its citizens from the toxic air.

The Elephant Care Association of South Africa (ECASA) responds to Dr. Adam Cruise’s article, ‘Rules of Engagement: South Africa to ban captive wildlife interactions for tourists’ The Elephant Care Association of South Africa is deeply concerned by Dr Cruise’s article,...