Institutions, controls, and Inter-organizational Trust

A critical challenge facing organizations today is the management of inter-organizational relationships. Unlike extant literature about this topic that considers trust as a monolithic concept the thesis uses a more nuanced approach that encompasses the different dimensions of trust as well as the link between them and relates these dimensions to the structural and institutional context of the relationship. It is argued that the decision to trust is not only calculative but also has a social orientation towards other actors and towards society as a whole. The empirical research consists of three studies presented in chapters two, three and four and uses data collected through two cross-sectional surveys. This thesis contributes to the extant literature in two ways. First, it studies how the interaction of different governance mechanisms influences performance. Results show that a combination of behaviour-based or output-based contract control mechanisms, social control and relational trust can be used to manage buyer-supplier relationships. Second, it enhances our understanding of how trust, an important type of governance, develops between inter-firm collaboration partners. The analyses in the thesis indicate that calculus-based trust is necessary to produce identification-based trust and that voluntary relational signaling both moderates and mediates the relationship between the two types of trust. Next, the results suggest that both the direct effect of institutional trust and its interaction with reliance on contracts have a positive association with calculus-based trust. Overall, this thesis contributes to the management accounting and control literature by providing insight into the causal processes that drive the relationships between the different trust, control and contract related concepts

Impact of Terrorism on International tourism demand

Tourists seek safe and secure destinations and avoid that of overwhelmed with terrorism. This study quantifies the relationship between terrorism and international tourism demand in 200 destination countries and regions for the period of 1995 to 2020. To achieve the objective, the study implied two-dimensional analyses by using the gravity model through Pooled ordinary least square estimator to pay special attention towards demand distribution. Our empirical results depict that, terrorism and terrorism in a destination country has a statistically insignificant relationship on international tourism demand, more specifically, here radical decline in GDP was observed in the sub-period 2006 – 2020 due to the global financial crisis and its aftershocks which badly affects tourist’s attraction to destination countries. This study pinpoints new insights for national tourism policymakers and business purposes.          &nbsp

Millimetre-wave antennas and systems for the future 5G

Editorial of the special issue on Millimetre-Wave Antennas and Systems for the Future 5

Laparoscopic-Assisted Management of Impalpable Testis in Patients Older than 10 Years

Laparoscopic-assisted single-stage orchiopexy appears to be a safe, effective procedure for intraabdominal testis in adolescent and older patients

Deep learning for religious and continent-based toxic content detection and classification

With time, numerous online communication platforms have emerged that allow people to express themselves, increasing the dissemination of toxic languages, such as racism, sexual harassment, and other negative behaviors that are not accepted in polite society. As a result, toxic language identification in online communication has emerged as a critical application of natural language processing. Numerous academic and industrial researchers have recently researched toxic language identification using machine learning algorithms. However, Nontoxic comments, including particular identification descriptors, such as Muslim, Jewish, White, and Black, were assigned unrealistically high toxicity ratings in several machine learning models. This research analyzes and compares modern deep learning algorithms for multilabel toxic comments classification. We explore two scenarios: the first is a multilabel classification of Religious toxic comments, and the second is a multilabel classification of race or toxic ethnicity comments with various word embeddings (GloVe, Word2vec, and FastText) without word embeddings using an ordinary embedding layer. Experiments show that the CNN model produced the best results for classifying multilabel toxic comments in both scenarios. We compared the outcomes of these modern deep learning model performances in terms of multilabel evaluation metrics

Qualitative Research in Applied Linguistics: A Practical Introduction, edited by Juanita Heigham and Robert Croker

This review makes a point in favor of the assertion made for the book that it is a practical introduction to the qualitative research in applied linguistics. The book consists of four parts: an overview of qualitative research, qualitative research methods, qualitative data collection methods, ethical practice issues and the writing of research reports. After proving a rich introduction to the qualitative research, the book discusses qualitative research approaches using a reader-friendly and interactive structure: pre-reading and post-reading questions along with the list of further readings. Then the data collection tools have been thoroughly discussed. What makes this book more useful is the use of illustrative examples for each qualitative research approach and data collection tool. The last part discusses core issues of ethics and drafting a research report. From the perspective of a novice researcher, it has achieved the goal of educating readers about qualitative research methods and data collection tools, as it gradually tracks the reader and provides them with a linking concept for a better understanding. However, reference to one study for both ethnography and case study remains a confusing point. Besides, the review suggests addition of some images to make reading of the book more interesting, especially for visual learners. Besides, a diagram should be given at the end of research methods chapters to outline the steps taken by researchers to do their studies

Authorship identification using ensemble learning

With time, textual data is proliferating, primarily through the publications of articles. With this rapid increase in textual data, anonymous content is also increasing. Researchers are searching for alternative strategies to identify the author of an unknown text. There is a need to develop a system to identify the actual author of unknown texts based on a given set of writing samples. This study presents a novel approach based on ensemble learning, DistilBERT, and conventional machine learning techniques for authorship identification. The proposed approach extracts the valuable characteristics of the author using a count vectorizer and bi-gram Term frequency-inverse document frequency (TF-IDF). An extensive and detailed dataset, All the news is used in this study for experimentation. The dataset is divided into three subsets (article1, article2, and article3). We limit the scope of the dataset and selected ten authors in the first scope and 20 authors in the second scope for experimentation. The experimental results of proposed ensemble learning and DistilBERT provide better performance for all the three subsets of the All the news dataset. In the first scope, the experimental results prove that the proposed ensemble learning approach from 10 authors provides a better accuracy gain of 3.14% and from DistilBERT 2.44% from the article1 dataset. Similarly, in the second scope from 20 authors, the proposed ensemble learning approach provides a better accuracy gain of 5.25% and from DistilBERT 7.17% from the article1 dataset, which is better than previous state-of-the-art studies

Tweet-to-Act: Towards Tweet-Mining Framework for Extracting Terrorist Attack-related Information and Reporting

The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific information, e.g., a terrorist attack, and alerts law enforcement, emergency services, and media outlets. Specifically, the proposed framework, Tweet-to-Act (T2A), employs word embedding to transform tweets into a vector space model and then utilizes theWord Mover’s Distance (WMD) to cluster tweets for the identification of incidents. To extract reliable and valuable information from a large dataset of short and informal tweets, the proposed framework employs sequence labeling with bidirectional Long Short-Term Memory based Recurrent Neural Networks (bLSTM-RNN). Extensive experimental results suggest that our proposed framework, T2A, outperforms other state-of-the-art methods that use vector space modeling and distance calculation techniques, e.g., Euclidean and Cosine distance. T2A achieves an accuracy of 96% and an F1-score of 86.2% on real-life datasets