Journal of Information and Organizational Sciences (JIOS)
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Forms and conversions of the economic capital of Croatian entrepreneurs in the computer programming industry with insights into variations of the company's development stages
No full textThe paper examines traditional knowledge about the role of financial and physical resources in entrepreneurship. Based on Bourdieu's theory, we search for an answer to the question of which forms of economic capital are used by Croatian entrepreneurs in the computer programming industry and what their value in the context of accessing other forms of capital is. The study is based on a qualitative methodological approach. In-depth interview technique accompanied by unstructured observations was used in data collection. In addition to primary data, research includes the use of qualitative and quantitative secondary data. Research suggests that in the computer programming industry, economic capital is just one tool in the conversion game of entrepreneurial capital. Moreover, the interviewed entrepreneurs attach the least importance to it in business, favoring the value of intangible resources such as specialized knowledge, business connections and personal acquaintances. The company's start-up phase is characterized by the entrepreneur's reliance on personal savings, which is replaced by financing based on retained earnings in the later stages of business. Other forms of growth financing, such as bank loans and recapitalization of external investors, are used by a minority of larger companies, with a good base of symbolic capital. The results on capital conversions indicate relatively easy conversion of economic capital into cultural capital and symbolic capital, and less frequent use of economic capital to create social capital. Fresh insights into the entrepreneurs' perceptions provided by the study expand existing knowledge about entrepreneurship within the computer programming industry, suggesting that it is an industry with huge potential for young talents without a personal financial base that is commonly considered a precondition for entering entrepreneurship
Towards a Combination of Metrics for Machine Translation
No full textIn this scholar, we compare three metrics for machine translation, from English to French and vice versa, and we give some combination formulas based on some schemes, algorithms, and machine learning tools. As an experimental dataset, we consider 10 English and French theses abstracts published in the web with four free in charge machine translation systems. Five combinations, with the same implicit weights, are considered namely: (BLEU+NIST), (BLEU+ (1-WER)), (NIST+(1-WER)), (BLEU+NIST+(1-WER)), and (FR(BLEU)+FR(NIST)+FR(WER)). These combinations are also considered differently through generating weights parameters on the basis of regression. The results of 12 formulas are computed and compared then in total. According to the obtained results, average regression combinations based on machine learning step are the best, especially with the three basic metrics, followed by average WER metric in the case of English to French. For French to English, (FR(BLEU)+FR(NIST)+FR(WER)) combination is the best followed respectively by the average regression combination with both first parameters (Reg(α,β)) and average BLEU basic metric. Another performance criterion is considered here, in the second position, namely: the number of times, over the 10 abstracts, where the formula is the best. Based on the obtained results, combination with regression based on the first and the last parameters (Reg(α,γ)) outperforms the others, in the case of English to French, with 3 times followed by Reg(β,γ), Reg(α,β,γ), NIST+(1-WER), and the basic metrics (BLEU, NIST, and WER) with 2 times for each of them. For French to English, the basic WER metric outperforms the others with three times followed by BLEU, (BLEU+ (1-WER)), (FR(BLEU)+FR(NIST)+FR(WER)), and Reg(α,γ) with 2 times for each of them. To note that there is a room of improvement for the combinations with1.0914 in the case of English to French and 1.01 in the case of French to English
A Modified Boosted Ensemble Classifier on Location Based Social Networking
No full textOne of the research issues that researchers are interested in is unbalanced data classification techniques. Boosting approaches like Wang's Boosting and Modified Boosted SVM (MBSVM) have been demonstrated to be more effective for unbalanced data. Our proposal The Modified Boosted Random Forest (MBRF) classifier is a Random Forest classifier that uses the Boosting approach. The main motivation of the study is to analyze sentiment of geotagged tweets understanding the state of mind of people at FIFA and Olympics datasets. Tree based model Random Forest algorithm using boosting approach classifies the tweets to build a recommendation system with an idea of providing commercial suggestions to participants, recommending local places to visit or perform activities. MBRF employs various strategies: i) a distance-based weight-update method based on K-Medoids ii) a sign-based classifier elimination technique. We have equally partitioned the datasets as 70% of data allocated for training and the remaining 30% data as test data. Our imbalanced data ratio measured 3.1666 and 4.6 for FIFA and Olympics datasets. We looked at accuracy, precision, recall and ROC curves for each event. The average AUC achieved by MBRF on FIFA dataset is 0.96 and Olympics is 0.97. A comparison of MBRF and Decision tree model using 'Entropy' proved MBRF better
Data-Centric Optimization Approach for Small, Imbalanced Datasets
No full textData-centric is a newly explored concept, where the attention is given to data optimization methodologies and techniques to improve model performance, rather than focusing on machine learning models and hyperparameter tunning. This paper suggests an effective data optimization methodology for optimizing imbalanced small datasets that improves machine learning model performance.
This paper is focused on providing an effective solution when the number of observations is not enough to construct a machine learning model with high values of the estimated magnitudes. For example, the majority of the observations are labeled as one class (majority class), and the rest as the other, commonly considered as the class of interest (minority class). The proposed methodology does not depend on the applied classification models, rather it is based on the properties of the data resampling approach to systematically enhance and optimize the training dataset. The paper examines numerical experiments applying the data centric optimization methodology, and compares with previously obtained results by other authors
Publication Ethics; List of Reviewers; Instructions for Authors
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Using the 3D Protein Structure as Key to Encrypt Images
No full textIn the digital world, information is exposed anytime, anywhere, to everybody, hence its privacy is a crucial matter. No matter how complicated the encryption algorithm is, it requires a strong and hard-to-break encryption key. Since the three-dimensional protein sequence structures are usually highly conserved, even better than DNA sequences, this work presents an innovative scheme for implementing the protein sequence and structural to build protein data tables that are then used to generate extremely strong encryption-keys. An image-encryption scheme designed to implement such encryption keys is developed and produced compatible security strength with existing encryption schemes. Prototype experiments resulted in an average normalized mean absolute error of 66.84%, an average peak signal to noise ratio of 6.85 dB, and comparable entropy with other cryptosystems. The obtained results make this scheme a promising color image protection technique for various applications
Digital transformation of strategic management of SMEs in the Czech Republic
No full textStrategic management of SMEs is perceived as crucial backbone of their business, as it impacts their business models and internal processes all the way up to digital innovation measured by the level of digital maturity. How do the strategic management factors influence the digital maturity? Through quantitative analysis of 76 respondents representing SMEs in the Czech Republic, the research data was collected and statistically tested. Results imply that strategic management factors affect the level of digital maturity. This paper contributes to prior literature by practical implementation of modified digital maturity model and by addressing the correlation of strategic management factors and the level of digital maturity. Limitations springing from sample site and environment are addressed and discussion on the results is conducted. Author proposes a conclusion that strategic management factors can be perceived as a driver of digital transformation, emphasizing the need for future research and practical discussion
Query Refinement into Information Retrieval Systems: An Overview
No full textQuery, expressing the user need and requirement, has an important role, in an information retrieval system, for reaching a high accuracy search. In this paper, we present an overview of the different refinement operations that the query may undergo, in the sake to enhance performance of an information retrieval system, such as: automatic query formulation through words prevision, query reformulation, query expansion, and query optimization
Usage of Cloud Computing Application by Students from Kurukshetra University: the Current State and Perspectives
No full textThe term "cloud computing" refers to a variety of tools and applications used by organizations to manage their work processes online. Apart from providing the expected benefits, the gadgets and applications of the cloud are helpful for education. The purpose of this study is not to examine the outcomes of cloud computing in universities, but to examine how students use cloud applications. A survey research design was used to examine how students of Kurukshetra University use cloud computing applications. 200 students from the streams of science, management, social science, and arts were randomly selected to answer a structured questionnaire. A response rate of 95% was achieved by 48 students in Science, 50 in Management, 48 in Social Science, and 44 in Arts who completed and returned the questionnaire on time. The analyzed data is presented in a table that includes the Likert scale mean, standard deviation, and the cross-table Chi-square test. In the study, most students highly preferred G Suite Cloud (57%) and Microsoft (40%) learning applications. It can be seen from the study that WhatsApp (98%), Facebook (96%), and YouTube (97%) are the most preferred cloud-based social media among the students, most students preferred Google Meet (95%) and Zoom (91%) meeting application, and most students preferred Google Drive (68%) and pCloud (67%) for data storage