International Journal of Modern Education and Computer Science @ijmecs
Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1096
Improving the Efficiency of Term Weighting in Set of Dynamic Documents
Статья научная
In real information systems, there are few static documents. On the other hand, there are too many documents that their content change during the time that could be considered as signals to improve the quality of information retrieval. Unfortunately, considering all these changes could be time-consuming. In this paper, a method has been proposed that the time of analyzing these changes could be reduced significantly. The main idea of this method is choosing a special part of changes that do not make effective changes in the quality of information retrieval; but it could be possible to reduce the analyzing time. To evaluate the proposed method, three different datasets selected from Wikipedia. Different factors have been assessed in term weighting and the effect of the proposed method investigated on these factors. The results of empirical experiments showed that the proposed method could keep the quality of retrieved information in an acceptable rate and reduce the documents' analysis time as a result.
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Improvised Scout Bee Movements in Artificial Bee Colony
Статья научная
In the basic Artificial Bee Colony (ABC) algorithm, if the fitness value associated with a food source is not improved for a certain number of specified trials then the corresponding bee becomes a scout to which a random value is assigned for finding the new food source. Basically, it is a mechanism of pulling out the candidate solution which may be entrapped in some local optimizer due to which its value is not improving. In the present study, we propose two new mechanisms for the movements of scout bees. In the first method, the scout bee follows a non-linear interpolated path while in the second one, scout bee follows Gaussian movement. Numerical results and statistical analysis of benchmark unconstrained, constrained and real life engineering design problems indicate that the proposed modifications enhance the performance of ABC.
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Increasing Teacher Competence in Cybersecurity Using the EU Security Frameworks
Статья научная
A study was undertaken to identify the missing skill and expertise of teachers and other stakeholders in the field of EU cyber security regulatory documents and frameworks. In order to increase the knowledge in this area and promote the EU security frameworks the model for the continuous building of teacher competence has been proposed and planned to be implemented in Ukraine. The proposed model will contribute towards improving the excellence of educators and academics, as well as increase competitiveness of educational programmes on cybersecurity among similar institutions in the EU countries. Various studies focused on the development of competence of the students to prepare them for the job market and build a comprehensive portfolio, education-business partnerships and collaboration. However, the issue of developing teacher expertise to achieve high quality of education remains open. We highlighted the importance of creating a cybersecurity ecosystem through the cooperation with different stakeholders and by implementing the model for continuous development of teacher competence. Before building the model the overview and analysis of different teacher professional development approaches were conducted. Analysis showed that for our goal the best suitable approach is a model that consists of two stages based on self-education and group education approaches with 7 processes inside. The study revealed that competence may be achieved through a number of activities which may be grouped under four generic categories: student and staff training, academic and business seminars, business-oriented roundtable debates, and research. The model uses as main methods of achieving a better quality of education in cybersecurity the development of new training courses and modernization of educational programs, and for raising awareness among businesses and regulatory bodies - the workshops and roundtables.
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Статья научная
Disruption era had affected to every sector, including higher education sector. Between 2015 and 2019, more than 130 private university were closed for a different of reasons. Basically, they are not ready to face the change. Innovation disruption created new situations, thus created new demand while destroying prior situations. Many businesses collapsed suddenly because they are not aware of the inflection point that suddenly destroys the business model that has been run for years. Nevertheless, the current situation gave hints to new thoughts, including the development business model called infinite game theory. This paper examined how strategic inflection points can have occurred in the higher education sector and the infinite games model can be an alternative strategy in order the higher education sector withstand disruptive innovation era.
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Indonesian Universities Readiness in Providing Professional HR in Geospatial Information
Статья научная
The need for geospatial data and information for development planning is major and inevitable at this time. Economic and social development requires the creation of a prosperous society in terms of meeting the adequacy of life and the extent to which environmental sustainability is maintained. The need to achieve both economic and social development goals requires data and information that can be provided by geospatial information. The question is how much is the opportunity that provides benefits for the community responded well by universities (PT) that provide experts in the field of geospatial information? To answer these questions, this research: 1) analyses the PT readiness from the aspect of human resource development in the field of geospatial information; 2) assess how much the ability of universities to provide experts in the field of geospatial information. The results of the study show that PT curriculums in Indonesia are fit with tiers 1 and 2 geospatial HR competencies in accordance with the DOLETA curriculum, and development is needed to meet levels 3 to 9. In terms of HR capabilities of the 41 PTs that carry out geographic education, only 2 PTs produce Geospatial HR. The paper offers e-learning or distance education is one of the approriate solution to fill in the gap between need and availability of Professional HR in Geospatial Information.
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Induction of Interactive Methods to Teach Software Engineering Course
Статья научная
Software engineering (SE) field is considered as a backbone of software industry. Students suffer many problems and difficulties when they approach to industry due to the old contents of SE course, lack of practice and inability in the subject area. The main reason of the SE course problems is the lack of coordination or gap between software industry and academia. This paper proposed a novel solution to enhance SE curriculum to bridge the gap between industry and academia. The proposed solution will assist students at optimal level in their professional careers to achieve goals of SE course. Survey research methodology is used to evaluate the proposed solution and the results of data analysis are highly encouraging. It is anticipated that the universities will find this research helpful to improve the quality of SE course.
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Статья научная
The phenomenon of product/business failure, as well as lack of environmental sustainability and learning limitations, is fast becoming a recurrent ‘disease’ for investors, designers, design sponsors and education policy makers in many developing countries with poor persuasiveness contributing a large quota to such failures. This has greatly hampered the education, poverty alleviation and developmental efforts of the governments of such societies. In a bid to curb this negative trend, children, who are major influencers in product purchase behaviours of adults, have been targeted specifically by persuasive designers, in an effort to both educate and adopt them as means of reaching the larger populace. However, most researches in current persuasive system designs are limited to the information communication/management technology or computerized environments. These systems are technology/internet-driven and many potential users, in reality, in the developing world, unlike the rest of the world is often made to believe, do not have open access to such systems. Unfortunately, the effectiveness of any persuasive system is dependent on its accessibility to its user(s). Technological backwardness (often concealed behind ostentatious self-deceptive facades) has led to the poor persuasiveness of local persuasive systems and products in the third worlds. Therefore, adopting a mixed method for establishing the factor(s) limiting the efficiency of the computer/electronic-human interaction persuasive systems (CHIPS) in South-West Nigeria (N=900), this study established the need to adopt more of the product/entity-human interaction persuasive system (PEHIPS) as an effective alternative for third world countries as, based on the study outcomes, the CHIPS proved less relatively effective in comparison to PEHIPS in rural regions. It however recommends the alternating adoption of a combination of both computerized and entity/product driven systems for the purpose of optimizing persuasive effectiveness in developing worlds.
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Information Culture Formation as the Most Promising Direction of Individual`s General Culture
Статья научная
The essence, structure, formation and conditions of culture have been investigated in the article. The types of culture have been studied in accordance with the development stages of civilization and different spheres. Various approaches on information culture as a part of common culture of a human have been analyzed. The formation issues of personal information culture have been reviewed according to the requirements of information society as a new development stage of a society. The features of human behavior in the virtual environment have been examined. The influence of education, profession and activity peculiarities on the formation of personal information culture has been investigated.
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Information Hiding Using Least Significant Bit Steganography and Cryptography
Статья научная
Steganalysis is the art of detecting the message's existence and blockading the covert communication. Various steganography techniques have been proposed in literature. The Least Significant Bit (LSB) steganography is one such technique in which least significant bit of the image is replaced with data bit. As this method is vulnerable to steganalysis so as to make it more secure we encrypt the raw data before embedding it in the image. Though the encryption process increases the time complexity, but at the same time provides higher security also. This paper uses two popular techniques Rivest, Shamir, Adleman (RSA) algorithm and Diffie Hellman algorithm to encrypt the data. The result shows that the use of encryption in Steganalysis does not affect the time complexity if Diffie Hellman algorithm is used in stead of RSA algorithm.
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Статья научная
Data security is one of the biggest concerns in cloud computing environment. Although the advantages of storing data in cloud computing environment is extremely high, there arises a problem related to data missing. CyberLiveApp (CLA) supports secure application development between multiple users, even though cloud users distinguish their vision privileges during storing of data. But CyberLiveApp failed to integrate the system with certain cloud-based computing environments on multi-server. Environmental Decision Support Systems (EDSS) move away the technical load and focus mainly on decision-making activities. EDDS does not have a secure collaborative decision-making experience on cloud services. To integrate the security level for multi-server cloud infrastructure, Information Interpretation Code on Multi-Server (IICM-S) is proposed in this paper. To ensure the information with relevance to security on cloud-based computing environments, Information Interpretation Code (IIC) algorithm is initially developed. Thus, IIC guarantee that all information pertaining to cloud is in secured condition in order to prove the trustworthiness of data. In addition, the multi-sever cloud infrastructure in IIC provides access point for secure information recovery from cloud data server. The multi-server cloud infrastructure with IIC algorithm performs the recovery task on multi-server cloud infrastructure. The Multi-server Information (MI) scheme measures the integrity level with effective data recovery process. The integrity level on multi-server cloud infrastructure is ensured using two components, verifier and verify shifter. MI scheme proficiently check integrity using these two components so that not only the data integrity is provided as well as security is ensured in all cases using IICM-S. Experiment is conducted in the Cloudsim platform on the factors such as average integration time on multi-server, security level, recovery efficiency level.
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Information Literacy and its Application in Nursing Education
Статья научная
Information literacy has been embedded by the university into the first year nursing curriculum. Embedding this literacy will not necessarily ensure the nursing graduates will apply this skill to the provision of high quality evidence-based health care. For this to happen information literacy skills gained in the classroom must contribute to sound decision making based on best practice evidence. This paper discusses the findings of a three phases research project designed to (i) determine the information literacy skills, confidence and problem solving abilities of students entering the university’s Bachelor of Nursing Program; (ii) determine if information literacy skills, confidence and problem solving abilities improve as a result of embedding information literacy instruction into a nursing course; and iii) ascertain whether there are any differences in information literacy skills, confidence and problem solving abilities based on the students demographic information. Data were collected in two sequential semesters using a questionnaire administered to the students. The response rates in semester one and two were 45 and 56 per cent respectively. Student confidence and awareness regarding information literacy is positively affected by learning experiences from semester one to semester two. Students indicated that they need both specific and regular instruction to adequately retain learning. Overall the study suggests that embedding information literacy instruction into the first year, first semester nursing program is beneficial. By the second semester the information literacy confidence and awareness of students increased as a result of intra-curricular instruction, however, problem solving skills need to be improved.
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Статья научная
This article presents the implementation of a machine learning-based face anti-spoofing method to enhance the security of an educational information portal for university students. The study addresses the challenge of preventing academic dishonesty by ensuring that only authorized individuals can complete intermediate and final assessment tasks. The proposed method leverages the Tiny neural network model, selected for its efficiency in compact data processing, alongside the dlib system in Python and the LCC_FASD dataset, which enables precise detection of 68 facial landmarks. Using a confusion matrix to evaluate performance, the method achieved a 94.47% accuracy in detecting spoofing attempts. These findings demonstrate the effectiveness of the proposed approach in safeguarding educational platforms and maintaining academic integrity.
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Статья научная
A method of choosing swarm optimization algorithms and using swarm intelligence for solving a certain class of optimization tasks in industry-specific geographic information systems was developed considering the stationarity characteristic of such systems. The method consists of 8 stages. Classes of swarm algorithms were studied. It is shown which classes of swarm algorithms should be used depending on the stationarity, quasi-stationarity or dynamics of the task solved by an industry geographic information system. An information model of geodata that consists in a formalized combination of their spatial and attributive components, which allows considering the relational, semantic and frame models of knowledge representation of the attributive component, was developed. A method of choosing optimization methods designed to work as part of a decision support system within an industry-specific geographic information system was developed. It includes conceptual information modeling, optimization criteria selection, and objective function analysis and modeling. This method allows choosing the most suitable swarm optimization method (or a set of methods).
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Information Technology for Gender Voice Recognition Based on Machine Learning Methods
Статья научная
The growing use of social networks and the steady popularity of online communication make the task of detecting gender from posts necessary for a variety of applications, including modern education, political research, public opinion analysis, personalized advertising, cyber security and biometric systems, marketing research, etc. This study aims to develop information technology for gender voice recognition by sound based on supervised learning using machine learning algorithms. A model, methods and means of recognition and gender classification of voice speech samples are proposed based on their acoustic properties and machine learning. In our voice gender recognition project, we used a model built based on the neural network using the TensorFlow library and Keras. The speaker’s voice was analysed for various acoustic features, such as frequency, spectral characteristics, amplitude, modulation, etc. The basic model we created is a typical neural network for text classification. It consists of the input layer, hidden layers, and the output layer. For text processing, we use a pre-trained word vector space such as Word2Vec or GloVe. We also used such techniques as dropout to prevent model overtraining, such activation functions as ReLU (Rectified Linear Unit) for non-linearity, and a softmax function in the last layer to obtain class probabilities. To train a model, we used the Adam optimizer, which is a popular gradient descent optimization method, and the “sparse categorical cross-entropy” loss function, since we are dealing with multi-class classification. After training the model, we saved it to a file for further use and evaluation of new data. The application of neural networks in our project allowed us to build a powerful model that can recognize a speaker’s gender by voice with high accuracy. The intelligent system was trained using machine learning methods with each of the methods being analysed for accuracy: K-Nearest Neighbours (98.10%), Decision Tree (96,69%), Logistic Regression (98.11%), Random Forest (96.65%), Support Vector Machine (98.26%), neural networks (98.11%). Additional techniques such as regularization and optimization can be used to improve model performance and prevent overtraining.
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Information Technology for Generating Lyrics for Song Extensions Based on Transformers
Статья научная
The article develops technology for generating song lyrics extensions using large language models, in particular the T5 model, to speed up, supplement, and increase the flexibility of the process of writing lyrics to songs with/without taking into account the style of a particular author. To create the data, 10 different artists were selected, and then their lyrics were selected. A total of 626 unique songs were obtained. After splitting each song into several pairs of input-output tapes, 1874 training instances and 465 test instances were obtained. Two language models, NSA and SA, were retrained for the task of generating song lyrics. For both models, t5-base was chosen as the base model. This version of T5 contains 223 million parameters. The analysis of the original data showed that the NSA model has less degraded results, and for the SA model, it is necessary to balance the amount of text for each author. Several text metrics such as BLEU, RougeL, and RougeN were calculated to quantitatively compare the results of the models and generation strategies. The value of the BLEU metric is the most diverse, and its value varies significantly depending on the strategy. At the same time, Rouge metrics have less variability and a smaller range of values. In total, for comparison, we used 8 different decoding methods for text generation supported by the transformers library, including Greedy search, Beam search, Diverse beam search, Multinomial sampling, Beam-search multinomial sampling, Top-k sampling, Top-p sampling, and Contrastive search. All the results of the lyrics comparison show that the best method for generating lyrics is beam search and its variations, including ray sampling. The contrastive search usually outperformed the usual greedy approach. The top-p and top-k methods do not have a clear advantage over each other, and in different situations, they produced different results.
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Innovative Approaches to Higher Education: Blended Learning in Kazakhstan
Статья научная
The research problem is based on the study of the possibilities of expanding methodological approaches, educational technologies, and educational programs for the implementation of blended learning and increasing the level of its effectiveness in the educational system of Kazakhstan. This study aims to identify the best conditions for implementing blended learning that would meet the technical capabilities of the university, the educational programs, and the interests and needs of all participants of the educational process. For this, the following data collection methods were used: online surveys, quantitative and qualitative analyses, and facilitation tools, such as World Café, Future Search, ranking, and Spearman's correlation analysis. The results show that more than half of the students (58%) and teachers (65%) were not satisfied with the existing structure of blended learning at the university. This research suggests involving all participants in the educating process when adopting the blended mode of learning to enhance the efficacy of the blended learning program. The practical significance of this research lies in its determination of the optimal conditions for implementing blended learning in the university programs of Kazakhstan. The engagement of all stakeholders in the Learning pathway in decision-making regarding hybrid education, taking into account the technical capabilities of universities and the individual needs of students and instructors, aims not only to address current issues but also to enhance the quality of education and prepare graduates to meet the demands of the contemporary labor market. Such an approach to research and innovation implementation in Kazakhstan's education could foster the development of more flexible, adaptive, and effective educational systems that meet the requirements of the modern world.
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Integrating Apple iPads into University Computing Courses
Статья научная
The use of mobile devices is becoming increasingly common in both society and in the K-12 environment. Products such as the Apple iPad and the Microsoft Surface, among others, have matured to a point where university faculty are striving to integrate this increasingly ubiquitous technology into the classroom and the curriculum. This paper represents a case study examining one attempt to integrate the use of tablets into five university-level computing courses during the 2015-2016 academic year. The author used a set of iPads and accompanying classroom technology (e.g. Apple TV, keyboards) in an attempt to engage students and build their problem-solving and collaborative skills. Student feedback suggests that students were engaged, and the results for the iPad's impact on problem-solving and collaborative skills improved over the course of the year. A number of challenges were observed, including inadequate student knowledge of tablets, wireless connectivity issues, student resistance to the group learning afforded by the iPads, and keeping the tablets charged and clean. Future plans for the study intend to address the challenges uncovered, using student and instructor feedback as an impetus for future development. This paper serves as an experiential report designed to inform other faculty who may be looking into similar projects.
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Integration Colour and Texture Features for Content-based Image Retrieval
Статья научная
Content-Based Image Retrieval offers an automatic way to extract visual image contents such as colour, texture, and shape so-called extracted features. Due to growing volume of digital images, Content-Based Image Retrieval is emerged to store and retrieved images from large scale databases. However, Content-Based Image Retrieval faces a challenge of meaning “Semantic gap” between machine and human conceptual. How to reduce this gap between colour and/or texture features that represent an object in the image? It is still the challenge that basically related to the effectiveness of image representation by extracted features and similarity measures between a query image features and database image features. Hence, different visual features have been proposed such as Gary Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Discrete Wavelet Transform (DWT) texture features that are extracted from gray-scale images. This paper presents an unsupervised algorithm that exploits data and score-level fusion to address the semantic gap. The algorithm first extracts mentioned features from colour images in HSV and YCbCr colour spaces to increase the effectiveness of image representation by integrating texture and colour visual information in terms of data-level fusion. Resulted similarity retrieval values are then fused in three versions of score-level fusion, summing values without weights, fixed, and adaptive weights using linear regression to raise relevant images in a ranked retrieved images list. WANG standard colour images are used to implement the algorithm. Rates of achievement in image retrievals are enhanced at both levels.
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Статья научная
The implementation of the concept of building an information society implies a widespread introduction of IT in all areas of modern society, including in the field of science. Here, the further progressive development and deepening of scientific research and connections presuppose a special role of e-science. E-science is closely connected with the innovative potential of IT, including the Internet technologies, the Internet of things, cyber-physical systems, which provide the means and solutions to the problems associated with the collection of scientific data, their storage, processing, and transmission. The integration of cyber-physical systems is accompanied by the exponential growth of scientific data that require professional management, analysis for the acquisition of new knowledge and the qualitative development of science. In the framework of e-science, cloud technologies are now widely used, which represent a centralized infrastructure with its inherent characteristic that is associated with an increase in the number of connected devices and the generation of scientific data. This ultimately leads to a conflict of resources, an increase in processing delay, losses, and the adoption of ineffective decisions. The article is devoted to the analysis of the current state and problems of integration of cyber-physical systems in the environment of e-science and ways to effectively solve key problems. The environment of e-science is considered in the context of a smart city. It presents the possibilities of using the cloud, fog, dew computing, and blockchain technologies, as well as a technological solution for decentralized processing of scientific data.
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Статья научная
The article develops a technology for finding tweet trends based on clustering, which forms a data stream in the form of short representations of clusters and their popularity for further research of public opinion. The accuracy of their result is affected by the natural language feature of the information flow of tweets. An effective approach to tweet collection, filtering, cleaning and pre-processing based on a comparative analysis of Bag of Words, TF-IDF and BERT algorithms is described. The impact of stemming and lemmatization on the quality of the obtained clusters was determined. Stemming and lemmatization allow for significant reduction of the input vocabulary of Ukrainian words by 40.21% and 32.52% respectively. And optimal combinations of clustering methods (K-Means, Agglomerative Hierarchical Clustering and HDBSCAN) and vectorization of tweets were found based on the analysis of 27 clustering of one data sample. The method of presenting clusters of tweets in a short format is selected. Algorithms using the Levenstein Distance, i.e. fuzz sort, fuzz set and Levenshtein, showed the best results. These algorithms quickly perform checks, have a greater difference in similarities, so it is possible to more accurately determine the limit of similarity. According to the results of the clustering, the optimal solutions are to use the HDBSCAN clustering algorithm and the BERT vectorization algorithm to achieve the most accurate results, and to use K-Means together with TF-IDF to achieve the best speed with the optimal result. Stemming can be used to reduce execution time. In this study, the optimal options for comparing cluster fingerprints among the following similarity search methods were experimentally found: Fuzz Sort, Fuzz Set, Levenshtein, Jaro Winkler, Jaccard, Sorensen, Cosine, Sift4. In some algorithms, the average fingerprint similarity reaches above 70%. Three effective tools were found to compare their similarity, as they show a sufficient difference between comparisons of similar and different clusters (> 20%). The experimental testing was conducted based on the analysis of 90,000 tweets over 7 days for 5 different weekly topics: President Volodymyr Zelenskyi, Leopard tanks, Boris Johnson, Europe, and the bright memory of the deceased. The research was carried out using a combination of K-Means and TF-IDF methods, Agglomerative Hierarchical Clustering and TF-IDF, HDBSCAN and BERT for clustering and vectorization processes. Additionally, fuzz sort was implemented for comparing cluster fingerprints with a similarity threshold of 55%. For comparing fingerprints, the most optimal methods were fuzz sort, fuzz set, and Levenshtein. In terms of execution speed, the best result was achieved with the Levenshtein method. The other two methods performed three times worse in terms of speed, but they are nearly 13 times faster than Sift4. The fastest method is Jaro Winkler, but it has a 19.51% difference in similarities. The method with the best difference in similarities is fuzz set (60.29%). Fuzz sort (32.28%) and Levenshtein (28.43%) took the second and third place respectively. These methods utilize the Levenshtein distance in their work, indicating that such an approach works well for comparing sets of keywords. Other algorithms fail to show significant differences between different fingerprints, suggesting that they are not adapted to this type of task.
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