Advances in database systems education: Methods, tools, curricula, and way forward.

Fundamentals of Database Systems is a core course in computing disciplines as almost all small, medium, large, or enterprise systems essentially require data storage component. Database System Education (DSE) provides the foundation as well as advanced concepts in the area of data modeling and its implementation. The first course in DSE holds a pivotal role in developing students' interest in this area. Over the years, the researchers have devised several different tools and methods to teach this course effectively, and have also been revisiting the curricula for database systems education. In this study a Systematic Literature Review (SLR) is presented that distills the existing literature pertaining to the DSE to discuss these three perspectives for the first course in database systems. Whereby, this SLR also discusses how the developed teaching and learning assistant tools, teaching and assessment methods and database curricula have evolved over the years due to rapid change in database technology. To this end, more than 65 articles related to DSE published between 1995 and 2022 have been shortlisted through a structured mechanism and have been reviewed to find the answers of the aforementioned objectives. The article also provides useful guidelines to the instructors, and discusses ideas to extend this research from several perspectives. To the best of our knowledge, this is the first research work that presents a broader review about the research conducted in the area of DSE.

Unusual Driver Behavior Detection in Videos Using Deep Learning Models.

Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers' recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver's abnormal behavior.

Serious game design model for language learning in the cultural context.

Digital learning environments have been gaining prominence during the last few years. In particular, the rising usage of mobile devices, including smartphones and tabs, has invited researchers to design and develop learning applications and games for such platforms. Mobile applications and games have been developed for learning languages like many other domains. However, most of these games are fun-based and lack a holistic design and development approach. Therefore, as a principal contribution, this research presents a theoretical model for designing language learning games in a cultural context. The proposed model combines the elements of sociocultural theory with the concepts and elements of gamification, keeping in view the requirements and educational settings, including level and mode of education, etc., to ensure the effectiveness and usability of the developed game. Subsequently, based on the proposed model, a Language Learning Game (LLG) has been designed and developed through a systematic process that involves game design, low-fidelity, and high-fidelity prototyping and its validation. The LLG has been evaluated comprehensively at different stages by incorporating standard methods. Whereby this research augments the existing set of heuristics by proposing a number of specialized heuristics for the evaluation of serious games to gauge their conformance to the cultural context. The evaluation results show that the game has overall usability scores of 90%. While the quasi-experiment-based pre-test and post-test have been conducted, the results reveal that the results obtained by LLG are statistically significantly better than adopted mobile application and traditional group.

An efficient algorithm for recognition of human actions.

Recognition of human actions is an emerging need. Various researchers have endeavored to provide a solution to this problem. Some of the current state-of-the-art solutions are either inaccurate or computationally intensive while others require human intervention. In this paper a sufficiently accurate while computationally inexpensive solution is provided for the same problem. Image moments which are translation, rotation, and scale invariant are computed for a frame. A dynamic neural network is used to identify the patterns within the stream of image moments and hence recognize actions. Experiments show that the proposed model performs better than other competitive models.

A versatile dataset for intrinsic plagiarism detection, text reuse analysis, and author clustering in Urdu.

Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.

A comprehensive review of security threats and solutions for the online social networks industry.

The term "cyber threats" refers to the new category of hazards that have emerged with the rapid development and widespread use of computing technologies, as well as our growing reliance on them. This article presents an in-depth study of a variety of security and privacy threats directed at different types of users of social media sites. Furthermore, it focuses on different risks while sharing multimedia content across social networking platforms, and discusses relevant prevention measures and techniques. It also shares methods, tools, and mechanisms for safer usage of online social media platforms, which have been categorized based on their providers including commercial, open source, and academic solutions.

Aspect based sentence segregated dataset of hybrid car's consumers online reviews.

Dataset presented in this paper is obtained from the top online automobile selling and purchasing websites. A total of 1000 reviews related to hybrid cars in the form of text reviews are extracted with the help of the Web Scraper tool. The dataset presents the customers sentiments in the form of reviews related to hybrid cars. Various aspects are taken into consideration while annotating the reviews such as driving, performance, comfort, safety features, interior, exterior and accessories. The annotation of data is done at three levels by three annotators i.e., (1) overall polarity of a review, (2) segregation of the sentence term in which aspect is discussed, (3) polarity of the discussed aspect. Cohen's Kappa score of 0.90 was achieved among the authors while annotating the reviews. Dataset can be used for sentiment analysis, information retrieving, lexicon analysis, and grammatical and morphological analysis.

Introductory programming course: review and future implications.

The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interesting dimensions of IPC, such as collaborative learning, cognitive assessments, and performance predictions. In addition to reviewing the recent advancements in IPC, this study proposes a new taxonomy of IPC research dimensions. Furthermore, based on the successful practices that are listed in the literature, some useful guidelines and advices for instructors have also been reported in this article. Lastly, this review presents some pertinent open research issues to highlight the future dimensions for IPC researchers.

A framework for the prediction of earthquake using federated learning.

Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

Mobile-assisted and gamification-based language learning: a systematic literature review.

Learning a new language is a challenging task. In many countries, students are encouraged to learn an international language at school level. In particular, English is the most widely used international language and is being taught at the school level in many countries. The ubiquity and accessibility of smartphones combined with the recent developments in mobile application and gamification in teaching and training have paved the way for experimenting with language learning using mobile phones. This article presents a systematic literature review of the published research work in mobile-assisted language learning. To this end, more than 60 relevant primary studies which have been published in well-reputed venues have been selected for further analysis. The detailed analysis reveals that researchers developed many different simple and gamified mobile applications for learning languages based on various theories, frameworks, and advanced tools. Furthermore, the study also analyses how different applications have been evaluated and tested at different educational levels using different experimental settings while incorporating a variety of evaluation measures. Lastly, a taxonomy has been proposed for the research work in mobile-assisted language learning, which is followed by promising future research challenges in this domain.

Study on survival, growth, haematology and body composition of Cyprinus carpio under different acute and chronic salinity regimes.

Pakistan's most of the land is less productive or no productivity at all due to erosion and salinity of the soil, which can be utilized to develop fisheries. The project, "Survival, growth and body composition of Cyprinus carpio under different salinity regimes" was undertaken in two phases. In the first phase susceptibility of Cyprinus carpio at four salinity levels in triplicate within 0-10 g L-1NaCl for 96 h in each aquarium was checked after one week acclamation at 0 g L-1, 2 g L-1 and 4 g L-1 NaCl. LC50 values varied from 7.67 to 10.65 g L-1 after 96 h for C. carpio. Percentage mortality of the fish and important water quality parameters after every 12 h were observed for a period of 96-h. Probit analysis showed that 96-h LC50 values ranged from 7.67 to 10.65 g L-1 . During experimental period aquaria water temperature ranged from 29.6 to 33.7 °C, pH values fluctuated between 7.8 and 9.7, Electrical conductivity values ranged from 2.40 to 20.13 dSm-1 and Dissolved oxygen ranged between 2.23 and 10 mg L-1. Sub-lethal salt concentration i.e. 0 g L-1 to 3 g L-1 NaCl upto 40 days showed that growth of C. carpio decreased with the increase of water salinity levels and ceased at 4 g L-1 salinity and increase in salinity have negatively affected hematological parameters.

An evaluation framework and comparative analysis of the widely used first programming languages.

Computer programming is the core of computer science curriculum. Several programming languages have been used to teach the first course in computer programming, and such languages are referred to as first programming language (FPL). The pool of programming languages has been evolving with the development of new languages, and from this pool different languages have been used as FPL at different times. Though the selection of an appropriate FPL is very important, yet it has been a controversial issue in the presence of many choices. Many efforts have been made for designing a good FPL, however, there is no ample way to evaluate and compare the existing languages so as to find the most suitable FPL. In this article, we have proposed a framework to evaluate the existing imperative, and object oriented languages for their suitability as an appropriate FPL. Furthermore, based on the proposed framework we have devised a customizable scoring function to compute a quantitative suitability score for a language, which reflects its conformance to the proposed framework. Lastly, we have also evaluated the conformance of the widely used FPLs to the proposed framework, and have also computed their suitability scores.