This study aims to check suitable software testing tools for testing microservices architectures, which have become increasingly popular in software development due to their advantages. However, testing microservices architectures poses unique challenges, and the e effectiveness of testing tools is investigated. The study will review the literature and conduct experiments using common testing tools on a microservices-based application to evaluate their e effectiveness in detecting bugs, vulnerabilities, and performance issues. The study will use a combination of quantitative and qualitative methods to analyse the data and provide insights into the strengths and weaknesses of different testing tools and their suitability for different types of microservices architectures. Ultimately, this study aims to contribute to the development of best practices for testing microservices architectures to help software developers and testers choose the most effective testing tools for their projects.
With more than seven billion people actively using the Internet, the number of cyber attacks has increased, and personal data breaches have become a concern among the general public. The COVID-19 pandemic has only increased the use of online platforms and services for work and leisure activities, which opens the door to more scams, viruses, and other cyber security breaches. Guided by SEO techniques and research regarding dangerous website and domain patterns, we have designed and implemented a visual system that tracks suspicious links on an active webpage and marks them in order to alert users to proceed with caution. Our AI utilizes linear regression to best detect trends in URL parsing, comparing them with registered unsafe links to see if they pose similar threats. The results reveal that AI isn’t entirely accurate since some trends are hard to decipher; however, it can reliably flag certain redirects and out-of-domain links that would otherwise remain hidden to users.
For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.
Software metrics have a direct link with measurement in software engineering. Correct measurement is the prior condition in any engineering fields, and software engineering is not an exception, as the size and complexity of software increases, manual inspection of software becomes a harder task. Most Software Engineers worry about the quality of software, how to measure and enhance its quality. The overall objective of this study was to asses and analysis’s software metrics used to measure the software product and process.
Video summarization of the segmented video is an essential process for video thumbnails, video surveillance and video downloading. Summarization deals with extracting few frames from each scene and creating a summary video which explains all course of action of full video with in short duration of time. The proposed research work discusses about the segmentation and summarization of the frames. A genetic algorithm (GA) for segmentation and summarization is required to view the highlight of an event by selecting few important frames required. The GA is modified to select only key frames for summarization and the comparison of modified GA is done with the GA.
Smart home is a house that uses information technology to control the electronic appliances, monitor the home environment and communicate with outer world. A sample house electric appliance monitor and control system that is one brand of the Smart home is addressed in this paper. This system integrates the AC power socket, low-power microcontroller and wireless communication into a wireless power socket that can be switched ON/OFF remotely through Internet. The system consists of three modules, that is, the Web Server Module, the Control Device Module, and the End Device Module which together provide an indoor wireless, and an outdoor remote control and monitor of home electric appliances. This paper presents the hardware and software implementation. The test results of the system have shown that it can be easily used for the smart home applications.
The Cell Broadband Engine (BE) is a heterogeneous 9-core microprocessor which initially saw the light in the Sony PlayStation 3. This paper describes the parallelization of a video processing application on the Cell BE, and the programming model chosen for this application. Serial implementations on PPE only and parallel implementations on PPE-SPE with 8 SPEs are described. This is followed by the presentation of the speedup comparisons with and without DMA and thread creation overhead times. The results presented in this paper demonstrate that the Cell BE processor can achieve a speedup of 10x on this application and shows good scalability with number of SPEs. When the input data size is at least 512x512, we showed that the speedup becomes limited by the number of memory transfer.
This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of extracted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which proves to take the frontal image in 0.27 second from video at most. The proposed system using six statistic values realizes 98.3% as an authentication rate.
The paper attempts to identify the impacts of five aspects of big data management competence in closed loop datafication system on service innovation performance and business performance in telecom industry in China. The research questions have been developed by integrating various theories (i.e. knowledge management theory, evolutionary perspective and resource-based theory) with the model of a telecom operator’s big data management competence in its closed loop datafication system for service innovation in China. The industry giant China Mobile is featured in the case study to demonstrate the relationships in big data environment. The findings strongly suggest that several propositions serve to support the notion of using of five aspects of management closed loop competence to promote service innovation. Specifically, the integrated management closed loop competence poses positive impact on service innovation performance. In addition, four
This study proposes a partially-combined forecasting framework for container throughput based on big data composed of structured historical data and unstructured data. Under the proposed framework, the structured data (the original time series) is firstly decomposed into linear component and nonlinear component. Seasonal auto-regression integrated moving average model (SARIMA) is adopted to capture and forecast the linear component, and a combined model, composed of least squares support vector regression (LSSVR) and artificial neural network (GP), is applied to modeling the nonlinear component. Next, unstructured data is analyzed by an expert system. With the synthesized expert judgment, the forecasts of linear and nonlinear components are integrated into a final forecast. For the illustration and verification purpose, an empirical study is conducted with the data of Qingdao Port. The results show that the model under the proposed framework significantly outperforms its competitive rivals.
This paper describes a face recognition algorithm using feature points of face parts, which is classified as a feature-based method. As recognition performance depends on the combination of adopted feature points, we utilize all reliable feature points effectively. From moving video input, well-conditioned face images with a frontal direction and without facial expression are extracted. To select such well-conditioned images, an iteratively minimizing variance method is used with variable input face images. This iteration drastically brings convergence to the minimum variance of 1 for a quarter to an eighth of all data, which means 3.75-7.5 Hz by frequency on average. Also, the maximum interval, which is the worst case, between the two values with minimum deviation is about 0.8 seconds for the tested feature point sample.
The novelty of this paper is a hybrid approach for modeling and implementing a real-time control strategy of a dc Servomotor angular speed by using a sliding mode control module. The sliding mode controller is embedded in an open-loop control system structure with a dc Buck converter to stabilize and regulate the converter output voltage, despite the load current changes and unregulated power supply input voltage. The motivation of this approach is that the most of the physical objects from real life exhibit in a naturally way a hybrid structure, i.e. a switching continuous discrete variable structure over the time. In our proposed embedded control system structure it is the dc Buck converter that exhibits a hybrid dynamics, modeled and implemented by using an academic evaluation version of software package AnyLogic 6.7. In addition the proposed hybrid real time control strategy proved their effectiveness among the formal languages, and through their use in different real time control systems industrial applications.
In this paper is proposed, implemented and evaluated a novel radial basis probabilistic neural network (RBPNN) based classification algorithm for classification fruit surface defects in color and texture of a very important fruit as orange. The proposed algorithm takes orange images as inputs then the texture and gray features of defect area are extracted by computing a gray level cooccurrence matrix and the defect areas are classified through an RBPNN-based classifier. The conducted experiments and the results reveal as the classification accuracy achieved is up to 88%.
This paper focuses on the detection of trend-based knowledge contained in sequential data. Some key concepts are redefined and an algorithm using the inertia test to dynamically partition a sequence to recognize trend partitions is also proposed. Experiment results prove the effectiveness of the algorithm, and exposing the limitation of traditional primitives. Some suggestions on parameter selection in practical application are given at the same time.
