Design of a Cryptographic Algorithm in the Form of an API in Order to Secure Monetary Transactions in a Supermarket

Supermarkets and large-scale retail stores are usually subject to huge monetary transactions for certain customers’ purchases. The computerization of these systems is common in supermarkets but the security of these transactions remains a mystery. This article presents an algorithm as an API based on symmetric cryptography that can enable end-to-end encryption of a monetary transaction in a supermarket. This algorithm is the first part of the complete supermarket management system which will be presented in the following article. The Python language and the Flask framework allow us to develop the algorithm as an independent component. Tests have been performed and our algorithm uses 98.49% less memory and 10.18% time saving than the AES algorithm.

Online and Offline Android Based Mobile Application for Mapping Health Facilities Using Google Map API. Case Study: Tanzania and Kenya Borders

The evolution of technology in 1990s resulted in the enormous growth of smartphones and the propagation of mobile applications (App) that marked new opportunities for healthcare centers and medical education. Apps have altered health services from patient’s health monitoring to specialist’s appointments and consultations from specialized health facilities. It can be argued that a healthy society can bring forth sustainable economic development to its full potential while an unhealthy society cannot. However, a free movement of people, labour and right to residence which was built across East Africa (EA) borders enabled Tanzania and Kenya borders to have enormous interactions. Subsequently, increase the risk of highly communicable diseases such as Tuberculosis and Sexually transmitted infections in such a way that medical attention is unavoidable along the borders. Statistically, Android Operating System (OS) owns 83% of Africa’s mobile OS market. In addition, 25,794,560 internet users reported by Tanzania Communications Regulatory Authority (TCRA) together with the 22.86 million internet users provided by Kenya Digital which is equivalent to 46% and 43% of internet penetration in year 2020, disclose the need for Android mobile application for mapping health facilities both online and offline using Google map API, which will solve residents’ need to healthcare services on the presence or shortage of internet connections;using either Swahili or English language via Smartphone devices. The App incorporates Monitoring and Evaluation (M & E) tool for tracking application usage which will ease Admin’s task to generate daily and monthly reports in Excel and Comma-Separated Values (CSV) formats. The developed system received positive feedback from EA citizens and residents in the Arusha region and Namanga border crossing where 90.2% of the system evaluation conducted between Dec 2020 and Apr 2021 agreed upon App usage.

Variable-length sequential dynamic features-based malware detection

In order to solve the problem that traditional signature-based malware detection systems are inefficacious in detecting new malware,a practical malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of malware detection.

Design and Development an Interactive On-the-Job Training Monitoring and Help Desk System with SMS for College of Information and Communication Technology

This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the College of Information and Communication Technology Nueva Ecija University of Science and Technology. The system made the OJT course procedure trouble-free by emerging a system accessible through the internet. Students have a user account, which gives them the aptitude to upload document files of their reports, thereby minimizing the time and energy spent traveling from the company’s location to the university and the other way around. Similarly, the OJT coordinators of the college are given their accounts to access and check the reports submitted by the students. The system is capable of generating reports and requirements in real-time, as long as all data is stored within the database and, therefore, the process is completed online. In addition, the system provides an interactive website that might help both students and coordinators to communicate instantaneously by having an online help desk where the students can ask related questions on their OJT course that the OJT coordinator and other students will answer. The coordinators can send a brief message service to the students enrolled within the OJT course through the utilization of the proposed system - this can be for the students who aren’t capable of opening their account more often, in order that they are still informed of the announcements they need to understand immediately. The interactive OJT help desk system with SMS can be used as a tool to help the students of the College of Information and Communication Technology (CICT) and the OJT coordinators in their tasks more conveniently.

ProSy: API-Based Synthesis with Probabilistic Model

Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first;thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.

Discovering API Directives from API Specifications with Text Classification

Application programming interface(API)libraries are extensively used by developers.To correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of APIs.Unfortunately,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API directives.In this paper,we propose an approach leveraging text classification to discover API directives from API specifications.Specifically,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of features.Then,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several classifiers.Given a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API directive.We have evaluated our approach over a publicly available annotated API directive corpus.The experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

How fast is it to city centers?The average travel speed as an indicator of road traffic accessibility potential

Transportation is the lifeblood of a modern metropolis.Accessibility generally refers to the interconnection between nodes in a regional traffic network.The purpose of the paper is to obtain more realistic and accurate measures of travel speed and to study the road traffic accessibility potential in cities.This study proposes a method for analyzing road traffic accessibility potential which is based on the average travel speed to city centers in off-peak times and which ranks 80 cities around the world.Based on the Suomi National Polar-Orbiting Partnership satellite’s visible-infrared imaging radiometer suite(NPP-VIIRS)night-time light data,urban built-up areas and city centers were extracted.Further,with the aid of the Google Maps application programming interface(API)network crawling technique,travel times and travel distances for several optimal routes to city centers by car were obtained.Feasible proposals for improving road traffic accessibility and planning urban transportation in different cities are presented.The proposed method offers a new possibility of analyzing traffic accessibility using internet data and geo-spatial methods.