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.

A SEQUENTIAL TESTING PROGRAM FOR PREDICTING AND IDENTIFICATING CARCINOGENS AND ITS APPLICATION

In this paper our studies about the sequential testing program for predicting and identificating carcinogens, sequential discriminant method and cost- effectiveness analysis are summarized. The analysis of our database of carcinogeniclty and genotoxicity of chemicals demonstrates the uncertainty . of short- term tests ( STTs ) to predict carcinogens and the results of most routine STTs are statistically dependent. We recommend the sequential testing program combining STTs and carclnogenicity assay, the optimal STT batteries, the rules of the sequential discrimination and the preferal choices of STTs tor specific chemical class. For illustrative pmposes the carclnogenicity prediction of several sample chamicals is presented. The results of cost-effectiveness analysis suggest that this program has vast social-economic effectiveness.

Call for papers Journal of Control Theory and Applications Special issue on Approximate dynamic programming and reinforcement learning

Approximate dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain and nonconvex environments over time.

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

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.

Focusing on individualized nutrition within the algorithmic diet: an in-depth look at recent advances in nutritional science, microbial diversity studies, and human health

A healthy balanced diet and a healthy lifestyle are very closely linked.Whichever the biological link is,it is overwhelming to understand.Modifications in how food is served,divided up,and supervised,such as the introduction of nutritional hygiene standards,food handling practices,and the entry of macro and micronutrients,have had a big impact on human health in the last few decades.Growing evidence indicates that our gut microbiota may affect our health in ways that are at least in part influenced by our diet and the ingredients used in the preparation of our food and drinks,as well as other factors.As a new problem,this one is getting a lot of attention,but it would be hard to figure out how the gut microbiota and nutrition molecules work together and how they work in certain situations.Genetic analysis,metagenomic characterization,configuration analysis of foodstuffs,and the shift to digital health information have provided massive amounts of data that might be useful in tackling this problem.Machine learning and deep learning methods will be employed extensively as part of this research in order to blend complicated data frames and extract crucial information that will be capable of exposing and grasping the incredibly delicate links that prevail between diet,gut microbiome,and overall wellbeing.Nutrition,well-being,and gut microorganisms are a few subjects covered in this field.It takes into account not only databases and high-speed technology,but also virtual machine problem-solving skills,intangible assets,and laws.This is how it works:Computer vision,data mining,and analytics are all discussed extensively in this study piece.We also point out limitations in existing methodologies and new situations that discovered in the context of current scientific knowledge in the decades to come.We also provide background on"bioinformatics"algorithms;recent developments may seem to herald a revolution in clinical research,pushing traditional techniques to the sidelines.Furthermore,their true potential rests in their ability to work in conjunction with,rather than as a substitute for,traditional research hypotheses and procedures.When new metadata propositions are made by focusing on easily understandable frameworks,they will always need to be rigorously validated and brought into question.Because of the huge datasets available,assumption analysis may be used to complement rather than a substitute for more conventional concept-driven scientific investigation.It is only by employing all of us that we will all increase the quality of evidence-based practice.

Tag clustering algorithm LMMSK: improved K-means algorithm based on latent semantic analysis

With the wide application of Web-2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system. © 2017 Beijing Institute of Aerospace Information.

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.

Development of a Support Tool for Rapid Application Integration of CIMS

Computer intergrated manufacturing system (CIMS) applications in manufacturing enterprises need new advanced support tools for application integration. This paper presents the system architecture and functions for CIMS application integration platform for manufacturing enterprises (MACIP). MACIP integrates a set of application software products and development tools as a complete system for CIMS implementation. MACIP includes a communication system, a global information system, three domain application sub integration platforms, an internet interface and an operation management and control system. The Client/Server structure, the object oriented method and the agent technique were used in developing MACIP. Good system openness, scalability, and maintenance are ensured by conforming to international standards and by using advanced system design software and management tools. MACIP can significantly reduce the implementation complexity, time, and cost of CIMS.

Feature Analysis of STEP Application Protocol and its Application

This paper uses semantic data modelling and CAD/CAPP/CAM integration to analyze the features defined in the STEP AP214, its characteristics of definition and distribution, and its influence on the complexity of CAD/CAPP/CAM application integration. A framework is developed for applications of CAD/CAPP/CAM integration based on the STEP to reduce the complexity of integration implementation. Two application program interfaces (API), namely, application protocol API and application API, are developed. The frameworks hierarchy structure and the application program interfaces between the layers make the integration of applications easier.

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

With the rapid development of artificial intelligence(AI),it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI.To explore the interaction mechanism of power system dynamic simulations and AI,a general design for AI-oriented power system dynamic simulators is proposed,which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces(APIs).With the support of APIs,simulation-assisted AI and AIassisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI.A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator.Tests of this prototype are carried out in four scenarios including sample generation,AI-based stability prediction,data-driven dynamic component modeling,and AI-aided stability control,which prove the validity,flexibility,and efficiency of the design and implementation for AI-oriented power system dynamic simulators.

A hazard analysis via an improved timed colored petri net with time–space coupling safety constraint

Petri nets are graphical and mathematical tools that are applicable to many systems for modeling, simulation, and analysis. With the emergence of the concept of partitioning in time and space domains proposed in avionics application standard software interface (ARINC 653), it has become difficult to analyze time–space coupling hazards resulting from resource partitioning using classical or advanced Petri nets. In this paper, we propose a time–space coupling safety constraint and an improved timed colored Petri net with imposed time–space coupling safety constraints (TCCP-NET) to fill this requirement gap. Time–space coupling hazard analysis is conducted in three steps: specification modeling, simulation execution, and results analysis. A TCCP-NET is employed to model and analyze integrated modular avionics (IMA), a real-time, safety-critical system. The analysis results are used to verify whether there exist time–space coupling hazards at runtime. The method we propose demonstrates superior modeling of safety-critical real-time systems as it can specify resource allocations in both time and space domains. TCCP-NETs can effectively detect underlying time–space coupling hazards. © 2016 Chinese Society of Aeronautics and Astronautics

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%.

A future triple entry accounting framework using blockchain technology

The study adopts a single case study approach to bring into conversation,ideas,and views of several scholars on triple entry accounting(TEA).The development of blockchain technology already drives the conscious move towards the TEA.The TEA is currently not being used in any significant way but is in a greater debate whether it is worth adopting such alternative accounting practices as the TEA.Shifting to the TEA system is challenging and at present,it is just a fascinating mental exercise.With these backdrops,the present study discusses the likelihood cases of future accounting practice namely:(Ⅰ)Sophisticated Accounting Software based on the double entry accounting(DEA);(Ⅱ)Combination of Blockchain and TEA;and(Ⅲ)Combination of disruptive technologies in addition to Blockchain and TEA.Finally,the study concludes with describing the basic architecture of a potential system of triple-entry accounting that could support the TEA system to deliver real-time insights into business operations.Lastly,the study plotted a hype cycle for accounting technologies to help global organizations identify the relevant accounting technologies and applications.

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.

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.

IMPLEMENTATION OF 3-VALUED PARACONSISTENT LOGIC PROGRAMMING TOWARDS DECISION MAKING SYSTEM OF AGENTS

Due to the rapid development of applications of artificial intelligence and robotics in recent years, the necessity of reasoning and decision making with uncertain and inaccurate information is increasing. Since robots in the real world are always exposed to behavioral inaccuracies and uncertainty arising from recognition methods, they may occasionally encounter contradictory facts during reasoning on action decision. Paraconsistent logic programming is promising to make appropriate action decisions even when an agent is exposed to such uncertain information or contradictory facts, but there has been no implementation of this programming to the best of our knowledge. We propose a resolution algorithm for the 3-valued paraconsistent logic programming system QMPT0 and its implementation on SWI-Prolog. We also describe an application of the 3-valued paraconsistent logic programming regarding agent decision making.