An Intelligent Platform for Software Component Mining and Retrieval.

The development of robotic applications necessitates the availability of useful, adaptable, and accessible programming frameworks. Robotic, IoT, and sensor-based systems open up new possibilities for the development of innovative applications, taking advantage of existing and new technologies. Despite much progress, the development of these applications remains a complex, time-consuming, and demanding activity. Development of these applications requires wide utilization of software components. In this paper, we propose a platform that efficiently searches and recommends code components for reuse. To locate and rank the source code snippets, our approach uses a machine learning approach to train the schema. Our platform uses trained schema to rank code snippets in the top k results. This platform facilitates the process of reuse by recommending suitable components for a given query. The platform provides a user-friendly interface where developers can enter queries (specifications) for code search. The evaluation shows that our platform effectively ranks the source code snippets and outperforms existing baselines. A survey is also conducted to affirm the viability of the proposed methodology.

A survey on common criteria (CC) evaluating schemes for security assessment of IT products.

Over the last few years, private and public organizations have suffered an increasing number of cyber-attacks owing to excessive exploitation of technological vulnerabilities. The major objective of these attacks is to gain illegal profits by extorting organizations which adversely impact their normal operations and reputation. To mitigate the proliferation of attacks, it is significant for manufacturers to evaluate their IT products through a set of security-related functional and assurance requirements. Common Criteria (CC) is a well-recognized international standard, focusing on ensuring security functionalities of an IT product along with the special emphasis on IS design and life-cycle. Apart from this, it provides a list of assurance classes, families, component, and elements based on which security EALs can be assigned to IT products. In this survey, we have provided a quick overview of the CC followed by the analysis of country-specific implementation of CC schemes to develop an understanding of critical factors. These factors play a significant role by providing assistance in IT products evaluation in accordance with CC. To serve this purpose, a comprehensive comparative analysis of four schemes belonging to countries including US, UK, Netherlands, and Singapore has been conducted. This comparison has aided to propose best practices for realizing an efficient and new CC scheme for the countries which have not designed it yet and for improving the existing CC schemes. Finally, we conclude the paper by providing some future directions regarding automation of the CC evaluation process.

Efficient Prediction of Missed Clinical Appointment Using Machine Learning.

Public health and its related facilities are crucial for thriving cities and societies. The optimum utilization of health resources saves money and time, but above all, it saves precious lives. It has become even more evident in the present as the pandemic has overstretched the existing medical resources. Specific to patient appointment scheduling, the casual attitude of missing medical appointments (no-show-ups) may cause severe damage to a patient's health. In this paper, with the help of machine learning, we analyze six million plus patient appointment records to predict a patient's behaviors/characteristics by using ten different machine learning algorithms. For this purpose, we first extracted meaningful features from raw data using data cleaning. We applied Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling Method (Adasyn), and random undersampling (RUS) to balance our data. After balancing, we applied ten different machine learning algorithms, namely, random forest classifier, decision tree, logistic regression, XG Boost, gradient boosting, Adaboost Classifier, Naive Bayes, stochastic gradient descent, multilayer perceptron, and Support Vector Machine. We analyzed these results with the help of six different metrics, i.e., recall, accuracy, precision, F1-score, area under the curve, and mean square error. Our study has achieved 94% recall, 86% accuracy, 83% precision, 87% F1-score, 92% area under the curve, and 0.106 minimum mean square error. Effectiveness of presented data cleaning and feature selection is confirmed by better results in all training algorithms. Notably, recall is greater than 75%, accuracy is greater than 73%, F1-score is more significant than 75%, MSE is lesser than 0.26, and AUC is greater than 74%. The research shows that instead of individual features, combining different features helps make better predictions of a patient's appointment status.

Expertise based skills management system to support resource allocation.

Skills Management is an essential concept of human resource management in which a skill inventory may be created for each employee and managers can assign tasks to workers based on worker's abilities. This concept is not fully practiced for two reasons: i) employee's skills are not effectively evaluated and documented, ii) tool support is deficient to manage this complex task. Ineffective skill management of an organization fizzle tasks assigned to the incompetent employees and this may lead to project failure. To fill up this gap, a survey is conducted across various software organizations to find out the best practices for the skill management and to gather requirements for skills management framework. Based on survey findings, a mathematical framework is proposed that calculates the soft and hard skills of employees automatically based on time and achievements as skill increases or decreases over time. In this framework, the Skills Calculation Engine (SCE) is developed for the managers to enhance the capacity of appropriate decisions making in assigning tasks to the rightly skilled workers. This framework is also useful for organizations as it can increase profitability as tasks are assigned to the most appropriate employees. The SCE is implemented as a Windows-based application to calculate skills, store skills in skills inventory, and assign tasks based on an employee's skills. The skills management tool is evaluated in a facilitated workshop; furthermore, a feature-wise comparison of the tool is also made with existing tools.

Monitoring social distancing under various low light conditions with deep learning and a single motionless time of flight camera.

The purpose of this work is to provide an effective social distance monitoring solution in low light environments in a pandemic situation. The raging coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has brought a global crisis with its deadly spread all over the world. In the absence of an effective treatment and vaccine the efforts to control this pandemic strictly rely on personal preventive actions, e.g., handwashing, face mask usage, environmental cleaning, and most importantly on social distancing which is the only expedient approach to cope with this situation. Low light environments can become a problem in the spread of disease because of people's night gatherings. Especially, in summers when the global temperature is at its peak, the situation can become more critical. Mostly, in cities where people have congested homes and no proper air cross-system is available. So, they find ways to get out of their homes with their families during the night to take fresh air. In such a situation, it is necessary to take effective measures to monitor the safety distance criteria to avoid more positive cases and to control the death toll. In this paper, a deep learning-based solution is proposed for the above-stated problem. The proposed framework utilizes the you only look once v4 (YOLO v4) model for real-time object detection and the social distance measuring approach is introduced with a single motionless time of flight (ToF) camera. The risk factor is indicated based on the calculated distance and safety distance violations are highlighted. Experimental results show that the proposed model exhibits good performance with 97.84% mean average precision (mAP) score and the observed mean absolute error (MAE) between actual and measured social distance values is 1.01 cm.

Incremental Construction for Scalable Component-Based Systems.

The availability of smart and intelligent sensors has changed the monitoring, control and maintenance of a conventional and advanced cyber-physical system used in public or private sectors of a society. For example, internet of things (IoT)-based health, agricultural and weather management systems. With the emergence of such sensors, along with the new ways to communicate or coordinate with them, we need to analyze and optimize the system construction processes. In this paper, to address the issue of scalability for bigger and complex systems based on sensors, we redefine an incremental construction process with an emphasis on behavior preservation and study the effectiveness of the use of software component models from the component-based development domain. In this paper, to deal with the issue of scalability, we investigate component-based development approaches with respect to our defined process and propose a taxonomy of component models with respect to component/system behavior. Moreover, based on the outcome of our analysis, we recommend the EX-MAN component model as the most suitable approach. We investigate incremental construction in the context of the three main categories of current component models, namely models where components are: (i) objects, (ii) architectural units and (iii) encapsulated components. Furthermore, to evaluate our defined process and selection of EX-MAN, we designed three examples of systems using our proposed process in EX-MAN component model.

Incremental Composition Process for the Construction of Component-Based Management Systems.

Cyber-physical systems (CPS) are composed of software and hardware components. Many such systems (e.g., IoT based systems) are created by composing existing systems together. Some of these systems are of critical nature, e.g., emergency or disaster management systems. In general, component-based development (CBD) is a useful approach for constructing systems by composing pre-built and tested components. However, for critical systems, a development method must provide ways to verify the partial system at different stages of the construction process. In this paper, for system architectures, we propose two styles: rigid architecture and flexible architecture. A system architecture composed of independent components by coordinating exogenous connectors is in flexible architecture style category. For CBD of critical systems, we select EX-MAN from flexible architecture style category. Moreover, we define incremental composition mechanism for this model to construct critical systems from a set of system requirements. Incremental composition is defined to offer preservation of system behaviour and correctness of partial architecture at each incremental step. To evaluate our proposed approach, a case study of weather monitoring system (part of a disaster management) system was built using our EX-MAN tool.