Recommender System for Configuration Management Process of Entrepreneurial Software Designing Firms

The rapid growth in software demand incentivizes software development organizations to develop exclusive software for their customers worldwide.This problem is addressed by the software development industry by software product line(SPL)practices that employ feature models.However,optimal feature selection based on user requirements is a challenging task.Thus,there is a requirement to resolve the challenges of software development,to increase satisfaction and maintain high product quality,for massive customer needs within limited resources.In this work,we propose a recommender system for the development team and clients to increase productivity and quality by utilizing historical information and prior experiences of similar developers and clients.The proposed system recommends features with their estimated cost concerning new software requirements,from all over the globe according to similar developers’and clients’needs and preferences.The system guides and facilitates the development team by suggesting a list of features,code snippets,libraries,cheat sheets of programming languages,and coding references from a cloud-based knowledge management repository.Similarly,a list of features is suggested to the client according to their needs and preferences.The experimental results revealed that the proposed recommender system is feasible and effective,providing better recommendations to developers and clients.It provides proper and reasonably well-estimated costs to perform development tasks effectively as well as increase the client’s satisfaction level.The results indicate that there is an increase in productivity,performance,and quality of products and a reduction in effort,complexity,and system failure.Therefore,our proposed system facilitates developers and clients during development by providing better recommendations in terms of solutions and anticipated costs.Thus,the increase in productivity and satisfaction level maximizes the benefits and usability of SPL in the modern era of technology.

Supply and Demand Oriented Energy Management in the Internet of Things

The Internet of Things (IoT) is emerging as an attractive paradigm involving physical perceptions, cyber interactions, social correlations and even cognitive thinking through a cyber-physical-social-thinking hyperspace. In this context, energy management with the purposes of energy saving and high efficiency is a challenging issue. In this work, a taxonomy model is established in reference to the IoT layers (i.e., sensor-actuator layer, network layer, and application layer), and IoT energy management is addressed from the perspectives of supply and demand to achieve green perception, communication, and computing. A smart home scenario is presented as a case study involving the main enabling technologies with supply-side, demand-side, and supply-demand balance considerations, and open issues in the field of IoT energy management are also discussed.

PEGA:probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations

Epigenetics’flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible.From the epigenetic perspective,the main limitations to improving the stability and accuracy of genetic algorithms are as follows:(1)the unchangeable nature of the external environment,which leads to excessive disorders in the changed phenotype after mutation and crossover;(2)the premature convergence due to the limited types of epigenetic operators.In this paper,a probabilistic environmental gradientdriven genetic algorithm(PEGA)considering epigenetic traits is proposed.To enhance the local convergence efficiency and acquire stable local search,a probabilistic environmental gradient(PEG)descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space.Moreover,to balance exploration and exploitation at different evolutionary stages,a variable nucleosome reorganization(VNR)operator is realized by dynamically adjusting the number of genes involved in mutation and crossover.Based on the above-mentioned operators,three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity.The experimental results on the open Congress on Evolutionary Computation-2017(CEC’17)benchmark over 10-,30-,50-,and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance.The ablation analysis demonstrates that for accuracy and stability,the fusion strategy of PEG and VNR are effective on 96.55%of the test functions and can improve the indicators by up to four orders of magnitude.Furthermore,the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.

Dataflow Management in the Internet of Things: Sensing,Control, and Security

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

Access Control and Authorization in Smart Homes: A Survey

With the rapid development of cyberspace and smart home technology, human life is changing to a new virtual dimension with several promises for improving its quality. Moreover, the heterogeneous, dynamic, and internet-connected nature of smart homes brings many privacy and security difficulties. Unauthorized access to the smart home system is one of the most harmful actions and can cause several trust problems and relationship conflicts between family members and invoke home privacy issues. Access control is one of the best solutions for handling this threat, and it has been used to protect smart homes and other Internet of Things domains for many years. This survey reviews existing access control schemes for smart homes, which concern the essential authorization requirements and challenges that need to be considered while designing an authorization framework for smart homes. Furthermore, we note the most critical challenges that other access control solutions neglect for smart homes.

Towards Rehabilitation at Home After Total Knee Replacement

In this paper,we present the design and implementation of an avatar-based interactive system that facilitates rehabilitation for people who have received total knee replacement surgeries.The system empowers patients to carry out exercises prescribed by a clinician at the home settings more effectively.Our system helps improve accountability for both patients and clinicians.The primary sensing modality is the Microsoft Kinect sensor,which is a depth camera that comes with a Software Development Kit(SDK).The SDK provides access to 3-dimensional skeleton joint positions to software developers,which significantly reduces the challenges in developing accurate motion tracking systems,especially for use at home.However,the Kinect sensor is not wellequipped to track foot orientation and its subtle movements.To overcome this issue,we augment the system with a commercial off-the-shelf Inertial Measurement Unit(IMU).The two sensing modalities are integrated where the Kinect serves as the primary sensing modality and the IMU is used for exercises where Kinect fails to produce accurate measurement.In this pilot study,we experiment with four rehabilitation exercises,namely,quad set,side-lying hip abduction,straight raise leg,and ankle pump.The Kinect is used to assess the first three exercises,and the IMU is used to assess the ankle pump exercise.