There's growing interest in developing applications for the Internet of Things. Such applications' main objective is to integrate technology into people's everyday lives, to be of service to them en masse. The form in which this integration is implemented, however, still leaves much room for improvement. Usually, the user must set parameters within the application. When the person's context changes, they have to manually reconfigure the parameters. What was meant to be a commodity in an unforeseen situation then becomes extra noise. This article describes a reference architecture that improves how people are integrated with the IoT, with smartphones doing the connecting. The resulting integration opens the way to new IoT scenarios supporting evolution towards the Internet of People.

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE.

As quantum computers mature, the complexity of quantum software increases. From initial standalone quantum algorithms, we are moving toward complex solutions combining quantum algorithms with traditional software. To build these, software engineering methods and abstractions are needed. Nowadays quantum computers are usually offered through the cloud, under a pay-per-use model, leading to the adoption of the service-oriented good practices dominant on the cloud. However, specific adaptations are needed to obtain the benefits of service-oriented computing while dealing with quantum hardware limitations. In this paper, we propose the Quantum API Gateway, an adaptation of the API Gateway pattern that takes into account the fact that quantum services cannot be deployed as traditional services. Instead, the Quantum API Gateway recommends the best quantum computer to run a given quantum service at run-time. As a proof of concept, an implementation of the Quantum API Gateway is provided for the Amazon Braket platform.

The development that quantum computing technologies are achieving is beginning to attract the interest of companies that could potentially be users of quantum software. Thus, it is perfectly feasible that during the next few years hybrid systems will start to appear integrating both the classical software systems of companies and new quantum ones providing solutions to problems that still remain unmanageable today. A natural way to support such integration is Service-Oriented Computing. While conceptually the invocation of a quantum software service is similar to that of a classical one, technically there are many differences and technological limitations, which refer to platform independence, decoupling, scalability, etc. To highlight these differences and the difficulties to develop quality quantum services, this paper takes a well-known problem to which a quantum solution can be provided, integer factorization, making use of the Amazon Braket quantum service platform. The exercise of trying to provide the factorization as a quantum service is carried out following the best practices, design patterns and standards existing in the implementation of classical services. This case study is used to highlight the rough edges and limitations that arise in the integration of classical-quantum hybrid systems using service-oriented computing. The conclusion of the study allows us to point out directions in which to focus research efforts in order to achieve effective quantum service-oriented computing.

Researchers from sociological disciplines could greatly benefit from collective information from the many people who use mobile devices to communicate via various social apps and services. However, processing that information is difficult because it's scattered among numerous social platforms. Furthermore, users are becoming increasingly concerned about how and by whom their information is being accessed. A new mobile-centric computing model allows sociological profiles of people to be generated, kept, and securely provided to third parties as a service. With this model, device owners can be fully aware and in control of how their information is accessed, while still contributing to collective sociological information.

Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend. People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food. The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process. Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information. We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions. However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations. The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags. In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a pre-filtering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations. The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins.