Active learning (AL) systems have become increasingly popular for various applications in machine learning (ML), including medical imaging, environmental monitoring, and geospatial analysis. These systems rely on inputs dynamically queried from people to enhance classification. Ensuring appropriate analyst trust in these systems presents a significant obstacle as analyst over- or underreliance may adversely affect a given application. Common AL strategies enhance classification models by asking analysts to provide labels for data points with the highest degree of uncertainty. However, such model-centric policies do not consider potential priming effects on the analyst and how they will affect people’s trust in the system post-training. We present an empirical study assessing how AL query policies and visualizations that enhance transparency in a classifier’s decisions influence trust in automated image classifiers. We found that query policy may significantly influence an analyst’s perception of system capabilities, while the level of visual transparency into classifier certainty may influence an analyst’s ability to perform a classification task. Our study informs the design of interactive labeling systems to help mitigate the effects of overreliance and calibrate appropriate trust in automated systems.

The number and variety of Web APIs is growing exponentially. Software engineers need to expend a significant amount of time and effort reading and understanding the accompanying documentation. In addition, system automation may use API to interact with each other. However, this is not always a simple task since the API documentation of a provider can be anything from a single HTML page description through to a complex structure with information spanning several pages. Understanding this wide variety of API documentation structures and styles is therefore a labor intensive and error-prone task for engineers. By providing a machine-learning platform that can extract and standardize API usage information, however, we believe we can accelerate the creation of API-enabled systems by using automation to simplify the task of understanding. In this paper we introduce a novel approach to automating and standardizing usage information about APIs, combining several machine-learning algorithms in order to extract key attributes from API documentation and generate a machine readable Open API Specification (OAS). We develop i) a content-based learning model that identifies the context of a block of extracted API features; ii) a signature-based machine learning model that recognizes a sequence of successful/unsuccessful extracted API endpoints; and iii) a deep mapping model that pinpoints fine-grained mapping of extracted API attributes to OAS objects. Results of our experiments show that the proposed approach successfully works with an accuracy of 99%, 94% and 97% for content-based learning, signature-based learning, and Deep Mapping of API attributes respectively. We then use the models to produce OAS compliant API Specifications for more than 2,585 public APIs, validate them via API calls and finally deploy the validated APIs to the RunMyProcess software automation platform.

The sequential decision-making problem with large-scale state spaces is an important and challenging topic for multitask reinforcement learning (MTRL). Training near-optimality policies across tasks suffers from prior knowledge deficiency in discrete-time nonlinear environment, especially for continuous task variations, requiring scalability approaches to transfer prior knowledge among new tasks when considering large number of tasks. This paper proposes a multitask policy adversarial learning (MTPAL) method for learning a nonlinear feedback policy that generalizes across multiple tasks, making cognizance ability of robot much closer to human-level decision making. The key idea is to construct a parametrized policy model directly from large high-dimensional observations by deep function approximators, and then train optimal of sequential decision policy for each new task by an adversarial process, in which simultaneously two models are trained: a multitask policy generator transforms samples drawn from a prior distribution into samples from a complex data distribution with higher dimensionality, and a multitask policy discriminator decides whether the given sample is prior distribution from human-level empirically derived or from the generator. All the related human-level empirically derived are integrated into the sequential decision policy, transferring human-level policy at every layer in a deep policy network. Extensive experimental testing result of four different WeiChai Power manufacturing data sets shows that our approach can surpass human performance simultaneously from cart-pole to production assembly control.

Large-scale machine coordination is a primary approach for flexible manufacturing, enabling large-scale autonomous machines to dynamically coordinate their actions in pursuit of a custom task. One of the key challenges for such large-scale systems is finding high-dimensional coordination decision-making policies. Multitask policy gradient algorithms can be used in search of high-dimensional policies, particularly in collaborative decision support systems and distributed control systems. However, it is difficult for these algorithms to learn online high-dimensional coordination control policies (CCP) from large-scale custom manufacturing tasks. This paper proposes a large-scale online multitask learning and decision-making approach, which can consecutively learn high-dimensional CCP in order to quickly coordinate machine actions online for large-scale custom manufacturing task. A large-scale online multitask leaning algorithm is developed, which is able to learn large-scale high-dimensional CCP in a flexible manufacturing scenario. An online stochastic planning algorithm is proposed, which online optimizes the Markov network structure in order to avoid expensive global search for the optimal policy. Experiments have been undertaken using a professional flexible manufacturing testbed deployed within a smart factory of Weichai Power in China. Results show the proposed approach to be more efficient when compared with previous works.

We are witnessing the emergence of a new technology paradigm, one which focuses on the large scale integration and simplification of distributed technologies along with their fundamental realignment around the needs of people and society. The new Human Centric Intelligent Society results from these changes is connecting information from many different sources across the physical and virtual worlds, using the power of integration to achieve new insights and to enact change in ways which are quicker and more context aware than ever before. The Human Centric integration will lead to safer, more comfortable and more prosperous societies, but before arriving to this status, we have several challenges for the existing platforms to be migrated and digitalized into a common Cloud Computing ecosystem, and enable the common things with a Web of Things capabilities that can enable new forms of social infrastructure that react to the needs of people in real time. In this work, we present the contributions and extensions to the EENA Public Safety Answering Points standards and data structure (PIDFLO), in order to enable it with the capabilities to be integrated with data coming from personal and wearable sensors (Internet of Things). In details, we have extended PIDF-LO with the support of OMA LwM2M Web Objects, in order to offer a scalable and interoperable integration of the Internet of Things and the European Emergencies Services based on Open Standards.

The Part environment is a software engineering environment (SEE) being built in the Pact project. The environment is being constructed on the PCTE interfaces. The project has defined an environment architecture which identifies Common Services. These are reusable tool components that factor out code that would otherwise have to be developed in several tools, and which significantly increase the integration of the environment. The main activity of the Part project is to develop method-independent tools, independent of a particular engineering technique for the production of software.