Probing the Landscape: Toward a Systematic Taxonomy of Online Peer Assessment Systems in Education

We present the research framework for a taxonomy of online educational peer-assessment systems. This framework enables researchers in technology-supported peer assessment to understand the current landscape of technologies supporting student peer review and assessment, specifically, its affordances and constraints. The framework helps identify the major themes in existing and potential research and formulate an agenda for future studies. It also informs educators and system design practitioners about use cases and design options

Assessing the Quality of Automatic Summarization for Peer Review in Education

ABSTRACT Technology supported peer review has drawn many interests from educators and researchers. It encourages active learning, provides timely feedback to students and multiple perspectives on their work. Currently, online peer review systems allow a student's work to be reviewed by a handful of their peers. While this is quite a good way to obtain a high degree of confidence, reading a large amount of feedback could be overwhelming. Our observation shows that the students even ignore some feedback when it gets too large. In this work, we try to automatically summarize the feedback by extracting the similar content that is mentioned by the reviewers, which would capture the strength and weaknesses of the work. We evaluate different auto summarization algorithms and length of the summary with educational peer review dataset, which was rated by a human. In general, the students found that medium-size generated summaries (5-10 sentences) encapsulate the context of the reviews, are able to convey the intent of the reviews, and help them to judge the quality of the work

User activity recognition for energy saving in smart home environment

In recent years, the consumption of electricity has increased considerably in the industrial, commercial and residential sectors. This has prompted a branch of research which attempts to overcome this problem by applying different information and communication technologies, turning houses and buildings into smart environments. In this paper, we propose and design an energy saving technique based on the relationship between the user's activities and electrical appliances in smart home environments. The proposed method utilizes machine learning techniques to automatically recognize the user's activities, and then a ranking algorithm is applied to relate activities and existing home appliances. Finally, the system gives recommendations to the user whenever it detects a waste of energy. Tests on a real database show that the proposed method can to save up to 35% of electricity in a smart home

Architecture for mixed criticality resource management in Internet of Things

We believe that the next big step in the field of Internet of Things (IoT) is to realize a virtual computing platform that provides access to heterogeneous group of device resources present in our living environments. By enabling 3rd party developers to access sensor and actuator resources present in a given environment in a same way they can access resources of a single mobile phone, the virtual computing platform would open a new market for the 3rd party IoT applications like the smart phones have done for mobile apps. To accomplish this vision, the virtual computing platform must be able to manage resource sharing between applications with differing criticality requirements for ensuring that the whole IoT system runs optimally. The main challenge is that the approach should be generic and extendable for future needs. To tackle this issue, we propose a two-level resource management architecture, where the necessary information about applications and resources are represented with machine-interpretable semantic descriptions based on the Semantic Web technologies. At the system level, these descriptions are used by the global resource manager for allocating resources to the applications based on their criticality and needs. At local level, each device is assigned with a local resource manager that schedules the access to resources provided by the device so that the performance of the more critical applications could be optimized at the expense of the less critical ones. To evaluate our approach in practice, we have implemented a reference implementation of the proposed architecture and demonstrated it through several applications with differing criticality levels. The results are very promising for managing mixed criticality applications in IoT

Industrial application development exploiting IoT vision and model driven programming

In recent years there has been a huge discussion about the IoT (Internet of Things) concept, and even more about IoT within industrial environment. The real IoT is a network of devices with local intelligence (sensors, lights, gas pumps, HVAC systems), which shares access & control mechanisms to push and pull status and command information from the networked world. However, there are still some issues, limiting the IoT diffusion: the devices involved are heterogeneous, with proprietary system of chips, protocols and interface; furthermore, there is a lack of development toolkits, enabling developers to create and evaluate IoT prototypes in simple and flexible manner. The ebbits [1] platform addressed those issues, taking advantages from the IoT vision and providing a middleware infrastructure for the integration of heterogeneous industrial devices and sensors, transforming them into web services and thus enabling their seamless integration into existing legacy systems. This paper will introduce the platform and its software architecture, describing features like semantic devices interoperability and entity virtualization. Furthermore, the paper will describe an innovative, IoT oriented, model driven development toolkit. This toolkit leverages on the semantic discovery service, allowing to dynamically selecting and locating available resources or devices, and provides a flexible instrument, including a graphical interface, that enables developers to compose mashup applications

The energy aware smart home

In this paper, we present a novel smart home system integrating energy efficiency features. The smart home application is built on top of Hydra, a middleware framework that facilitates the intelligent communication of heterogeneous embedded devices through an overlay P2P network. We interconnect common devices available in private households and integrate wireless power metering plugs to gain access to energy consumption data. These data are used for monitoring and analyzing consumed energy on device level in near real-time. Further, transparent information about the energy usage can be used to efficiently program and control home appliances depending on various factors, e.g. the electricity price. Making more and more data available to end-users, brings with it further challenges in the area of user interfaces. Hence, we complete the smart home system by intuitive user interfaces presenting energy consumption data in meaningful contexts and allowing end users to interact with their environment. We argue, that the combination of both, a technically sophisticated smart home application and at the same time transparent, intuitive user interfaces showing information regarding the energy usage, e.g. energy price, energy source, standby consumption etc., has the potential to bring the vision of the energy efficient smart home within reach

Bringing the Internet of Things along the manufacturing line: A case study in controlling industrial robot and monitoring energy consumption remotely

The Internet of Things (IoT) concept attracts considerable interest from the academia and industry. This paper provides a set of proof-of-concept prototype descriptions, based on such IoT exploitation, which aim at gathering real-time data along the manufacturing processes that enables a responsive production management and maintenance, including energy consumption and water usage monitoring at each stage of a production cycle. The presented work takes advantage of the ebbits platform, which provides a middleware infrastructure for integrating industrial sensors, devices and emerging wireless technology in the physical world, transforming them into web services that enable seamless integration into mainstream business system such as MES and ERP

Food Traceability Chain Supported by the Ebbits IoT Middleware

The paper presents the food traceability prototype, which was implemented as a pilot application of the FP7 EU project ebbits. The platform architecture, built upon the principles of the Internet of Things (IoT), People, and Services, is described in aspects of the supported interoperability and semantic orchestration of services involved in the food production chain. The platform represents physical objects as digital objects that go through different phases in the production chain. The information produced in each phase is stored by involved actors and could be retrieved back by the consumers through orchestrating services provided by the actors in the production chain. These services are resolved by a product service orchestration, which is supported by a semantic backend