A framework to support educational decision making in mobile learning

Learning Analytics in Mobile Learning is a challenging research topic, due to the distinguishing features of mobile learning. In fact, mobile learning is characterized by the learners’ mobility, the possibility of having localized data and information, the large amount of data that can be collected during a learning session, the affordances provided by the technologies and the social dynamics that characterize the context in which learning takes place. As a consequence, Learning Analytics in mobile learning requires original methodological approaches which enrich techniques already tested in different learning contexts (e.g., in Virtual Learning Environments) with specific strategies to deal with the complexity of mobile learning and manage the corresponding datasets. This paper presents a task-interaction framework to support educational decision-making in mobile learning. The framework is based on the relationships between the different types of interactions occurring in a mobile learning activity and the tasks which are pedagogically relevant for the learning activity. A case study has been designed to demonstrate the application of the task-interaction framework to learning scenarios based on the use of mobile devices.     

一个E-政府的规划框架及过程决策

用户需求、组织变化、政府法规和政策以及社会发展趋势对规划E-政府的IT组织有很大影响,对政府组织的挑战是规划、选择和实施“正确的”E-政府应用软件。提出了一个规划E-政府应用软件的框架,在战略上将可能的E-政府应用软件与本地政府组织相关的环境趋势绑定;在战术上将E-政府应用软件与应用软件和基础设施项目绑定。实证表明该框架能有效地结构化E-政府规划过程。     

A confidence-based framework for business to consumer (B2C) mobile commerce adoption

The Technology Acceptance Model (TAM) has been considered to be fundamental in determining the acceptance of new technology in the past decades. The two beliefs, ease of use and usefulness, in the model may not, however, fully explain the consumers’ behavior in an emerging environment, such as mobile commerce (m-commerce). This paper aims to develop a framework for m-commerce adoption in consumer decision-making processes. In this paper TAM has been adopted and extended to analyze successful m-commerce adoption. The key elements of the proposed confidence-based framework for B2C m-commerce adoption include psychological and behavioral factors. Psychological factors include history-based confidence, institution-based confidence and personality-based confidence. Behavioral factors include perceived ease of use and perceived usefulness of the mobile application and technology.     

A framework for benchmarking service process using data envelopment analysis and decision tree

With the increase in the influence of service industry on the economy, more and more firms are becoming concern about service and service management. Unlike tangible products, since service is a delivery system, designing and controlling delivery process are important in management of services. One of the most important issues in managing service delivery process is how to find out and improve inefficient process in an organization. Once inefficient processes are found, it should be determined which process is to be improved first, since it is impossible for any firm to improve all of their inefficient processes simultaneously. Then, in order to get some ideas about improvement, selecting a benchmark process is required. The objective of this paper is to provide a systematic approach for benchmarking service process. We used the integrated form of data envelopment analysis (DEA) and decision tree (DT) as a benchmarking method instead of using DEA alone. Although DEA is a useful technique for benchmarking, it fails to give any clear directions as to which process should be improved first. The proposed approach enables firm’s manager to find inefficient service units in a firm-level and inefficient processes in a service unit-level. The proposed approach also allows the manager to select which process to improve first and which process a benchmark for referencing. As a result, it can be helpful for operating and managing firms’ processes. To show how useful the suggested service process benchmarking framework is, an illustrative example is addressed.     

A testing framework for mobile computing software

We present a framework for testing applications for mobile computing devices. When a device is moved into and attached to a new network, the proper functioning of applications running on the device often depends on the resources and services provided locally in the current network. This framework provides an application-level emulator for mobile computing devices to solve this problem. Since the emulator is constructed as a mobile agent, it can carry applications across networks on behalf of its target device and allow the applications to connect to local servers in its current network in the same way as if they had been moved with and executed on the device itself. This paper also demonstrates the utility of this framework by describing the development of typical network-dependent applications in mobile and ubiquitous computing settings.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

A framework for decision support systems evaluation

In this paper, we present a framework for the Decision Support Systems evaluation problem. Using the Gorry-Scott Morton's framework for information systems, we develop several evaluation methods that structure the evaluation process. The framework determines the best methods of evaluation that are suitable to the characteristics of the Decision Support System concerned. Finally, we use the framework to evaluate two widely used Decision Support Systems.     

A conceptual framework for society-oriented decision support

Inspired by the operation of human social organisation, this paper presents a new architecture—a pyramid-committee—for developing society-oriented intelligence, whose structure imitates the organisation of human society in its decision making. The system takes a pyramid-like hierarchical structure with links in the pyramid forming a semi-lattice, which relate not only to nodes in the same layer, but also to others in different layers. The output of the system is a result of the negotiation and balancing of different interests. For such a system to function, the main difficulties concern the complicated relationships between different factors or agents. Focussing on the airport environment audit, we discuss the development of a model framework and the role of neural networks.     

SODPM: A sequence-oriented decision process model for unstructured group decision problems

Many approaches to the solutions of modern unstructured decision problems mainly involve modelling, information technology and group behaviour. The work of group decision-making can be viewed as a type of process plan that is reflected by its problem structure within which the thinking space is wide and innovative. This paper presents a Sequence-Oriented Decision Process Model (SODPM) that is based on the defined sequence of problem elements to help solve unstructured problems. A GDSSP (Group Decision Support System for Personnel Promotion) that embeds a predefined mechanism to perform decision process, decision model and decision choice is developed to experimentally demonstrate the SODPM. Empirical evaluation was conducted to derive the results for the research problems that include efficiency and group satisfaction. There were remarkable results: the SODPM can serve as a facilitative vehicle for opinion convergence and group satisfaction is highly positive. While the demonstrated domain for the example presented is personnel promotion, the proposed SODPM can be utilized to help solve the similar class of unstructured problems in other domains also.     

Energy-efficient multisite offloading policy using Markov decision process for mobile cloud computing

Mobile systems, such as smartphones, are becoming the primary platform of choice for a user’s computational needs. However, mobile devices still suffer from limited resources such as battery life and processor performance. To address these limitations, a popular approach used in mobile cloud computing is computation offloading, where resource-intensive mobile components are offloaded to more resourceful cloud servers. Prior studies in this area have focused on a form of offloading where only a single server is considered as the offloading site. Because there is now an environment where mobile devices can access multiple cloud providers, it is possible for mobiles to save more energy by offloading energy-intensive components to multiple cloud servers. The method proposed in this paper differentiates the data- and computation-intensive components of an application and performs a multisite offloading in a data and process-centric manner. In this paper, we present a novel model to describe the energy consumption of a multisite application execution and use a discrete time Markov chain (DTMC) to model fading wireless mobile channels. We adopt a Markov decision process (MDP) framework to formulate the multisite partitioning problem as a delay-constrained, least-cost shortest path problem on a state transition graph. Our proposed Energy-efficient Multisite Offloading Policy (EMOP) algorithm, built on a value iteration algorithm (VIA), finds the efficient solution to the multisite partitioning problem. Numerical simulations show that our algorithm considers the different capabilities of sites to distribute appropriate components such that there is a lower energy cost for data transfer from the mobile to the cloud. A multisite offloading execution using our proposed EMOP algorithm achieved a greater reduction on the energy consumption of mobiles when compared to a single site offloading execution.     

Sylvan: multi-core framework for decision diagrams

Decision diagrams, such as binary decision diagrams, multi-terminal binary decision diagrams and multi-valued decision diagrams, play an important role in various fields. They are especially useful to represent the characteristic function of sets of states and transitions in symbolic model checking. Most implementations of decision diagrams do not parallelize the decision diagram operations. As performance gains in the current era now mostly come from parallel processing, an ongoing challenge is to develop datastructures and algorithms for modern multi-core architectures. The decision diagram package Sylvan provides a contribution by implementing parallelized decision diagram operations and thus allowing sequential algorithms that use decision diagrams to exploit the power of multi-core machines. This paper discusses the design and implementation of Sylvan, especially an improvement to the lock-free unique table that uses bit arrays, the concurrent operation cache and the implementation of parallel garbage collection. We extend Sylvan with multi-terminal binary decision diagrams for integers, real numbers and rational numbers. This extension also allows for custom MTBDD leaves and operations and we provide an example implementation of GMP rational numbers. Furthermore, we show how the provided framework can be integrated in existing tools to provide out-of-the-box parallel BDD algorithms, as well as support for the parallelization of higher-level algorithms. As a case study, we parallelize on-the-fly symbolic reachability in the model checking toolset LTSmin. We experimentally demonstrate that the parallelization of symbolic model checking for explicit-state modeling languages, as supported by LTSmin, scales well. We also show that improvements in the design of the unique table result in faster execution of on-the-fly symbolic reachability.     

A framework for distributed management with mobile components

The increasing importance of networks and the growing numbers of devices and services that run on them necessitate effective network and systems management. The traditional centralized management paradigm alone is no longer sufficient for effective management solutions, primarily as it does not scale well. Distribution of management tasks is a promising approach. The distributed management framework (DMF) presented in this paper provides an environment that allows a broad range of management tasks to move and run anywhere within the managed system. In our approach, management tasks are lightweight applications that can be configured and downloaded dynamically as required, reducing the load on managed resources and simplifying the problem of management software updates. We present an object-oriented, Java-based implementation of the DMF and describe applications developed on this platform.     

A framework for context-aware self-adaptive mobile applications SPL

Mobile Applications are rapidly emerging as a convenient medium for using a variety of services. Over time and with the high penetration of smartphones in society, self-adaptation has become an essential capability required by mobile application users. In an ideal scenario, an application is required to adjust its behavior according to the current context of its use. This raises the challenge in mobile computing towards the design and development of applications that sense and react to contextual changes to provide a value-added user experience. In its general sense, context information can relate to the environment, the user, or the device status. In this paper, we propose a novel framework for building context aware and adaptive mobile applications. Based on feature modeling and Software Product Lines (SPL) concepts, this framework guides the modeling of adaptability at design time and supports context awareness and adaptability at runtime. In the core of the approach, is a feature meta-model that incorporates, in addition to SPL concepts, application feature priorities to drive the adaptability. A tool, based on that feature model, is presented to model the mobile application features and to derive the SPL members. A mobile framework, built on top of OSGI framework to dynamically adapt the application at runtime is also described.     

MAWS: A platform-independent framework for mobile agents using Web services

This paper proposes a framework for a multiplatform mobile agent system based on Web services. The framework includes a mobile agent factory, the mobile agents, and the host Web services. The mobile agents are created at the factory, migrate between Web services-enabled hosts, and execute at the destination hosts through invocation by XML-RPC. The transfer of mobile agents is accomplished through the employment of SOAP attachments where service handlers are used at the source and destination hosts to attach and detach agent files, respectively. The framework was implemented using the Java Web Service Developer Pack (JWSDP) and tested on 20 Linux and Windows hosts within a Wide Area Network (WAN). The system worked seamlessly on all designated hosts (proving interoperability) while comparing well in terms of throughput to three well-known mobile agent systems, namely, Voyager, Aglets, and Concordia. This paper demonstrates how the proposed system can interoperate with existing systems and serve as a model for interoperability among them. The paper also illustrates how the mobile agent can be secured against confidentiality and integrity attacks during transport and suggests ways for protecting it from malicious hosts as well as guarding hosts from malicious agents.     

A pairwise decision tree framework for hyperspectral classification

A novel pairwise decision tree (PDT) framework is proposed for hyperspectral classification, where no partitions and clustering are needed and the original C‐class problem is divided into a set of two‐class problems. The top of the tree includes all original classes. Each internal node consists of either a set of class pairs or a set of class pairs and a single class. The pairs are selected by the proposed sequential forward selection (SFS) or sequential backward selection (SBS) algorithms. The current node is divided into next‐stage nodes by excluding either class of each selected pair. In the classification, an unlabelled pixel is recursively classified into the next node, by excluding the less similar class of each node pair until the classification result is obtained. Compared to the single‐stage classifier approach, the pairwise classifier framework and the binary hierarchical classifier (BHC), experiments on an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a nine‐class problem demonstrated the effectiveness of the proposed framework.     

A general framework for time-aware decision support systems

In this paper we present a general framework for time-aware decision support systems. The framework uses the state-of-the-art tOWL language for the representation of temporal knowledge and enables temporal reasoning over the information that is represented in a knowledge base. Our approach uses state-of-the-art Semantic Web technology for handling temporal data. Through such an approach, the designer of a system can focus on the application intelligence rather than enforcing/checking data related restrictions manually. Also, there is an increased support for reuse of temporal reasoning tools across applications. We illustrate the applicability of our framework by building a market recommendations aggregation system. This system automatically collects market recommendations from online sources and, based on the past performance of the analysts that issued a recommendation, generates an aggregated recommendation in the form of a buy, hold, or sell advice. We illustrate the flexibility of our proposed system by implementing multiple methods for the aggregation of market recommendations.     

A framework for explaining reliance on decision aids

This study presents a framework for understanding task and psychological factors affecting reliance on advice from decision aids. The framework describes how informational asymmetries in combination with rational, motivational and heuristic factors explain human reliance behavior. To test hypotheses derived from the framework, 79 participants performed an uncertain pattern learning and prediction task. They received advice from a decision aid either before or after they expressed their own prediction, and received feedback about performance. When their prediction conflicted with that of the decision aid, participants had to choose to rely on their own prediction or on that of the decision aid. We measured reliance behavior, perceived and actual reliability of self and decision aid, responsibility felt for task outcomes, understandability of one's own reasoning and of the decision aid, and attribution of errors. We found evidence that (1) reliance decisions are based on relative trust, but only when advice is presented after people have formed their own prediction; (2) when people rely as much on themselves as on the decision aid, they still perceive the decision aid to be more reliable than themselves; (3) the less people perceive the decision aid's reasoning to be cognitively available and understandable, the less people rely on the decision aid; (4) the more people feel responsible for the task outcome, the more they rely on the decision aid; (5) when feedback about performance is provided, people underestimate both one's own reliability and that of the decision aid; (6) underestimation of the reliability of the decision aid is more prevalent and more persistent than underestimation of one's own reliability; and (7) unreliability of the decision aid is less attributed to temporary and uncontrollable (but not external) causes than one's own unreliability. These seven findings are potentially applicable for the improved design of decision aids and training procedures.     

SmartVisionApp: A framework for computer vision applications on mobile devices

In this paper a novel framework for the development of computer vision applications that exploit sensors available in mobile devices is presented. The framework is organized as a client–server application that combines mobile devices, network technologies and computer vision algorithms with the aim of performing object recognition starting from photos captured by a phone camera. The client module on the mobile device manages the image acquisition and the query formulation tasks, while the recognition module on the server executes the search on an existing database and sends back relevant information to the client. To show the effectiveness of the proposed solution, the implementation of two possible plug-ins for specific problems is described: landmark recognition and fashion shopping. Experiments on four different landmark datasets and one self-collected dataset of fashion accessories show that the system is efficient and robust in the presence of objects with different characteristics.     

Fractal: A mobile code-based framework for dynamic application protocol adaptation

The rapid growth of heterogeneous devices and diverse networks in our daily life, makes it is very difficult, if not impossible, to build a one-size-fits-all application or protocol, which can run well in such a dynamic environment. Adaptation has been considered as a general approach to address the mismatch problem between clients and servers; however, we envision that the missing part, which is also a big challenge, is how to inject and deploy adaptation functionality into the environment. In this paper we propose a novel application level protocol adaptation framework, Fractal, which uses the mobile code technology for protocol adaptation and leverages existing content distribution networks (CDN) for protocol adaptors (mobile codes) deployment. To the best of our knowledge, Fractal is the first application level protocol adaptation framework that considers the real deployment problem using mobile code and CDN. To evaluate the proposed framework, we have implemented two case studies: an adaptive message encryption protocol and an adaptive communication optimization protocol. In the adaptive message encryption protocol, Fractal always chooses a proper encryption algorithm according to different application requirements and device characteristics. And the adaptive communication optimization protocol is capable of dynamically selecting the best one from four communication protocols, including Direct sending, Gzip, Bitmap, and Vary-sized blocking, for different hardware and network configurations. In comparison with other adaptation approaches, evaluation results show the proposed adaptive approach performs very well on both the client side and server side. For some clients, the total communication overhead reduces 41% compared with no protocol adaptation mechanism, and 14% compared with the static protocol adaptation approach.