Knowledge representation and construction in hypermedia environments

Hypermedia information systems, because of their inherent qualities of nonlinearity and associative linking, offer the potential of facilitating enhanced knowledge representation and construction for users. These capabilities will be more fully realized if hypermedia information systems are developed and implemented based on constructivist learning theory. This paper examines the qualities of hypermedia systems that enable them to facilitate learning, discusses the background of current learning theories, and provides a brief view of a possible hypermedia system based on constructivist learning principles that could be developed through the use of intelligent autonomous agents.     

Knowledge Community: A Knowledge-Building System for Global Collaborative Project Learning

The wave of a knowledge economy drives today's education to equip students with knowledge building abilities. While collaborative learning has been proven to be an effective constructivist pedagogy, it is difficult to elicit, coordinate, and capture the corresponding knowledge construction process. The situation becomes more challenging when learning is conducted in a distributed environment in which the participants are scattered in different geographical locations. In this paper, we depict the theories, architecture, applications, and analysis of a Web-based computer-supported collaborative learning and knowledge-building system called Knowledge Community (KC), which currently serves a series of 3I (interdisciplinary, interschool, and international) Project Learning activities with more than 10 000 students and teachers participating globally. We also describe the corresponding 3I Project Learning model, a novel technology-enabled pedagogy in which learners perform collaborative, comparative study projects with peers from other countries. Technologically, KC is the use of Web technologies to provide a collaborative learning environment. Pedagogically, KC with its embedded learning theories has created a new learning culture that meets the demands of knowledge economy.     

Understanding behavioural intention to use information technology: Insights from humanitarian practitioners

The contemporary research in the area of individual technology adoption mainly focuses on commercial supply chains. However, limited research focuses on the context of humanitarian supply chains. This calls to develop structural models that can scrutinize the technology adoption behaviour of the users in the humanitarian context. Therefore, this study is an attempt to empirically examine the technology adoption behaviour of humanitarian organizations. It extends the unified theory of the acceptance and use of technology (UTAUT) model by integrating personal innovativeness and trust in technology with the behavioural intention to adopt technology in the humanitarian context. Data from 192 humanitarian practitioners, who have experienced a large number of disasters, is utilized to empirically validate the conceptual model. The structural equation modelling results show that - out of four constructs namely performance expectancy, effort expectancy, social influence and facilitating conditions under UTAUT - performance expectancy and effort expectancy significantly affect the IT adoption. Contrary to expectations, trust and personal innovation do not affect the behavioural intention. Also, personal innovation does not moderate the relationship between performance expectancy and effort expectancy. This underlines the need to foster a learning culture within these organizations. The efforts made by involved humanitarian organizations may be directed towards improving the level of education, skills and facilitating them with other resources such as appropriate IT and data mining training, so that the technology adoption becomes an integral part of their daily activities. Finally, detailed implications for humanitarian organizations are discussed.     

Is Usage a Missing Link in Explaining the Perceived Learning Outcome of Technology-Mediated Learning?

Information systems (IS) researchers have demonstrated that usage is a key variable in explaining the performance impact of information technology. However, existing technology-mediated learning (TML) studies have not examined the influence of usage on learning outcome and the factors that determine the usage of TML. To address this research gap, our study presents and tests a TML model by drawing insights from two research streams. First, following the IS literature, we incorporate the impact of technology usage on individual performance. Second, building on the social cognitive theory, we study the influences of self-efficacy beliefs (system and subject domain) and affective responses (affect and anxiety) on technology usage. Based on 503 matched responses collected using two-stage questionnaire surveys, our analyses confirm the significance of usage in mediating the effects of system self-efficacy and anxiety on perceived learning outcome, but not in mediating the effects of affect and subject-domain self-efficacy. We find strong support for the influences of self-efficacy beliefs on affective responses. Self-efficacy beliefs of the users are also observed to change over time and perceived learning outcome plays a significant role in explaining this change. Our research enhances the existing TML theory by producing useful insights regarding the influence of social cognitive factors of learners on the usage of TML and how usage mediates the influence of these variables on perceived learning outcome.     

Exploring the impact of social properties on routing in cognitive radio networks

In this paper, we study the social properties, including community, friendship, and individual selfishness of cognitive radio networks and analyze the effect of these social properties on the performance of routing protocols. We first introduce the concept of cooperation willingness considering social relations and individual selfishness. Then, we model the impact of cooperation willingness on the routing performance in cognitive radio networks. After that, we perform extensive simulations to evaluate the accuracy of our model. The simulation results show that our model matched the simulation results well. Finally, our results suggest that community members, friends, and nodes with lower individual selfishness should be selected in the forwarding routes.     

Models of Growth — Towards Fundamental Change in Learning Environments

This paper proposes that a major reason for the lack of change in education is not due to lack of ideas about learning on a micro or individual level, but rather is due to a lack of models for growth and change at a macro or systemic level. We critique existing models of growth and provide examples of broad social change in other fields. We look at their properties and use those as a guide to thinking about change in learning environments. We propose that there exists a grammar of school reform. We provide examples of attempts to facilitate fundamental change at a large scale, and attempt to synthesise their properties, leading to thinking about new models for growth.     

The Contingent Effects on Project Performance of Conducting Project Reviews and Deploying Project Management Offices

There is little empirical evidence on the benefits of deploying a project management office (PMO) and/or conducting project reviews. Drawing on the information processing view of organizations, goal setting and social cognitive theory, this study investigates the effects of those two organizational mechanisms on the project performance of organizations in the construction and information systems (IS) industries. The results show that the effects on project performance of deploying PMOs and conducting project reviews are contingent on task uncertainty. Construction organizations improve their project performance by conducting project reviews to provide performance feedback to the project teams, enabling them to make timely adjustments to their project management strategies. In contrast, IS services organizations improve their project performance by deploying PMOs to facilitate cross-project learning.     

认知无线电中一种感知节点集自适应选择算法

本文主要考虑认知网络中感知节点集的选择问题。联合谱感知技术虽然可以极大地提高认知系统的感知性能,但是随着参与感知的认知节点数目的增加,对系统资源的占用也会越来越多,使系统的传输效率下降。本文首先给出了认知网络中最优感知节点集的概念,接着分析了最优感知节点集的节点数目和平均接收信噪比所必须满足的条件,最后通过推导得到了在固定虚警概率条件下最优感知节点集的检测概率与它的节点数目和平均接收信噪比之间的关系表达式,并在此基础上提出了一种最优感知节点集的自适应选择算法。该算法不但能在认知网络中寻找最优感知节点集,同时还可以适应认知网络的动态拓扑变化。仿真结果证明了该算法的有效性。      

Understanding relationships among teleworkers' e-mail usage, e-mailrichness perceptions, and e-mail productivity perceptions under asoftware engineering environment

This study was undertaken to investigate the use of e-mail and its implications under a telework environment for distributed software engineering. For this, the relative strength between a social influence and individual attributes in affecting teleworkers' e-mail use was studied. Management support was used as the representative social influence, and age, status, and ease of use represented individual attributes. An examination was also made on how e-mail use, individual attributes, and management support affected the perceptions of e-mail's information richness and e-mail productivity. Two different types of surveys, log sheets and perception-based self-reports, as well as interviews and e-mail correspondences composed the data sources. Three hierarchical regression models were defined and tested for the hypothesis validation. Data analysis indicated that management support was a much more powerful indicator for teleworkers' media use than individual characteristics. Furthermore, although labeled as a relatively lean medium from the media richness theory perspective, e-mail could become an effective and richer communication tool through an active social construction process of management support. Finally, the management support and perception of e-mail as a rich medium were both highly influential in creating teleworkers' positive perception on e-mail productivity. This study rendered a strong indication that effective adoption of e-mail by teleworkers as an information-rich medium could benefit distributed work and distributed organizations through enhanced work productivity     

Neural network models in EMG diagnosis

In previous years, several computer-aided quantitative motor unit action potential (MUAP) techniques were reported. It is now possible to add to these techniques the capability of automated medical diagnosis so that all data can be processed in an integrated environment. In this study, the parametric pattern recognition (PPR) algorithm that facilitates automatic MUAP feature extraction and Artificial Neural Network (ANN) models are combined for providing an integrated system for the diagnosis of neuromuscular disorders. Two paradigms of learning for training ANN models were investigated, supervised, and unsupervised. For supervised learning, the back-propagation algorithm and for unsupervised learning, the Kohonen's self-organizing feature maps algorithm were used. The diagnostic yield for models trained with both procedures was similar and on the order of 80%. However, back propagation models required considerably more computational effort compared to the Kohonen's self-organizing feature map models. Poorer diagnostic performance was obtained when the K-means nearest neighbor clustering algorithm was applied on the same set of data     

行动学习理论下的高职青年教师教学能力提升路径研究

行动学习理论是以实际的工作任务或问题为导向, 以完成任务或解决问题为主线, 以个人能力为基础, 通 过在实际工作过程中对固有知识的质疑与反思, 以及团队支撑和同伴支持, 来提升自身的能力与水平。青 年教师是高职院校教师队伍的主力军, 其教学能力的高低对学校教学的影响较大, 需要学校给予更多的关 注。因此, 学院要根据青年教师的教学能力、专业背景、个人意愿, 以行动学习理论为指导, 以制度引领 为基础, 以专业支撑为核心, 以平台搭建为载体, 以自我提升为关键, 探寻提升高职青年教师教学能力的 路径与方法。     

Parametric optimization of software defined radio configurations using design of experiments

Cognitive radio (CR) systems incorporate learning and decision making into wireless and networking systems with the goal of improving performance and interoperability. Research has focused on artificial intelligence control and optimization of radio input parameters with little attention placed on identifying initialization parameters of cognitive engines or on testing methods. While CR techniques continue to advance, calibration and testing remain largely stagnant with reliance on ad hoc and highly application specific approaches. Given that cognitive radio will be deployed in a variety of environments with each requiring unique calibration, systematic procedures are needed. An approach founded in design of experiments provides a purposeful framework for performing testing and identification of initialization parameters with an efficient number of test cases. Response surface methodology designs identify representative knowledge of system performance including input parameter significance and quadratic estimation models of output metrics. An example of calibrating transmit-and-receive gain settings on a software-defined radio illustrates the use of the framework.     

How Do Knowledge Management Announcements Affect Firm Value? A Study of Firms Pursuing Different Business Strategies

The importance of knowledge management (KM) efforts is well recognized in the popular and academic press. However, KM efforts do not always lead to improved firm performance. This paper argues that different kinds of KM efforts may be appropriate for firms pursuing different business strategies, and the impact of a KM effort on firm performance would depend on whether the effort is aligned with the firm's business strategy. Prior literature on organizational learning, KM, and business strategy is used to develop these arguments, and also to identify the nature of KM efforts that would be most appropriate for firms pursuing Defender, Analyzer, and Prospector business strategies. An event study is used to evaluate the stock market reaction to a firm's public announcement of the KM effort. The empirical results, based on 103 KM announcements from 1995 to 2002, are consistent with the theory-based expectations, showing alignment between the KM effort and business strategy to be positively associated with impact on firm value. Some of the implications of these results for practice and future research are examined.     

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.     

Cognitive radio testing using psychometric approaches: applicability and proof of concept study

Cognitive radios promise efficient spectrum use and other performance improvements through use of machine learning to adapt the radios?? operational parameters to optimize performance; however, their flexibility complicates evaluation of cognitive radios?? performance. We propose to improve cognitive radio development and evaluation using approaches developed for efficiently measuring and testing human cognitive characteristics. Cognitive radio performance evaluation requirements and applicable psychometric approaches are described. Finally, a proof of concept application of a psychometric measurement technique to evaluate cognitive engine performance is presented for simulated channel conditions for multiple prioritizations of optimization goals.     

空间相关信道下一种合作感知节点集合选择算法

在空间相关信道条件下随着合作感知节点数目的增加,新增合作感知节点所带来的性能增益将会越来越小并趋近于零,同时系统资源使用效率也将会随着合作感知节点数目的增加而降低,因此在选择合作感知节点数目时需要在感知性能与资源使用效率之间进行折衷。本文提出了一种合作感知节点集合的贪婪选择算法。该算法首先在对感知性能和资源使用效率进行折衷的基础上得到判决门限,并依此判决门限选择满足条件的合作感知节点集合。仿真结果证明了所提出算法的有效性与可靠性。      

Evaluating a framework of theoretical hypotheses for animation learning

This paper presents a set of theoretical hypotheses suggesting various relationships between didactical setting and learning effects with animations. Particularly, we investigated whether individual flow-control adequately provides didactical means to reduce the cognitive load imposed by animations. We did not find an effect of individual flow control, probably due to the fact that this learning condition was embedded in a setting where not enough verbal information was offered together with the graphical animation. Overall the multimedia effects found in this study are in line with known principles of didactical multimedia design. Further, this study sheds light on theoretical aspects involved in the complex interaction between learning content, presentation, learning and resulting knowledge.     

Image quality assessment based on self-supervised learning and knowledge distillation

Deep neural networks have achieved great success in a wide range of machine learning tasks due to their excellent ability to learn rich semantic features from high-dimensional data. Deeper networks have been successful in the field of image quality assessment to improve the performance of image quality assessment models. The success of deep neural networks majorly comes along with both big models with hundreds of millions of parameters and the availability of numerous annotated datasets. However, the lack of large-scale labeled data leads to the problems of over-fitting and poor generalization of deep learning models. Besides, these models are huge in size, demanding heavy computation power and failing to be deployed on edge devices. To deal with the challenge, we propose an image quality assessment based on self-supervised learning and knowledge distillation. First, the self-supervised learning of soft target prediction given by the teacher network is carried out, and then the student network is jointly trained to use soft target and label on knowledge distillation. Experiments on five benchmark databases show that the proposed method is superior to the teacher network and even outperform the state-of-the-art strategies. Furthermore, the scale of our model is much smaller than the teacher model and can be deployed in edge devices for smooth inference.     

基于情感刺激的语音抑郁症检测分析

抑郁症是一种常见的精神障碍疾病,早期的检测和诊断对抑郁症预防和治疗至关重要。基于语音的抑郁症检测是当前计算机辅助检测方法中的一种高效、便捷的手段。为了探索不同的情感刺激是否对语音抑郁症检测存在影响,本文首先构建了抑郁症分析声学特征集,接着使用非参数检验的方法分析不同情感刺激性下抑郁与非抑郁个体之间声学特征的显著性差异,再采用情感刺激（积极、消极、中性）和任务类型（文本朗读、问答）组合的实验设计,通过机器学习和深度学习算法分别构建语音抑郁症检测模型。实验结果证明使用情感刺激会对抑郁症检测任务产生一定程度的影响,并且消极的情感刺激更容易诱发抑郁相关的情绪,对个体的发音特性产生影响,进而取得比积极刺激和中性语音更好的检测效果。     

Determinants of the intention to use non-medical insertable digital devices: The case of Chile and Colombia

In the past, thinking of carrying electronic devices inside our bodies was only posed by non-real scenarios. The emergence of insertable devices has changed this. Since this technology is still in its initial development stages, few studies have investigated factors that influence its acceptance. This paper analyzes the predictors of the intention to use non-medical insertable devices in two Latin American contexts. We used partial least squares structural equation modeling to examine whether six constructs predicted intention to use insertable devices. A questionnaire was administered to undergraduate students located in Colombia and Chile (n = 672). We also examined whether these predictors influenced intention differently for both of them. Four common constructs significantly and positively influenced both Chilean and Colombian respondents to use insertable devices (hedonic motivation, habit, performance expectancy, and social influence). Also, the habit has a complementary mediating effect on the relationship between social influence and behavioral intention. By contrast, effort expectations were a positive and significant predictor, but only among Chilean respondents. Findings suggest that when technologies are emerging, well-known predictors of intention (e.g., performance and effort expectations) are less influential than predictors related to self-efficacy (e.g., habit and hedonic motivation). The use of insertable devices has a significant impact on society. Thus, a better understanding of what motivates their use has implications for both academia and industry.