The impact of computer use on cognitive development in young children: A theoretical analysis

Technological innovations of the past have extended our muscles and our senses and have also increased the number and variety of our social interactions. These increased social interactions tend to produce changes in social institutions which in turn affect child rearing and mental development. When young children experience increased social interactions this can have a negative effect upon mental development. Computer technology is unique in that it decreases as well as increases the number and frequency of social interactions. Moreover, only computers increase the number of self-self interactions. If these computer related trends continue, and if technology makes it possible for children to engage in self-self interactions, mental development will be enhanced.     

A theoretical foundation of variability in component-based development

Component-Based Development (CBD) is revolutionizing the process of building applications by assembling pre-built reusable components. Components should be designed more for inter-organizational reuse, rather than intra-organization reuse through domain analysis which captures the commonality of the target domain. Moreover, the minor variations within the commonality should also be modeled and reflected in the design of components so that family members can effectively customize the components for their own purpose. To carry out domain analysis effectively and design widely reusable components, precise definitions of variability-related terms and a classification of variability types must be made. In this paper, we identify the fundamental difference between conventional variability and component variability, and present five types of variability and three kinds of variability scopes. Each type of variability is precisely defined for its applicable situations and guidelines. Having a formal view on variability, not only the domain analysis but also component customization can be effectively carried out in a precise manner.     

A theoretical analysis of recursive identification methods

In this paper five different recursive identification methods will be analyzed and compared, namely recursive versions of the least squares method, the instrumental variable method, the generalized least squares method, the extended least squares method and the maximum likelihood method. They are shown to be similar in structure and need of computer storage and time. Making use of recently developed theory for asymptotic analysis of recursive stochastic algorithms, these methods are examined from a theoretical viewpoint. Possible convergence points and their global and local convergence properties are studied. The theoretical analysis is illustrated and supplemented by simulations.     

一种面向网络连通性的关键网络元素脆弱性分析方法

准确评估网络系统脆弱性对于网络安全规划和风险管理至关重要.现有网络脆弱性分析方法大多利用单一特性识别脆弱元素,随着网络系统复杂化进程加快,多元化的脆弱性识别显得尤为重要.从攻击者的角度出发,提出一种面向网络连通性的关键元素脆弱性分析方法,识别网络中具有多重重要身份且破坏代价小的网络元素,利用局部分析措施确定网络关键元素并将网络连通性作为脆弱性衡量指标,以识别出关键元素中可致使网络连通性特定降级的最小代价集合.仿真结果表明,所提出方案对脆弱元素的定位更加准确,可为网络安全防护措施的制定提供有效且可靠的参考.     

A theoretical and experimental investigation of graph theoretical measures for land development in satellite imagery

Today's commercial satellite images enable experts to classify region types in great detail. In previous work, we considered discriminating rural and urban regions. However, a more detailed classification is required for many purposes. These fine classifications assist government agencies in many ways including urban planning, transportation management, and rescue operations. In a step toward the automation of the fine classification process, this paper explores graph theoretical measures over grayscale images. The graphs are constructed by assigning photometric straight-line segments to vertices, while graph edges encode their spatial relationships. We then introduce a set of measures based on various properties of the graph. These measures are nearly monotonic (positively correlated) with increasing structure (organization) in the image. Thus, increased cultural activity and land development are indicated by increases in these measures - without explicit extraction of road networks, buildings, residences, etc. These latter, time consuming (and still only partially automated) tasks can be restricted only to "promising" image regions, according to our measures. In some applications our measures may suffice. We present a theoretical basis for the measures followed by extensive experimental results in which the measures are first compared to manual evaluations of land development. We then present and test a method to focus on, and (pre)extract, suburban-style residential areas. These are of particular importance in many applications, and are especially difficult to extract. In this work, we consider commercial IKONOS data. These images are orthorectified to provide a fixed resolution of 1 meter per pixel on the ground. They are, therefore, metric in the sense that ground distance is fixed in scale to pixel distance. Our data set is large and diverse, including sea and coastline, rural, forest, residential, industrial, and urban areas.     

A theoretical analysis based on causal inference and single-instance learning

Applied Intelligence - Although using single-instance learning methods to solve multi-instance problems has achieved excellent performance in many tasks, the reasons for this success still lack a...     

A theoretical development for the computer generation and display of piecewise polynomial surfaces

Two algorithms for parametric piecewise polynomial evaluation and generation are described. The mathematical development of these algorithms is shown to generalize to new algorithms for obtaining curve and surface intersections and for the computer display of parametric curves and surfaces.     

Topological analysis of cloud service connectivity

Relying on services in the cloud involves manifold availability risks and concerns. This article focuses on the network reachability of cloud services. We present a study on cloud outages and causes, and analyze the topological connectivity of major cloud service providers (CSPs) by graph-based measures. Our approach is based on the construction and integration of an empirical dataset describing the connections between Autonomous Systems (ASs) of organizations that form the Internet backbone. According to our findings, though the ASs of CSPs generally appear to be better connected than an average AS, they also vastly differ in several connectivity measures, sometimes by more than an order of magnitude. Our results help to identify well-connected CSPs and CSPs that could potentially suffer more from Internet outages, if no additional path redundancy is provided. Our approach can be used by CSPs to assess connectivity beyond their own premises. It can also support cloud service customers during benchmarking and selection of CSPs when high availability is a critical requirement.     

Effective connectivity analysis of fMRI data based on network motifs

Exploring effective connectivity between neuronal assemblies at different temporal and spatial scales is an important issue in human brain research from the perspective of pervasive computing. At the same time, network motifs play roles in network classification and analysis of structural network properties. This paper develops a method of analyzing the effective connectivity of functional magnetic resonance imaging (fMRI) data by using network motifs. Firstly, the directed interactions between fMRI time-series are analyzed based on Granger causality analysis (GCA), by which the complex network is built up to reveal the causal relationships among different brain regions. Then the effective connectivity in complex network is described with a variety of network motifs, and the statistical properties of fMRI data are characterized according to the network motifs topological parameters. Finally, the experimental results demonstrate that the proposed method is feasible in testing and measuring the effective connectivity of fMRI data.     

A theoretical investigation of feature partitioning principal component analysis methods

Principal Component Analysis (PCA) is a well-known linear dimensionality reduction technique in the literature. It extracts global principal components (PCs) and lacks in capturing local variations in its global PCs. To overcome the issues of PCA, Feature Partitioning based PCA (FP-PCA) methods were proposed; they extract local PCs from subpatterns and they are not sensitive to global variations across the subpatterns. Subsequently, SubXPCA was proposed as a novel FP-PCA approach which extracts PCs by utilizing both global and local information; it was proved to be efficient in terms of computational time and classification. It is observed that there is no detailed theoretical investigation done on the properties of FP-PCA methods. Such theoretical analysis is essential to provide generalized and formal validation of the properties of the FP-PCA methods. In this paper, our focus is to show SubXPCA as an alternative to PCA and other FP-PCA methods by proving analytically the various properties of SubXPCA related to summarization of variance, feature orders, and subpattern sizes. We prove analytically that (i) SubXPCA approaches PCA in terms of summarizing variance with increase in number of local principal components of subpatterns; (ii) SubXPCA is robust against feature orders (permutations) of patterns and variety of partitions (subpattern sizes); (iii) SubXPCA shows higher dimensionality reduction as compared to other FP-PCA methods. These properties of SubXPCA are demonstrated empirically upon UCI Waveform and ORL face data sets.     

Issues in representation of molecular structure the development of molecular connectivity.

Significant issues in the representation of molecular structure and the development of the molecular connectivity paradigm are presented. In the molecular connectivity paradigm, molecular structure is represented directly. Kier and Hall developed the method by creating ways to encode electronic information based on the paradigm developed from the Randi? branching index. The simple and valence delta values were created to encode atomic and valence-state electronic information through counts of sigma, pi, and lone pair electrons. A family of indices was created to provide a wide range of structure information. The key aspects of the development are presented and discussed in such a way as to reveal, at least in part, the imaginative thinking involved in the process. Possible future roles for molecular connectivity chi indices are discussed.     

A theoretical analysis of bagging as a linear combination of classifiers

We apply an analytical framework for the analysis of linearly combined classifiers to ensembles generated by bagging. This provides an analytical model of bagging misclassification probability as a function of the ensemble size, which is a novel result in the literature. Experimental results on real data sets confirm the theoretical predictions. This allows us to derive a novel and theoretically grounded guideline for choosing bagging ensemble size. Furthermore, our results are consistent with explanations of bagging in terms of classifier instability and variance reduction, support the optimality of the simple average over the weighted average combining rule for ensembles generated by bagging, and apply to other randomization-based methods for constructing classifier ensembles. Although our results do not allow to compare bagging misclassification probability with the one of an individual classifier trained on the original training set, we discuss how the considered theoretical framework could be exploited to this aim.     

A replicated quasi-experimental study on the influence of personality and team climate in software development

This article reports a replication of a quasi-experimental study analyzing how personality factors and team climate influence software development team effectiveness, product quality and team member satisfaction. The replication was designed on the basis of the original quasi-experimental study, both of which were run in an academic setting. In the original study, data were collected from a sample of 35 three-member developer teams. All these teams used an adaptation of extreme programming (XP) to the academic environment to develop the same software system. In the replication, the data were collected from a sample of 34 three- or four-member developer teams working on the same software project. Student teams used a common object-oriented software development paradigm to solve the set problem and applied the Unified Process. In both studies all teams were formed at random, and their members were blind to the quasi-experimental conditions and hypotheses. The replication of this empirical study aims to verify the results of the original quasi-experiment. It examines, first, whether personality factors (neuroticism, extroversion, agreeableness, openness to experience and conscientiousness) are related to the quality of the developed software and team member satisfaction and, second, whether the preferences, perceptions and preferences-perceptions fit for the four team climate factors (participative safety, support for innovation, team vision and task orientation) are related to the quality of the developed software and team member satisfaction. The results of the replication corroborate some of the findings of the original study. On the one hand, the results revealed that there is a significant correlation between the extroversion personality factor and software quality, but no significant correlation between the extroversion personality factor and team satisfaction. Also, we found that the perception of team climate where participative safety is high is related to better quality software. We observed significant relationships between the perception of the four team climate factors and team member satisfaction. Additionally, the results showed a positive relationship between software quality and teams in which the real climate perception at the end of the project is better than preferences stated by team members at the outset of the project for the participative safety factor. Finally, we found that teams where the real climate is better than the stated preferences for the team orientation factor exhibit a direct and positive relationship to team member satisfaction.     

The effect of imbalanced data sets on LDA: A theoretical and empirical analysis

This paper demonstrates that the imbalanced data sets have a negative effect on the performance of LDA theoretically. This theoretical analysis is confirmed by the experimental results: using several sampling methods to rebalance the imbalanced data sets, it is found that the performances of LDA on balanced data sets are superior to those of LDA on imbalanced data sets.     

A unified model of knowledge sharing behaviours: theoretical development and empirical test

Research and practice on knowledge management (KM) have shown that information technology alone cannot guarantee that employees will volunteer and share knowledge. While previous studies have linked motivational factors to knowledge sharing (KS), we took a further step to thoroughly examine this theoretically and empirically. We developed a unified model that is comprehensive and yet parsimonious, based on the decomposed theory of planned behaviour (DTPB) with three sets of critical antecedents: psychological, organisational and technological that are theorised to influence KS behaviours. Results of a field survey of knowledge workers support the majority of hypothesised relationships, and explained 41.3% of the variance in the actual KS behaviours and 60.8% of the variance in the intention to share knowledge. These results far exceed the predictive powers achieved by previous studies. Among our significant findings include a strong positive influence of perceived enjoyment in helping others (PEH) and a strong negative influence of perceived loss of knowledge power (PLK). Based on the findings, we discussed the study's implications for research and practice.     

Persuasive messages on information system acceptance: A theoretical extension of elaboration likelihood model and social influence theory

Firms invest millions of dollars in the introduction of new information systems for long-term benefit. If employees are not willing to accept a new information system, such investments may be wasted. Employee acceptance of a new information system is in part determined by external influences. However, previous research has neglected the paths of persuasive strategies and external social influences on information system acceptance. Linkages between persuasive strategies and external social influences are also scarce. By integrating social influence theory and an elaboration likelihood model, this study explores the influence of persuasive messages (source credibility and argument quality) on social influence, affective response and cognitive response. This study also investigates the interrelationships among affective response, cognitive response and behavior intention. Furthermore, the moderating roles of social influences on the impact of affective response and cognitive response on behavior intention are identified.     

博弈控制论简述

博弈控制论是近年来出现的博弈与控制的交叉学科,虽然它融合了博弈和控制双方的工具和方法,但无论从研究对象,研究方法与现有的结论看,它都不完全从属于经典博弈或经典控制的范畴,因此,是一个具有自身特色的新学术生长点.本文的目的是对这个新方向做一个简述介绍,内容包括:i)基于博弈的控制;ii)基于势博弈的优化;iii)博弈中的状态空间方法;iv)国内几个研究博弈控制论的团队.最后,对博弈控制论的未来做一展望.     

A theoretical perspective on social agency

In interacting with artificial social agents, novel forms of sociality between humans and machines emerge. The theme of Social Agency between humans and robots is of emerging importance. In this paper key theoretical issues are discussed in a preliminary exploration of the concept. We try to understand what Social Agency is and how it is created by, negotiated with, and attributed to artificial agents. This is done in particular considering socially situated robots and by exploring how people recognize and accept social agents. The interplay between humans and agents is investigated through dynamics of interpretation, signification and attribution. The ultimate goal of this research is to explore the challenges and opportunities brought by the design of socially intelligent agents.

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