• Asia pacific journal of information systems
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• v.10 no.2
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• pp.71-95
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• 2000

Despite repeated exhortation about the importance of social and human dimensions of systems development, socio-organizational issues continue to be neglected and ignored in the current information systems practice. A review of the human information processing literature suggests that the reasons for this continuing lack of attention to social issues may be found in the limitations of human cognition and information processing capacities. Bostrom and Heinen(1978) and Kumar and Bjorn-Anderson(1990) also suggest that the inadequate attention to social problems and issues by the analyst could originate from the analysts limited problem perception. This research explores how the representation forms of information systems(IS) methodology used in understanding and modeling the problem situation affect such systems development problem perception. Typically, a system development methodology prescribes the use of system models(i.e., system representations) to understand, analyze, evaluate, and design the information system. Given the size and complexity of information systems, and the abstraction and simplification underlying the modeling process, system representations usually depict only a limited set of aspects of the system. Thus, a methodology whose representations are limited to technical aspects will tend to limit the analyst's perspective to a technical one only(Kumar & Welke, 1990). Following the same line of argument, in contrast, it is the conjecture of this study that a methodology which specifies both social and technical aspects of IS development will help the analyst develop a more comprehensive view of the IS problem domain. Based on the above concept, a theoretical model was first developed which explained the systems analysts cognitive process. Drawing on this model, a research model was developed hypothesizing the impacts of representation forms on problem identification. The model was tested using a laboratory experiment with 70 individual subjects. A special computer software was developed with a hypermedia authoring tool to conduct the experiments in order to avoid experimenter biases and to maintain consistency in administrating repeated experiments. The program, designed to replace the experimenter, consisted of functions such as presenting the subjects with problem material, asking the subjects questions, and saving the typed answers of the subjects. The results indicate that representation forms strongly influence problem identification. It was found that the use of the socio-technical representation form led to the findings of more social problems than the use of technical representation form. The results imply significant effects of representation forms on problem findings and also suggest that the use of adequate representation forms may help overcome dysfunctional effects of our limited information processing capacity.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.277-301
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• 2009

The purpose of this study is to examine those factors affecting Male Abusive Behavior on Intimate Female Partner Violence. The primary aim of this study is to determine the association between Intimate Partner Violence and coping strategies of male abusive behavior. The sample included 121 male abusive behavior who are referred by Counsel for Family Violence. For statistical analysis, descriptive statistical methods and hierarchical multiple regression were employed. Results indicated that male abusive behavior expressed more aggressive cognitive biases and irrational beliefs than nonviolent men. Both of problem-solving and avoidance coping to deal with relationship conflicts were related to abusive behavior of male. Specially, men who used higher levels of avoidance coping strategies was more likely related to physical abuse, less use of problem-solving coping was related to psychological abuse. Hostility and low marital satisfaction have also been associated with Intimate Partner Violence. Drinking is a risk factor for psychological abuse. Results are discussed implication for developing theoretical and interventional meanings of social welfare practice.

• The Korean Journal of Coaching Psychology
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• v.4 no.1
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• pp.1-19
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• 2020

The study was intended to explore the relevance of the variables below, assuming that the intolerance of uncertainty and dichotomous thinking would represent sequential serialized effects in the relationship between perfectionistic self-presentation and social anxiety. The data of this study were collected on questionnaire survey of 252 adult men and women in Seoul using perfectionistic self-presentation scale, social interaction anxiety scale, social phobia scale, intolerance of uncertainty scale and dichotomous thinkingI-30R as index, which results are as follow. First, intolerance of uncertainty and dichotomous thinking showed perfect mediation effects on the relationship between perfectionistic self-presentation and social anxiety. Intolerance of uncertainty and dichotomous thinking were identified as contributing factors to the development and preservation of social anxiety by perfectionistic self-presenters. Second, in the relationship between perfectionistic self-presentation and dichotomous thinking, intolerance of uncertainty showed mediation effect. And in the relationship between intolerance of uncertainty and social anxiety, dichotomous thinking showed mediation effect. This suggested that if tolerance of uncertainty was deficient, it was likely to lead to dichotomous thinking. And a dichotomous thinking has prompted or accelerated negative cognitive biases resulting from intolerance of uncertainty, triggering and deepening social anxiety. Lastly, the limitations of this study and future research direction were suggested.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.