Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.
Some of basic aspects of soil potassium with special reference to soil physical properties were discussed. Data in the Official Soil Series Description(Korea) was analyzed according to soil type, land form, and soil texture to find soil potassium status which may explain different response to potassium application. Exchangeable potassium contents decreased with soil depth irrespective of soil type, land form and soil texture. Change in degree of potassium saturation within soil profile was not so clear as exchangeable potassium but the degree of potassium saturation of A horizon was highest among soil horizon. Soils of terrace and mountain foot slope showed high values both in exchangeable potassium and degree of potassium sauration and only these two soils were classified as soils having exchangeable potassium higher than 0.3 meq per 100g of soil and degree of potassium saturation higher than 5.0%. Exchangeable potassium of fine loamy and fine clayey soils is higher than 0.3 meq per 100g of soil but degree of potassium saturation is lower than 4.0%. Degree of potassium saturation of sandy soils exceeds 5.0% but exchangeable potassium is very low. Soils of rolling, hilly, unmatured and alpine land soils have lower exchangeable potassium and show lower degree of potassium saturation. The highest distribution of exchangeable potassium content irrespective of soil horizons was shown in the range of 0.1-0.2 meq per 100g of soil. The highest distribution of degree of potassium saturation was in the range of 2.0-3.0% in A horizon and 1.0-2.0% in B and C horizons. Of the soil series concerned in this analysis, 27.3% in A horizon, 11.1% in B horizon and 4.0% in C horizon had exchangeable potassium higher than 0.3 meq per 100g of soil and 18.0% in A horizon, 6.3% in B horizon, and 4.1% in C horizon showed degree of potassium saturation higher than 5.0%. The low response of potassium application only to soils in terrace and mountain foot slope may be resulted from the high exchangeable potassium content and high degree of potassium saturation. It is concluded that a great response of potassium application to soils is expected especially in dry season.
Objectives: The gold standard for diagnosing obstructive sleep apnea syndrome (OSAS) is nocturnal polysomnography (NPSG). This is rather expensive and somewhat inconvenient, however, and consequently simpler and cheaper alternatives to NPSG have been proposed. Oximetry is appealing because of its widespread availability and ease of application. In this study, we have evaluated whether oximetry alone can be used to diagnose or screen OSAS. The diagnostic performance of an analysis algorithm using arterial oxygen saturation (
This studies designed to work out a theoretical framework on the experience of sexual abuse from the perspective of grounded theory in an effort to provide more practical and efficient nursing intervention for female victims. The subcategories identified were "sexual abuse", "threatening", "absent mindness", "embarrassment", "horripilation", "dizziness", "wondrousness", "filthiness", "sexual curiosity", "violence level", "victim's age", "neighbors response", "victims personality", "common experience", "sexual abuse information", "family relations", "level of familiarity", "hiding", "suppression", "self-torture", "self-protection", "avoidance", "asking aid", "withdrawal", "hatred", "confusion", "dodging, "remmant", and "pursuing". The 29 subcategories given above were further integrated into 16 categories such as "victimizedness", "being astounded", "filthiness", "degree", "developmental stage", "response pattern", "personality", "rarity", "information availability", "family support", "cover-up", "escaping", "informing", "negative internalization", and "positive pursuit of change". The core categories linked to all the other categories turned out to be "being taken aback" and "filthiness" incorporating the relevant subcategories. A total of 23 theoretical hypothesis emerged in the process of analyzing data. 1. the grater sexual curiosity, the weaker the senses of being taken aback and filthiness. 2. The weaker sexual curiosity, the stronger the senses of being taken aback and filthiness. 3. The stronger the level of violence, The more violent the senses of being taken aback and filthiness. 4. The lower the level of violence, the weaker the senses of being taken aback and filthiness. 5. The younger the victims, the stronger the senses of being taken aback and filthiness. 6. The older the victims, The weaker the senses of being taken aback and filthiness. 7. 'Escaping' will transpire regardless of the given circumstances. 8. The weaker the senses of being taken aback and filthiness, the more probable 'informing' and 'escaping' transpire. 9. The stronger the senses of being taken aback and filthiness, the more probable 'informing' and 'escaping' transpire. 10. The more protective the response from 'informing' and 'escaping' transpire around, the more likely the response to being taken aback' and 'filthiness' will be 'informing' and 'escaping'. 11. The more repelling the response from around, the more likely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping'. 12. The more open minded the personality of the subject, the more likely the response to 'being taken aback' and 'filthiness' will be 'informing' and 'escaping'. 13. The more closed the personality of tile subject, the more likely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping'. 14. The more frequent the experience of sexual abuse, the more likely the response to 'being taken aback' and 'filthiness' will be 'informing' and 'escaping'. 15. The less frequent the experience of sexual abuse, the more lilely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping'. 16. The more available information concerning sexual abuses, the more likely response to 'being taken aback' and 'filthiness' will be 'informing' and 'escaping. 17. The less available information concerning sexual abuses, the more likely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping'. 18. The more cohesive the family of the subject, the more likely the response to 'being taken aback' and 'filthiness' will be 'informing' and 'escaping'. 19. The less cohesive the family of the subject, the more likely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping'. 20. The less familiar the subject is with the abuser, the more likely the response to 'being taken aback' and 'filthiness' will be 'informing' and 'escaping'. 21. The less familiar the subject is with the abuser, the more likely the response to 'being taken aback' and 'filthiness' will be 'covering-up' and 'escaping. 22. The more likely the response to 'being taken aback' and 'filthiness' is 'informing and 'escaping', the more positive changes the subject will pursue. 23. The more likely the response to 'being taken aback' and 'filthiness' is 'covering-up' and 'escaping', the more negative changes the subject will pursue. The following four hypotheses were conformed in the process of data analysis. 1) In case the level of violence is strong but 'being taken aback' and 'filthiness' in weak because of strong sexual curiosity and also if information concerning sexual abuse is not readily available and the frequency is low, negative internationalization marked by 'covering-up' and 'escaping' will take place despite the fact the subject is open-minded, the family is cohesive and the abuser is unfamiliar. 2) In case the level of violence is weak but 'being taken aback' and 'filthiness' is weak combined with weak sexual curiosity and also if information concerning sexual abuse is readily available and the response from around is protective and the frequency is high, the subject will pursue positive changes to 'being taken aback' and 'filthiness', further aided by the fact that the subject is open-minded, the family is cohesive and the abuser is unfamiliar. 3) In case the level of violence is strong and 'being taken abuse' and 'filthiness' is strong because of weak sexual curiosity and also if information concerning sexual abuse is reading available and the response from around is readily available and the response from around is protective and the frequency is low, the subject will persue positive changes marked by 'informing' and 'escaping' despite the fact that the family cohesion is weak and the abuser is familiar. 4) In case the level of violence is strong and 'being taken aback' and 'filthiness' is strong because of weak sexual curiosity and also if information concerning sexual abuse is not readily available and the response from around is respelling and the frequency is low negative internalization like 'covering-up' and 'escaping' will take place, further aggravated by the fact that the subject's personality is closed, family cohesion is weak, and subject is familiar. On the basis of the above finding, it is recommended that nursing intervention should focus on promoting the milieu conductive to the victims pursuing positive changes along with the adequate aids from protection facilities as well as from the people around them.
Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.
Personalized services directly and indirectly acquire personal data, in part, to provide customers with higher-value services that are specifically context-relevant (such as place and time). Information technologies continue to mature and develop, providing greatly improved performance. Sensory networks and intelligent software can now obtain context data, and that is the cornerstone for providing personalized, context-specific services. Yet, the danger of overflowing personal information is increasing because the data retrieved by the sensors usually contains privacy information. Various technical characteristics of context-aware applications have more troubling implications for information privacy. In parallel with increasing use of context for service personalization, information privacy concerns have also increased such as an unrestricted availability of context information. Those privacy concerns are consistently regarded as a critical issue facing context-aware personalized service success. The entire field of information privacy is growing as an important area of research, with many new definitions and terminologies, because of a need for a better understanding of information privacy concepts. Especially, it requires that the factors of information privacy should be revised according to the characteristics of new technologies. However, previous information privacy factors of context-aware applications have at least two shortcomings. First, there has been little overview of the technology characteristics of context-aware computing. Existing studies have only focused on a small subset of the technical characteristics of context-aware computing. Therefore, there has not been a mutually exclusive set of factors that uniquely and completely describe information privacy on context-aware applications. Second, user survey has been widely used to identify factors of information privacy in most studies despite the limitation of users' knowledge and experiences about context-aware computing technology. To date, since context-aware services have not been widely deployed on a commercial scale yet, only very few people have prior experiences with context-aware personalized services. It is difficult to build users' knowledge about context-aware technology even by increasing their understanding in various ways: scenarios, pictures, flash animation, etc. Nevertheless, conducting a survey, assuming that the participants have sufficient experience or understanding about the technologies shown in the survey, may not be absolutely valid. Moreover, some surveys are based solely on simplifying and hence unrealistic assumptions (e.g., they only consider location information as a context data). A better understanding of information privacy concern in context-aware personalized services is highly needed. Hence, the purpose of this paper is to identify a generic set of factors for elemental information privacy concern in context-aware personalized services and to develop a rank-order list of information privacy concern factors. We consider overall technology characteristics to establish a mutually exclusive set of factors. A Delphi survey, a rigorous data collection method, was deployed to obtain a reliable opinion from the experts and to produce a rank-order list. It, therefore, lends itself well to obtaining a set of universal factors of information privacy concern and its priority. An international panel of researchers and practitioners who have the expertise in privacy and context-aware system fields were involved in our research. Delphi rounds formatting will faithfully follow the procedure for the Delphi study proposed by Okoli and Pawlowski. This will involve three general rounds: (1) brainstorming for important factors; (2) narrowing down the original list to the most important ones; and (3) ranking the list of important factors. For this round only, experts were treated as individuals, not panels. Adapted from Okoli and Pawlowski, we outlined the process of administrating the study. We performed three rounds. In the first and second rounds of the Delphi questionnaire, we gathered a set of exclusive factors for information privacy concern in context-aware personalized services. The respondents were asked to provide at least five main factors for the most appropriate understanding of the information privacy concern in the first round. To do so, some of the main factors found in the literature were presented to the participants. The second round of the questionnaire discussed the main factor provided in the first round, fleshed out with relevant sub-factors. Respondents were then requested to evaluate each sub factor's suitability against the corresponding main factors to determine the final sub-factors from the candidate factors. The sub-factors were found from the literature survey. Final factors selected by over 50% of experts. In the third round, a list of factors with corresponding questions was provided, and the respondents were requested to assess the importance of each main factor and its corresponding sub factors. Finally, we calculated the mean rank of each item to make a final result. While analyzing the data, we focused on group consensus rather than individual insistence. To do so, a concordance analysis, which measures the consistency of the experts' responses over successive rounds of the Delphi, was adopted during the survey process. As a result, experts reported that context data collection and high identifiable level of identical data are the most important factor in the main factors and sub factors, respectively. Additional important sub-factors included diverse types of context data collected, tracking and recording functionalities, and embedded and disappeared sensor devices. The average score of each factor is very useful for future context-aware personalized service development in the view of the information privacy. The final factors have the following differences comparing to those proposed in other studies. First, the concern factors differ from existing studies, which are based on privacy issues that may occur during the lifecycle of acquired user information. However, our study helped to clarify these sometimes vague issues by determining which privacy concern issues are viable based on specific technical characteristics in context-aware personalized services. Since a context-aware service differs in its technical characteristics compared to other services, we selected specific characteristics that had a higher potential to increase user's privacy concerns. Secondly, this study considered privacy issues in terms of service delivery and display that were almost overlooked in existing studies by introducing IPOS as the factor division. Lastly, in each factor, it correlated the level of importance with professionals' opinions as to what extent users have privacy concerns. The reason that it did not select the traditional method questionnaire at that time is that context-aware personalized service considered the absolute lack in understanding and experience of users with new technology. For understanding users' privacy concerns, professionals in the Delphi questionnaire process selected context data collection, tracking and recording, and sensory network as the most important factors among technological characteristics of context-aware personalized services. In the creation of a context-aware personalized services, this study demonstrates the importance and relevance of determining an optimal methodology, and which technologies and in what sequence are needed, to acquire what types of users' context information. Most studies focus on which services and systems should be provided and developed by utilizing context information on the supposition, along with the development of context-aware technology. However, the results in this study show that, in terms of users' privacy, it is necessary to pay greater attention to the activities that acquire context information. To inspect the results in the evaluation of sub factor, additional studies would be necessary for approaches on reducing users' privacy concerns toward technological characteristics such as highly identifiable level of identical data, diverse types of context data collected, tracking and recording functionality, embedded and disappearing sensor devices. The factor ranked the next highest level of importance after input is a context-aware service delivery that is related to output. The results show that delivery and display showing services to users in a context-aware personalized services toward the anywhere-anytime-any device concept have been regarded as even more important than in previous computing environment. Considering the concern factors to develop context aware personalized services will help to increase service success rate and hopefully user acceptance for those services. Our future work will be to adopt these factors for qualifying context aware service development projects such as u-city development projects in terms of service quality and hence user acceptance.