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Background: Accurate identification of primary pathogens in foot infections remains challenging due to the diverse microbiome. Conventional culture may show false-positive or false-negative growth, leading to ineffective postoperative antibiotic treatment. Next-generation sequencing (NGS) has been explored as an alternative to standard culture in orthopedic infections. NGS is highly sensitive and can detect an entire bacterial genome along with genes conferring antibiotic resistance in a given sample. We investigated the potential use of NGS for accurate identification and quantification of microbes in infected diabetic foot ulcer (DFU). We hypothesize that NGS will aid identification of dominant pathogen and provide a more complete profile of microorganisms in infected DFUs compared to the standard culture method. Methods: Data were prospectively collected from 30 infected DFU patients who underwent operative treatment by a fellowship-trained orthopedic foot and ankle surgeon from October 2018 to September 2019. The average age of the patient was 60.4 years. Operative procedures performed were irrigation and debridement (12), toe or ray amputation (13), calcanectomies (4), and below-the-knee amputation (1). Infected bone specimens were obtained intraoperatively and processed for standard culture and NGS. Concordance between the standard culture and NGS was assessed. Results: In 29 of 30 patients, pathogens were identified by both NGS and culture, with a concordance rate of 70%. In standard culture, Staphylococcus aureus (58.6%) was the most common pathogen, followed by coagulase-negative Staphylococcus (24.1%), Corynebacterium striatum (17.2%), and Enterococcus faecalis (17.2%). In NGS, Finegoldia magna (44.8%) was the most common microorganism followed by S. aureus (41.4%), and Anaerococcus vaginalis (24.1%). On average, NGS revealed 5.1 (range, 1-11) pathogens in a given sample, whereas culture revealed 2.6 (range, 1-6) pathogens. Conclusion: NGS is an emerging molecular diagnostic method of microbial identification in orthopedic infection. It frequently provides different profiles of microorganisms along with antibiotic-resistant gene information compared to conventional culture in polymicrobial foot infection. Clinical use of NGS for management of foot and ankle infections warrants further investigation. Level of Evidence: Level II, diagnostic study.

South Korea's COVID-19 control strategy has been widely emulated. Korea's ability to rapidly achieve disease control in early 2020 without a "Great Lockdown" despite its proximity to China and high population density make its achievement particularly intriguing. This paper helps explain Korea's pre-existing capabilities which enabled the rapid and effective implementation of its COVID-19 control strategies. A systematic assessment across multiple domains demonstrates that South Korea's advantages in controlling its epidemic are owed tremendously to legal and organizational reforms enacted after the MERS outbreak in 2015. Successful implementation of the Korean strategy required more than just a set of actions, measures and policies. It relied on a pre-existing legal framework, financing arrangements, governance and a workforce experienced in outbreak management.

Recent work has addressed opinion inferences that arise when opinions are expressed toward +/-effect events, events that positively or negatively affect entities. Many words have mixtures of senses with different +/-effect labels, and therefore word sense disambiguation is needed to exploit +/-effect information for sentiment analysis. This paper presents a knowledge-based +/-effect coarse-grained sense disambiguation method based on selectional preferences modeled via topic models. The method achieves an overall accuracy of 0.83, which represents a significant improvement over three competitive baselines.

This special issue of the Journal of Research in Curriculum and Instruction was developed to disseminate current research within global citizenship education in light of the confusions in theory and inaction in practice in the field. In this special issue, guest editors and authors from Canada, Scotland, Japan, Korea, and the United States consider the competing demands placed on, and opportunities available to educators concerned with global citizenship education. Data has been collected across local, national, international, and virtual spaces utilizing qualitative and quantitative research methods to provide an insightful look into the nature of global citizenship education inside and outside of K-16 contexts. The findings of these papers call attention to innovations and frustrating gaps in the field.

Opinion inference arises when opinions are expressed toward states and events which positive or negatively affect entities, i.e., benefactive and malefactive events. This paper addresses creating a lexicon of such events, which would be helpful to infer opinions. Verbs may be ambiguous, in that some meanings may be benefactive and others may be malefactive or neither. Thus, we use WordNet to create a sense-level lexicon. We begin with seed senses culled from FrameNet and expand the lexicon using WordNet relationships. The evaluations show that the accuracy of the approach is well above baseline accuracy.

Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities (+/-effect events). This paper addresses methods for creating a lexicon of such events, to support such work on opinion inference. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To maximize the effectiveness of different types of information, we combine a graph-based method using WordNet 1 relations and a standard classifier using gloss information. A hybrid between the two gives the best results. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set.

Sentiment analysis requires human efforts to construct clue lexicons and/or annotations for machine learning, which are considered domain-dependent. This paper presents a sentiment analysis method where clues are learned automatically with a minimum training data at a sentence level. The main strategy is to learn and weight sentiment-revealing clues by first generating a maximal set of candidates from the annotated sentences for maximum recall and learning a classifier using linguistically-motivated composite features at a later stage for higher precision. The proposed method is geared toward detecting negative sentiment sentences as they are not appropriate for suggesting contextual ads. We show how clue-based sentiment analysis can be done without having to assume availability of a separately constructed clue lexicon. Our experimental work with both Korean and English news corpora shows that the proposed method outperforms word-feature based SVM classifiers. The result is especially encouraging because this relatively simple method can be used for documents in new domains and time periods for which sentiment clues may vary.

This paper presents user requirements for camera-based mobile applications in touch screen devices for blind people. We conducted a usability testing for a color reading application on Android OS. In the testing, participants were asked to evaluate three different types of interfaces of the application in order to identify user requirements and preferences. The results of the usability testing presented that (1) users preferred short depth of menu hierarchy, (2) users needed both manual and automatic camera shooting modes although they preferred manual to automatic mode, (3) the initial audio help was more useful for users than in---time help, (4) users wanted the OS supported screen reader function to be turned off during the color reading, and (5) users required tactile feedback to identify touch screen boundary.

Compared to traditional text classification with a flat category set or a small hierarchy of categories, classifying web pages to a large-scale hierarchy such as Open Directory Project (ODP) and Yahoo! Directory is challenging. While a recently proposed "deep" classification method makes the problem tractable, it still suffers from low classification performance. A major problem is the lack of training data, which is unavoidable with such a huge hierarchy. Training pages associated with the category nodes are short, and their distributions are skewed. To alleviate the problem, we propose a new training data selection strategy and a naive Bayes combination model, which utilize both local and global information. We conducted a series of experiments with the ODP hierarchy containing more than 100,000 categories to show that the proposed method of using both local and global information indeed helps avoiding the training data sparseness problem, outperforming the state-of-art method.

This paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more contextual-information such as topic or domain is essential. In our system, we first automatically extract sentiment clues in different domains by our observation. We identified that a sentiment clue is often syntactically related to a sentiment topic in a sentence, which is defined as a primary subject of sentiment expression, such as event, company, and person. We bootstrap from a small set of seed clues and generate new clues by utilizing linguistic dependencies and collocation information between sentiment clues and sentiment topics. Next, we learn a domain-specific sentiment classifier for each domain with the newly aggregated clues. We ran experiments to see how the bootstrapping algorithm to converge and aggregate new clues and verified that the extracted domain-context features are more effective than generally-used features in sentiment analysis by running them on the same sentiment classifier.

As complex indoor-robot systems are developed and deployed into the real-world, the demand for human-robot interaction is increasing. Mixed-initiative human-robot interaction is a good method to coordinate actions of a human and a robot in a complementary fashion. In order to support such interactions, we employ scripts that are rich, flexible, and extensible for a robotpsilas interactions in a variety of situations. Scripts are amenable for expressing knowledge in an applicable form, especially describing a sequence of actions in organizing tasks. In this paper, we propose a script-based cognitive architecture for collaboration, which is based on three-level cognitive models. It incorporates dynamic Bayesian network (DBN) to automatically govern action sequences in the scripts and detect userpsilas intention or goal. Starting from an understanding of user initiatives, our intelligent task manager suggests the most relevant initiatives for an efficient collaboration. DBN has been evaluated in real indoor task scenarios for its efficacy in interaction reduction, error minimization, and task satisfaction.

Software component has been developed lively. However, the measurement of software component quality is relatively less researched against conventional software quality in practice. This paper introduces an in-house component quality model which includes metrics for component quality evaluation, tailoring guidelines for evaluations, and reporting formats of evaluations. Also, we present the examples of quality evaluation with it. Recently, to formalize and visualize the quality of S/W components in a quantitative way we have applied this component quality model to embedded system development projects so that component developers can control component quality to build reusable and quality components while enabling cost reduction and quality improvement. In addition, we've improved component quality model for the proper tailoring when applied in embedded system domain.

Data-rich webpages are providing an increasingly important data source for web applications. While the problem of data object recognition is intensively discussed, it is mostly addressed as a separated process from the frontier task of relevant webpage identification. In this paper, we propose a method to leverage the classification result of data-rich webpages for efficient and scalable data object recognition. A novel context information is proposed, which can be inferred from the webpage classification and exploited in the bottom-up data object recognition. Experimental results show that the context information brings a 19% improvement in the running efficiency of the bottom- up data object recognition.

Mood classification for blogs is useful in helping user-to-agent interaction for a variety of applications involving the web, such as user modeling, recommendation systems, and user interface fields. It is challenging at the same time because of the diversity of the characteristics of bloggers, their experiences, and the way moods are expressed. As an attempt to handle the diversity, we combine multiple sources of evidence for a mood type. Support vector machine based mood classifier (SVMMC) is integrated with mood flow analyzer (MFA) that incorporates commonsense knowledge obtained from the general public (i.e. ConceptNet), the affective norms english words (ANEW) list, and mood transitions. In combining the two different approaches, we employ a statistically weighted voting scheme based on the support vector machine (SVM). For evaluation, we have built a mood corpus consisting of manually annotated blogs, which amounts to over 4000 blogs. Our proposed method outperforms SVMMC by 5.68% in precision. The improvement is attributed to the strategy of choosing more trustable classification results in an interleaving fashion between the SVMMC and our MFA.

In spite of the long history of intelligent humanoid robots, endowing human-like commonsense knowledge to a robot is still a difficult problem. Since a knowledge base consisting of a large number of rules and facts is not an efficient structure that can express situational and background knowledge for humanoid robots, more compact yet forgiving representation is required. Our proposal is to employ a script design that contains richness and diversity needed for robot's task planning, in conjunction with a robust cognitive architecture called EM-ONE, the latest extant account of an implemented cognitive architecture. The script structure has its advantages in flexibility and extensibility for a variety of situations or tasks at hand, along with reusability. As the number of scripts increases, the coverage for diverseness of human-robot interaction (HRI) situation grows. In this paper, we discuss three cognitive models used as our cognitive architecture basis and describe our efforts for generating task scripts in a semi-automatic way by reusing the already existing scripts.

Many software development methodology support systems (M. Light, 2003) have emerged with fundamental features such as, process definition, and process publication including organizational process manuals and online documentation. However, the major tools are lacking features such as the enactment of multiple methodologies in one project, the reuse of project assets for project performance, and the quantitative project management with the purpose of development process improvement in both project and organization level. For this reason, we have built the PRiME system, which is software process & project management system based on SPEM (software process engineering metamodel) (http://www.omg.org/technology/documents/formal/spem.htm) compliant software development methodologies. It basically supports the effective definition, reference, implementation, and enhancement of methodology providing the integrated functionalities in software development project. Also its primary sub-systems have been built as a pluggable component type, called eclipse plug-in within the eclipse PDE (plug-in development environment) for compatibility with CASE or software development tools.

Software processes are slowly penetrating the software development industry. The fact that software process has to be applied throughout the whole lifecycle of software development is particularly true for successful projects. Therefore, SPI (Software Process Improvement) is an ongoing activity throughout project duration. In this paper, we suggest process management system which is needed to assist SPI activities. To enhance the quality of software product, the proposed system supports internal assessors to conduct the gap analysis compliant with ISO IS 15504. Later, it has evolved as a process mentoring tool for developers, as well as SPI tool for software development organizations

Since user interface directly affects the usability of software, more systematic and efficient methods and tools that support user interface development are needed. Most of user interface development tools currently existing are focused on implementation phase, such as supporting using and managing GUI components, organizing and arranging layout, and testing prototype interfaces. These kinds of tools or environments fall short of answering key questions such as how components in a given dialog box can be used to accomplish a particular user task. Furthermore, it is not easy to learn or use them because they require using programming or scripting language in order to specify user interaction. This paper describes our tool, USE, which provides extracting GUI components from the user/task model and examining the usability of designed user interface through the storyboarding mechanism.

Background : Shivering is a common postanesthetic complication. Because all shivering patients feel uncomfortable and increase oxygen consumption, various attempts have been made to prevent its occurrence or to control it. Among the pharmacological methods of treating shivering, meperidine has been known to be the most effective. This study was designed to evaluate whether there was any difference among meperidine, fentanyl, doxapram and normal saline in the treatment of post-anesthetic shivering. Methods : Forty patients (ASA class I or II) who showed postoperative shivering were randomly assigned into four groups(n=10): Normal saline group: normal saline 5 ml, Doxapram group: doxapram 1.5 mg/kg, Meperidine group: meperidine 25 mg, Fentanyl group: fentanyl 25 g. And all patients received routine care: oxygen by T-piece and heat-reflective blanketrol (cincinati Subzero, U.S.A.). Evaluation of the state of shivering was done every 5 minutes from the beginning of the treatment by the same investigator who had injected the drugs intravenously for treatment of shivering. The age, sex, weight and duration of surgery were recorded. Results: There were no significant statistical differences in age, sex, weight and duration of surgery among the four groups. By 5 minutes, 90% of doxapram group and 30% of meperidine group had stopped shivering. By 10 minutes, 90% of doxapram group and 70% of meperidine group had stopped shivering. But in fentanyl and normal saline group, only 20% had stoppd shivering by 10 minutes. Conclusions: We conclude that both meperidine and doxapram are effective on post-anesthetic shivering. In cases of patient with respiratory depression, doxapram is especially effective because it stimulates the respiratory center. (Korean J Anesthesiol 1998; 34: 389∼393)