• Journal of Information Science Theory and Practice
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• v.2 no.4
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• pp.31-48
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• 2014

The proliferation of many interactive Topic Detection and Tracking (iTDT) systems has motivated researchers to design systems that can track and detect news better. iTDT focuses on user interaction, user evaluation, and user interfaces. Recently, increasing effort has been devoted to user interfaces to improve TDT systems by investigating not just the user interaction aspect but also user and task oriented evaluation. This study investigates the combination of the bag of words and named entities approaches implemented in the iTDT interface, called Interactive Event Tracking (iEvent), including what TDT tasks these approaches facilitate. iEvent is composed of three components, which are Cluster View (CV), Document View (DV), and Term View (TV). User experiments have been carried out amongst journalists to compare three settings of iEvent: Setup 1 and Setup 2 (baseline setups), and Setup 3 (experimental setup). Setup 1 used bag of words and Setup 2 used named entities, while Setup 3 used a combination of bag of words and named entities. Journalists were asked to perform TDT tasks: Tracking and Detection. Findings revealed that the combination of bag of words and named entities approaches generally facilitated the journalists to perform well in the TDT tasks. This study has confirmed that the combination approach in iTDT is useful and enhanced the effectiveness of users' performance in performing the TDT tasks. It gives suggestions on the features with their approaches which facilitated the journalists in performing the TDT tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2952-2971
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• 2016

Outsourcing jobs to a public cloud is a cost-effective way to address the problem of satisfying the peak resource demand when the local cloud has insufficient resources. In this paper, we studied the management of deadline-constrained bag-of-tasks jobs on hybrid clouds. We presented a binary nonlinear programming (BNP) problem to model the hybrid cloud management which minimizes rent cost from the public cloud while completes the jobs within their respective deadlines. To solve this BNP problem in polynomial time, we proposed a heuristic algorithm. The main idea is assigning the task closest to its deadline to current core until the core cannot finish any task within its deadline. When there is no available core, the algorithm adds an available physical machine (PM) with most capacity or rents a new virtual machine (VM) with highest cost-performance ratio. As there may be a workload imbalance between/among cores on a PM/VM after task assigning, we propose a task reassigning algorithm to balance them. Extensive experimental results show that our heuristic algorithm saves 16.2%-76% rent cost and improves 47.3%-182.8% resource utilizations satisfying deadline constraints, compared with first fit decreasing algorithm, and that our task reassigning algorithm improves the makespan of tasks up to 47.6%.

• The Journal of the Acoustical Society of Korea
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• v.33 no.4
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• pp.248-254
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• 2014

Among acoustic signal analysis tasks, acoustic context awareness is one of the most formidable tasks in terms of complexity since it requires sophisticated understanding of individual acoustic events. In conventional context awareness methods, individual acoustic event detection or recognition is employed to generate a relevant decision on the impending context. However this approach may produce poorly performing decision results in practical situations due to the possibility of events occurring simultaneously or the acoustically similar events that are difficult to distinguish with each other. Particularly, the babble noise acoustic event occurring at a bus or subway environment may create confusion to context awareness task since babbling is similar in any environment. Therefore in this paper, a frequency-cepstral feature vector is proposed to mitigate the confusion problem during the situation awareness task of binary decisions: bus or metro. By employing the Support Vector Machine (SVM) as the classifier, the proposed feature vector scheme is shown to produce better performance than the conventional scheme.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.7-8
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• 2018

This study proposes an analysis of the Word2vec-based machine learning classifiers for the sake of opinion mining tasks. As a bench-marking method, BOW (Bag-of-Words) was adopted. On the basis of utilizing the Word2vec and BOW as feature extraction methods, we applied Laptop and Restaurant dataset to LR, DT, SVM, RF classifiers. The results showed that the Word2vec feature extraction yields more improved performance.

• IE interfaces
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• v.17 no.1
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• pp.46-55
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• 2004

Various characteristics of the object being lifted are known to affect the biomechanical, physiological, and psychophysical stresses. The object characteristics to be considered in the design process of lifting tasks are weight, shape, stiffness, and availability of handles and similar coupling devices. In this study, a prototype Polypropylene laminated bag with carrying handles was designed to decrease the physical stress of people who handle these bags. Physiological and psychophysical approaches as well as subjective ratings were applied to evaluate the effects of handles provided on the designed PP laminated bag. Statistical analysis showed that the VO2, heart rate, blood pressure, and Borg-RPE score for PP laminated fertilizer bag with carrying handles were significantly lower than those bags without handles. Moreover, Maximum Acceptable Lifting Endurance Time(MALET) measure, newly developed in this study, for bags with handles was significantly higher than those for bags without handles. It is thus recommended that the various types of bags and boxes be equipped with handles to reduce the musculoskeletal, physiological, psychophysical, and subjective perceived stresses.

• Journal of the Korea Society of Computer and Information
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• v.24 no.2
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• pp.171-177
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• 2019

This paper aims to find the most effective feature selection method for the sake of opinion mining tasks. Basically, opinion mining tasks belong to sentiment analysis, which is to categorize opinions of the online texts into positive and negative from a text mining point of view. By using the five product groups dataset such as apparel, books, DVDs, electronics, and kitchen, TF-IDF and Bag-of-Words(BOW) fare calculated to form the product review feature sets. Next, we applied the feature selection methods to see which method reveals most robust results. The results show that the stacking classifier based on those features out of applying Information Gain feature selection method yields best result.

• Journal of Fashion Business
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• v.24 no.1
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• pp.15-28
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• 2020

The purpose of this study was to develop and propose creative smart bags in emotional e-textiles using LEDs that inform smartphone users of motion-induced luminescence and ringing of cell phones. The LED light-emitting operation tasks produced in the study were applied to each of the three design smart bags, setting the five cases of luminance by a call initiated, absent phone, rejecting answering phone, texting, and motion-induced luminescence. In the male laptop bags of LED luminous images using wappen, 10 LEDs could be separated by a total of three pins to display the luminous mode, and all 10 LEDs became a total of five luminous patterns, including all that illuminate and those that illuminate randomly. E-wappen rendered the motif a strong sense of visibility and performed six roles on phone rings and texting. To develop a women's tote bag, we did a laser cut and attached the leather strips and placed 10 triangular LEDs to form a geometric LED e-textile. It provides the possibility of transforming simple design from traditional fashion into a more interesting and various smart designs. An entertainment smart bag using graphic design was constructed by applying a tilt sensor to look like a light in the night sky by shaking and moving the bag. The graphic design and composition of LEDs indicate that LEDs and fashion item are applied in harmony rather than heterogeneous, enabling them to be applied as fashion-oriented wearable smart products.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.9
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• pp.359-366
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• 2021

A number of construction companies in Korea invest considerable human and financial resources to construct a system for managing apartment defect data and for categorizing repair tasks. Thus, this study proposes machine learning models to automatically classify defect complaint text-data into one of the sub categories of 'finishing work' (i.e., one of the defect repair tasks). In the proposed models, we employed two word representation methods (Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF)) and two machine learning classifiers (Support Vector Machine, Random Forest). In particular, we conducted both binary- and multi- classification tasks to classify 9 sub categories of finishing work: home appliance installation work, paperwork, painting work, plastering work, interior masonry work, plaster finishing work, indoor furniture installation work, kitchen facility installation work, and tiling work. The machine learning classifiers using the TF-IDF representation method and Random Forest classification achieved more than 90% accuracy, precision, recall, and F1 score. We shed light on the possibility of constructing automated defect classification systems based on the proposed machine learning models.

• Food Science and Preservation
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• v.24 no.7
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• pp.915-922
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• 2017

This study aimed in onion production by evaluating cost and labor efficiencies of onion storage methods using either a ton-bag or a wire-steel pallet. New methods using ton-bag and wire-steel pallets were developed and applied to postharvest tasks, such as harvest packaging, transportation, and storage. The storage parameters evaluated for their effect on the logistics of onion production were: working duration, working hours, and cost expenditure. The longitudinal tensile strength of the ton-bag developed in this study was 16% higher than that of the conventional ton-bag. The wire-steel pallet developed in this study had 10% more storage capacity in a low-temperature storage room, and its truck loading capacity was more than doubled compared to that of the conventional steel pallet. There was no difference in the wasting rate during bulk storage between the newly developed wire-steel pallet and the conventional steel pallet, for 500 kg of onions. However, the bulk storage of 1,000 kg of onions using the wire-steel pallet was not found to be suitable, because the wasting rate of onions stored using the wire-pallet was 3.7% higher than that of onions stored using a conventional steel pallet. The time and the total investing costs for the bulk method decreased by 50.1% and 46.1%, respectively, compared to those for conventional harvest. In the bulk storage using the wire-steel pallet, the total storage cost decreased by 28.8%. Thus, it is estimated that we could have saved 18.3 billion won if the wire-steel pallet method about 30% of the total onion production (1,298,749 M/T) in 2016.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.104-110
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• 2016

Dense local image descriptors like SIFT are fruitful for capturing salient information about image, shown to be successful in various image-related tasks when formed in bag-of-words representation (i.e., histograms). In this paper we consider to utilize these dense local descriptors in the object tracking problem. A notable aspect of our tracker is that instead of adopting a point estimate for the target model, we account for uncertainty in data noise and model incompleteness by maintaining a distribution over plausible candidate models within the Bayesian framework. The target model is also updated adaptively by the principled Bayesian posterior inference, which admits a closed form within our Dirichlet prior modeling. With empirical evaluations on some video datasets, the proposed method is shown to yield more accurate tracking than baseline histogram-based trackers with the same types of features, often being superior to the appearance-based (visual) trackers.