• International Journal of Knowledge Content Development & Technology
• /
• v.13 no.1
• /
• pp.75-100
• /
• 2023

With research data generation on the rise, Institutional Repositories (IR) are one of the tools to manage it. However, the variety of data practices across institutions, domains, communities, etc., often requires dedicated studies in order to identify the research data management (RDM) require- ments and mapping them to IR features to support them. In this study, we investigated the data practices for a few national universities in North Macedonia, including 110 participants from different departments. The methodology we adopted to this end enabled us to derive some of the key RDM requirements for a variety of data-related activities. Finally, we mapped these requirements to 6 features that our participants asked for in an IR solution: (1) create (meta)data and documentation, (2) distribute, share, and promote data, (3) provide access control, (4) store, (5) backup, and (6) archive. This list of IR features could prove useful for any university that has not yet established an IR solution.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.195-207
• /
• 2022

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

• Human Ecology Research
• /
• v.55 no.1
• /
• pp.81-92
• /
• 2017

Father participation in child rearing is gaining its importance in the field of child development. Previous literature consistently suggests the importance of parental efficacy and educational support on children's school life. However, studies which that focus exclusively on the role of the father's role are highly limited. Based on previous literature, we hypothesized that parental efficacy would directly and indirectly affect children's school life via parental educational support at home. We aimed to expand on the previous literature by exploring the relations within fathers. Participants were 662 fathers whose children attended elementary, middle, and high school. We measured parental efficacy, educational support at home, and their children's school life were measured. Descriptive statistics were first conducted to investigate the general features of participants as well as the correlations between study variables. The research model was analyzed using structural equation modeling in AMOS 20.0. Results proved the direct effect of father's parental efficacy on father's educational support at home as well as children's school life. Fathers' educational support at home mediated the effect of parental efficacy on children's school life. Our results suggests that fathers who perceive themselves as efficient parents provides a higher level of educational support at home for their children; consequently, children of those fathers in turn adjust better in school and achieve higher grades. Also, the father's parental efficacy also indirectly exerts positive effects on children's school life via heightened educational support at home. An importance of paternal participation and support in child rearing and education was highly suggested. Relevant policy issues regarding paternal educational support were discussed.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.13 no.1
• /
• pp.12-18
• /
• 2013

Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of- the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with ${\mu}$ and ${\beta}$ bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.

• Journal of Sensor Science and Technology
• /
• v.25 no.4
• /
• pp.285-290
• /
• 2016

Human sensing is an important but challenging technology. Unlike other methods for sensing humans, a vision sensor has many advantages, and there has been active research in automatic human detection in camera images. The combination of Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is currently one of the most successful methods in vision-based human detection. However, extracting HOG features from an image is computer intensive, and it is thus hard to employ the HOG method in real-time processing applications. This paper describes an efficient solution to this speed problem of the HOG method. Our method obtains edge information of an image and finds candidate regions where humans very likely exist based on the distribution pattern of the detected edge points. The HOG features are then extracted only from the candidate image regions. Since complex HOG processing is adaptively done by the guidance of the simpler edge detection step, human detection can be performed quickly. Experimental results show that the proposed method is effective in various images.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.11
• /
• pp.2824-2838
• /
• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.06a
• /
• pp.163-165
• /
• 2014

In this paper, we propose a world's first programmable video processing platform for video quality enhancement of 8K ($7680{\times}4320$) UHD (Ultra High Definition) TV at 60 frames per second. To support huge computation and memory bandwidth of video quality enhancement for 8K resolution, the proposed platform has unique features like symmetric multi-cluster architecture for data partitioning, ring data-path between clusters to support data pipelining, on-the-fly processing architecture to reduce DDR bandwidth, flexible hardware to accelerating common kernel in video enhancement algorithms. In addition to those features, general programmability of SRP (Samsung reconfigurable processor) as main core of the proposed platform makes it possible to upgrade continuously video enhancement algorithm even after the platform is fixed. This ability is very important because algorithms for 8K DTV is under development. The proposed sub-system has been embedded into SoC (System on Chip) and new 8K UHD TV using the programmable SoC is expected at CES2015 for the first time in the world.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.6 no.1
• /
• pp.37-44
• /
• 2013

In this paper, we propose the pulmonary nodule detection system based on voxel classification. The proposed system consists of three main steps. In the first step, we segment lung volume. In the second step, the lung structures are initially segmented. In the last step, we classify the nodules using voxel classification. To describe characteristics of each voxel, we extract the log-polar sampling based features. Support Vector Machine is applied to the extracted features to classify into nodules and non-nodules.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.7
• /
• pp.1531-1536
• /
• 2011

We propose an automatic swearword filter system for online game chatting by using Support Vector Machines(SVM). We collected chatting sentences from online games and tagged them as normal sentences or swearword included sentences. We use n-gram syllables and lexical-part of speech (POS) tags of a word as features and select useful features by chi square statistics. Each selected feature is represented as binary weight and used in training SVM. SVM classifies each chatting sentence as swearword included one or not. In experiment, we acquired overall 90.4% of F1 accuracy.

• Journal of Information Processing Systems
• /
• v.9 no.1
• /
• pp.103-116
• /
• 2013

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotation-invariant texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts.