Vol. 42, No. 12,
Dec. 2015
Digital Library
Efficient Parallel CUDA Random Number Generator on NVIDIA GPUs
In this paper, we implemented a parallel random number generation program on GPU"s, which are known for high performance computing, using LCG (Linear Congruential Generator). Random numbers are important in all fields requiring the use of randomness, and LCG is one of the most widely used methods for the generation of pseudo-random numbers. We explained the parallel program using the NVIDIA CUDA model and MPI(Message Passing Interface) and showed uniform distribution and performance results. We also used a Monte Carlo algorithm to calculate pi(π) comparing the parallel random number generator with cuRAND, which is a CUDA library function, and showed that our program is much more efficient. Finally we compared performance results using multi-GPU’s with those of ideal speedups.
A Fast and Scalable Image Retrieval Algorithms by Leveraging Distributed Image Feature Extraction on MapReduce
Hwan-Jun Song, Jin-Woo Lee, Jae-Gil Lee
With mobile devices showing marked improvement in performance in the age of the Internet of Things (IoT), there is demand for rapid processing of the extensive amount of multimedia big data. However, because research on image searching is focused mainly on increasing accuracy despite environmental changes, the development of fast processing of high-resolution multimedia data queries is slow and inefficient. Hence, we suggest a new distributed image search algorithm that ensures both high accuracy and rapid response by using feature extraction of distributed images based on MapReduce, and solves the problem of memory scalability based on BIRCH indexing. In addition, we conducted an experiment on the accuracy, processing time, and scalability of this algorithm to confirm its excellent performance.
Flash Operation Group Scheduling for Supporting QoS of SSD I/O Request Streams
Eungyu Lee, Sun Won, Joonwoo Lee, Kanghee Kim, Eyeehyun Nam
As SSDs are increasingly being used as high-performance storage or caches, attention is increasingly paid to the provision of SSDs with Quality-of-Service for I/O request streams of various applications in server systems. Since most SSDs are using the AHCI controller interface on a SATA bus, it is not possible to provide a differentiated service by distinguishing each I/O stream from others within the SSD. However, since a new SSD interface, the NVME controller interface on a PCI Express bus, has been proposed, it is now possible to recognize each I/O stream and schedule I/O requests within the SSD for differentiated services. This paper proposes Flash Operation Group Scheduling within NVME-based flash storage devices, and demonstrates through QEMU-based simulation that we can achieve a proportional bandwidth share for each I/O stream.
Framework-assisted Selective Page Protection for Improving Interactivity of Linux Based Mobile Devices
Seungjune Kim, Jungho Kim, Seongsoo Hong
While Linux-based mobile devices such as smartphones are increasingly used, they often exhibit poor response time. One of the factors that influence the user-perceived interactivity is the high page fault rate of interactive tasks. Pages owned by interactive tasks can be removed from the main memory due to the memory contention between interactive and background tasks. Since this increases the page fault rate of the interactive tasks, their executions tend to suffer from increased delays. This paper proposes a framework-assisted selective page protection mechanism for improving interactivity of Linux-based mobile devices. The framework-assisted selective page protection enables the run-time system to identify interactive tasks at the framework level and to deliver their IDs to the kernel. As a result, the kernel can maintain the pages owned by the identified interactive tasks and avoid the occurrences of page faults. The experimental results demonstrate the selective page protection technique reduces response time up to 11% by reducing the page fault rate by 37%.
Energy-aware Selective Compression Scheme for Solar-powered Wireless Sensor Networks
Min Jae Kang, Semi Jeong, Dong Kun Noh
Data compression involves a trade-off between delay time and data size. Greater delay times require smaller data sizes and vice versa. There have been many studies performed in the field of wireless sensor networks on increasing network life cycle durations by reducing data size to minimize energy consumption; however, reductions in data size result in increases of delay time due to the added processing time required for data compression. Meanwhile, as energy generation occurs periodically in solar energy-based wireless sensor networks, redundant energy is often generated in amounts sufficient to run a node. In this study, this excess energy is used to reduce the delay time between nodes in a sensor network consisting of solar energy-based nodes. The energy threshold value is determined by a formula based on the residual energy and charging speed. Nodes with residual energy below the threshold transfer data compressed to reduce energy consumption, and nodes with residual energy above the threshold transfer data without compression to reduce the delay time between nodes. Simulation based performance verifications show that the technique proposed in this study exhibits optimal performance in terms of both energy and delay time compared with traditional methods.
Agent-Based Modeling and Simulation Methodology using Social-Level Characteristics: A Case Study on Self-Adaptive Smart Grid and Military Domain Systems using Tropos
Agent-based modeling and simulation (ABMS) is used to model of market and social phenomena by utilizing agents’ fine-grained behaviors and interactions that cannot be implemented in a conventional simulation. However, ABMS represents irrational agents and hinders the achievement of individual or overall goals since ABMS is based on agent-based software, which follows the principle of rationality at the knowledge level [1]. This problem was solved in the agent-based software engineering (ABSE) field by using behavior laws for the social level [2]. However, they still do not propose the specific development methodology for how to develop the social level in a systematic way. Therefore, in order to propose agent-based modeling and simulation methods that reflect the behavior laws of social level characteristics, our study used the Tropos that can combine ABSE and social behavior laws for the presentation of concrete tasks and deliverables for each development step by step. In addition, the proposed method will be specified through experiments with specific application examples and case studies on the self-adaptive smart grid and the military domain system.
Automatic Detection of Off-topic Documents using ConceptNet and Essay Prompt in Automated English Essay Scoring
This work presents a new method that can predict, without the use of training data, whether an input essay is written on a given topic. ConceptNet is a common-sense knowledge base that is generated automatically from sentences that are extracted from a variety of document types. An essay prompt is the topic that an essay should be written about. The method that is proposed in this paper uses ConceptNet and an essay prompt to decide whether or not an input essay is off-topic. We introduce a way to find the shortest path between two nodes on ConceptNet, as well as a way to calculate the semantic similarity between two nodes. Not only an essay prompt but also a student"s essay can be represented by concept nodes in ConceptNet. The semantic similarity between the concepts that represent an essay prompt and the other concepts that represent a student"s essay can be used for a calculation to rank “on-topicness” ; if a low ranking is derived, an essay is regarded as off-topic. We used eight different essay prompts and a student-essay collection for the performance evaluation, whereby our proposed method shows a performance that is better than those of the previous studies. As ConceptNet enables the conduction of a simple text inference, our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required.
A Method of Feature Extraction on Motor Imagery EEG Using FLD and PCA Based on Sub-Band CSP
The brain-computer interface obtains a user"s electroencephalogram as a replacement communication unit for the disabled such that the user is able to control machines by simply thinking instead of using hands or feet. In this paper, we propose a feature extraction method based on a non-selected filter by SBCSP to classify motor imagery EEG. First, we divide frequencies (4～40 Hz) into 4-Hz units and apply CSP to each Unit. Second, we obtain the FLD score vector by combining FLD results. Finally, the FLD score vector is projected onto the optimal plane for classification using PCA. We use BCI Competition III dataset IVa, and Extracted features are used as input for LS-SVM. The classification accuracy of the proposed method was evaluated using 10×10 fold cross-validation. For subjects ‘aa’, ‘al’, ‘av’, ‘aw’, and ‘ay’, results were 85.29 ± 0.93%, 95.43 ± 0.57%, 72.57 ± 2.37%, 91.82 ± 1.38%, and 93.50 ± 0.69%, respectively.
Estimation of Wrist Movements based on a Regression Technique for Wearable Robot Interfaces
Recently, the development of practical wearable robot interfaces has resulted in the emergence of wearable robots such as arm prosthetics or lower-limb exoskeletons. In this paper, we propose a novel method of wrist movement intention estimation based on a regression technique using electromyography of human bio-signals. In daily life, changes in user arm position changes cause decreases in performance by modulating EMG signals. Therefore, we propose an estimation method for robust wrist movement intention for arm position changes, combining several movement intention models based on the regression technique trained by different arm positions. In our experimental results, our method estimates wrist movement intention more accurately than previous methods.
A Method for Selecting Software Reliability Growth Models Using Trend and Failure Prediction Ability
YongJun Park, Bup-Ki Min, Hyeon Soo Kim
Software Reliability Growth Models (SRGMs) are used to quantitatively evaluate software reliability and to determine the software release date or additional testing efforts using software failure data. Because a single SRGM is not universally applicable to all kinds of software, the selection of an optimal SRGM suitable to a specific case has been an important issue. The existing methods for SRGM selection assess the goodness-of-fit of the SRGM in terms of the collected failure data but do not consider the accuracy of future failure predictions. In this paper, we propose a method for selecting SRGMs using the trend of failure data and failure prediction ability. To justify our approach, we identify problems associated with the existing SRGM selection methods through experiments and show that our method for selecting SRGMs is superior to the existing methods with respect to the accuracy of future failure prediction.
A Label Inference Algorithm Considering Vertex Importance in Semi-Supervised Learning
Byonghwa Oh, Jihoon Yang, Hyun-Jin Lee
Semi-supervised learning is an area in machine learning that employs both labeled and unlabeled data in order to train a model and has the potential to improve prediction performance compared to supervised learning. Graph-based semi-supervised learning has recently come into focus with two phases: graph construction, which converts the input data into a graph, and label inference, which predicts the appropriate labels for unlabeled data using the constructed graph. The inference is based on the smoothness assumption feature of semi-supervised learning. In this study, we propose an enhanced label inference algorithm by incorporating the importance of each vertex. In addition, we prove the convergence of the suggested algorithm and verify its excellence.
Linking Korean Predicates to Knowledge Base Properties
Yousung Won, Jongseong Woo, Jiseong Kim, YoungGyun Hahm, Key-Sun Choi
Relation extraction plays a role in for the process of transforming a sentence into a form of knowledge base. In this paper, we focus on predicates in a sentence and aim to identify the relevant knowledge base properties required to elucidate the relationship between entities, which enables a computer to understand the meaning of a sentence more clearly. Distant Supervision is a well-known approach for relation extraction, and it performs lexicalization tasks for knowledge base properties by generating a large amount of labeled data automatically. In other words, the predicate in a sentence will be linked or mapped to the possible properties which are defined by some ontologies in the knowledge base. This lexical and ontological linking of information provides us with a way of generating structured information and a basis for enrichment of the knowledge base.
A Conceptual Framework for Aging Diagnosis Using IoT Devices
Jae Yoo Lee, Jin Cheul Park, Soo Dong Kim
With the emergence of Internet-of-Things (IoT) computing, it has become possible to acquire users’ health-related contexts from various IoT devices and to diagnose their biological aging through analysis of the IoT health contexts. However, previous work on methods of aging diagnosis used a fixed list of aging diagnosis factors, making it difficult to handle the variability of users’ IoT health contexts and to dynamically adapt the addition and deletion of aging diagnosis factors. This paper proposes a design and methods for a dynamically adaptable aging diagnosis framework that acquires a set of IoT health contexts from various IoT devices based on a set of aging diagnosis factors of the user. By using the proposed aging diagnosis framework, aging diagnosis methods can be applied without considering the variability of IoT health contexts and aging diagnosis factors can be dynamically added and deleted.
AGB (Ancestral Genome Browser): A Web Interface for Browsing Reconstructed Ancestral Genomes
Daehwan Lee, Jongin Lee, Woon-Young Hong, Eunji Jang, Jaebum Kim
With the advancement of next-generation sequencing (NGS) technologies, various genome browsers have been introduced. Because existing browsers focus on comparison of the genomic data of extant species, however, there is a need for a genome browser for ancestral genomes and their evolution. In this paper, we introduce a genome browser, AGB (Ancestral Genome Browser), that displays ancestral genome data reconstructed from existing species. With AGB, it is possible to trace genomic variations that occurred during evolution in a simple and intuitive way. We explain the capability of AGB in terms of visualizing ancestral genomic information and evolutionary genomic variations. AGB is now available at http://bioinfo.konkuk.ac.kr/genomebrowser/.
A Design of Effective Inference Methods and Their Application Guidelines for Supporting Various Medical Analytics Schemes
Moon Kwon Kim, Hyun Jung La, Soo Dong Kim
As a variety of personal medical devices appear, it is possible to acquire a large number of diverse medical contexts from the devices. There have been efforts to analyze the medical contexts via software applications. In this paper, we propose a generic model of medical analytics schemes that are used by medical experts, identify inference methods for realizing each medical analytics scheme, and present guidelines for applying the inference methods to the medical analytics schemes. Additionally, we develop a PoC inference system and analyze real medical contexts to diagnose relevant diseases so that we can validate the feasibility and effectiveness of the proposed medical analytics schemes and guidelines of applying inference methods.
A Network-Aware Congestion Control Scheme for Improving the Performance of C-TCP over HBDP Networks
Junyeol Oh, Dooyeol Yun, Kwangsue Chung
While today’s networks have been shown to exhibit HBDP (High Bandwidth Delay Product) characteristics, the legacy TCP increases the size of the congestion window slowly and decreases the size of the congestion window drastically such that it is not suitable for HBDP Networks. In order to solve this problem with the legacy TCP, many congestion control TCP mechanisms have been proposed. C-TCP (Compound-TCP) is a hybrid TCP which is a synergy of delay-based and loss-based approaches. C-TCP adapts the decreasing rate of the delay window without considering the congestion level, leading to degradation of performance. In this paper, we propose a new scheme to improve the performance of C-TCP. By controlling the increasing and decreasing rates according to the congestion level of the network, our proposed scheme can improve the bandwidth occupancy and fairness of C-TCP. Through performance evaluation, we show that our proposed scheme offers better performance in HBDP networks as compared to the legacy C-TCP.
Malware Classification System to Support Decision Making of App Installation on Android OS
Hong Ryeol Ryu, Yun Jang, Taekyoung Kwon
Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.
Dynamic Power Management for Webpage Loading on Mobile Devices
As the performance of mobile devices has increased, high-end multicore CPUs have become the norm in smartphones. However, such high performance devices are exposed to the problem of battery depletion due to the energy consumption caused by software performance, and despite increases in battery capacity. The required resources are dynamic and varied, and further user interaction is highly random; thus, Linux-based power management such as DVFS is needed to fulfill requirements. In order to reduce power consumption, we propose a method to restrict the CPU frequency of data download while maintaining user reactivity. This can supplement the weakness of existing Linux-based power management techniques like DVFS in relation to webpage loading. Through the implementation of our method at the application level, we confirm that energy consumption from webpage loading is reduced.
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