Advances in Parallel and Distributed Computing and Ubiquitous Services

This research focuses on one prominent many-core prototype—the Intel’s Single-chip Cloud Computer (SCC). We address the performance problem of shared virtual memory consistency for this cache in-coherent architecture. Aiming to keep data on-chip as much as possible to reduce memory accesses external to the chip, we propose two techniques to leverage the cache hierarchy to its full and make data reside in the on-chip scratchpad memory. First, targeted at the architectural specificity of the hardware, we redesigned the traditional software distributed shared memory (SDSM) to allow shared data to be treated transparently like private memory so that the cache hierarchy can be fully utilised without sacrificing memory consistency. Second, we propose a distance-aware page allocation scheme that samples access frequencies and selects the most frequently-recently used pages to be stored on the on-chip scratchpad memory.

Mobile phones are getting smarter and people have been using them for many different proposes. Recently, more and more people have begun using their mobile phones as a method of payment for online shopping and banking. Mobile payments have become easier than ever. Present security issues of mobile payments, however, still require improvement. This paper aims to summarize the idea of mobile payments and analyze the research of existing secure mobile payment protocols by using MPPS (Mobile Payment Protocol Security) framework. As a result, this paper will give researchers tools to standardize current protocol and share new developments.

Protocol Oblivious Forwarding (POF) is a recently proposed technology that enables a protocol independent data plane under the context of Software-Defined Networking (SDN). In this paper, we present POFOX, a SDN controller for POF. POFOX employs the full potentials of POF devices by allowing a protocol oblivious data plane, and it provides a simple programming model similar to POX. Based on POFOX, we construct a network testbed, and experimentally illustrate that POFOX can effectively manage the POF network, and provide the controlling functionality with high performances.

Recommender Systems (RS) is widely employed in information retrieval in social networks due to the prevalence of social networking services. Since the matrix factorization (MF) model has a good expandability, social information is easy to be integrated into the model. In general, researchers convert social information to social regularization. Moreover, similarity function is the key in social regularization to constrain the MF objective function. Previous researchers defined the similarity of users’ rating behavior on the same items as users’ interest in similarity. However, they neglected two problems: First, the friendship is a superficial social network that cannot reflect the intimacy among users. Second, the superficial social network generally cannot represent users’ interest in similarity. Recently, researchers have found that both the number of co-friends and friends sub-graph improve users’ interest in similarity, but they do not give a mathematical definition. In this paper, we use these two factors to design two new similarity functions. To use them in the MF-based RS, we come up with two kinds of social regularization for each similarity function. Compared with previous social regularization, our methods can more precisely explain users’ interest similarity. The experimental analysis on a large dataset shows that our approaches improve the performance of the state-of-the-art social recommendation model.

Virtual microscopy is an evolving medical application used for teaching and learning at universities. We have developed a peer-to-peer based solution called Omentum, aiming at bringing virtual microscopy to an Internet-scale community. Omentum has to manage more than 10,000 large proprietary microscopic images that are converted to easily dividable JPEG-trees, each consisting of millions of very small-scaled image parts. In this paper we propose a portable and platform independent user space file system (USPFS) for addressing the application-specific access patterns, security concerns and data integrity. USPFS is able to efficiently manage huge capacities (roughly 9 × 10¹⁸ slices with 9,000 Petabytes each) with a theoretically infinite number of storable objects while providing highly important platform independency, data integrity checks as well as an easily extendable API. The evident metadata overhead is only 0.3 % and the performance evaluation shows promising results for both read and write operations.

In contrast with the increasing popularity of heterogeneous systems, programming on these systems remains complex and time-consuming. Developers have to access heterogeneous processors through explicitly and error-prone operations provided by low-level approaches like OpenCL. We present OCLS (OpenCL Simplified), a high-level abstraction based framework and its implementation as a minimal library on the top of OpenCL. OCLS shields hardware details, simplifies the development process and handles the environment configuration and data movement implicitly. Its APIs act like ordinary functions and require little prior training. OCLS thus reduces heterogeneous programming effort and relieves the programmers of low-level programming. We evaluated OCLS across a set of different benchmarks. The size of benchmarks rewritten in OCLS reduced by an average ratio of 35.4 %. In the experiment on both GPU and Intel MIC platforms with data sets in different size, OCLS yielded better performance than original OpenCL programs and showed a good stability and portability.

Architecture combining Content-Centric Network (CCN) and Software-Defined Network (SDN) has gradually attracted more attention. We have realized a prototype of CCN using Protocol-Oblivious Forwarding (POF), called Software-Defined Content Network (SDCN). And SDCN does not rely on the IP. CCN supports the unique in-network caching, so that caching strategies become a challenge. CCN lacks of global recognition for the whole network that leads to unreasonable resource allocation. This paper focuses on collecting topology information in SDCN and constructing a hierarchical cache model of the network. We discuss how to distribute cache capacity based on node value under the total storage budget for the network. A cache strategy based on node value is also proposed, which places contents on the nodes with different values according to their popularity. Experimental results show that the cache performance in SDCN was improved.

Internet of Event-Driven Control Things (IoEVCT), the large-scale event-based control systems based on the Internet-of-Things (IoT) can be composed of a series of sensors, controllers, and actuators. It is wirelessly connected by the internet of events between sensors and actuators. The perceptible objects are considered as not only a time-based, controlled system at the micro level, but also as an event-driven, controlled system at the macro level of large-scale IoT. This paper introduces our initial effort to implement a newly designed simulation framework for quantitative power measurement when a large number of sensors and actuators are connected wirelessly to control certain plants. Power consumption of the whole system can be divided into power consumed by a microcontroller unit (MCU) in sensors and actuators. Using the proposed architecture, we can range from the low-level viewed power via an MCU to the whole power of the large scale of control things. We try to substitute each physical MCU in the sensor and actuator devices with the emulation based on an instruction set simulator (ISS) to reduce the physical cost caused by experiments in real environment. Using the proposed simulation framework, our study shows a possibility that the sensing, controlling, and actuating process in the wirelessly connected control systems over the large-scale IoT can be analyzed in terms of the power consumption, which is affected by various environmental causes around IoT.

For effective cooperative learning organize group is important. Member in the group to the interaction between the group members are to be composed of heterogeneous. However, the average ability of the group needed to solve a given task to a fair evaluation in cooperative learning should be similar to each other between the groups. In this paper, we propose greedy approach to find partitions with high homogeneity in a group and high heterogeneity between groups.

In the power industry, the focus has been almost exclusively on implementing equipment that can keep the power system reliable. Until recently, communications and information flows have been considered of peripheral importance. However, increasingly the Information Infrastructure that supports the monitoring and control of the power system has come to be critical to the reliability of the power system. Communication protocols are one of the most critical parts of power system operations, both responsible for retrieving information from field equipment for sending control commands. We studied the wind energy SCADA system for IEC 61850 communication protocol. We focus on security for reliable SCADA system now. We want to introduce security concept required in SCADA systems.

We proposed automatic speech recognition (ASR) error management method using recurrent neural network (RNN) based syllable prediction for spoken dialog applications. ASR errors are detected and corrected by syllable prediction. For accurate prediction of a next syllable, we used a current syllable, previous syllable context, and phonetic information of next syllable which is given by ASR error. The proposed method can correct ASR errors only with a text corpus which is used for training of the target application, and it means that the method is independent to the ASR engine. The method is general and can be applied to any speech based application such as spoken dialog systems.

Recently, mobile devices are gradually increasingly used even in companies and governmental institutions and the exchange of company data and military secret data through mobile devices is increased. As a result, the illegitimate leakage and collection of user data related to mobile devices has greatly increased. In this paper, we propose a method of managing the sensitive data of a mobile device using an escrow server. The proposed scheme is advantageous in that it is capable of storing the entrusted sensitive data of a user within the mobile device and verifying the validity of app software installed on the mobile device using an escrow server. Thereby it enhances data protection, minimizes damage resulting from the exposure of data attributable to the loss of a mobile device, and prevents the installation of illegitimate software.

Color volume datasets of the human body, such as Visible Human or Visible Korean, describe realistic anatomical structures. However, imperfect segmentation of these color volume datasets, which are typically generated manually or semi-automatically, produces poor-quality rendering results. We propose an interactive high-quality visualization method using GPU-based refinements to support the study of anatomical structures. To smoothly represent the boundaries of a region-of-interest (ROI), we apply Gaussian filtering to the opacity values of the color volume. Morphological grayscale erosion operations are performed to shrink the boundaries, which are expanded by the Gaussian filtering. We implement these operations on GPUs for the sake of fast refinements. As a result, our method delivered high-quality result images with smooth boundaries providing considerably faster refinements, sufficient for interactive renderings as the ROI changes, compared to CPU-based method.

In the indoor ubiquitous computing environment where Global Positioning System (GPS) cannot be utilized, the approach to calculate the locations of Unmanned Aerial Vehicles (UAVs) is the core technique to control multiple UAVs. To calculate the locations of UAVs, the distance between Access Points (APs) and UAVs should be measured accurately given that the location of UAVs is obtained on the basis of the distance between APs and UAVs. In this paper, we propose a method to measure the distance between a single beacon and a single AP in an indoor ubiquitous computing environment. We assume that the beacon is attached to the bottom of a UAV. In the indoor experiment, while transferring a beacon, the distances between the beacon and an AP were measured and tuned. Therefore, the accumulated difference between the real beacon location and the calculated beacon location was reduced by 31.1 %.

The locations of residents are utilized not only for recognizing the situation of indoor ubiquitous computing environments but also for controlling devices and expressing intention. This paper proposes a novel user interface to utilize the locations of users as control signals in indoor ubiquitous computing environments. The locations of users are estimated by analyzing the distance between a beacon and APs.

The high incidence of heinous crime is increasing to use of CCTV. However, CCTV has been used to obtain evidence rather than crime prevention. Also it shows a weak effect about preventing crime. To solve the weak effort, we propose a Flexible Multi-level Regression (FMR) model that should estimate a dangerous situation for the pedestrian. The FMR model is tracking the behavior of between pedestrians from multiple CCTV that are located in different locations. The FMR has a prediction logic that should estimate an abnormal situation to analyze the possibility of crime by using the Regression and Apriori algorithm. The FMR model can be usefully used to prevent the crime because of an immediate response and rapid situation assessment.

Recently, human-centered technology development is in the limelight. It reflects user characteristics, including the physical and psychological characteristics of humans. In particular, numerous intelligent systems have been developed to prove humans with the services required for a living space. In this study, a middleware system was developed to analyze a user’s emotions using brain waves to control the brightness and color of light accordingly. The middleware in a smart building analyzes the brain waves acquired from the sensors of each household and uses this information to appropriately control the brightness and color of the light in the proper space. Such a system could be utilized in a variety of fields. For example, an intelligent apartment could provide a comfortable indoor environment, as well as save energy, and light therapy could be used to treat depression and insomniac.

Human interaction is related to the development and maintenance of communication. Communication is largely divided into verbal communication and non-verbal communication. Verbal communication involves the use of a word or words. Non-verbal communication is the use of body language. Gestures belong to non-verbal communication. It is possible to represent various types of motion. For this reason, gestures are spotlighted as a means of implementing an NUI/NUX in the field of HCI and HRI. In this paper, using Kinect and the geometric characteristics of the hand, we propose method for recognizing the number of fingers and detecting the hand area. Because Kinect provides a color image and depth image at the same time, it is easy to understand a gesture. The finger number is identified by calculating the length of the outline and central point of the hand.

Organizational communication structure affects the nature of human interactions and information flow which in its own turn can lead to a competitive advantage in the knowledge economy. However, in addition to that, social relationships between individuals in an organization can also be utilized to produce positive returns. In this article we emphasize the role of individual structural importance within an organizational informal communication structure as a mechanism for knowledge flow and speeding up organizational learning. Our experimental results indicate the fact that structural position of individuals within their informal communication networks can help the network members to have a better access to ongoing information exchange processes in the organization. The results of our analyses also show that through an informal communication network of people in the form of scale-free connectivity pattern organizational learning is faster comparing to small-world connectivity style.

In this paper, a boost chopper using PV (Photovoltaics) system and PWM (Pulse Width Modulation) voltage type power converter were constructed to provide a pleasant environment to the patients in the hospital wards by controlling temperature, humidity and air-conditioning and heating. For the stable modulation of solar cell, synchronizing signal and control signal were processed using one chip microprocessor. In this thesis, in addition, grid voltage was detected and this grid voltage and inverter output were operated at the same phase for the phase locking with PWM voltage source inverter so that surplus power could be linked to grid. This characteristic were applied on the temperature and humidity sensors in the general buildings and buildings having specific purposes such as hospitals. The good dynamic characteristic of inverter could be obtained by these applications. Also, PWM voltage inverter maintains a high power factor and low-frequency harmonic output so that power can be supplied in the load as well as system.

In this paper, we discuss the problem of the input-output linearization method for the time-varying nonlinear systems with model uncertainties. The uncertainty of model is presented in all systems, the primary concern in the system analysis. In particular, in this study, while proving the input-output linearization theorem for time-varying nonlinear systems with uncertainty, the uncertainty bounded range of models is derived. It can be seen that the expansion of the input-output linearization for the time-invariant nonlinear system with uncertainty. The Algorithm is simulated and tested by MATLAB. The results show that this algorithm which is more effective routing protocol prolongs the network lifetime.

In this paper, based on core technologies such as overcoming a place’s limitations, light that can substitute for the sunlight, automation, nutrient supply system and temperature, and intelligent situation recognition for solar power generation, geothermal HVAC (heating, ventilating, and air conditioning), a plant growth analysis system for vegetation factories was designed. The system is likely to improve the freshness of agricultural products through order and planned productions, to create new markets through the convergence of the IT and BT industries, and to promote convenience in farming and comfort in workspaces through automatic control, robot development, etc. In addition, the system is expected to offer opportunities for urban residents to experience and learn the whole process of a plant’s growth; to provide a leisurely life, such as a downtown oasis, to those who are tired of the dreary city life; to prevent environmental pollution through the effective use of recycled resources; and to produce and stably supply diverse agricultural products all year round, regardless of the weather.

The entrepreneurship required for a creative economy is one that promotes job creation through the creativity of CEOs who have entrepreneurial spirit as well as excellent knowledge and technology. This entrepreneurship plays a critical role in the construction of a venture business ecosystem. Meanwhile, big data, until now, have consisted only of numbers and texts that are specified and standardized by certain structured rules. Nowadays, however, big data analysis methods are being developed to gain information and business opportunities from new aspects through the use of nonstandard data. The big data technology is becoming a core element in the new digital age rather than just a trend, and the big data strategies are progressing from the testing stage to the implementation stage. In particular, as the importance of nonstandard data is increasing, limitations of the conventional system analysis appeared, and the analysis methods of advanced analytics are being highlighted. The scope of big data is anticipated to expand in enterprises with the emergence of many application cases, such as the real-time use of the data. The business model for the formation of the entrepreneurial ecosystem of the creative economy can assist the construction of an entrepreneurial platform as a catalyst for the stimulation of innovative business start-ups. This entrepreneurial platform can produce such effects as fast product development, commercialization of technology, risk reduction, and job creation. In this study, the operating model formation according to the characteristics of the industry or the business model in operation, the profit creation sources, the scope of value provision, and the priorities with regard to the required data were examined. Furthermore, cases related to the performance of big data business models of advanced corporations were examined, and a big data business model for the formation of the entrepreneurial ecosystem of the creative economy in the 21st century was derived.

Unlike in the past, where diaries and paper calendars were mostly used, the rapid increase in the use of computers and smartphones of late has demanded a function with which the users can confirm and attach their schedules anytime, anywhere. Especially, in the busy and flexible modern life, fast schedule coordination targeting a majority has become increasingly necessary. As the conventional schedule management methods have had problems in coordinating a common schedule that satisfies the majority in cooperation and group work systems, in this study, a smart group scheduling function was developed, with focus on intelligent decision scheduling, unlike in the conventional schedule management programs. Further, the function proposed in this paper, whose process deduces a result shared by majority people in a group, can be concluded within a system based on individuals’ opinions and information; therefore, it is considered the system has the potential to contribute to democratic and horizontal work execution.

The client-server system in the old finance and banking circle was operated in such a way as to respond to a relatively small number of devices, such as ATMs (automated teller machines) and personal computers. Due to the significant increase in the use of smart devices at the present time, however, the number of devices that need to be connected to financial transaction servers continually skyrockets. In this paper, a mapper that can analyze the information of the messages sent from newly added terminals and then link to the MCA was designed, which would allow new terminals to be added without correcting the programs. In addition, as myriads of terminals need to be added, the programs cannot be corrected on a case-to-case basis; in addition, it is actually impossible to correct all the programs. Thus, the mapper, an automatic mapping system, was developed to add terminals automatically. As a result, the mapper is likely to minimize the labor and money that need to be invested for mapping works and to establish a definitive standard in using various mapping tools, thus preventing losses accrued from the use of each different tool.

The traffic accident statistics show that fatigue (drowsiness) and drunk driving are the major causes of traffic accidents. Therefore, it is important to detect and prevent driving in fatigue condition. The conventional fatigue detection technologies use methods that detect a driver’s drowsiness from the direction of the face, the eye closing speed, etc., using cameras and various senses. Such technologies, however, are not only expensive but also have positional detection limitations as cameras and sensors are used, thereby restricting the driver’s behavior. In this study, a simple method of detecting fatigue condition based on HRV (Heart Rate Variability) data is presented. The proposed method can greatly reduce the cost of drowsiness prevention system for safe driving.

Information security is a great challenge for most organizations in today’s information world, especially the insider problem. With the help of malwares, insiders can search and steal valuable files easily and safely in an organization’s network. In this paper, we collect a dataset of file access behaviors for normal processes and malware processes. We analyze the dataset and find several features in which normal processes and malware processes show significant differences, a file access behavior model is given based on these features, and we apply both semi-supervised and unsupervised approaches to verify the effectiveness of our model, experimental results demonstrate that our model is effective in distinguishing between file access behaviors of normal processes and malware processes.

Support Vector Machine (SVM) classifier with Histogram of Orientated Gradients (HOG) feature is one of the most popular techniques used for vehicle detection in recent years. In this paper, we study the effect of HOG parameter values on the performance and computing time of vehicle detection. The aim of this paper is to explore the relationship between performance/computing time and HOG parameter values, and eventually to guide finding the most appropriate parameter set to meet specific problem constrains.

Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades because of its easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task of dynamically maintaining users’ similarity list is challenged by cold-start problem and scalability problem. Recently, several methods are presented on addressing the two problems. However, these methods require mn steps to compute the similarity list against the kNN attack, where m and n are the number of items and users in the system respectively. Observing that the k new users from the kNN attack, with enough recommendation data, have the same rating list, we present a faster algorithm, TwinSearch, to avoid computing and sorting the similarity list for each new user repeatedly to save the time. The computational cost of our algorithm is 1/125 of the existing methods. Both theoretical and experimental results show that the TwinSearch Algorithm achieves better running time than the traditional method.