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Purpose The study aimed to evaluate the safety and clinical efficacy of transcatheter arterial embolization (TAE) for the treatment of arterial esophageal bleeding. Methods Nine patients (8 male, 1 female; mean age, 62.3±7.5 years) who underwent TAE for arterial esophageal bleeding between January 2004 and January 2020 were included. Preceding endoscopic treatment was unsuccessful in five patients and was not attempted in four patients due to the non-cooperation of the patients in endoscopic treatment. The etiologies of bleeding were esophageal cancer (n=4), Mallory-Weiss syndrome (n=3), erosive esophagitis (n=1), and esophageal ulcer (n=1). Technical and clinical success, recurrent bleeding, procedure-related complications, and clinical outcomes were retrospectively reviewed. Results The angiographic findings for bleeding were contrast media extravasation (n=8) or tumor staining without a definite bleeding focus (n=1). The bleeding focus at the distal esophagus (n=8) was the left gastric artery, whereas that at the middle esophagus (n=1) was the right bronchial artery. Technical success was achieved in all patients. The embolic agents were n-butyl cyanoacrylate (NBCA, n=5), gelatin sponge particles (n=2), microcoils (n=1), and NBCA with gelatin sponge particles (n=1). Clinical success was achieved in 77.8% of cases (7/9); two patients with recurrent bleeding one day after the first TAE showed culprit arteries different from the bleeding foci at the first TAE. One patient who underwent embolization of both the left and short gastric arteries died of gastric infract/perforation one month after TAE. Conclusion TAE can be an alternative to the treatment of arterial esophageal bleeding. TAE can be attempted in the treatment of recurrent bleeding, but there is a risk of ischemia/infarct in the gastrointestinal tract involved.

Video frame interpolation has increasingly attracted attention in computer vision and video processing fields. When motion patterns in a video are complex, large and non-linear (exceptional motion), the generated intermediate frame is blurred and likely to have large artifacts. In this paper, we propose a novel video frame interpolation considering the exceptional motion patterns. The proposed video frame interpolation takes into account an exceptional motion map that contains the location and intensity of the exceptional motion. The proposed method consists of three parts, which are optical flow based frame interpolation, exceptional motion detection, and frame refinement. The optical flow based frame interpolation predicts an optical flow which is used to synthesize the pre-generated intermediate frame. The exceptional motion detection detects the position and intensity of complex and large motion with the current frame and the previous frame sequence. The frame refinement focuses on the exceptional motion region of the pre-generated intermediate frame by using the exceptional motion map. The proposed video frame interpolation can be robust against the exceptional motion including complex and large motion. Experimental results showed that the proposed video frame interpolation achieved high performance on various public video datasets and especially on videos with exceptional motion patterns.

Cloud computing is now known as the most cost-effective platform for delivering big data and artificial intelligence services over the Internet to enterprises and cloud consumers. However, despite many recent security developments, many cloud consumers continue to express great concern about using these platforms because they still have significant vulnerabilities. Typically, Economic Denial of Sustainability (EDoS) attacks exploit the pay-as-you-go billing mechanisms used by cloud service providers, so that a cloud customer is forced to pay an extra fee for the additional resources triggered by the attack activities. In our previous work, we already proposed an system to mitigate such EDoS attacks. Overall, this previous work presented an effective system for detecting abnormal events; however, the false-alarm rates still remain relatively high and detection rates are low, because abnormal events could be caused by the cloud customer. Furthermore, our previous work still consumes a large number of computing resources. Therefore, in this paper, we propose an enhanced scheme to detect and mitigate EDoS attacks efficiently and reliably. Our proposed scheme is composed of online and offline phases, implementing a gated recurrent unit, which not only can capture complex temporal dependence relations in the data, but also can reduce the vanishing gradient problems in time series. First, to reflect the normal patterns, our proposed scheme learns accurate representations of multivariate time series. Next, these representations are used to reconstruct input data. Finally, the reconstruction probabilities not only can be used to find anomalies, but also can provide interpretations. The proposed scheme also introduces a self-adjusting threshold to reduce error rates, whereas existing solutions normally use a hard threshold to analyze the anomalies, which causes increasing error rates. Our comprehensive analysis of the results shows outstanding performance compared to other solutions and our previous work.

OBJECTIVE Clinical outcomes of patients who undergo transarterial chemoembolization (TACE) for single small hepatocellular carcinoma (HCC) are not consistent, and may differ based on certain imaging findings. This retrospective study was aimed at determining the efficacy of pre-TACE CT or MR imaging findings in predicting survival outcomes in patients with small HCC upon being treated with TACE. Besides, the study proposed to build a risk prediction model for these patients. MATERIALS AND METHODS Altogether, 750 patients with functionally good hepatic reserve who received TACE as the first-line treatment for single small HCC between 2004 and 2014 were included in the study. These patients were randomly assigned into training (n = 525) and validation (n = 225) sets. RESULTS According to the results of a multivariable Cox analysis, three pre-TACE imaging findings (tumor margin, tumor location, enhancement pattern) and two clinical factors (age, serum albumin level) were selected and scored to create predictive models for overall, local tumor progression (LTP)-free, and progression-free survival in the training set. The median overall survival time in the validation set were 137.5 months, 76.1 months, and 44.0 months for low-, intermediate-, and high-risk groups, respectively (p < 0.001). Time-dependent receiver operating characteristic curves of the predictive models for overall, LTP-free, and progression-free survival applied to the validation cohort showed acceptable areas under the curve values (0.734, 0.802, and 0.775 for overall survival; 0.738, 0.789, and 0.791 for LTP-free survival; and 0.671, 0.733, and 0.694 for progression-free survival at 3, 5, and 10 years, respectively). CONCLUSION Pre-TACE CT or MR imaging findings could predict survival outcomes in patients with small HCC upon treatment with TACE. Our predictive models including three imaging predictors could be helpful in prognostication, identification, and selection of suitable candidates for TACE in patients with single small HCC.

Wireless sensor networks (WSNs) suffer not only from short lifetimes because of limited energy but also from an energy imbalance between the nodes close to the sink and the other nodes. To fundamentally resolve the issue of short lifetimes, recent studies have utilized environmental energy, such as solar power. Additionally, WSNs that employ energy-aware dynamic topology control are also being studied to address the energy imbalance. This paper proposes an improved collection tree protocol (CTP) scheme, called solar-CTP, that uses the two approaches of energy-harvesting and energy-aware topology control simultaneously. The proposed scheme is derived from the CTP scheme, which is a widely adopted data collection strategy designed for typical battery-based WSNs with a fixed sink. We tailor the CTP scheme for solar-powered WSNs operating with a mobile sink. Performance verification confirms that our scheme significantly reduces the number of blackout nodes compared to other CTP variants, thus increasing the amount of data collected by the sink.

Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall; however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions.

Cloud computing is currently considered the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnerabilities. A new type of attack called an economic denial of sustainability (EDoS) attack exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, a few solutions have been proposed, including hard-threshold and machine learning-based solutions. Among them, long short-term memory (LSTM)-based solutions achieve much higher accuracy and false-alarm rates than hard-threshold and other machine learning-based solutions. However, LSTM requires a long sequence length of the input data, leading to a degraded performance owing to increases in the calculations, the detection time, and consuming a large number of computing resources of the defense system. We, therefore, propose a two-phase deep learning-based EDoS detection scheme that uses an LSTM model to detect each abnormal flow in network traffic, however, the LSTM model requires only a short sequence length of five of the input data. Thus, the proposed scheme can take advantage of the efficiency of the LSTM algorithm in detecting each abnormal flow in network traffic, while reducing the required sequence length of the input data. A comprehensive performance evaluation shows that our proposed scheme outperforms the existing solutions in terms of accuracy and resource consumption.

Due to muscle dystrophy, diabetes patients suffer from foot gait movement and need to define whether their muscle contraction-relaxation is normal. However, the way to determine diabetic foot is inconvenient and may give stress to patients during the test by attaching multiple electrodes on their foot. Also, after measuring the EMG, it takes several hours or days to figure out whether the testing subject has a diabetic foot. Therefore, we developed an EMG measured diabetic determination system by machine learning protocol based on LSTM (Long Short-Term Memory). We tested 15 individuals with normal glycemia and 15 diabetic patients who have been suffered more than 10 years with fasting glucose levels over 180mg/dl. Every patient’s EMG wave pulse was measured for one minute with 3-second terms of contraction and relaxation for three times, Subjects’ wave pulse was correct as normal and diabetes to clear machine learning process. Every second 150 data were created and the middle range was normalized. Numbers between 0 and 1 range were used as a y normalization for machine learning test training. After test learning, we input normal and diabetes wave pulse patterns whether this LSTM model could make a clear definition. (Fig 1.) As result, this machine learning model could distinguish 88.21% of diabetes patients’ feet by comparing foot EMG wave pulse, but 11.79% of diabetes foot cannot be determined due to an inefficient data pool. Disclosure D. Kim: None. M. Park: None. D. Jee: None. G. Im: None. J. Lee: None. J. Shin: None. J. Kim: None. W. Kim: None. Funding Korea Institute of Startup & Entrepreneurship Development

Gastrointestinal (GI) bleeding is a common complication of various endoscopic procedures. In most cases, GI bleeding responds to conservative management. And if indicated, endoscopic hemostasis is performed. However, if endoscopic hemostasis fails due to massive bleeding or hemodynamically unstable patients, alternative treatment options, such as endovascular hemostasis or surgery, should be considered. As endoscopic procedures have become more diverse, there have been reports of interventional treatment of endoscopic, procedure-related GI bleeding. In this article we will discuss the endovascular diagnosis and treatment of endoscopic, procedure-related GI bleeding.

The fifth-generation (5G) heterogeneous networks (HetNets) are gaining attention to be a key enabler that provides promising infrastructure for the massive proliferation of Internet of Things (IoT) devices and their services. However, one of the key challenges that the IoT terminals face is the inter-cell interference (ICI) problem since the 5G HetNets are generally deployed based on a co-channel model that overlays numerous pico eNodeBs (eNBs) on top of macro eNBs grid on the same frequency band. In order to overcome the ICI problem, we propose a relay-assisted communication approach by which the data of interfered IoT terminal (iIT) in the ICI area is relayed, via a device-to-device connection, to its neighboring IoT terminals which has good signal to and from the network. The key component in this proposed scheme is the relay selection algorithm which aims at maximizing the network resource availability at the highest priority, as well as device data rate. Firstly, resource availability maximization (RAmax) function determines an eNB that has maximum resource availability among all neighboring eNBs of the iIT to be a gateway node for the relay connection to the network (referred to as reNB). Among the IoT terminals associated with the reNB, a relay IoT terminal (called rIT) linking iIT and reNB is selected by a condition of maximum channel quality to the reNB. Simulation results show that our proposed algorithm increases total network throughput and the number of simultaneously served ITs by 44% and 20%, respectively.

Software-defined networking (SDN) is the key outcome of extensive research efforts over the past few decades toward transforming the Internet infrastructure to be more programmable, configurable, and manageable. However, critical cyber-threats in the SDN-based cloud environment are rising rapidly, in which distributed denial-of-service (DDoS) attack is one of the most damaging cyber attacks. In this paper, we propose an efficient solution to tackle DDoS attacks in the SDN-based cloud environment. We first introduce a new hybrid machine learning model based on support vector machine and self-organizing map algorithms to improve the traffic classification. Then, we propose an enhanced history-based IP filtering scheme ( $eHIPF$ ) to improve the attack detection rate and speed. Finally, we introduce a novel mechanism that combines both the hybrid machine learning model and the $eHIPF$ scheme to make a DDoS attack defender for the SDN-based cloud environment. The testbed is implemented in an SDN-based cloud with service function chaining. Through practical experiments, the proposed DDoS attack defender is proven to outperform existing mechanisms for DDoS attack classification and detection. The comprehensive experiments conducted with various DDoS attack levels prove that the proposed mechanism is an effective, innovative approach to defend DDoS attacks in the SDN-based cloud.

In this paper, we propose a history-based dynamic timeout scheme to alleviate the flow table overflow attack which is one of typical attacks against Software Defined Networks (SDN). We investigated hard timeout and idle timeout used in OpenFlow which is the most popular protocol for SDN, and developed the proposed scheme that dynamically adjusts both hard timeout and idle timeout to reduce the number of flow rules. The experiment results shows it can protect SDN switches from the flow table overflow efficiently.

Cloud computing is now considered to be the most cost-effective platform for offering business and consumer IT services over the Internet. However, it is prone to new vulnera-bilities. Specifically, a newly discovered type of attack, called an economic-denial-of-sustainability attack known as EDoS, exploits the pay-per-use model to scale up the resource usage over time to the degree that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, we propose an effective solution in the SDN-based cloud computing environment. We first introduce a machine-learning-based approach adopting a framework called MAD-GAN which applies an unsupervised multivariate anomaly detection technique based on Genera-tive Adversarial Networks (GANs), using the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) to detect EDoS attacks. Its main idea is to produce an anomaly score at each time step by learning a multivariate attribute. We then generate a dynamic threshold score to compare with the anomaly score produced to classify the network traffic as EDoS traffic or normal traffic. By realistic tests, our proposed scheme is demonstrated to outperform existing methods for EDoS attack detection. The detailed experiments conducted with different levels of EDoS attacks show that the proposed scheme is an efficient, innovative approach to defend EDoS attacks in the SDN-based cloud.

In this paper, we propose a novel video frame interpolation method via exceptional motion-aware synthesis, in which accurate optical flow could be estimated even with exceptional motion patterns. Specifically, we devise two deep learning modules: exceptional motion detection and frame interpolation with refined flow. The motion detection module detects the position and intensity of exceptional motion patterns in current frame given the past frame sequence. The flow refinement module refines the pre-estimated optical flow for synthesizing the intermediate frame using the information of exceptional motion. The proposed modules improve the quality of the synthesized intermediate frame by making the optical flow robust against exceptional case of motion. Experimental results showed that the proposed method outperforms the state-of-the-art methods qualitatively and quantitatively.

In Cloud Computing, a new type of attack, called Economic Denial of Sustainability (EDoS) attack, exploits the pay-per-use model to scale up the resource usage over time to the extent that the cloud user has to pay for the unexpected usage charge. To prevent EDoS attacks, we propose an efficient solution in the SDN-based cloud computing environment. In this paper, we first apply an unsupervised learning approach called Long Short-Term Memory (LSTM), which is a multivariate time series anomaly detection, to detect EDoS attacks. Its key idea is to try to predict values of the resource usage of a cloud consumer (CPU load, memory usage and etc). Furthermore, unlike other existing proposals using a predefined threshold to classify the anomalies which generate high rate errors, in this work, we utilize a dynamic error threshold which delivers much better performance. Through practical experiments, the proposed EDoS attack defender is proven to outperform existing mechanisms for EDoS attack detection. Furthermore, it also outperforms some of the machine-learning-based methods, which we conducted the experiment ourselves. The comprehensive experiments conducted with various EDoS attack levels prove that the proposed mechanism is an effective, innovative approach to defense EDoS attacks in the SDN-based cloud.

This paper introduces a video retrieval tool for the 2020 Video Browser Showdown (VBS). The tool enhances the user’s video browsing experience by ensuring full use of video analysis database constructed prior to the Showdown. Deep learning based object detection, scene text detection, scene color detection, audio classification and relation detection with scene graph generation methods have been used to construct the data. The data is composed of visual, textual, and auditory information, broadening the scope to which a user can search beyond visual information. In addition, the tool provides a simple and user-friendly interface for novice users to adapt to the tool in little time.

Cloud computing is one of the most modernized technology for the modern world. Along with the developments in the cloud infrastructure comes the risk of attacks that exploit the cloud services to exhaust the usage-based resources. A new type of general denial attack, called “economic denial of sustainability” (EDoS), exploits the pay-per-use service to scale-up resource usage normally and gradually over time, finally bankrupting a service provider. The stealthiness of EDoS has made it challenging to detect by most traditional mechanisms for the detection of denial-of-service attacks. Although some recent research has shown that multivariate time recurrent models, such as recurrent neural networks (RNN) and long short-term memory (LSTM), are effective for EDoS detection, they have some limitations, such as a long processing time and information loss. Therefore, an efficient EDoS detection scheme is proposed, which utilizes an attention technique. The proposed attention technique mimics cognitive attention, which enhances the critical features of the input data and fades out the rest. This reduces the feature selection processing time by calculating the query, key and value scores for the network packets. During the EDoS attack, the values of network features change over time. The proposed scheme inspects the changes of the attention scores between packets and between features, which can help the classification modules distinguish the attack flows from network flows. On another hand, our proposal scheme speeds up the processing time for the detection system in the cloud. This advantage benefits the detection process, but the risk of the EDoS is serious as long as the detection time is delayed. Comprehensive experiments showed that the proposed scheme can enhance the detection accuracy by 98%, and the computational speed is 60% faster compared to previous techniques on the available datasets, such as KDD, CICIDS, and a dataset that emerged from the testbed. Our proposed work is not only beneficial to the detection system in cloud computing, but can also be enlarged to be better with higher quality of training and technologies.

Software-Defined Networking (SDN) is a new programmable networking model that features the detachment of control and data planes. In this network, the network brain is an SDN controller that is used to centrally monitor and control the data plane based on the OpenFlow protocol and applications located in the application layer. In recent years, a vast number of issues relating to security have been seriously debated for this networking paradigm, especially the large-scale model. In particular, flooding attacks have been on the rise, providing great challenges for the SDN architecture to cope with. In this paper, we present a novel mechanism using the Self-Organizing Map (SOM) application to solve the performance bottleneck and overload problems for the upper layers in a large-sized SDN in case of flooding attacks. Our proposed approach integrates a Distributed Self-Organizing Map (DSOM) system to OpenFlow Switches instead of using a standalone SOM. By exploiting SDN advantages, such as flexibility and overhead reduction, we implement and test both a DSOM system and a single SOM system on multi-criteria to compare the performance of our introduced system. Our experimental results show that the DSOM solution can effectively detect abnormal traffic, solve bottleneck problems and increase the system reaction speed to attack traffic, while presenting a smaller overhead to the network system.

Cooperative awareness among vehicles is required to make driving safer. Inter-vehicle communication is achieved by beaconing in the vicinity of the respective vehicles. The beaconing includes the exchange of periodic messages on each vehicle’s status information. The shorter the period of the beacon message transmission is, the higher the status information accuracy is. Accordingly, driving is safer. However, frequent exchanges of the periodic beacon cause wireless channel congestion and packet loss. To address the trade-off problem between the accuracy of information and the channel load, this paper presents a scheme to determine the optimum beacon interval for guaranteeing fundamental accuracy, while maintaining a moderate load on the communication channel. A performance evaluation using a computer simulation showed that the proposed scheme guarantees sufficient information accuracy for effectively avoiding inter-vehicle collisions and information overload. Furthermore, the proposed scheme is compatible with, and can be directly applied to, the current vehicular ad hoc network system implementation.

Service Function Chaining (SFC) provides a special capability that defines an ordered list of network services as a virtual chain and makes a network more flexible and manageable. However, SFC is vulnerable to various attacks caused by compromised switches, especially the middlebox-bypass attack. In this paper, we propose a system that can detect not only middlebox-bypass attacks but also other incorrect forwarding actions by compromised switches. The existing solutions to protect SFC against compromised switches and middlebox-bypass attacks can only solve individual problems. The proposed system uses both probe-based and statistics-based methods to check the probe packets with random pre-assigned keys and collect statistics from middleboxes for detecting any abnormal actions in SFC. It is shown that the proposed system takes only 0.08 ms for the packet processing while it prevents SFC from the middlebox-bypass attacks and compromised switches, which is the negligible delay.

Software-Defined Networking (SDN) brings us an opportunity to manage the network more efficiently with the separation of control and data planes. However, SDN is still vulnerable to existing threats from the security point of view. Especially, SYN Flooding Attack, one of the typical Denial of Service attacks, may not only exhaust the resource of a victim but also paralyze the entire SDN network by a large number of control messages between controllers and SDN switches. Although various approaches have been proposed to defend the SYN flooding attack, they still have some drawbacks such as packet processing overload and delay. Therefore, this paper proposes an efficient SYN flooding defense scheme utilizing the TCP Time Out mechanism and Round-Trip Time (RTT). The experiment results show the proposed scheme can defend the attack with low bandwidth occupation between the controller and SDN switches and little computing resources.

Public DNS servers have been deployed more and more because of some reasons such as faster web browsing or censorship bypassing. However, they are open to anyone, which can be used as a tool for network attacks. A typical attack using public DNS servers is a DNS amplification attack. In this paper, we propose a DNS amplification attack defense system which can block response messages from unsolicited DNS queries in the SDN environment by using the flow control technique. The system is emerged into the SDN controller to manages all the flow rules of the switches. The results show that our system can maintain stable bandwidth and prevent the DNS amplification attack effectively.

Realistic image synthesis is to generate an image that is perceptually indistinguishable from an actual image. Generating realistic looking images with large variations (e.g., large spatial deformations and large pose change), however, is very challenging. Handing large variations as well as preserving appearance needs to be taken into account in the realistic looking image generation. In this paper, we propose a novel realistic looking image synthesis method, especially in large change demands. To do that, we devise generative guiding blocks. The proposed generative guiding block includes realistic appearance preserving discriminator and naturalistic variation transforming discriminator. By taking the proposed generative guiding blocks into generative model, the latent features at the layer of generative model are enhanced to synthesize both realistic looking- and target variation- image. With qualitative and quantitative evaluation in experiments, we demonstrated the effectiveness of the proposed generative guiding blocks, compared to the state-of-the-arts., This work is accepted in ICIP 2019

This study dealt with developing an accident model for rural signalized intersections with random parameter negative binomial method. The limitation of previous count models(especially, Poisson/Negative Binomial model) is not to explain the integrated variations in terms of time and the distinctive characters a specific point/segment has. This drawback of the traditional count models results in the underestimation of the standard error(t-value inflation) of the derived coefficient and finally affects the low-reliability of the whole model. To solve this problem, this study improves the limitation of traditional count models by suggesting the use of random parameter which takes account of heterogeneity of each point/segment. Through the analyses, it was found that the increase of traffic flow and pedestrian facilities on minor streets had positive effects on the increase of traffic accidents. Left turning lanes and median on major streets reduced the number of accidents. The analysis results show that the random parameter modeling is an effective method for investigating the influence on traffic accident from road geometries. However, this study could not analyze the effects of sequential changes of driving conditions including geometries and safety facilities. Language: ko

Pilot- and industrial-scale mineral carbonation plants to remove CO2 through a reaction with Ca(OH)2 were built in South Korea to address concerns related to global warming. To simulate mineral carbonation reactors with complex gas–liquid–solid interacting flow patterns, a computational fluid dynamics (CFD)-based compartmental model was developed. In the model, the reactors were divided into hundreds of zones, and each zone was assumed to be a single homogeneous reactor. Mass and heat balance equations were formulated for each zone and the entire reactor, separately. The mass flow rates between adjacent zones and initial CO2 holdup at each zone were calculated from CFD simulations, and a kinetic model, which included all the involved reactions, was built in MATLAB. The total CO2 removal efficiency, pH, and temperature changes as well as concentration profiles of CO2 and other species were predicted during batch operations. To validate the performance of the model, the simulated results were compared with the real operation data from the pilot- and industrial-scale plants. The errors at steady states were within 7% without any adjustable parameters. Furthermore, the model was used to predict the performances of reactors 2.5 and 10 times larger than the industrial-scale reactor.

Through Virtual Machine technology(VM), VMs can be packed into much fewer number of physical servers than that of VMs. Since even an idle physical server wastes more than 60% of max power consumption, it has been considered as one of energy saving technologies to minimize the number of physical servers by using the knapsack problem solution based on the computing resources. However, this paper shows that this tightly packed consolidation may not achieve the efficient energy saving. Instead, a service pattern-based VM consolidation algorithm is proposed. The proposed algorithm takes the service time of each VM into account, and consolidates VMs to physical servers in the way to minimize energy consumption. The comprehensive simulation results show that the proposed algorithm gains more than 30% power saving.

Hierarchical macro/small cell deployment is one of the most promising technologies to enhance cell coverage and network capacity in 3GPP LTE/LTE-Advanced systems. However, latency of inbound mobility to the small cell is a critical obstacle to its deployment because it takes much time to detect an associated small cell due to its sparse deployment and to identify the target small cell because of the delay in the verification procedure required before performing inbound handover to the target. Therefore, in order to invigorate the deployment of the LTE small cells, the inbound handover latency must be reduced. To tackle the problem, this paper proposes an efficient handover strategy for inbound mobility to LTE small cells. Performance evaluation by computer simulation shows that the proposed scheme effectively reduces the inbound handover latency. Furthermore, the proposed scheme is compatible and can be directly applied with small overhead to the current LTE/LTE Advanced system implementation.

Software defined networking (SDN) technology promises a new bright future to IP network. Significant number of SDN researches have been done so far to facilitate network operation and management efficiently. However, until now there are only few frameworks to support emulation, verification, and implementation of the SDN. Mininet is one of the most popular tools because of the openness, cost effectiveness, and full Openflow support. By default, Mininet helps to create a stand alone typical SDN network along with lightweight clients. In this article, we contribute the extended functions of network topology emulation in Mininet based on Virtualbox, such as Internet connection, independent OS clients, standard routing controller, and automatic flow logging. It is highly expected to bring an easier and convenient facility to SDN research and training.

Axillary lymph node (ALN) status is an important prognostic factor for overall breast cancer survival. In current clinical practice, ALN status is evaluated before surgery via multimodal imaging and physical examination. Mammography is typically suboptimal for complete ALN evaluation. Currently, ultrasonography is widely used to evaluate ALN status; nonetheless, results may vary according to operator. Ultrasonography is the primary imaging modality for evaluating ALN status. Other imaging modalities including contrast-enhanced magnetic resonance imaging, computed tomography, and positron emission tomography/computed tomography can play additional roles in axillary nodal staging.The purpose of this article is (1) to review the strengths and weaknesses of current imaging modalities for nodal staging in breast cancer patients and (2) to discuss updated guidelines for ALN management with regard to preoperative ALN imaging.

We proposed a half-wall structure in the in-plane switching (IPS) configuration of the nanoencapsulated liquid crystal (LC) display for reducing a driving voltage. The IPS electrodes were fabricated on top of the half-walls to enhance electric field strength through the whole LC layer. In addition, we demonstrated a self-masking process for the half-wall structure and the IPS electrodes without any additional mask-aligning process. (C) 2017 Optical Society of America

This paper proposes a proactive optimal resource allocation scheme for elastic security Virtualized Network Functions (VNFs) in the Service Function Chaining on the Software Defined Network Function Virtualization (SDNFV-enabled) cloud environment. We firstly analyze our system model, and transform them into M/M/1/∞ and M/M/k queueing model. Then we define mathematical requirements by analyzing the new VNF resource allocation function and estimating the total number of packets in an SFC i system. From these requirements, we finally propose a proactive resource allocation optimizer with solvable and practical constraints.

This study explored factors affecting traffic accidents in roadway segments with and without lighting systems using a random parameter negative binomial model. This study sought to make up for a shortcoming of the fixed parameter model that constrained the estimated parameters to be fixed across observations, by applying random parameters that can take into account unobserved heterogeneity. Three variables had a random parameter among nine significant variables in segments with lighting systems, while seven of the eleven significant variables in a segment without a lighting system had random parameters. The different influence of interstate highway geometrics on vehicle crashes with and without lighting systems found through this study considering unobserved heterogeneity may hopefully help reduce accident frequencies and consider installation of lighting systems on interstate highways in the future.

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

Until now, several research on the relationship of traffic crash occurrences and geometric had been conducted and revealed that projects of road alignment, geometric improvement and hazardous segment selection reduced the number of accidents and accident severities. However, such variables did not consider the non-homogeneous characteristics of roadway segments due to the difficulty of data collection, which results in under-estimation of the standard error affecting the overall modeling goodness-of-fit. This study highlights the importance of non-homogeneity by looking at the effect of the non-homogeneous geometric variables through the modeling process. The model delivers meaningful results when using some geometric variables without relevant geometrics' variables.지금까지 교통사고발생과 기하구조와의 관계파악을 위한 모형정립에 관한 연구가 많이 이루어져 왔다 . 이러한 연구들은 도로선형, 기하구조의 개선 혹은 위험구간 선정 등에 사용되어 교통사고 건수 및 사고심각도를 줄이는데 기여를 하여왔다. 하지만, 모형정립에 사용되었던 변수들은 자료수집 부족 등의 이유로 변수 혹은 대상구간이 가지고 있는 기하구조의 비동질성을 고려하지 못한 측면이 있었으며 , 이는 모형 정립시 계수의 표준오차값이 과소 추정되어 모형전체의 신뢰성에 영향을 미쳐왔다 . 따라서, 이번 연구에서는 사용되는 변수의 비동질성 고려가 모형의 결과에 미치는 영향을 알아봄으로써, 비동질성의 중요성을 파악하고자 하는데 목적이 있다 . 그 결과, 모든 기하구조에 대한 비동질성을 고려하지는 못하였으나 , 몇몇 사용된 기하구조 변수들의 경우 , 의미 있는 결과가 도출되었다 .

Group key management (GKM) in mobile communication is important to enable access control for a group of users. A major issue in GKM is how to minimize the communication cost for group rekeying. To design the optimal GKM, researchers have assumed that all group members have the same leaving probabilities and that the tree is balanced and complete to simplify analysis. In the real mobile computing environment, however, these assumptions are impractical and may lead to a large gap between the impractical analysis and the measurement in real-life situations, thus allowing for GKM schemes to incorporate only a specific number of users. In this paper, we propose a new GKM framework supporting more general cases that do not require these assumptions. Our framework consists of two algorithms: one for initial construction of a basic key-tree and another for optimizing the key-tree after membership changes. The first algorithm enables the framework to generate an optimal key-tree that reflects the characteristics of users' leaving probabilities, and the second algorithm allows continual maintenance of communication with less overhead in group rekeying. Through simulations, we show that our GKM framework outperforms the previous one which is known to be the best balanced and complete structure.

In this paper, we propose a new mechanism for counteracting ARP (Address Resolution Protocol) poisoning-based Man-in-the-Middle (MITM) attacks in a subnet, where wired and wireless nodes can coexist. The key idea is that even a new node can be protected from an ARP cache poisoning attack if the mapping between an IP and the corresponding MAC addresses is resolved through fair voting among neighbor nodes under the condition that the number of good nodes is larger than that of malicious nodes. Providing fairness in voting among the nodes that are heterogeneous in terms of the processing capability and access medium is quite a challenge. We attempt to achieve fairness in voting using the uniform transmission capability of Ethernet LAN cards and smaller medium access delays of Ethernet than for wireless LAN. Although there is another scheme that resolves the same issue based on voting, i.e. MR-ARP, the voting fairness is improved further by filtering the voting reply messages from the too-early responding nodes, and the voting-related key parameters are determined analytically considering the fairness in voting. This paper shows that fairness in voting can be achieved using the proposed approach, overcoming the limitations of other voting-based schemes, and ARP poisoning-based MITM attacks can be mitigated in a more generalized environment through experiments.

In this paper, we propose a new type of IAM clustering architecture for the efficiency of user authentication and authorization in the Inter-Cloud environment. clustering architecture allows users to easily use un-registered services with their registered authentication and access permissions through pre-Access Agreement. through this paper, we explain our authentication protocol and IAM clustering architecture components.

In a computed tomography (CT) scan, fat deposition in the urinary bladder wall is seen as a linear hypoattenuating band surrounded by soft tissue density. It is uncommon, but is often seen in normal cases. However, there is no report of fat deposition in the urinary bladder wall in Korea. The authors encountered a 62-year-old male patient who showed an incidental hypoattenuating band in the urinary bladder wall on abdominal CT. The patient showed no clinical signs related to fat deposition in the urinary bladder wall. When the patient's previous abdominal CT was retrospectively reviewed, the same CT finding was seen. This linear hypoattenuating band within the urinary bladder wall should be considered as a normal CT finding, although it is uncommon.