Previous works have studied the throughput allocation of the heterogeneous storage system consisting of SSD and HDD in the dynamic setting where users are not all present in the system simultaneously, but those researches make multiple servers as one large resource pool, and cannot cope with the multi-server environment. We design a dynamic throughput allocation mechanism named DAM, which can handle the throughput allocation of multiple heterogeneous servers in the dynamic setting, and can provide a number of desirable properties. The experimental results show that DAM can make one dynamic throughput allocation of multiple servers for making sure users' local allocations in each server, and can provide one efficient and fair throughput allocation in the whole system.

Emerging solid state drives (SSDs) based on a next-generation memory technology have been recently released in market. In this work, we call them low-latency SSDs because the device latency of them is an order of magnitude lower than that of conventional NAND flash SSDs. Although low-latency SSDs can drastically reduce an I/O latency perceived by an application, the overhead of OS processing included in the I/O latency has become noticeable because of the very low device latency. Since the OS processing is executed on a CPU core, its operating frequency should be maximized for reducing the OS overhead. However, a higher core frequency causes the higher CPU power consumption during I/O accesses to low-latency SSDs. Therefore, we propose the device utilization-aware DVFS (DU-DVFS) technique that periodically monitors the utilization of a target block device and applies dynamic voltage and frequency scaling (DVFS) to CPU cores executing I/O-intensive processes only when the block device is fully utilized. In this case, DU-DVFS can reduce the CPU power consumption without hurting performance because the delay of OS processing incurred by decreasing the core frequency can be hidden. Our evaluation with 28 I/O-intensive workloads on a real server containing an Intel® Optane™ SSD demonstrates that DU-DVFS reduces the CPU power consumption by 41.4% on average (up to 53.8%) with a negligible performance degradation, compared to a standard DVFS governor on Linux. Moreover, the evaluation with multiprogrammed workloads composed of I/O-intensive and non-I/O-intensive programs shows that DU-DVFS is also effective for them because it can apply DVFS only to CPU cores executing I/O-intensive processes.

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.

In recent years, multi-party computation (MPC) frameworks based on replicated secret sharing schemes (RSSS) have attracted the attention as a method to achieve high efficiency among known MPCs. However, the RSSS-based MPCs are still inefficient for several heavy computations like algebraic operations, as they require a large amount and number of communication proportional to the number of multiplications in the operations (which is not the case with other secret sharing-based MPCs). In this paper, we propose RSSS-based three-party computation protocols for modular exponentiation, which is one of the most popular algebraic operations, on the case where the base is public and the exponent is private. Our proposed schemes are simple and efficient in both of the asymptotic and practical sense. On the asymptotic efficiency, the proposed schemes require O(n)-bit communication and O(1) rounds,where n is the secret-value size, in the best setting, whereas the previous scheme requires O(n2)-bit communication and O(n) rounds. On the practical efficiency, we show the performance of our protocol by experiments on the scenario for distributed signatures, which is useful for secure key management on the distributed environment (e.g., distributed ledgers). As one of the cases, our implementation performs a modular exponentiation on a 3,072-bit discrete-log group and 256-bit exponent with roughly 300ms, which is an acceptable parameter for 128-bit security, even in the WAN setting.

In the obnoxious facility game, we design mechanisms that output a location of an undesirable facility based on the locations of players reported by themselves. The benefit of a player is defined to be the distance between her location and the facility. A player may try to manipulate the output of the mechanism by strategically misreporting her location. We wish to design a λ-group strategy-proof mechanism i.e., for every group of players, at least one player in the group cannot gain strictly more than λ times her primary benefit by having the entire group change their reports simultaneously. In this paper, we design a k-candidate λ-group strategy-proof mechanism for the obnoxious facility game in the metric defined by k half lines with a common endpoint such that each candidate is a point in each of the half-lines at the same distance to the common endpoint as other candidates. Then, we show that the benefit ratio of the mechanism is at most 1+2/(k-1)λ. Finally, we prove that the bound is nearly tight.

Dedicated Short Range Communication (DSRC) is currently standardized as a leading technology for the implementation of Vehicular Networks. Non-safety application in DSRC is emerging beyond the initial safety application. However, it suffers from a typical issue of low data delivery ratio in urban environments, where static and moving obstacles block or attenuate the radio propagation, as well as other technical issues such as temporal-spatial restriction, capital cost for infrastructure deployments and limited radio coverage range. On the other hand, Content-Centric Networking (CCN) advocates ubiquitous in-network caching to enhance content distribution. The major characteristics of CCN are compatible with the requirements of vehicular networks so that CCN could be available by vehicular networks. In this paper, we propose a CCN-based vehicle-to-vehicle (V2V) communication scheme on the top of DSRC standard for content dissemination, while demonstrate its feasibility by analyzing the frame format of Beacon and WAVE service advertisement (WSA) messages of DSRC specifications. The simulation-based validations derived from our software platform with OMNeT++, Veins and SUMO in realistic traffic environments are supplied to evaluate the proposed scheme. We expect our research could provide references for future more substantial revision of DSRC standardization for CCN-based V2V communication.

This letter presents the safety confirmation system based on Near Field Communication (NFC) and Ultra High Frequency (UHF) band Radio Frequency IDentification (RFID) tags. Because these RFID tags can operate without the need for internal batteries, the proposed safety confirmation system is effective during large-scale disasters that cause loss of electricity and communication infrastructures. Sharing safety confirmation data between the NFC and UHF band RFID tags was studied to confirm the feasibility of the data sharing. The prototype of the proposed system was fabricated, confirming the feasibility of the proposed safety confirmation system.

In the context of Cyber-Physical System (CPS), analyzing the real world data accumulated in cyberspace would improve the efficiency and productivity of various social systems. Towards establishing data-driven society, it is desired to share data safely and smoothly among multiple services. In this paper, we propose a scheme that services authenticate users using information registered on a blockchain. We show that the proposed scheme has resistance to tampering and a spoofing attack.

Mining software artifacts is a useful way to understand the source code of software projects. Topic modeling in particular has been widely used to discover meaningful information from software artifacts. However, software artifacts are unstructured and contain a mix of textual types within the natural text. These software artifact characteristics worsen the performance of topic modeling. Among several natural language pre-processing tasks, removing stop words to reduce meaningless and uninteresting terms is an efficient way to improve the quality of topic models. Although many approaches are used to generate effective stop words, the lists are outdated or too general to apply to mining software artifacts. In addition, the performance of the topic model is sensitive to the datasets used in the training for each approach. To resolve these problems, we propose an automatic stop word generation approach for topic models of software artifacts. By measuring topic coherence among words in the topic using Pointwise Mutual Information (PMI), we added words with a low PMI score to our stop words list for every topic modeling loop. Through our experiment, we proved that our stop words list results in a higher performance of the topic model than lists from other approaches.

This paper proposes an anomaly prediction method based on k-means clustering that assumes embedded devices with memory constraints. With this method, by checking control system behavior in detail using k-means clustering, it is possible to predict anomalies. However, continuing clustering is difficult because data accumulate in memory similar to existing k-means clustering method, which is problematic for embedded devices with low memory capacity. Therefore, we also propose k-means clustering to continue clustering for infinite stream data. The proposed k-means clustering method is based on online k-means clustering of sequential processing. The proposed k-means clustering method only stores data required for anomaly prediction and releases other data from memory. Due to these characteristics, the proposed k-means clustering realizes that anomaly prediction is performed by reducing memory consumption. Experiments were performed with actual data of control system for anomaly prediction. Experimental results show that the proposed anomaly prediction method can predict anomaly, and the proposed k-means clustering can predict anomalies similar to standard k-means clustering while reducing memory consumption. Moreover, the proposed k-means clustering demonstrates better results of anomaly prediction than existing online k-means clustering.

Epidemic cyber incidents are caused by malicious websites using exploit kits. The exploit kit facilitate attackers to perform the drive-by download (DBD) attack. However, it is reported that malicious websites using an exploit kit have similarity in their website structure (WS)-trees. Hence, malicious website identification techniques leveraging WS-trees have been studied, where the WS-trees can be estimated from HTTP traffic data. Nevertheless, the defensive component of the exploit kit prevents us from capturing the WS-tree perfectly. This paper shows, hence, a new WS-tree construction procedure by using the fact that a DBD attack happens in a certain duration. This paper proposes, moreover, a new malicious website identification technique by clustering the WS-tree of the exploit kits. Experiment results assuming the D3M dataset verify that the proposed technique identifies exploit kits with a reasonable accuracy even when HTTP traffic from the malicious sites are partially lost.

Herugolf and Makaro are Nikoli's pencil puzzles. We study the computational complexity of Herugolf and Makaro puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

As the demand of data reliability becomes more and more larger, most of today's storage systems adopt erasure codes to assure the data could be reconstructed when suffering from physical device failures. In order to fast recover the lost data from a single failure, recovery optimization methods have attracted a lot of attention in recent years. However, most of the existing optimization methods focus on homogeneous devices, ignoring the fact that the storage devices are usually heterogeneous. In this paper, we propose a new recovery optimization method named HSR (Heterogeneous Storage Recovery) method, which uses both loads and speed rate among physical devices as the optimization target, in order to further improve the recovery performance for heterogeneous devices. The experiment results show that, compared to existing popular recovery optimization methods, HSR method gains much higher recovery speed over heterogeneous storage devices.

In order to evaluate the vehicle detection method, it is necessary to know the correct vehicle position considered as “ground truth”. We propose indices considering subjective evaluation in vehicle detection utilizing IoU. Subjective evaluation experiments were carried out with respect to misregistration from ground truth in vehicle detection.

Today, trust plays a central role in services in distributed environments. Conventionally deployed trust has been based on static framework in which a server responds to a service request under statically determined policies. However, in accordance with evolution of distributed environments empowered with IoT and federated access mechanisms, dynamic behavior must be analyzed and taken into service provision, which conventional trust cannot properly handle. In this paper, we propose an extension of PDP (Policy Decision Point) in which assertions together with service requests are evaluated. Furthermore, the evaluation may be dynamically configured in dynamically evolving trust environment. We propose an elastic trust model in view of dynamic trust environment. This enables intuitionistic modeling of typical concrete elastic distributed services.

A fast cross-validation algorithm for model selection in kernel ridge regression problems is proposed, which is aiming to further reduce the computational cost of the algorithm proposed by An et al. by eigenvalue decomposition of a Gram matrix.

Power line communication (PLC) networks play an important role in home networks and in next generation hybrid networks, which provide higher data rates (Gbps) and easier connectivity. The standard medium access control (MAC) protocol of PLC networks, IEEE 1901, uses a special carrier sense multiple access with collision avoidance (CSMA/CA) mechanism, in which the deferral counter technology is introduced to avoid unnecessary collisions. Although PLC networks have achieved great commercial success, MAC layer analysis for IEEE 1901 PLC networks received limited attention. Until now, a few studies used renewal theory and strong law of large number (SLLN) to analyze the MAC performance of IEEE 1901 protocol. These studies focus on saturated conditions and neglect the impacts of buffer size and traffic rate. Additionally, they are valid only for homogeneous traffic. Motivated by these limitations, we develop a unified and scalable analytical model for IEEE 1901 protocol in unsaturated conditions, which comprehensively considers the impacts of traffic rate, buffer size, and traffic types (homogeneous or heterogeneous traffic). In the modeling process, a multi-layer discrete Markov chain model is constructed to depict the basic working principle of IEEE 1901 protocol. The queueing process of the station buffer is captured by using Queueing theory. Furthermore, we present a detailed analysis for IEEE 1901 protocol under heterogeneous traffic conditions. Finally, we conduct extensive simulations to verify the analytical model and evaluate the MAC performance of IEEE 1901 protocol in PLC networks.

In this letter, we investigate the physical layer security in multi-user multi-relay networks, where each relay is not merely a traditional helper, but at the same time, can become a potential eavesdropper. We first propose an efficient low-complexity user and relay selection scheme to significantly reduce the amount of channel estimation as well as the amount of potential links for comparison. For the proposed scheme, we derive the closed-form expression for the lower bound of ergodic secrecy rate (ESR) to evaluate the system secrecy performance. Simulation results are provided to verify the validity of our expressions and demonstrate how the ESR scales with the number of users and relays.

Accurate segmentation of the region in the iris picture has a crucial influence on the reliability of the recognition system. In this letter, we present an end to end deep neural network based on U-Net. It uses dense connection blocks to replace the original convolutional layer, which can effectively improve the reuse rate of the feature layer. The proposed method takes U-net's skip connections to combine the same-scale feature maps from the upsampling phase and the downsampling phase in the upsampling process (merge layer). In the last layer of downsampling, it uses dilated convolution. The dilated convolution balances the iris region localization accuracy and the iris edge pixel prediction accuracy, further improving network performance. The experiments running on the Casia v4 Interval and IITD datasets, show that the proposed method improves segmentation performance.

In order to cope with recent growth of mobile data traffic and emerging various services, world-wide system trials for the fifth-generation (5G) mobile communication system that dramatically extends capability of the fourth-generation mobile communication system are being performed to launch its commercial service in 2020. In addition, research and development of new radio access technologies for 5G evolution and beyond 5G systems are beginning to be made all over the world. This paper introduces our recent activities on 5G transmission experiments that aim to validate Massive MIMO technologies using higher frequency bands such as SHF/EHF bands, that is, 5G experimental trials. Recent results of 5G system trials to create new services and applications in 5G era in cooperation with partners in vertical industries are also introduced.