Collaborative filtering (CF) has been widely used in recommender systems to generate personalized recommendations. However, recommender systems using CF are vulnerable to shilling attacks, in which attackers inject fake profiles to manipulate recommendation results. Thus, shilling attacks pose a threat to the credibility of recommender systems. Previous studies mainly derive features from characteristics of item ratings in user profiles to detect attackers, but the methods suffer from low accuracy when attackers adopt new rating patterns. To overcome this drawback, we derive features from properties of item popularity in user profiles, which are determined by users' different selecting patterns. This feature extraction method is based on the prior knowledge that attackers select items to rate with man-made rules while normal users do this according to their inner preferences. Then, machine learning classification approaches are exploited to make use of these features to detect and remove attackers. Experiment results on the MovieLens dataset and Amazon review dataset show that our proposed method improves detection performance. In addition, the results justify the practical value of features derived from selecting patterns.

Pair-wise testing is an effective test planning technique for finding interaction faults using a small set of test cases. Constraint elicitation is an important process in the pair-wise testing design since constraints determine the test space; however, the constraint elicitation process has not been well studied. It usually requires manual capturing and precise definition of constraints. In this paper, we propose a constraint elicitation process that helps combinatorial test design. Our elicitation process consists of two steps: parameter combination identification and value pair determination. We conduct experiments on some test models, and demonstrate that some extracted rules match constraints and others helps to define constraints.

Deterioration of sewer pipes is one of very important problems in Japan. Sewer inspections have been carried out mainly by visual check or wired remote robots with a camera. However, such inspection schemes involve high labor and/or monetary cost. Sewer inspection with boat-type video cameras or unwired robots takes a long time to check the result of the inspection because video data are obtained after the equipment is retrieved from the pipe. To realize low cost, safe and quick inspection of sewer pipes, we have proposed a sewer inspection system using drifting wireless sensor nodes. Water, soil, and the narrow space in the pipe make the long-range and high throughput wireless radio communication difficult. Therefore, we have to identify suitable radio frequency and antenna configuration based on wireless communication characteristics in sewer pipes. If the frequency is higher, the Fresnel zone, the needed space for the line of sight is small, but the path loss in free space is large. On the other hand, if the frequency is lower, the size of the Fresnel zone is large, but the path loss in free space is small. We conducted wireless communication experiments using 920MHz, 2.4GHz, and 5GHz band off-the-shelf devices in an experimental underground pipe. The measurement results show that the wireless communication range of 5GHz (IEEE 802.11a) is over 8m in a 200mm-diameter pipe and is longer than 920MHz (ARIB STD-T108), 2.4GHz (IEEE 802.11g, IEEE 802.15.4) band at their maximum transmission power. In addition, we confirmed that devices that use IEEE 802.11a and 54Mbps bit rate can transmit about 43MB data while they are in the communication range of an AP and drift at 1m/s in a 200mm-diameter pipe, and it is bigger than one of devices that use other bit rate.

In this paper, a new non-uniform weight-updating scheme for adaptive digital beamforming (DBF) is proposed. The unique feature of the letter is that the effective working range of the beamformer is extended and the computational complexity is reduced by introducing the robust DBF based on worst-case performance optimization. The robust parameter for each weight updating is chosen by analyzing the changing rate of the Direction of Arrival (DOA) of desired signal in LEO satellite communication. Simulation results demonstrate the improved performance of the new Non-Uniform Weight-Updating Beamformer (NUWUB).

Energy harvesting wireless sensor networks (EH-WSNs) are being actively studied in order to solve the problems faced by battery-operated WSNs, namely the cost for battery replacement and the negative impact on the environment. In EH-WSNs, each node harvests ambient energy, such as light, heat, vibration, and uses it for sensing, computations, and wireless communications, where the amount of harvested energy of each node varies depending on their environments. MAC protocols for EH-WSNs need to be designed to achieve high throughput and fairness, however, the conventional MAC protocols proposed for EH-WSNs do not adapt to the harvesting rate of each node, resulting in poor fairness. In this paper, we propose a fair MAC protocol based on polling scheme for EH-WSNs. The proposed scheme adjusts contention probability of each node according to its harvesting rate, thereby increasing the throughput of nodes with low harvesting rate. We evaluate throughput and fairness of the proposed fair polling scheme by theoretical analysis and computer simulations, and show that the proposed scheme can improve fairness with little degradation of the overall network throughput.

Silver electrical contacts are separated at constant speed and break arcs are generated between them in a 200V-450VDC and 10A resistive circuit. The motion of the break arcs is restricted by some surrounding alumina plates. Transverse magnetic field of a permanent magnet is applied to the break arcs. Changing the supply voltage and the height of a wall located at the upper side of the break arcs, the arc lengthening time and motion of the break arcs are investigated. As a result, the higher supply voltage causes an increase of the arc lengthening time. The arc lengthening time increases significantly when the break arcs expand into the whole of the surrounding walls.

An overview of the evolution of intelligent transport systems (ITS) supported by advances in information and communication technologies is presented. Focusing on a sensing platform as one of the ITS applications, this paper presents a survey on vehicular ad hoc network-based geographic routing. In addition to the minimum requirement of street-awareness based on street maps, traffic and packet-awareness are considered essential to achieve acceptable packet delivery performance. In particular, in addition to statistical information, real-time traffic and packet level information are indispensable for making routing protocols feasible and effective. Considering traffic conditions that are highly space- and time-dependent, static nodes can be used to assist with geographic routing, and a protocol workable under a partial deployment of static nodes is considered.

NoSQL systems have become vital components to deliver big data services due to their high horizontal scalability. However, existing NoSQL systems rely on experienced administrators to configure and tune the wide range of configurable parameters for optimized performance. In this work, we present a configuration management framework for NoSQL systems, called xConfig. With xConfig, its users can first identify performance sensitive parameters and capture the tuned parameters for different workloads as configuration policies. Next, based on tuned policies, xConfig can be implemented as the corresponding configuration optimiaztion system for the specific NoSQL system. Also it can be used to analyze the range of configurable parameters that may impact the runtime performance of NoSQL systems. We implement a prototype called HConfig based on HBase, and the parameter tuning strategies for HConfig can generate tuned policies and enable HBase to run much more efficiently on both individual worker node and entire cluster. The massive writing oriented evaluation results show that HBase under write-intensive policies outperforms both the default configuration and some existing configurations while offering significantly higher throughput.

The scan segmentation method is an efficient solution to deal with the test power problem; However, the use of multiple capture cycles may cause capture violations, thereby leading to fault coverage loss. This issue is much more severe in at-speed testing. In this paper, two scan partition schemes based on complex networks clustering ara proposed to minimize the capture violations without increasing test-data volume and extra area overhead. In the partition process, we use a more accurate notion, spoiled nodes, instead of violation edges to analyse the dependency of flip-flops (ffs), and we use the shortest-path betweenness (SPB) method and the Laplacian-based graph partition method to find the best combination of these flip-flops. Beyond that, the proposed methods can use any given power-unaware set of patterns to test circuits, reducing both shift and capture power in at-speed testing. Extensive experiments have been performed on reference circuit ISCAS89 and IWLS2005 to verify the effectiveness of the proposed methods.

In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.

This paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a well-known prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works.

In this paper, we propose a multi-cell structure backscatter based wireless-powered communication network (WPCN) where a number of backscatter cells are locally separated, each containing a subset of users around a carrier emitter. The multi-cell structure backscatter based WPCN can be implemented in two ways, namely time-division multiplexing (TDM) and frequency-division multiplexing (FDM). Here users harvest energy from the carrier signal transmitted by the carrier emitter, and then transmit their own information in a passive way via the reflection of the carrier signal using frequency-shift keying modulation. We characterize the energy-free condition and the signal-to-noise ratio (SNR) outage zone in a backscatter based WPCN. Also, a backscatter based harvest-then-transmit protocol is adopted to maximize the sum-throughput of all users by optimally allocating time for energy harvesting and information transmission. Numerical results demonstrate that the backscatter based WPCN ensures an increased long-range coverage and a diminished SNR outage zone compared to conventional radio based WPCNs. Also, comparing the two types of multi-cell structure backscatter based WPCN, TDM within each backscatter cell and FDM across backscatter cells versus FDM within each backscatter cell and TDM across backscatter cells, numerical results confirm that which one yields a better performance.

Dynamic Voltage/Frequency Scaling (DVFS) allows designers to improve energy efficiency through adjusting supply voltage at runtime in order to meet the workload demand. Previous works solving real-time DVFS problems often refer to the canonical schedules with the exponential length. Other solutions for online scheduling depend on empirical or stochastic heuristics, which potentially result in frequent fluctuations of voltage/speed scaling. This paper aims at increasing the schedule predictability using period transformation in the pinwheel task model and improves the control on power-awareness by decreasing the speeds of as many tasks as possible to the same level. Experimental results show the maximum energy savings of 6% over the recent Dynamic Power Management (DPM) method and 12% over other slack reclamation algorithms.

In this work, the novel fingerprinting evaluation parameter, which is called the punishment cost, is proposed. This parameter can be calculated from the designed matrix, the punishment matrix, and the confusion matrix. The punishment cost can describe how well the result of positioning is in the designated grid or not, by which the conventional parameter, the accuracy, cannot describe. The experiment is done with real measured data on weekdays and weekends. The results are considered in terms of accuracy and the punishment cost. Three well-known machine learning algorithms, i.e. Decision Tree, k-Nearest Neighbors, and Artificial Neural Network, are verified in fingerprinting positioning. In experimental environment, Decision Tree can perform well on the data from weekends whereas the performance is underrated on the data from weekdays. The k-Nearest Neighbors has proper punishment costs, even though it has lower accuracy than that of Artificial Neural Network, which has moderate accuracies but lower punishment costs. Therefore, other criteria should be considered in order to select the algorithm for indoor positioning. In addition, punishment cost can facilitate the conversion spot positioning to floor positioning without data modification.

As the demand for higher transmission rates and spectral efficiency is steadily increasing, the research and development of novel mobile communication systems has gained momentum. This paper focuses on providing a comprehensive survey of research and development activities on fifth generation mobile communication systems in Japan. We try to survey a vast area of wireless communication systems and the developments that led to future 5G systems.

Information-Centric Networking (ICN) treats contents as first class citizens and adopts name-based routing for content distribution and retrieval. Content names rather than IP addresses are directly used for routing. However, due to the location-independent naming and the huge namespace, name-based routing faces scalability and efficiency issues including large routing tables and high path stretches. This paper proposes a universal Scalable Name-based Geometric Routing scheme (SNGR), which is a careful synthesis of geometric routing and name resolution. To provide scalable and efficient underlying routing, a universal geometric routing framework (GRF) is proposed. Any geometric routing scheme can be used directly for name resolution based on GRF. To implement an overlay name resolution system, SNGR utilizes a bi-level grouping design. With this design, a resolution node that is close to the consumer can always be found. Our theoretical analyses guarantee the performance of SNGR, and experiments show that SNGR outperforms similar routing schemes in terms of node state, path stretch, and reliability.

Information floating delivers information to mobile nodes in specific areas without meaningless spreading of information by permitting mobile nodes to directly transfer information to other nodes by wireless links in designated areas called transmittable areas. In this paper, we assume that mobile nodes change direction at intersections after receiving such information as warnings and local advertisements and that an information source remains in some place away from the transmittable area and continuously broadcasts information. We analyze performance of information floating under these assumptions to explore effects of the behavior changes of mobile nodes, decision deadline of the behavior change, and existence of a fixed source on information floating. We theoretically analyze the probability that a node cannot receive information and also derive the size of each transmittable area so that this probability is close to desired values.

Fine-grained power gating (FGPG) is a power-saving technique by switching off circuit blocks while the blocks are idle. Although FGPG can reduce power consumption without compromising computational performance, switching the power supply on and off causes energy overhead. To prevent power increase caused by the energy overhead, in our prior research we proposed an FGPG control method of the operating system(OS) based on pre-analyzing applications' power usage. However, modern computing systems have a wide variety of use cases and run many types of application; this makes it difficult to analyze the behavior of all these applications in advance. This paper therefore proposes a new FGPG control method without profiling application programs in advance. In the new proposed method, the OS monitors a circuit's idle interval periodically while application programs are running. The OS enables FGPG only if the interval time is long enough to reduce the power consumption. The experimental results in this paper show that the proposed method reduces power consumption by 9.8% on average and up to 17.2% at 25°C. The results also show that the proposed method achieves almost the same power-saving efficiency as the previous profile-based method.

We present a hard-wired central patter generator (CPG) hardware network that reproduces the periodic oscillations of the typical gaits, namely, walk, trot, and bound. Notably, the three gaits are generated by a single parameter, i.e., the battery voltage EMLR, which acts like a signal from the midbrain's locomotor region. One CPG is composed of two types of hardware neuron models, reproducing neuronal bursting and beating (action potentials), and three types of hardware synapse models: a gap junction, excitatory and inhibitory synapses. When four hardware CPG models were coupled into a Z4 symmetry network in a previous study [22], two neuronal oscillation patterns corresponding to four-legged animal gaits (walk and bound) were generated by manipulating a single control parameter. However, no more than two neuronal oscillation patterns have been stably observed on a hard-wired four-CPG hardware network. In the current study, we indicate that three neuronal oscillation patterns (walk, trot, and bound) can be generated by manipulating a single control parameter on a hard-wired eight-CPG (Z4 × Z2 symmetry) hardware network.

Forming Business Continuity Planning (BCP) is recognized as a significant counter-measure against future large-scale disasters by private enterprises after the 2011 Great East Japan Earthquake more than before. Based on a questionnaire survey, this paper reports business recovery conditions of private enterprises in Miyagi Prefecture affected by the disaster. Analyzing the results of questionnaire, it suggests some important points: (1) estimation of long-term internal/external factors that influence business continuity, (2) development of concrete pre-disaster framework, (3) multi-media-based advertising strategy, and (4) re-allocation of resources.