In this letter, we propose a round robin-based wear leveling (RRWL) for flash memory systems. RRWL uses a block erase table (BET), which is composed of a bit array and saves the erasure histories of blocks. BET can use one-to-one mode to increase the performance of wear leveling or one-to-many mode to reduce memory consumption. However, one-to-many mode decreases the accuracy of cold block information, which results in the lifetime degradation of flash memory. To solve this problem, RRWL consistently uses one-to-one mode based on round robin method to increase the accuracy of cold block identification, with reduced memory size of BET, like in one-to-many mode. Experiments show that RRWL increases the lifetime of flash memory by up to 47% and 14%, compared with BET and HaWL, respectively.

The development of smart devices has led to the growth of Location-Based Social Networking Services (LBSNSs). In this paper, we introduce an l-Close Range Friends query that finds all l-hop friends of a user within a specified range. We also propose a query processing method on Social Grid Index (SGI). Using real datasets, the performance of our method is evaluated.

Most existing methods on research collaborator recommendation focus on promoting collaboration within a specific discipline and exploit a network structure derived from co-authorship or co-citation information. To find collaboration opportunities outside researchers' own fields of expertise and beyond their social network, we present an interdisciplinary collaborator recommendation method based on research content similarity. In the proposed method, we calculate textual features that reflect a researcher's interests using a research grant database. To find the most relevant researchers who work in other fields, we compare constructing a pairwise similarity matrix in a feature space and exploiting existing social networks with content-based similarity. We present a case study at the Graduate University for Advanced Studies in Japan in which actual collaborations across departments are used as ground truth. The results indicate that our content-based approach can accurately predict interdisciplinary collaboration compared with the conventional collaboration network-based approaches.

In this paper, robust stability of nonlinear feedback systems with unknown disturbance is considered by using the operator-based right coprime factorization method. For dealing with the unknown disturbance, a new design scheme and a nonlinear controller are given. That is, robust stability of the nonlinear systems with unknown disturbance is guaranteed by combining right coprime factorization with the proposed controller. Simultaneously, adverse effects resulting from the disturbance are removed by using the proposed nonlinear operator controller. Finally, a simulation example is given to show the effectiveness of the proposed design scheme of this paper.

In Twitter-like services, countless messages are being posted in real-time every second all around the world. Timely knowledge about what kinds of information are diffusing in social media is quite important. For example, in emergency situations such as earthquakes, users provide instant information on their situation through social media. The collective intelligence of social media is useful as a means of information detection complementary to conventional observation. We have developed a system for monitoring and analyzing information diffusion data in real-time by tracking retweeted tweets. A tweet retweeted by many users indicates that they find the content interesting and impactful. Analysts who use this system can find tweets retweeted by many users and identify the key people who are retweeted frequently by many users or who have retweeted tweets about particular topics. However, bursting situations occur when thousands of social media messages are suddenly posted simultaneously, and the lack of machine resources to handle such situations lowers the system's query performance. Since our system is designed to be used interactively in real-time by many analysts, waiting more than one second for a query results is simply not acceptable. To maintain an acceptable query performance, we propose a capacity control method for filtering incoming tweets using extra attribute information from tweets themselves. Conventionally, there is a trade-off between the query performance and the accuracy of the analysis results. We show that the query performance is improved by our proposed method and that our method is better than the existing methods in terms of maintaining query accuracy.

In this paper, a Boolean algebra approach is proposed to encode various acceptability semantics for abstract argumentation frameworks, where each semantics can be equivalently encoded into several Boolean constraint models based on Boolean matrices and a family of Boolean operations between them. Then, we show that these models can be easily translated into logic programs, and can be solved by a constraint solver over Boolean variables. In addition, we propose some querying strategies to accelerate the calculation of the grounded, stable and complete extensions. Finally, we describe an experimental study on the performance of our encodings according to different semantics and querying strategies.

Given an undirected graph G, an edge dominating set is a subset F of edges such that each edge not in F is adjacent to some edge in F, and computing the minimum size of an edge dominating set is known to be NP-hard. Since the size of any edge dominating set is at least half of the maximum size µ(G) of a matching in G, we study the problem of testing whether a given graph G has an edge dominating set of size ⌈µ(G)/2⌉ or not. In this paper, we prove that the problem is NP-complete, whereas we design an O*(2.0801µ(G)/2)-time and polynomial-space algorithm to the problem.

Similarity search is an important and fundamental problem, and thus widely used in various fields of computer science including multimedia, computer vision, database, information retrieval, etc. Recently, since loitering behavior often leads to abnormal situations, such as pickpocketing and terrorist attacks, its analysis attracts increasing attention from research communities. In this paper, we present AntiLoiter, a loitering discovery system adopting efficient similarity search on surveillance videos. As we know, most of existing systems for loitering analysis, mainly focus on how to detect or identify loiterers by behavior tracking techniques. However, the difficulties of tracking-based methods are known as that their analysis results are heavily influenced by occlusions, overlaps, and shadows. Moreover, tracking-based methods need to track the human appearance continuously. Therefore, existing methods are not readily applied to real-world surveillance cameras due to the appearance discontinuity of criminal loiterers. To solve this problem, we abandon the tracking method, instead, propose AntiLoiter to efficiently discover loiterers based on their frequent appearance patterns in longtime multiple surveillance videos. In AntiLoiter, we propose a novel data structure Luigi that indexes data using only similarity value returned by a corresponding function (e.g., face matching). Luigi is adopted to perform efficient similarity search to realize loitering discovery. We conducted extensive experiments on both synthetic and real surveillance videos to evaluate the efficiency and efficacy of our approach. The experimental results show that our system can find out loitering candidates correctly and outperforms existing method by 100 times in terms of runtime.

Class imbalance has drawn much attention of researchers in software defect prediction. In practice, the performance of defect prediction models may be affected by the class imbalance problem. In this paper, we present an approach to evaluating the performance stability of defect prediction models on imbalanced datasets. First, random sampling is applied to convert the original imbalanced dataset into a set of new datasets with different levels of imbalance ratio. Second, typical prediction models are selected to make predictions on these new constructed datasets, and Coefficient of Variation (C·V) is used to evaluate the performance stability of different models. Finally, an empirical study is designed to evaluate the performance stability of six prediction models, which are widely used in software defect prediction. The results show that the performance of C4.5 is unstable on imbalanced datasets, and the performance of Naive Bayes and Random Forest are more stable than other models.

A miniaturized and bandwidth-enhanced implantable antenna is designed for wireless biotelemetry in the medical implantable communications service (MICS) frequency band of 402-405MHz. To reduce the antenna size and enhance the available bandwidth with regard to the reflection coefficients, a meandered planar inverted-F antenna (PIFA) structure is adopted on a dielectric/ferrite substrate which is an artificial magneto-dielectric material. The potential of the proposed antenna for the intended applications is verified through prototype fabrication and measurement with a 2/3 human muscle phantom. Good agreement is observed between the simulation and measurement in terms of resonant characteristics and gain radiation patterns; the bandwidth is enhanced in comparison with that of the ferrite-removed antenna, and antenna gain of -27.7dB is obtained in the measurement. Allowances are made for probable fabrication inaccuracies and practical operating environments. An analysis of 1-g SAR distribution is conducted to confirm compliance with the specific absorption rate limitation (1.6W/kg) of the American National Standards Institute (ANSI).

In synchronous LSI circuits, memory subsystems such as Flip-Flops and SRAMs are essential components and latches are the base elements of the common memory logics. In this paper, a stability analysis method for latches operating in a low voltage region is proposed. The butterfly curve of latches is a key for analyzing a retention failure of latches. This paper discusses a modeling method for retention stability and derives an analytical stability model for latches. The minimum supply voltage where the latches can operate with a certain yield can be accurately derived by a simple calculation using the proposed model. Monte-Carlo simulation targeting 65nm and 28nm process technology models demonstrates the accuracy and the validity of the proposed method. Measurement results obtained by a test chip fabricated in a 65nm process technology also demonstrate the validity. Based on the model, this paper shows some strategies for variation tolerant design of latches.

Many distributed systems use a replication mechanism for reliability and availability. On the other hand, application developers have to consider minimum consistency requirement for each application. Therefore, a replication protocol that supports multiple consistency models is required. Multi-Consistency Data Replication (McRep) is a proxy-based replication protocol and can support multiple consistency models. However, McRep has a potential problem in that a replicator relaying all request and reply messages between clients and replicas can be a performance bottleneck and a Single-Point-of-Failure (SPoF). In this paper, we introduce the multi-consistency support mechanism of McRep to a combined state-machine and deferred-update replication protocol to eliminate the performance bottleneck and SPoF. The state-machine and deferred-update protocols are well-established approaches for fault-tolerant data management systems. But each method can ensure only a specific consistency model. Thus, we adaptively select a replication method from the two replication bases. In our protocol, the functionality of the McRep's replicator is realized by clients and replicas. Each replica has new roles in serialization of all transactions and managing all views of the database, and each client has a new role in managing status of its transactions. We have implemented and evaluated the proposed protocol and compared to McRep. The evaluation results show that the proposed protocol achieved comparable throughput of transactions to McRep. Especially the proposed protocol improved the throughput up to 16% at a read-heavy workload in One-Copy. Finally, we demonstrated the proposed failover mechanism. As a result, a failure of a leader replica did not affect continuity of the entire replication system unlike McRep.

In this paper, a built-in test circuit for an electrical interconnect test method is proposed to detect an open defect occurring at an interconnect between an IC and a printed circuit board. The test method is based on measuring the supply current of an inverter gate in the test circuit. A time-varying signal is provided to an interconnect as a test signal by the built-in test circuit. In this paper, the test circuit is evaluated by SPICE simulation and by experiments with a prototyping IC. The experimental results reveal that a hard open defect is detectable by the test method in addition to a resistive open defect and a capacitive open one at a test speed of 400 kHz.

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a low-dimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition.

The unconditionally stable (US) Laguerre-FDTD method has recently attracted significant attention for its high efficiency and accuracy in modeling fine structures. One of the most attractive characteristics of this method is its marching-on-in-order solution scheme. This paper presents Hermite-Rodriguez functions as another type of orthogonal basis to implement a new 2-D US solution scheme.

For phase stabilization of two-tone coherent millimeter-wave/microwave carrier generation, two types of phase detection schemes were devised based on lightwave interferometric technique, the Mach-Zehnder interferometric method and the pseudo Mach-Zehnder interferometric method. The former system showed clear eye patterns at both OOK and PSK modulations of 1 Gbit/s on the 12.5-GHz carrier. The latter system demonstrated the error-free transmission at OOK modulation of 11 Gbit/s on the 100-GHz carrier.

Technological developments in wireless communication have led to an increasing demand for radio frequencies. This has necessitated the practice of spectrum sharing to ensure optimal usage of the limited frequencies, provided this does not cause interference. This paper presents a framework for managing an unexpected situation in which a primary user experiences harmful interference with regard to database-driven secondary use of spectrum allocated to the primary user towards 5G mobile networks, where the primary user is assumed to be a radar system. In our proposed framework, the primary user informs a database that they are experiencing harmful interference. Receiving the information, the database updates a primary exclusive region in which secondary users are unable to operate in the licensed spectrum. Subsequent to the update, this primary exclusive region depends on the knowledge about the secondary users when the primary user experiences harmful interference, knowledge of which is stored in the database. We assume a circular primary exclusive region centered at a primary receiver and derive an optimal radius of the primary exclusive region by applying stochastic geometry. Then, for each type of knowledge stored in the database for the secondary user, we evaluate the optimal radius for a target probability that the primary user experiences harmful interference. The results show that the more detailed the knowledge of the secondary user's density and transmission power stored in the database, the smaller the radius that has to be determined for the primary exclusive region after the update and the more efficient the spatial reuse of the licensed spectrum that can be achieved.

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 letter we present some easily checkable necessary conditions for a polynomial with positive coefficients to have all its zeros in a prescribed sector in the left half of the complex plane. As an auxiliary result, we also obtain a new necessary condition for the Hurwitz stability.

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.