In encryption schemes, the sender may not generate randomness properly if generating randomness is costly, and the sender is not concerned about the security of a message. The problem was studied by the first author (2016), and was formalized in a game-theoretic framework. In this work, we construct an encryption scheme with an optimal round complexity on the basis of the mechanism of repeated games.

In mobile and wireless networks, controlling data delivery latency is one of open problems due to the stochastic nature of wireless channels, which are inherently unreliable. This paper explores how the current best-effort throughput-oriented wireless services might evolve into latency-sensitive enablers of new mobile applications such as remote three-dimensional (3D) graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the standard latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible delay of data re-ordering, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse data delivery over multiple paths with uncorrelated outage likelihoods; and (ii) forward packet-loss protection (FPP), creating encoding redundancy for proactive recovery of intolerably delayed data without end-to-end retransmissions. Analysis and evaluation are based on traces of real life traffic, which is measured in live carrier-grade long term evolution (LTE) networks and campus WiFi networks, due to no such system/environment yet to verify the importance of spatial diversity and encoding redundancy. Analysis and evaluation reveal the seriousness of the latency problem and that the proposed FPP with spatial diversity and encoding redundancy can minimize the delay of re-ordering. Moreover, a novel FPP effectiveness coefficient is proposed to explicitly represent the effectiveness of EPP implementation.

Creating new content based on existing original work is becoming popular especially among amateur creators. Such new content is called derivative work and can be transformed into the next new derivative work. Such derivative work creation is called “N-th order derivative creation.” Although derivative creation is popular, the reason an individual derivative work was created is not observable. To infer the factors that trigger derivative work creation, we have proposed a model that incorporates three factors: (1) original work's attractiveness, (2) original work's popularity, and (3) derivative work's popularity. Based on this model, in this paper, we describe a public web service for browsing derivation factors called Songrium Derivation Factor Analysis. Our service is implemented by applying our model to original works and derivative works uploaded to a video sharing service. Songrium Derivation Factor Analysis provides various visualization functions: Original Works Map, Derivation Tree, Popularity Influence Transition Graph, Creator Distribution Map, and Creator Profile. By displaying such information when users browse and watch videos, we aim to enable them to find new content and understand the N-th order derivative creation activity at a deeper level.

This paper proposes a triangular active charge injection method to reduce resonant power supply noise by injecting the adequate amount of charge into the supply line of the LSI in response to the current consumption of the core circuit. The proposed circuit is composed of three key components, a voltage drop detector, an injection controller circuit and a canceling capacitor circuit. In addition to the theoretical analysis of the proposed method, the measurement results indicate that our proposed method with active capacitor can realize about 14% noise reduction compared with the original noise amplitude. The proposed circuit consumes 25.2 mW in steady state and occupies 0.182 mm2.

Image boundary detection or image segmentation is an important step in image analysis. However, choosing appropriate parameters for boundary detection algorithms is necessary to achieve good boundary detection results. Image boundary detection fusion with unsupervised parameters can output a final consensus boundary, which is generally better than using unsupervised or supervised image boundary detection algorithms. In this study, we theoretically examine why image boundary detection fusion can work well and we propose a mixture model for image boundary detection fusion (MMIBDF) to achieve good consensus segmentation in an unsupervised manner. All of the segmentation algorithms are treated as new features and the segmentation results obtained by the algorithms are the values of the new features. The MMIBDF is designed to sample the boundary according to a discrete distribution. We present an inference method for MMIBDF and describe the corresponding algorithm in detail. Extensive empirical results demonstrate that MMIBDF significantly outperforms other image boundary detection fusion algorithms and the base image boundary detection algorithms according to most performance indices.

Early reviews, posted on online review sites shortly after products enter the market, are useful for estimating long-term evaluations of those products and making decisions. However, such reviews can be influenced easily by anomalous reviewers, including malicious and fraudulent reviewers, because the number of early reviews is usually small. It is therefore challenging to detect anomalous reviewers from early reviews and estimate long-term evaluations by reducing their influences. We find that two characteristics of heterogeneity on actual review sites such as Amazon.com cause difficulty in detecting anomalous reviewers from early reviews. We propose ideas for consideration of heterogeneity, and a methodology for computing reviewers' degree of anomaly and estimating long-term evaluations simultaneously. Our experimental evaluations with actual reviews from Amazon.com revealed that our proposed method achieves the best performance in 19 of 20 tests compared to state-of-the-art methodologies.

To prepare antireflection coating (ARC) by wet process is important technology for low cost fabrication of solar cells. In this research, we consider the optical reflectance of a three layer stack structure of ARC films on the pyramidally textured single-crystalline silicon substrates. Each layer of the ARC films is deposited by a spin-coating method. The triple layers consist of SiO2, SiO2-TiO2 mixture, and TiO2 films from air to the silicon substrate in that order, and the refractive index is slightly increased from air to the substrate. Light reflection can be reduced further mainly due to graded index effect. The optimized three layer structure ARC shows that the reflectance is below 0.048 at the wavelength of 600 nm.

This study proposes a maximum-likelihood-estimation method for a quadrotor UAV given the existence of sensor delays. The state equation of the UAV is nonlinear, and thus, we propose an approximated method that consists of two steps. The first step estimates the past state based on the delayed output through an extended Kalman filter. The second step involves calculating an estimate of the present state by simulating the original system from the past to the present. It is proven that the proposed method provides an approximated maximum-likelihood-estimation. The effectiveness of the estimator is verified by performing experiments.

To help with decision making, online shoppers tend to go through both a list of a product's features and functionality provided by the vendor, as well as a list of reviews written by other users. Unfortunately, this process is ineffective when the buyer is confronted with large amounts of information, particularly when the buyer has limited experience with and knowledge of the product. In order to avoid this problem, we propose a framework of purpose-oriented recommendation that presents a ranked list of products suitable for a designated user purpose by identifying important product features to fulfill the purpose from online reviews. As technical foundation for realizing the framework, we propose several methods to mine relation between user purposes and product features from the consumer reviews. Using digital camera reviews on Amazon.com, the experimental results show that our proposed method is both effective and stable, with an acceptable rate of precision and recall.

In this study, a plasma loop tube is presented as a tunable VHF-UHF band plasma antenna. In plasma medium, wave radiation mechanism is due to ionized gas instead of metal. Meanwhile, the most important advantage of plasma elements is electronic tunability rather than the rigid and fixed features of metals. Here, we employ an external magnetic field as a background to affect the plasma without any shape, gas or source manipulation. Finite difference time domain (FDTD) is performed for plasma antenna analysis. The FDTD formulation should be adapted to fluid modeling of plasma in the anisotropic zone in the presence of an external magnetic field. The bandwidth coverage of 700MHz is obtained by designing correctly. Parametric study in return loss, gain and radiation pattern are studied here and other new points are presented as well.

Evolvable hardware (EHW) based on field-programmable gate arrays (FPGAs) opens up new possibilities towards building efficient adaptive system. State of the art EHW systems based on virtual reconfiguration and dynamic partial reconfiguration (DPR) both have their limitations. The former has a huge area overhead and circuit delay, and the later has slow configuration speed and low flexibility. Therefore a general low-cost fast hybrid reconfiguration architecture is proposed in this paper, which merges the high flexibility of virtual reconfiguration and the low resource cost of DPR. Moreover, the bitstream relocation technology is introduced to save the bitstream storage space, and the discrepancy configuration technology is adopted to reduce reconfiguration time. And an embedded RAM core is adopted to store bitstreams which accelerate the reconfiguration speed further. The proposed architecture is evaluated by the online evolution of digital image filter implemented on the Xilinx Virtex-6 FPGA development board ML605. And the experimental results show that our system has lower resource overhead, higher operating frequency, faster reconfiguration speed and less bitstream storage space in comparison with the previous works.

Self-paced learning (SPL) gradually trains the data from easy to hard, and includes more data into the training process in a self-paced manner. The advantage of SPL is that it has an ability to avoid bad local minima, and the system can improve the generalization performance. However, SPL's system needs an expert to judge the complexity of data at the beginning of training. Generally, this expert does not exist in the beginning, and is learned by gradually training the samples. Based on this consideration, we add an uncertainty of complexity judgment into SPL's system, and propose a self-paced learning with uncertainty prior (SPUP). For efficiently solving our system optimization function, an iterative optimization and statistical simulated annealing method are introduced. The final experimental results indicate that our SPUP has more robustness to the outlier and achieves higher accuracy and less error than SPL.

This paper shows an optimal spreading sequence in the Weyl sequence class, which is similar to the set of the Oppermann sequences for asynchronous CDMA systems. Sequences in Weyl sequence class have the desired property that the order of cross-correlation is low. Therefore, sequences in the Weyl sequence class are expected to minimize the inter-symbol interference. We evaluate the upper bound of cross-correlation and odd cross-correlation of spreading sequences in the Weyl sequence class and construct the optimization problem: minimize the upper bound of the absolute values of cross-correlation and odd cross-correlation. Since our optimization problem is convex, we can derive the optimal spreading sequences as the global solution of the problem. We show their signal to interference plus noise ratio (SINR) in a special case. From this result, we propose how the initial elements are assigned, that is, how spreading sequences are assigned to each users. In an asynchronous CDMA system, we also numerically compare our spreading sequences with other ones, the Gold codes, the Oppermann sequences, the optimal Chebyshev spreading sequences and the SP sequences in Bit Error Rate. Our spreading sequence, which yields the global solution, has the highest performance among the other spreading sequences tested.

Efficient optic disc (OD) segmentation plays a significant role in retinal image analysis and retinal disease screening. In this paper, we present a full-automatic segmentation approach called double boundary extraction for the OD segmentation. The proposed approach consists of the following two stages: first, we utilize an unsupervised learning technology and statistical method based on OD boundary information to obtain the initial contour adaptively. Second, the final optic disc boundary is extracted using the proposed LSO model. The performance of the proposed method is tested on the public DIARETDB1 database and the experimental results demonstrate the effectiveness and advantage of the proposed method.

In this paper, we first propose two batch blind source separation and equalization algorithms based on support vector regression (SVR) for linear time-invariant multiple input multiple output (MIMO) systems. The proposed algorithms combine the conventional cost function of SVR with error functions of classical on-line algorithm for blind equalization: both error functions of constant modulus algorithm (CMA) and radius directed algorithm (RDA) are contained in the penalty term of SVR. To recover all sources simultaneously, the cross-correlations of equalizer outputs are included in the cost functions. Simulation experiments show that the proposed algorithms can recover all sources successfully and compensate channel distortion simultaneously. With the use of iterative re-weighted least square (IRWLS) solution of SVR, the proposed algorithms exhibit low computational complexity. Compared with traditional algorithms, the new algorithms only require fewer samples to achieve convergence and perform a lower residual interference. For multilevel signals, the single algorithms based on constant modulus property usually show a relatively high residual error, then we propose two dual-mode blind source separation and equalization schemes. Between them, the dual-mode scheme based on SVR merely requires fewer samples to achieve convergence and further reduces the residual interference.

Given an edge-weighted bipartite digraph G=(A,B;E), the Bipartite Traveling Salesman Problem (BTSP) asks to find the minimum cost of a Hamiltonian cycle of G, or determine that none exists. When |A|=|B|=n, the BTSP can be solved using polynomial space in O*(42nnlog n) time by using the divide-and-conquer algorithm of Gurevich and Shelah (SIAM Journal of Computation, 16(3), pp.486-502, 1987). We adapt their algorithm for the bipartite case, and show an improved time bound of O*(42n), saving the nlog n factor.

In this paper, we define a wireless sensor network with multiple types of sensors as a wireless heterogeneous sensor network (WHSN), and propose an efficient query dissemination scheme (EDT) in the WHSN. The EDT based on total dominant pruning can forward queries to only the nodes with data requested by the user, thereby reducing unnecessary packet transmission. We show that the EDT is suitable for the WHSN environment through a variety of simulations.

Understanding which refactoring transformations were performed is in demand in modern software constructions. Traditionally, many researchers have been tackling understanding code changes with history data derived from version control systems. In those studies, problems of the traditional approach are pointed out, such as entanglement of multiple changes. To alleviate the problems, operation histories on IDEs' code editors are available as a new source of software evolution data nowadays. By replaying such histories, we can investigate past code changes in a fine-grained level. However, the prior studies did not provide enough evidence of their effectiveness for detecting refactoring transformations. This paper describes an experiment in which participants detect refactoring transformations performed by other participants after investigating the code changes with an operation-replay tool and diff tools. The results show that both approaches have their respective factors that pose misunderstanding and overlooking of refactoring transformations. Two negative factors on divided operations and generated compound operations were observed in the operation-based approach, whereas all the negative factors resulted from three problems on tangling, shadowing, and out-of-order of code changes in the difference-based approach. This paper also shows seven concrete examples of participants' mistakes in both approaches. These findings give us hints for improving existing tools for understanding code changes and detecting refactoring transformations.

Previous research has shown that transcripts generated by automatic speech recognition (ASR) technologies can improve the listening comprehension of non-native speakers (NNSs). However, we still lack a detailed understanding of how ASR transcripts affect listening comprehension of NNSs. To explore this issue, we conducted two studies. The first study examined how the current presentation of ASR transcripts impacted NNSs' listening comprehension. 20 NNSs engaged in two listening tasks, each in different conditions: C1) audio only and C2) audio+ASR transcripts. The participants pressed a button whenever they encountered a comprehension problem, and explained each problem in the subsequent interviews. From our data analysis, we found that NNSs adopted different strategies when using the ASR transcripts; some followed the transcripts throughout the listening; some only checked them when necessary. NNSs also appeared to face difficulties following imperfect and slightly delayed transcripts while listening to speech - many reported difficulties concentrating on listening/reading or shifting between the two. The second study explored how different display methods of ASR transcripts affected NNSs' listening experiences. We focused on two display methods: 1) accuracy-oriented display which shows transcripts only after the completion of speech input analysis, and 2) speed-oriented display which shows the interim analysis results of speech input. We conducted a laboratory experiment with 22 NNSs who engaged in two listening tasks with ASR transcripts presented via the two display methods. We found that the more the NNSs paid attention to listening to the audio, the more they tended to prefer the speed-oriented transcripts, and vice versa. Mismatched transcripts were found to have negative effects on NNSs' listening comprehension. Our findings have implications for improving the presentation methods of ASR transcripts to more effectively support NNSs.

This letter presents a novel tensor voting mechanism — analytic tensor voting (ATV), to get rid of the difficulties in original tensor voting, especially the efficiency. One of the main advantages is its explicit voting formulations, which benefit the completion of tensor voting theory and computational efficiency. Firstly, new decaying function was designed following the basic spirit of decaying function in original tensor voting (OTV). Secondly, analytic stick tensor voting (ASTV) was formulated using the new decaying function. Thirdly, analytic plate and ball tensor voting (APTV, ABTV) were formulated through controllable stick tensor construction and tensorial integration. These make the each voting of tensor can be computed by several non-iterative matrix operations, improving the efficiency of tensor voting remarkably. Experimental results validate the effectiveness of proposed method.