Recently, Hof et al. extended the type-2 Duman and Salehi (DS2) bound to generalized decoding, which was introduced by Forney, with decision criterion FR. From this bound, they derived two significant bounds. One is the Shulman-Feder bound for generalized decoding (GD) with the binary-input output-symmetric channel. The other is an upper bound for an ensemble of linear block codes, by applying the average complete weight distribution directly to the DS2 bound for GD. For the Shulman-Feder bound for GD, the authors derived a condition under which an upper bound is minimized at an intermediate step and show that this condition yields a new bound which is tighter than Hof et al.'s bound. In this paper, we first extend this result for non-binary linear block codes used over a class of symmetric channels called the regular channel. Next, we derive a new tighter bound for an ensemble of linear block codes, which is based on the average weight distribution.

Behaviors of an object-oriented system can be visualized as reverse-engineered sequence diagrams from execution traces. This approach is a valuable tool for program comprehension tasks. However, owing to the massiveness of information contained in an execution trace, a reverse-engineered sequence diagram is often afflicted by a scalability issue. To address this issue, many trace summarization techniques have been proposed. Most of the previous techniques focused on reducing the vertical size of the diagram. To cope with the scalability issue, decreasing the horizontal size of the diagram is also very important. Nonetheless, few studies have addressed this point; thus, there is a lot of needs for further development of horizontal summarization techniques. We present in this paper a method for identifying core objects for trace summarization by analyzing reference relations and dynamic properties. Visualizing only interactions related to core objects, we can obtain a horizontally compactified reverse-engineered sequence diagram that contains system's key behaviors. To identify core objects, first, we detect and eliminate temporary objects that are trivial for a system by analyzing reference relations and lifetimes of objects. Then, estimating the importance of each non-trivial object based on their dynamic properties, we identify highly important ones (i.e., core objects). We implemented our technique in our tool and evaluated it by using traces from various open-source software systems. The results showed that our technique was much more effective in terms of the horizontal reduction of a reverse-engineered sequence diagram, compared with the state-of-the-art trace summarization technique. The horizontal compression ratio of our technique was 134.6 on average, whereas that of the state-of-the-art technique was 11.5. The runtime overhead imposed by our technique was 167.6% on average. This overhead is relatively small compared with recent scalable dynamic analysis techniques, which shows the practicality of our technique. Overall, our technique can achieve a significant reduction of the horizontal size of a reverse-engineered sequence diagram with a small overhead and is expected to be a valuable tool for program comprehension.

In this letter, we consider a multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) system, in which the confidential message intended for the information receiver (IR) should be kept secret from the energy receiver (ER). Our goal is to design the optimal transmit covariance matrix so as to maximize the secrecy energy efficiency (SEE) of the system while guaranteeing the secrecy rate, energy harvesting and transmit power constraints. To deal with the original non-convex optimization problem, we propose an alternating optimization (AO)- based algorithm and also prove its convergence. Simulation results show that the proposed algorithm outperforms conventional design methods in terms of SEE.

The growth mechanisms of three-dimensionally (3D) faceted InGaN quantum wells (QWs) on (=1=12=2) GaN substrates are discussed. The structure is composed of (=1=12=2), {=110=1}, and {=1100} planes, and the cross sectional shape is similar to that of 3D QWs on (0001). However, the 3D QWs on (=1=12=2) and (0001) show quite different inter-facet variation of In compositions. To clarify this observation, the local thicknesses of constituent InN and GaN on the 3D GaN are fitted with a formula derived from the diffusion equation. It is suggested that the difference in the In incorporation efficiency of each crystallographic plane strongly affects the surface In adatom migration.

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

This paper focuses mainly on issues related to the pricing of American options under a fuzzy environment by taking into account the clustering of the underlying asset price volatility, leverage effect and stochastic jumps. By treating the volatility as a parabolic fuzzy number, we constructed a Levy-GJR-GARCH model based on an infinite pure jump process and combined the model with fuzzy simulation technology to perform numerical simulations based on the least squares Monte Carlo approach and the fuzzy binomial tree method. An empirical study was performed using American put option data from the Standard & Poor's 100 index. The findings are as follows: under a fuzzy environment, the result of the option valuation is more precise than the result under a clear environment, pricing simulations of short-term options have higher precision than those of medium- and long-term options, the least squares Monte Carlo approach yields more accurate valuation than the fuzzy binomial tree method, and the simulation effects of different Levy processes indicate that the NIG and CGMY models are superior to the VG model. Moreover, the option price increases as the time to expiration of options is extended and the exercise price increases, the membership function curve is asymmetric with an inclined left tendency, and the fuzzy interval narrows as the level set α and the exponent of membership function n increase. In addition, the results demonstrate that the quasi-random number and Brownian Bridge approaches can improve the convergence speed of the least squares Monte Carlo approach.

In this letter, we explored the usage of spatio-temporal information in one unified framework to improve the performance of multichannel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.

This paper enhances the QoE of audio and video multicast transmission over a wireless LAN by means of reliable groupcast schemes. We use GCR (GroupCast with Retries) Unsolicited Retry and GCR Block ACK as reliable groupcast schemes; they are standardized by IEEE 802.11aa. We assume that a wireless access point transmits audio and video streams to several terminals connected to the access point by groupcast. We compare three schemes: Groupcast with EDCA (Enhanced Distributed Channel Access), GCR Unsolicited Retry and GCR Block ACK. We perform computer simulations under various network conditions to assess application-level QoS and evaluate QoE by a subjective experiment. As a result, we find that the most effective scheme depends on network conditions.

Rehabilitation training with pedaling wheelchair in combination with functional electrical stimulation (FES) can be effective for decreasing the risk of falling significantly. Automatic adjustment of cycling speed and making a turn without standstill has been desired for practical applications of the training with mobile FES cycling. This study aimed at developing closed-loop control system of cycling speed with the pedaling wheelchair. Considering clinical practical use with no requirement of extensive modifications of the wheelchair, measurement method of cycling speed with inertial motion measurement units (IMUs) was introduced, and fuzzy controller for adjusting stimulation intensity to regulate cycling speed was designed. The developed prototype of closed-loop FES control system achieved appropriately cycling speed for the different target speeds in most of control trials with neurologically intact subjects. In addition, all the control trials of low speed cycling including U-turn achieved maintaining the target speed without standstill. Cycling distance and cycling time increased with the closed-loop control of low cycling speed compensating decreasing of cycling speed caused by muscle fatigue. From these results, the developed closed-loop fuzzy FES control system was suggested to work reliably in mobile FES cycling.

An important step for improving software analyzability is applying refactorings during the maintenance phase to remove bad smells, especially the long method bad smell. Long method bad smell occurs most frequently and is a root cause of other bad smells. However, no research has proposed an approach to repeating refactoring identification, suggestion, and application until all long method bad smells have been removed completely without reducing software analyzability. This paper proposes an effective approach to identifying refactoring opportunities and suggesting an effective refactoring set for complete removal of long method bad smell without reducing code analyzability. This approach, called the long method remover or LMR, uses refactoring enabling conditions based on program analysis and code metrics to identify four refactoring techniques and uses a technique embedded in JDeodorant to identify extract method. For effective refactoring set suggestion, LMR uses two criteria: code analyzability level and the number of statements impacted by the refactorings. LMR also uses side effect analysis to ensure behavior preservation. To evaluate LMR, we apply it to the core package of a real world java application. Our evaluation criteria are 1) the preservation of code functionality, 2) the removal rate of long method characteristics, and 3) the improvement on analyzability. The result showed that the methods that apply suggested refactoring sets can completely remove long method bad smell, still have behavior preservation, and have not decreased analyzability. It is concluded that LMR meets the objectives in almost all classes. We also discussed the issues we found during evaluation as lesson learned.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Response property is a kind of liveness property. Response property problem is defined as follows: Given two activities α and β, whenever α is executed, is β always executed after that? In this paper, we tackled the problem in terms of Workflow Petri nets (WF-nets for short). Our results are (i) the response property problem for acyclic WF-nets is decidable, (ii) the problem is intractable for acyclic asymmetric choice (AC) WF-nets, and (iii) the problem for acyclic bridge-less well-structured WF-nets is solvable in polynomial time. We illustrated the usefulness of the procedure with an application example.

In this paper, we propose a boundary-aware superpixel segmentation method, which could quickly and exactly extract superpixel with a non-iteration framework. The basic idea is to construct a minimum spanning tree (MST) based on structure edge to measure the local similarity among pixels, and then label each pixel as the index with shortest path seeds. Intuitively, we first construct MST on the original pixels with boundary feature to calculate the similarity of adjacent pixels. Then the geodesic distance between pixels can be exactly obtained based on two-round tree recursions. We determinate pixel label as the shortest path seed index. Experimental results on BSD500 segmentation benchmark demonstrate the proposed method obtains best performance compared with seven state-of-the-art methods. Especially for the low density situation, our method can obtain the boundary-aware oversegmentation region.

Based on Bezout's theorem, the feasibility condition for interference alignment (IA) is established in a two-way small cell network where part of cells transmit in downlink while the others in uplink. Moreover, the sufficient and necessary condition for the two-way network to achieve as many degrees of freedom (DoFs) as the traditional one-way network is presented. We find that in certain cases every small cell can independently decide to work in either downlink mode or uplink mode as needed without causing performance degradation of IA.

One of the most promising compression methods for XML documents is the one that translates a given document to a tree grammar that generates it. A feature of this compression is that the internal structures are kept in production rules of the grammar. This enables us to directly manipulate the tree structure without decompression. However, previous studies assume that a given XML document does not have data values because they focus on direct retrieval and manipulation of the tree structure. This paper proposes a direct update method for XML documents with data values and shows the effectiveness of the proposed method based on experiments conducted on our implemented tool.

Formal series are a natural extension of formal languages by associating each word with a value called a coefficient or a weight. Among them, recognizable series and algebraic series can be regarded as extensions of regular languages and context-free languages, respectively. The coefficient of a word w can represent quantities such as the cost taken by an operation on w, the probability that w is emitted. One of the possible applications of formal series is the string counting in quantitative analysis of software. In this paper, we define the counting problems for formal series and propose algorithms for the problems. The membership problem for an automaton or a grammar corresponds to the problem of computing the coefficient of a given word in a given series. Accordingly, we define the counting problem for formal series in the following two ways. For a formal series S and a natural number d, we define CC(S,d) to be the sum of the coefficients of all the words of length d in S and SC(S,d) to be the number of words of length d that have non-zero coefficients in S. We show that for a given recognizable series S and a natural number d, CC(S,d) can be computed in O(η log d) time where η is an upper-bound of time needed for a single state-transition matrix operation, and if the state-transition matrices of S are commutative for multiplication, SC(S,d) can be computed in polynomial time of d. We extend the notions to tree series and discuss how to compute them efficiently. Also, we propose an algorithm that computes CC(S,d) in square time of d for an algebraic series S. We show the CPU time of the proposed algorithm for computing CC(S,d) for some context-free grammars as S, one of which represents the syntax of C language. To examine the applicability of the proposed algorithms to string counting for the vulnerability analysis, we also present results on string counting for Kaluza Benchmark.

This paper proposes a novel domain adaptation method that can utilize out-of-domain text resources and partially domain matched text resources in language modeling. A major problem in domain adaptation is that it is hard to obtain adequate adaptation effects from out-of-domain text resources. To tackle the problem, our idea is to carry out model merger in a latent variable space created from latent words language models (LWLMs). The latent variables in the LWLMs are represented as specific words selected from the observed word space, so LWLMs can share a common latent variable space. It enables us to perform flexible mixture modeling with consideration of the latent variable space. This paper presents two types of mixture modeling, i.e., LWLM mixture models and LWLM cross-mixture models. The LWLM mixture models can perform a latent word space mixture modeling to mitigate domain mismatch problem. Furthermore, in the LWLM cross-mixture models, LMs which individually constructed from partially matched text resources are split into two element models, each of which can be subjected to mixture modeling. For the approaches, this paper also describes methods to optimize mixture weights using a validation data set. Experiments show that the mixture in latent word space can achieve performance improvements for both target domain and out-of-domain compared with that in observed word space.

Research on permutation polynomials over the finite field F22k with significant cryptographical properties such as possibly low differential uniformity, possibly high nonlinearity and algebraic degree has attracted a lot of attention and made considerable progress in recent years. Once used as the substitution boxes (S-boxes) in the block ciphers with Substitution Permutation Network (SPN) structure, this kind of polynomials can have a good performance against the classical cryptographic analysis such as linear attacks, differential attacks and the higher order differential attacks. In this paper we put forward a new construction of differentially 4-uniformity permutations over F22k by modifying the inverse function on some specific subsets of the finite field. Compared with the previous similar works, there are several advantages of our new construction. One is that it can provide a very large number of Carlet-Charpin-Zinoviev equivalent classes of functions (increasing exponentially). Another advantage is that all the functions are explicitly constructed, and the polynomial forms are obtained for three subclasses. The third advantage is that the chosen subsets are very large, hence all the new functions are not close to the inverse function. Therefore, our construction may provide more choices for designing of S-boxes. Moreover, it has been checked by a software programm for k=3 that except for one special function, all the other functions in our construction are Carlet-Charpin-Zinoviev equivalent to the existing ones.

Regarding IEEE 802.11 wireless local area networks (WLANs), many researchers are focusing on signal-to-noise ratio (SNR)-based rate adaptation schemes, because these schemes have the advantage of accurately selecting transmission rates that suit the channel. However, even SNR-based rate adaptation schemes work poorly in a rapidly varying channel environment. If a transmitter cannot receive accurate rate information due to fast channel fading, it encounters continuous channel errors, because the cycle of rate adaptation and rate information feedback breaks. A well-designed request-to-send/clear-to-send (RTS/CTS) frame exchange policy that accurately reflects the network situation is an indispensable element for enhancing the performance of SNR-based rate adaptation schemes. In this paper, a novel rate adaptation scheme called adaptive RTS/CTS-exchange and rate prediction (ARRP) is proposed, which adapts the transmission rate efficiently for variable network situations, including rapidly varying channels. ARRP selects a transmission rate by predicting the SNR of the data frame to transmit when the channel condition becomes worse. Accordingly, ARRP prevents continuous channel errors through a pre-emptive transmission rate adjustment. Moreover, ARRP utilizes an efficient RTS/CTS frame exchange algorithm that considers the number of contending stations and the current transmission rate of data frames, which drastically reduces both frame collisions and RTS/CTS-exchange overhead simultaneously. Simulation results show that ARRP achieves better performance than other rate adaptation schemes.

A problem of global stabilization of a class of approximately feedback linearized systems is considered. A new system structural feature is the presence of non-trivial diagonal terms along with nonlinearity, which has not been addressed by the previous control results. The stability analysis reveals a new relationship between the time-varying rates of system parameters and system nonlinearity along with our controller. Two examples are given for illustration.