Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low-quality source code before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.

Effective emergency resource allocation is essential to guarantee a successful emergency disposal, and it has become a research focus in the area of emergency management. Emergency event logs are accumulated in modern emergency management systems and can be analyzed to support effective resource allocation. This paper proposes a novel approach for efficient emergency resource allocation by mining emergency event logs. More specifically, an emergency event log with various attributes, e.g., emergency task name, emergency resource type (reusable and consumable ones), required resource amount, and timestamps, is first formalized. Then, a novel algorithm is presented to discover emergency response process models, represented as an extension of Petri net with resource and time elements, from emergency event logs. Next, based on the discovered emergency response process models, the minimum resource requirements for both reusable and consumable resources are obtained, and two resource allocation strategies, i.e., the Shortest Execution Time (SET) strategy and the Least Resource Consumption (LRC) strategy, are proposed to support efficient emergency resource allocation decision-making. Finally, a chlorine tank explosion emergency case study is used to demonstrate the applicability and effectiveness of the proposed resource allocation approach.

Contamination of water resources with pathogenic microorganisms excreted in human feces is a worldwide public health concern. Surveillance of fecal contamination is commonly performed by routine monitoring for a single type or a few types of microorganism(s). To design a feasible routine for periodic monitoring and to control risks of exposure to pathogens, reliable statistical algorithms for inferring correlations between concentrations of microorganisms in water need to be established. Moreover, because pathogens are often present in low concentrations, some contaminations are likely to be under a detection limit. This yields a pairwise left-censored dataset and complicates computation of correlation coefficients. Errors of correlation estimation can be smaller if undetected values are imputed better. To obtain better imputations, we utilize side information and develop a new technique, the asymmetric Tobit model which is an extension of the Tobit model so that domain knowledge can be exploited effectively when fitting the model to a censored dataset. The empirical results demonstrate that imputation with domain knowledge is effective for this task.

Standard neural machine translation (NMT) is on the assumption that the document-level context is independent. Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network. The capacity of the memory network that detecting the most relevant part of the current sentence from memory renders a natural solution to model the rich document-level context. In this work, the proposed document-aware memory network is implemented to enhance the Transformer NMT baseline. Experiments on several tasks show that the proposed method significantly improves the NMT performance over strong Transformer baselines and other related studies.

We propose a new method for improving the recognition performance of phonemes, speech emotions, and music genres using multi-task learning. When tasks are closely related, multi-task learning can improve the performance of each task by learning common feature representation for all the tasks. However, the recognition tasks considered in this study demand different input signals of speech and music at different time scales, resulting in input features with different characteristics. In addition, a training dataset with multiple labels for all information sources is not available. Considering these issues, we conduct multi-task learning in a sequential training process using input features with a single label for one information source. A comparative evaluation confirms that the proposed method for multi-task learning provides higher performance for all recognition tasks than individual learning for each task as in conventional methods.

Applications of continuous-time (CT) comparator include relaxation oscillators, pulse width modulators, and so on. CT comparator receives a differential input and outputs a strobe ideally when the differential input crosses zero. Unlike the DT comparators with positive feedback circuit, amplifiers consuming static power must be employed in CT comparators to amplify the input signal. Therefore, minimization of comparator delay under the constraint of power consumption often becomes an issue. This paper analyzes transient behavior of a CT comparator. Using “constant delay approximation”, the comparator delay is derived as a function of input slew rate, number of stages of the preamplifier, and device parameters in each block. This paper also discusses optimum design of the CT comparator. The condition for minimum comparator delay is derived with keeping power consumption constant. The results include that the optimum DC gain of the preamplifier is e∼e3 per stage depending on the element which dominates load capacitance of the preamplifier.

In this paper, for the purpose of clarifying the desired ITS information and communication systems considering both safety and social feasibility to prevention overengineering, using a microscopic traffic flow simulator, we discuss the required information acquisition rate of three types of safety driving support systems, that is, the sensor type and the communication type, the sensor and communication fusion type. Performances are evaluated from the viewpoint of preventing overengineering performance using the “TsRm evaluation method” that considers a vehicle approaching within the range of R meters within T seconds as the vehicle with a high possibility of collision, and that evaluates only those vehicles. The results show that regarding the communication radius and the sensing range, overengineering performance may be estimated when all vehicles in the evaluation area are used for evaluations without considering each vehicle's location, velocity and acceleration as in conventional evaluations. In addition, it is clarified that the sensor and communication fusion type system is advantageous by effectively complementing the defects of the sensor type systems and the communication type systems.

Water detection is important for machine vision applications such as visual inspection and robot motion planning. In this paper, we propose an approach to per-pixel water detection on unknown surfaces with a hyperspectral image. Our proposed method is based on the water spectral characteristics: water is transparent for visible light but translucent/opaque for near-infrared light and therefore the apparent near-infrared spectral reflectance of a surface is smaller than the original one when water is present on it. Specifically, we use a linear combination of a small number of basis vector to approximate the spectral reflectance and estimate the original near-infrared reflectance from the visible reflectance (which does not depend on the presence or absence of water) to detect water. We conducted a number of experiments using real images and show that our method, which estimates near-infrared spectral reflectance based on the visible spectral reflectance, has better performance than existing techniques.

Well annotated dataset is crucial to the training of object detectors. However, the production of finely annotated datasets for object detection tasks is extremely labor-intensive, therefore, cloud sourcing is often used to create datasets, which leads to these datasets tending to contain incorrect annotations such as inaccurate localization bounding boxes. In this study, we highlight a problem of object detection with noisy bounding box annotations and show that these noisy annotations are harmful to the performance of deep neural networks. To solve this problem, we further propose a framework to allow the network to modify the noisy datasets by alternating refinement. The experimental results demonstrate that our proposed framework can significantly alleviate the influences of noise on model performance.

In this letter, we will prove that chaotic binary sequences generated by the tent map and Walsh functions are i.i.d. (independent and identically distributed) and orthogonal to each other.

In this paper, we present an algorithm that counts the number of empty quadrilaterals whose corners are chosen from a given set S of n points in general position. Our algorithm can separately count the number of convex or non-convex empty quadrilaterals in O(T) time, where T denotes the number of empty triangles in S. Note that T varies from Ω(n2) and O(n3) and the expected value of T is known to be Θ(n2) when the n points in S are chosen uniformly and independently at random from a convex and bounded body in the plane. We also show how to enumerate all convex and/or non-convex empty quadrilaterals in S in time proportional to the number of reported quadrilaterals, after O(T)-time preprocessing.

Optical code-division multiple-access (CDMA) techniques provide multi-user data transmission services in optical wireless and fiber communication systems. Several signature codes, such as modified prime sequence codes (MPSCs), generalized MPSCs (GMPSCs) and modified pseudo-orthogonal M-sequence sets, have been proposed for synchronous optical CDMA systems. In this paper, a new scheme is proposed for synchronous optical CDMA to increase the number of users and, consequently, to increase the total data rate without increasing the chip rate. The proposed scheme employs a GMPSC and an extended bi-orthogonal code which is a unipolar code generated from a bipolar Walsh code. Comprehensive comparisons between the proposed scheme and several conventional schemes are shown. Moreover, bit error rate performance and energy efficiency of the proposed scheme are evaluated comparing with those of the conventional optical CDMA schemes under atmospheric propagation environment.

Permutation polynomials and their compositional inverses are crucial for construction of Maiorana-McFarland bent functions and their dual functions, which have the optimal nonlinearity for resisting against the linear attack on block ciphers and on stream ciphers. In this letter, we give the explicit compositional inverse of the permutation binomial $f(z)=z^{2^{r}+2}+alpha zinmathbb{F}_{2^{2r}}[z]$. Based on that, we obtain the dual of monomial bent function $f(x)={ m Tr}_1^{4r}(x^{2^{2r}+2^{r+1}+1})$. Our result suggests that the dual of f is not a monomial any more, and it is not always EA-equivalent to f.

The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.

We propose a new lattice-based digital signature scheme MLWRSign by modifying Dilithium, which is one of the second-round candidates of NIST's call for post-quantum cryptographic standards. To the best of our knowledge, our scheme MLWRSign is the first signature scheme whose security is based on the (module) learning with rounding (LWR) problem. Due to the simplicity of the LWR, the secret key size is reduced by approximately 30% in our scheme compared to Dilithium, while achieving the same level of security. Moreover, we implemented MLWRSign and observed that the running time of our scheme is comparable to that of Dilithium.

The extended visual cryptography scheme (EVCS) proposed by Ateniese et al. is one of variations of the visual cryptography scheme such that a secret image is recovered by superimposition of certain qualified collections of shares, where cover images are visible on respective shares. In this paper, we give a new definition of the EVCS for improving visibility of the recovered secret image as well as the cover images. We formulate the problem to construct the basis matrices of the EVCS with the minimum pixel expansion as an integer programming problem. We solve the integer programming problem for general access structures with less than or equal to five participants and show that basis matrices with a smaller pixel expansion can be obtained for certain cases. We also analyze security of the EVCS meeting the new definition from an information-theoretic viewpoint. We give a condition under which any forbidden collection of shares does not reveal any additional information on not only a secret image but also the cover images that are not visible on the other shares.

In this paper, we examine techniques for improving the throughput of unlicensed radio systems such as wireless LANs (WLANs) to take advantage of multi-radio access to mobile broadband, which will be important in 5G evolution and beyond. In WLANs, throughput is reduced due to mixed standards and the degraded quality of certain frequency channels, and thus control techniques and an architecture that provide efficient control over WLANs are needed to solve the problem. We have proposed a technique to control the terminal connection dynamically by using the multi-radio of the AP. Furthermore, we have proposed a new control architecture called WiSMA for efficient control of WLANs. Experiments show that the proposed method can solve those problems and improve the WLAN throughput.

In this letter, we give a recursive construction of q-ary almost periodic complementary pairs (APCPs) based on an interleaving technique of sequences and Kronercker product. Based on this construction, we obtain new quaternary APCPs with new lengths.

Predicting the malfunction timing of wind turbines is essential for maintaining the high profitability of the wind power generation business. Machine learning methods have been studied using condition monitoring system data, such as vibration data, and supervisory control and data acquisition (SCADA) data, to detect and predict anomalies in wind turbines automatically. Autoencoder-based techniques have attracted significant interest in the detection or prediction of anomalies through unsupervised learning, in which the anomaly pattern is unknown. Although autoencoder-based techniques have been proven to detect anomalies effectively using relatively stable SCADA data, they perform poorly in the case of deteriorated SCADA data. In this letter, we propose a power-curve filtering method, which is a preprocessing technique used before the application of an autoencoder-based technique, to mitigate the dirtiness of SCADA data and improve the prediction performance of wind turbine degradation. We have evaluated its performance using SCADA data obtained from a real wind-farm.

The Tokyo subway is one of the most complex subway networks in the world and it is difficult to compute a visually readable metro map using existing layout methods. In this paper, we present a new method that can generate complex metro maps such as the Tokyo subway network. Our method consists of two phases. The first phase generates rough metro maps. It decomposes the metro networks into smaller subgraphs and partially generates rough metro maps. In the second phase, we use a local search technique to improve the aesthetic quality of the rough metro maps. The experimental results including the Tokyo metro map are shown.