Software maintenance is an important activity in the software lifecycle. Software maintenance does not only mean removing faults found after software release. Software needs extensions or modifications of its functions owing to changes in the business environment and software maintenance also refers to them. To help users and service suppliers benchmark work efficiency for software maintenance, and to clarify the relationships between software quality, work efficiency, and unit cost of staff, we used a dataset that includes 134 data points collected by the Economic Research Association in 2012, and analyzed the factors that affected the work efficiency of software maintenance. In the analysis, using a multiple regression model, we clarified the relationships between work efficiency and programming language and productivity factors. To analyze the influence to the quality, relationships of fault ratio was analyzed using correlation coefficients. The programming language and productivity factors affect work efficiency. Higher work efficiency and higher unit cost of staff do not affect the quality of software maintenance.

In an internet of things (IoT) system using an energy harvesting device and a secondary (2nd) battery, regardless of the age of the 2nd battery, the power management shortens the lifespan of the entire system. In this paper, we propose a scheme that extends the lifetime of the energy harvesting-based IoT system equipped with a Lithium 2nd battery. The proposed scheme includes several policies of using a supercapacitor as a primary energy storage, limiting the charging level according to the predicted harvesting energy, swinging the energy level around the minimum stress state of charge (SOC) level, and delaying the charge start time. Experiments with natural solar energy measurements based on a battery aging approximation model show that the proposed method can extend the operation lifetime of an existing IoT system from less than one and a half year to more than four years.

Multi-time negotiation which repeats negotiations many times under the same conditions is an important class of automated negotiation. We propose a meta-strategy that selects an agent's individual negotiation strategy for multi-time negotiation. Because the performance of the negotiating agents depends on situational parameters, such as the negotiation domains and the opponents, a suitable and effective individual strategy should be selected according to the negotiation situation. However, most existing agents negotiate based on only one negotiation policy: one bidding strategy, one acceptance strategy, and one opponent modeling method. Although the existing agents effectively negotiate in most situations, they do not work well in particular situations and their utilities are decreased. The proposed meta-strategy provides an effective negotiation strategy for the situation at the beginning of the negotiation. We model the meta-strategy as a multi-armed bandit problem that regards an individual negotiation strategy as a slot machine and utility of the agent as a reward. We implement the meta-strategy as the negotiating agents that use existing effective agents as the individual strategies. The experimental results demonstrate the effectiveness of our meta-strategy under various negotiation conditions. Additionally, the results indicate that the individual utilities of negotiating agents are influenced by the opponents' strategies, the profiles of the opponent and its own profiles.

This paper shows design and implementation of an image-based vibration detection system on a field-programmable gate array (FPGA), aiming at application to tremor suppression for microsurgery assistance systems. The system can extract a vibration component within a user-specified frequency band from moving images in real-time. For fast and robust detection, we employ a statistical approach using dense optical flow to derive vibration component, and design a custom hardware based on the Lucas-Kanade (LK) method to compute optical flow. And for band-pass filtering without phase delay, we implement the band-limited multiple Fourier linear combiner (BMFLC), a sort of adaptive band-pass filter which can recompose an input signal as a mixture of sinusoidal signals with multiple frequencies within the specified band, with no phase delay. The whole system is implemented as a deep pipeline on a Xilinx Kintex-7 XC7K325T FPGA without using any external memory. We employ fixed-point arithmetic to reduce resource utilization while maintaining accuracy close to double-precision floating-point arithmetic. Empirical experiments reveal that the proposed system extracts a high-frequency tremor component from hand motions, with intentional low-frequency motions successfully filtered out. The system can process VGA moving images at 60fps, with a delay of less than 1 µs for the BMFLC, suggesting effectiveness of the deep pipelined architecture. In addition, we are planning to integrate a CNN-based segmentation system for improving detection accuracy, and show preliminary software evaluation results.

In this paper, we propose a novel single-template strategy based on a mean template set and locally/globally weighted dynamic time warping (LG-DTW) to improve the performance of online signature verification. Specifically, in the enrollment phase, we implement a time series averaging method, Euclidean barycenter-based DTW barycenter averaging, to obtain a mean template set considering intra-user variability among reference samples. Then, we acquire a local weighting estimate considering a local stability sequence that is obtained analyzing multiple matching points of an optimal match between the mean template and reference sets. Thereafter, we derive a global weighting estimate based on the variable importance estimated by gradient boosting. Finally, in the verification phase, we apply both local and global weighting methods to acquire a discriminative LG-DTW distance between the mean template set and a query sample. Experimental results obtained on the public SVC2004 Task2 and MCYT-100 signature datasets confirm the effectiveness of the proposed method for online signature verification.

In emergency communication systems research, aerial wireless relay networks (AWRNs) using multicopter unmanned aerial vehicles (UAVs) have been proposed. The main issue of the AWRNs is how to minimize the delay time of packet transmissions since it is not easy to supply many multicopters to cover a wide area. In this paper, we review the flight schemes and their delay time for the AWRNs. Furthermore, the network has specific issues such as multicopters' drops due to their battery capacity depletion and inclination of moving multicopters. The inclination of multicopters affects the received power, and the communication range changes based on the inclination as well. Therefore, we clarify the effect of these issues on the delay time.

A compact optical polarization converter (PC) based on slot waveguide has been proposed in this study. Utilizing the high refractive index contrast between a Si waveguide and SiO2 cladding on the silicon-on-insulator platform, the light beam can be strongly confined in a slot waveguide structure. The proposed PC consists of a square waveguide and an L-shape cover waveguide. Since the overall structure is symmetrically distributed along the axis rotated 45-degree from the horizontal direction, the optical axis of this PC lies in the direction with equi-angle from two orthogonally polarized modes of the input and output ends, which leads to a high polarization conversion efficiency (PCE). 3D FDTD simulation results illustrate that a TE-to-TM mode conversion is achieved with a device length of 8.2 µm, and the PCE exceeds 99.8%. The structural tolerance and wavelength dependence of the PC have also been discussed in detail.

We demonstrated 1-Tb/s-class transmissions of field-deployed large-core low-loss fiber links, which is compliant with ITU-T G.654.E, using our newly developed real-time transponder consisting of a state-of-the-art 16-nm complementary metal-oxide-semiconductor (CMOS) based digital signal processing application-specific integrated circuit (DSP-ASIC) and an indium phosphide (InP) based high-bandwidth coherent driver modulator (HB-CDM). In this field experiment, we have achieved record transmission distances of 1122km for net data-rate 1-Tb/s transmission with dual polarization-division multiplexed (PDM) 32 quadrature amplitude modulation (QAM) signals, and of 336.6 km for net data-rate 1.2-Tb/s transmission with dual PDM-64QAM signals. This is the first demonstration of applying hybrid erbium-doped fiber amplifier (EDFA) and backward-distributed Raman amplifier were applied to terrestrial G.654.E fiber links. We also confirmed the stability of signal performance over field fiber transmission in wavelength division multiplexed (WDM) condition. The Q-factor fluctuations respectively were only less than or equal to 0.052dB and 0.07dB for PDM-32QAM and PDM-64QAM signals within continuous measurements for 60 minutes.

This paper proposes two algorithms, namely Server-User Matching (SUM) algorithm and Extended Server-User Matching (ESUM) algorithm, for the distributed server allocation problem. The server allocation problem is to determine the matching between servers and users to minimize the maximum delay, which is the maximum time to complete user synchronization. We analyze the computational time complexity. We prove that the SUM algorithm obtains the optimal solutions in polynomial time for the special case that all server-server delay values are the same and constant. We provide the upper and lower bounds when the SUM algorithm is applied to the general server allocation problem. We show that the ESUM algorithm is a fixed-parameter tractable algorithm that can attain the optimal solution for the server allocation problem parameterized by the number of servers. Numerical results show that the computation time of ESUM follows the analyzed complexity while the ESUM algorithm outperforms the approach of integer linear programming solved by our examined solver.

At ASIACRYPT 2018, Castryck, Lange, Martindale, Panny and Renes proposed CSIDH, which is a key-exchange protocol based on isogenies between elliptic curves, and a candidate for post-quantum cryptography. However, the implementation by Castryck et al. is not constant-time. Specifically, a part of the secret key could be recovered by the side-channel attacks. Recently, Meyer, Campos, and Reith proposed a constant-time implementation of CSIDH by introducing dummy isogenies and taking secret exponents only from intervals of non-negative integers. Their non-negative intervals make the calculation cost of their implementation of CSIDH twice that of the worst case of the standard (variable-time) implementation of CSIDH. In this paper, we propose a more efficient constant-time algorithm that takes secret exponents from intervals symmetric with respect to the zero. For using these intervals, we need to keep two torsion points on an elliptic curve and calculation for these points. We evaluate the costs of our implementation and that of Meyer et al. in terms of the number of operations on a finite prime field. Our evaluation shows that our constant-time implementation of CSIDH reduces the calculation cost by 28% compared with the implementation by Mayer et al. We also implemented our algorithm by extending the implementation in C of Meyer et al. (originally from Castryck et al.). Then our implementation achieved 152 million clock cycles, which is about 29% faster than that of Meyer et al. and confirms the above reduction ratio in our cost evaluation.

We aim at HDR imaging with simple processing while preventing spatial resolution degradation in multiple-exposure-time image sensor where the exposure time is controlled for each pixel. The contributions are the proposal of image interpolation by motion area detection and pixel adaptive weighting method by overexposure and motion blur detection.

Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, light-weight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a light-weight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

This paper introduces the maximization version of the k-path vertex cover problem, called the MAXIMUM K-PATH VERTEX COVER problem (MaxPkVC for short): A path consisting of k vertices, i.e., a path of length k-1 is called a k-path. If a k-path Pk includes a vertex v in a vertex set S, then we say that v or S covers Pk. Given a graph G=(V, E) and an integer s, the goal of MaxPkVC is to find a vertex subset S⊆V of at most s vertices such that the number of k-paths covered by S is maximized. The problem MaxPkVC is generally NP-hard. In this paper we consider the tractability/intractability of MaxPkVC on subclasses of graphs. We prove that MaxP3VC remains NP-hard even for split graphs. Furthermore, if the input graph is restricted to graphs with constant bounded treewidth, then MaxP3VC can be solved in polynomial time.

With the rapid evolution and increase of cyberthreats in recent years, it is necessary to detect and understand it promptly and precisely to reduce the impact of cyberthreats. A darknet, which is an unused IP address space, has a high signal-to-noise ratio, so it is easier to understand the global tendency of malicious traffic in cyberspace than other observation networks. In this paper, we aim to capture global cyberthreats in real time. Since multiple hosts infected with similar malware tend to perform similar behavior, we propose a system that estimates a degree of synchronizations from the patterns of packet transmission time among the source hosts observed in unit time of the darknet and detects anomalies in real time. In our evaluation, we perform our proof-of-concept implementation of the proposed engine to demonstrate its feasibility and effectiveness, and we detect cyberthreats with an accuracy of 97.14%. This work is the first practical trial that detects cyberthreats from in-the-wild darknet traffic regardless of new types and variants in real time, and it quantitatively evaluates the result.

This paper proposes an efficient server selection scheme in successive participation scenario with participating-domain segmentation. The scheme is utilized by distributed processing systems for real-time interactive communication to suppress the communication latency of a wide-area network. In the proposed scheme, users participate for server selection one after another. The proposed scheme determines a recommended server, and a new user selects the recommended server first. Before each user participates, the recommended servers are determined assuming that users exist in the considered regions. A recommended server is determined for each divided region to minimize the latency. The new user selects the recommended available server, where the user is located. We formulate an integer linear programming problem to determine the recommended servers. Numerical results indicate that, at the cost additional computation, the proposed scheme offers smaller latency than the conventional scheme. We investigate different policies to divide the users' participation for the recommended server finding process in the proposed scheme.

Outlier detection in a data set is very important in performing proper data mining. In this paper, we propose a method for efficiently detecting outliers by performing cluster analysis using the DS algorithm improved from the k-means algorithm. This method is simpler to detect outliers than traditional methods, and these detected outliers can quantitatively indicate “the degree of outlier”. Using this method, we detect abnormal trading days from OHLCs for S&P500 and FTSA, which are typical and world-wide stock indexes, from the beginning of 2005 to the end of 2015. They are defined as non-steady trading days, and the conditions for becoming the non-steady markets are mined as new knowledge. Applying the mined knowledge to OHLCs from the beginning of 2016 to the end of 2018, we can predict the non-steady trading days during that period. By verifying the predicted content, we show the fact that the appropriate knowledge has been successfully mined and show the effectiveness of the outlier detection method proposed in this paper. Furthermore, we mutually reference and comparatively analyze the results of applying this method to multiple stock indexes. This analyzes possible to visualize when and where social and economic impacts occur and how they propagate through the earth. This is one of the new applications using this method.

Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classification that classify a driver is significantly drowsy or not. Multi-level drowsiness estimation, that detects not only significant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car system. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting significant drowsiness for binary classification. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are effective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.

This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.

A unified decision scheme for the classification and localization of cable faults is proposed based on a two-step procedure. Having basis in the time domain reflectometry (TDR), the proposed scheme is capable of determining not only the locations but also types of faults in a cable without an excessive additional computational burden compared to other TDR-based schemes. Results from simulation and experiments with measured real data demonstrate that the proposed scheme exhibits a higher rate of correct decision than the conventional schemes in localizing and classifying faults over a wide range of the location of faults.

The packet error rate (PER) performance of multi-hop STBC based cooperative and diversity relaying systems are studied. These systems consist of a source, a destination, and two relay stations in each hop. From in-lab experiments, it is confirmed that the cooperative relaying system has better PER performance than the diversity relaying system with highly correlated channels.