AI (artificial intelligence) has grown at an overwhelming speed for the last decade, to the extent that it has become one of the mainstream tools that drive the advancements in science and technology. Meanwhile, the paradigm of edge computing has emerged as one of the foremost areas in which applications using the AI technology are being most actively researched, due to its potential benefits and impact on today's widespread networked computing environments. In this paper, we evaluate two major entry-level offerings in the state-of-the-art edge device technology, which highlight increased computing power and specialized hardware support for AI applications. We perform a set of deep learning benchmarks on the devices to measure their performance. By comparing the performance with other GPU (graphics processing unit) accelerated systems in different platforms, we assess the computational capability of the modern edge devices featuring a significant amount of hardware parallelism.

History data of edit operations are more beneficial than those stored in version control systems since they provide detailed information on how source code was changed. Meanwhile, a large number of recorded edit operations discourage developers and researchers from roughly understanding the changes. To assist with this task, it is desirable that they easily obtain traceability links for changed program elements over two source code snapshots before and after a code change. In this paper, we propose a graph representation called Operation History Graph (OHG), which presents code change information with such traceability links that are inferred from the history of edit operations. An OHG instance is generated by parsing any source code snapshot restored by edit histories and combining resultant abstract syntax trees (ASTs) into a single graph structure. To improve the performance of building graph instances, we avoided simply maintaining every program element. Any program element presenting the inner-structure of methods and non-changed elements are omitted. In addition, we adopted a lightweight static analysis for type name resolving to reduce required memory resource in the analysis while the accuracy of name resolving is preserved. Moreover, we assign a specific ID to each node and edge in the graph instance so that a part of the graph data can be separately stored and loaded on demand. These decisions make it feasible to build, manipulate, and store the graph with limited computer resources. To demonstrate the usefulness of the proposed operation history graph and verify whether detected traceability links are sufficient to reveal actual changes of program elements, we implemented tools to generate and manipulate OHG instances. The evaluation on graph generation performance shows that our tool can reduce the required computer resource as compared to another tool authors previously proposed. Moreover, the evaluation on traceability shows that OHG provides traceability links with sufficient accuracy as compared to the baseline approach using GumTree.

In this Letter, we propose a blind and robust multiple watermarking scheme using Contourlet transform and singular value decomposition (SVD). The host image is first decomposed by Contourlet transform. Singular values of Contourlet coefficient blocks are adopted to embed watermark information, and a fast calculation method is proposed to avoid the heavy computation of SVD. The watermark is embedded in both low and high frequency Contourlet coefficients to increase the robustness against various attacks. Moreover, the proposed scheme intrinsically exploits the characteristics of human visual system and thus can ensure the invisibility of the watermark. Simulation results show that the proposed scheme outperforms other related methods in terms of both robustness and execution time.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

Event-triggered control is a method that the control input is updated only when a certain condition is satisfied (i.e., an event occurs). In this paper, event-triggered control over a sensor network is studied based on the notion of uniformly ultimate boundedness. Since sensors are located in a distributed way, we consider multiple event-triggering conditions. In uniformly ultimate boundedness, it is guaranteed that if the state reaches a certain set containing the origin, the state stays within this set. Using this notion, the occurrence of events in the neighborhood of the origin is inhibited. First, the simultaneous design problem of a controller and event-triggering conditions is formulated. Next, this problem is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the proposed method is demonstrated by a numerical example.

The Benchmarking Working Group of IETF has defined a benchmarking methodology for IPv6 transition technologies including stateless NAT64 (also called SIIT) in RFC 8219. The aim of our effort is to design and implement a test program for SIIT gateways, which complies with RFC 8219, and thus to create the world's first standard free software SIIT benchmarking tool. In this paper, we overview the requirements for the tester on the basis of RFC 8219, and make scope decisions: throughput, frame loss rate, latency and packet delay variation (PDV) tests are implemented. We fully disclose our design considerations and the most important implementation decisions. Our tester, siitperf, is written in C++ and it uses the Intel Data Plane Development Kit (DPDK). We also document its functional tests and its initial performance estimation. Our tester is distributed as free software under GPLv3 license for the benefit of the research, benchmarking and networking communities.

Stochastic geometry analysis of wireless backhaul networks with beamforming in roadside environments is provided. In particular, a new model to analyze antenna gains, interference, and coverage in roadside environments of wireless networks with Poisson point process deployment of BSs is proposed. The received interference from the BSs with wired backhaul (referred to as anchored BS or A-BS) and the coverage probability of a typical BS are analyzed under different approximations of the location of the serving A-BS and combined antenna gains. Considering the beamforming, the coverage probability based on the aggregate interference consisting of the direct interference from the A-BSs and reflected interference from the BSs with wireless backhaul is also derived.

This paper presents a conformal retrodirective metagrating with multi-azimuthal-angle operating ability. First, a flat metagrating composed of a periodic array of single rectangular patch elements, two-layer stacked substrates, and a ground plane is implemented to achieve one-directional retroreflection at a specific angle. The elevation angle of the retroreflection is manipulated by precisely tuning the value of the period. To control the energy coupling to the retrodirective mode, the dimensions of the length and width of the rectangular patch are investigated under the effect of changing the substrate thickness. Three values of the length, width, and thickness are then chosen to obtain a high retroreflection power efficiency. Next, to create a conformal design operating simultaneously at multiple azimuthal angles, the rectangular patch array using a flexible ultra-thin guiding layer is conformed to a dielectric cylindrical substrate backed by a perfect electric conductor ground plane. Furthermore, to further optimize the retroreflection efficiency, two circular metallic plates are added at the two ends of the cylindrical substrate to eliminate the specular reflection inside the space of the cylinder. The measured radar cross-section shows a power efficiency of the retrodirective metagrating of approximately 91% and 93% for 30° retrodirected elevation angle at the azimuthal angles of 0° and 90°, respectively, at 5.8GHz.

Smart business management has been built to efficiently carry out enterprise business activities and improve its business outcomes in a global business circumstance. Firms have applied their smart business to their business activities in order to enhance the smart business results. The outcome of an enterprise's smart business fulfillment has to be managed and measured to effectively establish and control the smart business environment based on its business plan and business departments. In this circumstance, we need the measurement framework that can reasonably gauge a firm's smart business output in order to control and advance its smart business ability. This research presents a measurement instrument for an enterprise smart business performance in terms of a general smart business outcome. The developed measurement scale is verified on its validity and reliability through factor analysis and reliability analysis based on previous literature. This study presents an 11-item measurement tool that can reasonably gauge a firm smart business performance in both of finance and non-finance perspective.

According to the aging society, it is getting more important for software industry to secure human resources including senior developers. To enhance the performance of senior developers, we should clarify the strengths and weaknesses of senior developers, and based on that, we should reconsider software engineering education and development support tools. To a greater or lesser extent, many cognitive abilities would be affected by aging, and we focus on the human memory as one of such abilities. We performed preliminary analysis based on the assumption. In the preliminary experiment, we prepared programs in which the influence of human memory performance (i.e., the number of variables remembered in the short-term memory) on reading speed is different, and measured time for subjects to understand the programs. As a result, we observed that the code reading speed of senior subjects was slow, when they read programs in which the influence of human memory performance is larger.

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.

Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called “conditional information leakage given the eavesdropper's received signals,” which expresses the amount of information that an eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length n, while the complexity for computing the usual information leakage is exponential in n. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.

In this paper, we consider interference suppression for a full-duplex (FD) multiuser system based on single-carrier transmission in frequency-selective channels where a FD base-station (BS) simultaneously communicates with half-duplex (HD) uplink and downlink mobile users. We propose a design method for time-domain filtering where the filters in the BS transmitter suppress inter-symbol interference (ISI) and downlink inter-user interference (IUI); those in the BS receiver, self-interference, ISI, and uplink IUI; and those in the downlink mobile users, co-channel interference (CCI) without the channel state information of the CCI channels. Simulation results indicate that the FD system based on the proposed method outperforms the conventional HD system and FD system based on multicarrier transmission.

In recent years, with the improvement of health awareness, people have paid more and more attention to proper meal. Existing research has shown that a proper meal can help people prevent lifestyle diseases such as diabetes. In this research, by attaching sensors to the tableware, the information during the meal can be captured, and after processing and analyzing it, the meal information, such as time and sequence of meal, can be obtained. This paper introduces how to use supervised learning and multi-instance learning to deal with meal information and a detailed comparison is made. Three supervised learning algorithms and two multi-instance learning algorithms are used in the experiment. The experimental results showed that although the supervised learning algorithms have achieved good results in F-score, the multi-instance learning algorithms have achieved better results not only in accuracy but also in F-score.

A robust adaptive beamforming algorithm is proposed based on the precise interference-plus-noise covariance matrix reconstruction and steering vector estimation of the desired signal, even existing large gain-phase errors. Firstly, the model of array mismatches is proposed with the first-order Taylor series expansion. Then, an iterative method is designed to jointly estimate calibration coefficients and steering vectors of the desired signal and interferences. Next, the powers of interferences and noise are estimated by solving a quadratic optimization question with the derived closed-form solution. At last, the actual interference-plus-noise covariance matrix can be reconstructed as a weighted sum of the steering vectors and the corresponding powers. Simulation results demonstrate the effectiveness and advancement of the proposed method.

Expectation propagation (EP) decoding is proposed for sparse superposition coding in orthogonal frequency division multiplexing (OFDM) systems. When a randomized discrete Fourier transform (DFT) dictionary matrix is used, the EP decoding has the same complexity as approximate message-passing (AMP) decoding, which is a low-complexity and powerful decoding algorithm for the additive white Gaussian noise (AWGN) channel. Numerical simulations show that the EP decoding achieves comparable performance to AMP decoding for the AWGN channel. For OFDM systems, on the other hand, the EP decoding is much superior to the AMP decoding while the AMP decoding has an error-floor in high signal-to-noise ratio regime.

Low Probability of Intercept (LPI) radar waveform has complex and diverse modulation schemes, which cannot be easily identified by the traditional methods. The research on intrapulse modulation LPI radar waveform recognition has received increasing attention. In this paper, we propose an automatic LPI radar waveform recognition algorithm that uses a multi-resolution fusion convolutional neural network. First, signals embedded within the noise are processed using Choi-William Distribution (CWD) to obtain time-frequency feature images. Then, the images are resized by interpolation and sent to the proposed network for training and identification. The network takes a dual-channel CNN structure to obtain features at different resolutions and makes features fusion by using the concatenation and Inception module. Extensive simulations are carried out on twelve types of LPI radar waveforms, including BPSK, Costas, Frank, LFM, P1~P4, and T1~T4, corrupted with additive white Gaussian noise of SNR from 10dB to -8dB. The results show that the overall recognition rate of the proposed algorithm reaches 95.1% when the SNR is -6dB. We also try various sample selection methods related to the recognition task of the system. The conclusion is that reducing the samples with SNR above 2dB or below -8dB can effectively improve the training speed of the network while maintaining recognition accuracy.

In this paper we analyze the interval algorithm for random number generation proposed by Han and Hoshi in the case of Markov coin tossing. Using the expression of real numbers on the interval [0,1), we first establish an explicit representation of the interval algorithm with the representation of real numbers on the interval [0,1) based one number systems. Next, using the expression of the interval algorithm, we give a rigorous analysis of the interval algorithm. We discuss the difference between the expected number of the coin tosses in the interval algorithm and their upper bound derived by Han and Hoshi and show that it can be characterized explicitly with the established expression of the interval algorithm.

Due to recent technology progress based on big-data processing, many applications present irregular or unpredictable communication patterns among compute nodes in high-performance computing (HPC) systems. Traditional communication infrastructures, e.g., torus or fat-tree interconnection networks, may not handle well their matchmaking problems with these newly emerging applications. There are already many communication-efficient application mapping algorithms for these typical non-random network topologies, which use nearby compute nodes to reduce the network distances. However, for the above unpredictable communication patterns, it is difficult to efficiently map their applications onto the non-random network topologies. In this context, we recommend using random network topologies as the communication infrastructures, which have drawn increasing attention for the use of HPC interconnects due to their small diameter and average shortest path length (ASPL). We make a comparative study to analyze the impact of application mapping performance on non-random and random network topologies. We propose using topology embedding metrics, i.e., diameter and ASPL, and list several diameter/ASPL-based application mapping algorithms to compare their job scheduling performances, assuming that the communication pattern of each application is unpredictable to the computing system. Evaluation with a large compound application workload shows that, when compared to non-random topologies, random topologies can reduce the average turnaround time up to 39.3% by a random connected mapping method and up to 72.1% by a diameter/ASPL-based mapping algorithm. Moreover, when compared to the baseline topology mapping method, the proposed diameter/ASPL-based topology mapping strategy can reduce up to 48.0% makespan and up to 78.1% average turnaround time, and improve up to 1.9x system utilization over random topologies.

In this work, we introduce notions of quantum frequency arrangements consisting of quantum frequency squares, cubes, hypercubes and a notion of orthogonality between them. We also propose a notion of quantum mixed orthogonal array (QMOA). By using irredundant mixed orthogonal array proposed by Goyeneche et al. we can obtain k-uniform states of heterogeneous systems from quantum frequency arrangements and QMOAs. Furthermore, some examples are presented to illustrate our method.