In this paper we propose an extension of the Attention Branch Network (ABN) by using instance segmentation for generating sharper attention maps for action recognition. Methods for visual explanation such as Grad-CAM usually generate blurry maps which are not intuitive for humans to understand, particularly in recognizing actions of people in videos. Our proposed method, Object-ABN, tackles this issue by introducing a new mask loss that makes the generated attention maps close to the instance segmentation result. Further the Prototype Conformity (PC) loss and multiple attention maps are introduced to enhance the sharpness of the maps and improve the performance of classification. Experimental results with UCF101 and SSv2 shows that the generated maps by the proposed method are much clearer qualitatively and quantitatively than those of the original ABN.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.

We characterized prion disease by comparing brain functional connectivity network (BFCN), which were constructed by 16-ch scalp-recorded electroencephalograms (EEGs). The connectivity between each pair of nodes (electrodes) were computed by synchronization likelihood (SL). The BFCN was applied to graph theory to discriminate prion disease patients from healthy elderlies and dementia groups.

Permutation codes are error-correcting codes over symmetric groups. We focus on permutation codes under Chebyshev (l∞) distance. A permutation code invented by Kløve et al. is of length n, size 2n-d and, minimum distance d. We denote the code by φn,d. This code is the largest known code of length n and minimum Chebyshev distance d > n/2 so far, to the best of the authors knowledge. They also devised efficient encoding and hard-decision decoding (HDD) algorithms that outperform the bounded distance decoding. In this paper, we derive a tight upper bound of decoding error probability of HDD. By factor graph formalization, we derive an efficient maximum a-posterior probability decoding algorithm for φn,d. We explore concatenating permutation codes of φn,d=0 with binary outer codes for more robust error correction. A naturally induced pseudo distance over binary outer codes successfully characterizes Chebyshev distance of concatenated permutation codes. Using this distance, we upper-bound the minimum Chebyshev distance of concatenated codes. We discover how to concatenate binary linear codes to achieve the upper bound. We derive the distance distribution of concatenated permutation codes with random outer codes. We demonstrate that the sum-product decoding performance of concatenated codes with outer low-density parity-check codes outperforms conventional schemes.

This paper first introduces the structure of a shipboard equipment control cabinet and the preliminary design of electromagnetic shielding, then introduces the principle of low-frequency magnetic field shielding, and uses silicon steel sheet to shield the low-frequency magnetic field of shipboard equipment control equipment. Based on ANSYS Maxwell simulation software, the low-frequency magnetic field radiation emission of the equipment's conducted harmonic peak frequency point is simulated. Finally, according to MIL-STD-461G test standard, the low-frequency magnetic field radiation emission test is carried out, which meets the limit requirements of the standard. The low-frequency magnetic field shielding technology has practical value. The low-frequency magnetic field radiation emission simulation based on ANSYS Maxwell proposed in this paper is a useful attempt for the quantitative simulation of radiation emission.

Self-directed learning in an appropriately designed environment can help learners retain knowledge tied to experience and motivate them to learn more. For teachers, however, it is difficult to design an environment to give to learners and to give feedback that reflects respect for their independent efforts, while for learners, it is difficult to set learning objectives on their own and to construct knowledge correctly based on their own efforts. In this research, we developed a learning support system that provides a mechanism for constructing an observational learning environment using virtual space and that encourages self-directed knowledge discovery. We confirmed that this system contributes to a learner's structural understanding and its retention and to a greater desire to learn at a level comparable to that of concept map creation, another active learning method.

This paper focuses on the potential value and future prospects of using virtual reality (VR) technology in online education. In detailing online education and the latest VR technology, we focus on metaverse construction and artificial intelligence (AI) for educational VR use. In particular, we describe a virtual university campus in which on-demand VR lectures are conducted in virtual lecture halls, automated evaluations of student learning and training using machine learning, and the linking of multiple digital campuses.

Quadrature voltage-controlled oscillators (VCOs) with current-weight-average and voltage-weight-average phase-adjusting architectures are studied. The phase adjusting equalizes the oscillation frequency to the LC-resonant frequency. The merits of the equalization are explained by using Leeson's phase noise equation and the impulse sensitivity function (ISF). Quadrature VCOs with the phase-adjusting architectures are fabricated using 180-nm TSMC CMOS and show low-phase-noise performances compared to a conventional differential VCO. The ISF analysis and small-signal analysis also show that the drawbacks of the current-weight-average phase-adjusting and voltage-weight-average phase-adjusting architectures are current-source noise effect and large additional capacitance, respectively. A voltage-average-adjusting circuit with a source follower at its input alleviates the capacitance increase.

Deep neural networks (DNNs) perform well for image recognition, speech recognition, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a data sample created by adding a small amount of noise to an original sample in such a way that it is difficult for humans to identify but that will cause the sample to be misclassified by a target model. In a military environment, adversarial examples that are correctly classified by a friendly model while deceiving an enemy model may be useful. In this paper, we propose a method for generating a selective adversarial example that is correctly classified by a friendly gait recognition system and misclassified by an enemy gait recognition system. The proposed scheme generates the selective adversarial example by combining the loss for correct classification by the friendly gait recognition system with the loss for misclassification by the enemy gait recognition system. In our experiments, we used the CASIA Gait Database as the dataset and TensorFlow as the machine learning library. The results show that the proposed method can generate selective adversarial examples that have a 98.5% attack success rate against an enemy gait recognition system and are classified with 87.3% accuracy by a friendly gait recognition system.

Intelligent reconfigurable surfaces (IRS) have attracted much attention from both industry and academia due to their performance improving capability and low complexity for 6G wireless communication systems. In this letter, we introduce an IRS-assisted space-time line code (STLC) technique. The STLC was introduced as a promising technique to acquire the optimal diversity gain in 1×2 single-input multiple-output (SIMO) channel without channel state information at receiver (CSIR). Using the cosine similarity theorem, we propose a novel phase-steering technique for the proposed IRS-assisted STLC technique. We also mathematically characterize the proposed IRS-assisted STLC technique in terms of outage probability and bit-error rate (BER). Based on computer simulations, it is shown that the results of analysis shows well match with the computer simulation results for various communication scenarios.

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

In this letter, we propose an energy beamforming empowered relaying scheme for a batteryless IoT network, where wireless-powered relays are deployed between the hybrid access point (HAP) and batteryless IoT devices to assist the uplink information transmission from the devices to the HAP. In particular, the HAP first exploits energy beamforming to efficiently transmit radio frequency (RF) signals to transfer energy to the relays and as the incident signals to enable the information backscattering of batteryless IoT devices. Then, each relay uses the harvested energy to forward the decoded signals from its corresponding batteryless IoT device to the HAP, where the maximum-ratio combing is used for further performance improvement. To maximize the network sum-rate, the joint optimization of energy beamforming vectors at the HAP, network time scheduling, power allocation at the relays, and relection coefficient at the users is investigated. As the formulated problem is non-convex, we propose an alternating optimization algorithm with the variable substitution and semi-definite relaxation (SDR) techniques to solve it efficiently. Specifically, we prove that the obtained energy beamforming matrices are always rank-one. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve a significant sum-rate gain.

This paper proposes multi-input physical layer network coding (multi-input PLNC) for high speed wireless communication in two-dimensional wireless multihop networks. In the proposed PLNC, all the terminals send their packets simultaneously for the neighboring relays to maximize the network throughput in the first slot, and all the relays also do the same to the neighboring terminals in the second slot. Those simultaneous signal transmissions cause multiple signals to be received at the relays and the terminals. Signal reception in the multi-input PLNC uses multichannel filtering to mitigate the difficulties caused by the multiple signal reception, which enables the two-input PLNC to be applied. In addition, a non-linear precoding is proposed to reduce the computational complexity of the signal detection at the relays and the terminals. The proposed multi-input PLNC makes all the terminals exchange their packets with the neighboring terminals in only two time slots. The performance of the proposed multi-input PLNC is confirmed by computer simulation. The proposed multi-input physical layer network coding achieves much higher network throughput than conventional techniques in a two-dimensional multihop wireless network with 7 terminals. The proposed multi-input physical layer network coding attains superior transmission performance in wireless hexagonal multihop networks, as long as more than 6 antennas are placed on the terminals and the relays.

Web application second-order vulnerabilities first inject malicious code into the persistent data stores of the web server and then execute it at later sensitive operations, causing severe impact. Nevertheless, the dynamic features, the complex data propagation, and the inter-state dependencies bring many challenges in discovering such vulnerabilities. To address these challenges, we propose DISOV, a web application property graph (WAPG) based method to discover second-order vulnerabilities. Specifically, DISOV first constructs WAPG to represent data propagation and inter-state dependencies of the web application, which can be further leveraged to find the potential second-order vulnerabilities paths. Then, it leverages fuzz testing to verify the potential vulnerabilities paths. To verify the effectiveness of DISOV, we tested it in 13 popular web applications in real-world and compared with Black Widow, the state-of-the-art web vulnerability scanner. DISOV discovered 43 second-order vulnerabilities, including 23 second-order XSS vulnerabilities, 3 second-order SQL injection vulnerabilities, and 17 second-order RCE vulnerabilities. While Black Widow only discovered 18 second-order XSS vulnerabilities, with none second-order SQL injection vulnerability and second-order RCE vulnerability. In addition, DISOV has found 12 0-day second-order vulnerabilities, demonstrating its effectiveness in practice.

This study examined whether distance learning in a first-year PBL courses in the first unit of instruction improves the effectiveness of subsequent group work learning over face-to-face learning. The first-year PBL consisted of three units: an input unit, a group work unit and an outcomes presentation unit. In 2017/2018, the input unit was conducted in the classroom with face-to-face learning. In 2017, a workshop was held in addition to face-to-face learning in classroom. In 2020/2021, the input unit was conducted with distance learning. In the years, approximately 100 people completed the questionnaire. A preliminary check confirmed that the average score of students' self-assessment of their own social skills were not significantly different among the four years. Analysis showed that in 2018, the perceived efficacy in the group work unit depended on learners' high social skills. Alternatively, in 2017/2020/2021, the perceived efficacy in group work was not dependent on learners' social skills. This suggests that distance learning and face-to-face learning with workshop learning, instead of full face-to-face learning for the units placed before the group work unit facilitates the learning efficacy of the group work unit, even for students with social skill concerns.

In this study, we have proposed a millimeter-wave (MMW) single-pixel imaging (SPI) system with a liquid-crystal (LC) mask cell. The LC cell functions as an electrically switchable mask based on the change in absorption properties, which depend on the orientation of the LC. We investigated the influence of noise on the measured and estimated data (reconstructed image). The proposed system exhibited moderate robustness against random noise (that were added) compared to raster scan-based and Hadamard matrix-based SPI systems. Finally, the results of some demonstrative experiments were introduced to ensure the applicability of the constructed MMW-SPI system, and steps for improving the reconstructed image quality were discussed.

We present a mathematical formulation of a trust metric using a quality and quantity pair. Under a certain assumption, we regard trust as an additive value and define the soundness of a trust computation as not to exceed the total sum. Moreover, we point out the importance of not only soundness of each computed trust but also the stability of the trust computation procedure against changes in trust value assignment. In this setting, we define trust composition operators. We also propose a trust computation protocol and prove its soundness and stability using the operators.

On an overlay network where a number of nodes work autonomously in a decentralized way, the efficiency of broadcasts has a significant impact on the performance of distributed systems built on the network. While a broadcast method using a spanning tree produces a small number of messages, the routing path lengths are prone to be relatively large. Moreover, when multiple nodes can be source nodes, inefficient broadcasts often occur because the efficient tree topology differs for each node. To address this problem, we propose a novel protocol in which a source node selects an efficient tree from multiple spanning trees when broadcasting. Our method shortens routing paths while maintaining a small number of messages. We examined path lengths and the number of messages for broadcasts on various topologies. As a result, especially for a random graph, our proposed method shortened path lengths by approximately 28% compared with a method using a spanning tree, with almost the same number of messages.

The mean, median, and mode are usually calculated from univariate observations as the most basic representative values of a random variable. To measure the spread of the distribution, the standard deviation, interquartile range, and modal interval are also calculated. When we analyze continuous relations between a pair of random variables from bivariate observations, regression analysis is often used. By minimizing appropriate costs evaluating regression errors, we estimate the conditional mean, median, and mode. The conditional standard deviation can be estimated if the bivariate observations are obtained from a Gaussian process. Moreover, the conditional interquartile range can be calculated for various distributions by the quantile regression that estimates any conditional quantile (percentile). Meanwhile, the study of the modal interval regression is relatively new, and spline regression models, known as flexible models having the optimality on the smoothness for bivariate data, are not yet used. In this paper, we propose a modal interval regression method based on spline quantile regression. The proposed method consists of two steps. In the first step, we divide the bivariate observations into bins for one random variable, then detect the modal interval for the other random variable as the lower and upper quantiles in each bin. In the second step, we estimate the conditional modal interval by constructing both lower and upper quantile curves as spline functions. By using the spline quantile regression, the proposed method is widely applicable to various distributions and formulated as a convex optimization problem on the coefficient vectors of the lower and upper spline functions. Extensive experiments, including settings of the bin width, the smoothing parameter and weights in the cost function, show the effectiveness of the proposed modal interval regression in terms of accuracy and visual shape for synthetic data generated from various distributions. Experiments for real-world meteorological data also demonstrate a good performance of the proposed method.