Universities collect and process a massive amount of Personal Identifiable Information (PII) at registration and throughout interactions with individuals. However, student PII can be exposed to the public by uploading documents along with university notice without consent and awareness, which could put individuals at risk of a variety of different scams, such as identity theft, fraud, or phishing. In this paper, we perform an in-depth analysis of student PII leakage at Vietnamese universities. To the best of our knowledge, we are the first to conduct a comprehensive study on student PII leakage in higher educational institutions. We find that 52.8% of Vietnamese universities leak student PII, including one or more types of personal data, in documents on their websites. It is important to note that the compromised PII includes sensitive types of data, student medical record and religion. Also, student PII leakage is not a new phenomenon and it has happened year after year since 2005. Finally, we present a study with 23 Vietnamese university employees who have worked on student PII to get a deeper understanding of this situation and envisage concrete solutions. The results are entirely surprising: the employees are highly aware of the concept of student PII. However, student PII leakage still happens due to their working habits or the lack of a management system and regulation. Therefore, the Vietnamese university should take a more active stand to protect student data in this situation.

Recent studies have shown that concurrent transmission with precise time synchronization enables reliable and efficient flooding for wireless networks. However, most of them require all nodes in the network to forward packets a fixed number of times to reach the destination, which leads to unnecessary energy consumption in both one-to-one and many-to-one communication scenarios. In this letter, we propose G1M address this issue by reducing redundant packet forwarding in concurrent transmissions. The evaluation of G1M shows that compared with LWB, the average energy consumption of one-to-one and many-to-one transmission is reduced by 37.89% and 25%, respectively.

This paper presents a deep network based on unrolling the diffusion process with the morphological Laplacian. The diffusion process is an iterative algorithm that can solve the diffusion equation and represents time evolution with Laplacian. The diffusion process is applied to smoothing of images and has been extended with non-linear operators for various image processing tasks. In this study, we introduce the morphological Laplacian to the basic diffusion process and unwrap to deep networks. The morphological filters are non-linear operators with parameters that are referred to as structuring elements. The discrete Laplacian can be approximated with the morphological filters without multiplications. Owing to the non-linearity of the morphological filter with trainable structuring elements, the training uses error back propagation and the network of the morphology can be adapted to specific image processing applications. We introduce two extensions of the morphological Laplacian for deep networks. Since the morphological filters are realized with addition, max, and min, the error caused by the limited bit-length is not amplified. Consequently, the morphological parts of the network are implemented in unsigned 8-bit integer with single instruction multiple data set (SIMD) to achieve fast computation on small devices. We applied the proposed network to image completion and Gaussian denoising. The results and computational time are compared with other denoising algorithm and deep networks.

For massive multiple-input multiple-output (MIMO) communication systems, simple linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) can achieve near-optimal detection performance with reduced computational complexity. However, such linear detectors always involve complicated matrix inversion, which will suffer from high computational overhead in the practical implementation. Due to the massive parallel-processing and efficient hardware-implementation nature, the neural network has become a promising approach to signal processing for the future wireless communications. In this paper, we first propose an efficient neural network to calculate the pseudo-inverses for any type of matrices based on the improved Newton's method, termed as the PINN. Through detailed analysis and derivation, the linear massive MIMO detectors are mapped on PINNs, which can take full advantage of the research achievements of neural networks in both algorithms and hardwares. Furthermore, an improved limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is studied as the learning algorithm of PINNs to achieve a better performance/complexity trade-off. Simulation results finally validate the efficiency of the proposed scheme.

We thank Kamata et al. (2023) [1] for their interest in our work [2], and for providing an explanation of the quasi-linear kernel from a viewpoint of multiple kernel learning. In this letter, we first give a summary of the quasi-linear SVM. Then we provide a discussion on the novelty of quasi-linear kernels against multiple kernel learning. Finally, we explain the contributions of our work [2].

We present our data-collection and deep neural network (DNN)-training scheme for extracting the optical status from signals received by digital coherent optical receivers in fiber-optic networks. The DNN is trained with unlabeled datasets across multiple administrative network domains by combining federated learning and unsupervised learning. The scheme allows network administrators to train a common DNN-based encoder that extracts optical status in their networks without revealing their private datasets. An early-stage proof of concept was numerically demonstrated by simulation by estimating the optical signal-to-noise ratio and modulation format with 64-GBd 16QAM and quadrature phase-shift keying signals.

In this paper, a wireless communication network that uses unmanned aerial vehicles (UAVs) in the sky to transmit information between ground users is considered. We highlight a delay-tolerant network, where information is relayed in a store-and-forward fashion by establishing two types of intermittent communication links: between a UAV and a user (UAV-to-user) and between UAVs (UAV-to-UAV). Thus, a flight algorithm that controls the movement of the UAVs is crucial in achieving rapid information transmission. Our study proposes new flight algorithms that simultaneously consider the two types of communication links. In UAV-to-UAV links, the direct information transmission between two UAVs and the indirect transmission through other UAVs are considered separately. The movement of the UAVs is controlled by solving an optimization problem at certain time intervals, with a variable consideration ratio of the two types of links. In addition, we investigate not only the case where all UAVs move cooperatively but also the case where each UAV moves autonomously. Simulation results show that the proposed algorithms are effective. Moreover, they indicate the existence of an optimal consideration ratio of the two types of communication and demonstrate that our approach enables the control of frequencies of establishing the communication links. We conclude that increasing the frequency of indirect communication between UAVs improves network performance.

When mission-critical applications are provided over a network, high availability is required in addition to a low delay. This paper proposes a multi-homing network design model, named MHND, that achieves low delay, high availability, and the order guarantee of events. MHND maintains the event occurrence order with a multi-homing configuration using conservative synchronization. We formulate MHND as an integer linear programming problem to minimize the delay. We prove that the distributed server allocation problem with MHND is NP-complete. Numerical results indicate that, as a multi-homing number, which is the number of servers to which each user belongs, increases, the availability increases while increasing the delay. Noteworthy, two or more multi-homing can achieve approximately an order of magnitude higher availability compared to that of conventional single-homing at the expense of a delay increase up to two times. By using MHND, flexible network design is achieved based on the acceptable delay in service and the required availability.

Non-Terrestrial-Network (NTN) can provide seamless and ubiquitous connectivity of massive devices. Thus, the feeder links between satellites and gateways need to provide essentially high data transmission rates. In this paper, we focus on a typical high-capacity scenario, i.e., LEO-IoT, to find an optimal satellite selection schema to maximize the capacity of feeder links. The proposed schema is able to obtain the optimal mapping among all the satellites and gateways. By comparing with maximum service time algorithm, the proposed schema can construct a more balanced and reasonable connection pattern to improve the efficiency of the gateways. Such an advantage will become more significant as the number of satellites increases.

In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.

This paper proposes overloaded multiple input multiple output (MIMO) bi-directional communication with physical layer network coding (PLNC) to enhance the transmission speed in heterogeneous wireless multihop networks where the number of antennas on the relay is less than that on the terminals. The proposed overloaded MIMO communication system applies precoding and relay filtering to reduce computational complexity in spite of the transmission speed. An eigenvector-based filter is proposed for the relay filter. Furthermore, we propose a technique to select the best filter among candidates eigenvector-based filters. The performance of the proposed overloaded MIMO bi-directional communication is evaluated by computer simulation in a heterogeneous wireless 2-hop network. The proposed filter selection technique attains a gain of about 1.5dB at the BER of 10-5 in a 2-hop network where 2 antennas and 4 antennas are placed on the relay and the terminal, respectively. This paper shows that 6 stream spatial multiplexing is made possible in the system with 2 antennas on the relay.

Radar-based sensing and concealed weapon detection technologies have been attracting attention as a measure to enhance security screening in public facilities and various venues. For these applications, the security check must be performed without impeding the flow of people, with minimum human effort, and in a non-contact manner. We developed technologies for a high-throughput walk-through security screening called Invisible Sensing (IVS) and implemented them in a prototype system. The IVS system consists of dual planar radar panels facing each other and carries out an inspection based on a multi-region screening approach as a person walks between the panels. Our imaging technology constructs a high-quality radar image that compensates for motion blur caused by a person's walk. Our detection technology takes multi-view projected images across the multiple regions as input to enable real-time whole-body screening. The IVS system runs its functions by pipeline processing to achieve real-time screening operation. This paper presents our IVS system along with these key technologies and demonstrates its empirical performance.

Precision microwave measurement systems require highly stable oscillators with both excellent long-term and short-term stability. Compared to components used in laboratory instruments, dielectric resonator oscillators (DRO) offer low phase noise with greatly reduced mechanical complexity. To further enhance performance, phase-locked loop (PLL) stabilization can be used to eliminate drift and provide precise frequency control. In this work, the design of a low-cost DRO concept is presented and its performance is evaluated through simulations and measurements. An open-loop phase noise of -107.2 dBc/Hz at 10 kHz offset frequency and 12.8 GHz output frequency is demonstrated. Drift and phase noise are reduced by a PLL, so that a very low jitter of under 29.6 fs is achieved over the entire operating bandwidth.

In this paper, we describe a wavelength-division multiplexing visible-light communication (VLC) system using two colored light-emitting diodes (LEDs) with similar emission wavelengths. A multi-input multi-output signal-separation method using a neural network is proposed to cancel the optical cross chatter caused by the spectral overlap of LEDs. The experimental results demonstrate that signal separation using neural networks can be achieved in wavelength-multiplexed VLC systems with a bit error rate of less than 3.8×10-3 (forward error correction limit). Furthermore, the simulation results reveal that the carrier-to-noise ratio (CNR) is improved by 2dB for the successive interference canceller (SIC) compared to the zero-forcing method.

FPGA clusters that consist of multiple FPGA boards have been gaining interest in recent times. Massively parallel processing with a stand-alone heterogeneous FPGA cluster with SoC- style FPGAs and mid-scale FPGAs is promising with cost-performance benefit. Here, we propose such a heterogeneous FPGA cluster with FiC and M-KUBOS cluster. FiC consists of multiple boards, mounting middle scale Xilinx's FPGAs and DRAMs, which are tightly coupled with high-speed serial links. In addition, M-KUBOS boards are connected to FiC for ensuring high IO data transfer bandwidth. As an example of massively parallel processing, here we implement genomic pattern search. Next-generation sequencing (NGS) technology has revolutionized biological system related research by its high-speed, scalable and massive throughput. To analyze the genomic data, short read mapping technique is used where short Deoxyribonucleic acid (DNA) sequences are mapped relative to a known reference sequence. Although several pattern matching techniques are available, FM-index based pattern search is perfectly suitable for this task due to the fastest mapping from known indices. Since matching can be done in parallel for different data, the massively parallel computing which distributes data, executes in parallel and gathers the results can be applied. We also implement a data compression method where about 10 times reduction in data size is achieved. We found that a M-KUBOS board matches four FiC boards, and a system with six M-KUBOS boards and 24 FiC boards achieved 30 times faster than the software based implementation.

Existing simple routing protocols (e.g., OSPF, RIP) have some disadvantages of being inflexible and prone to congestion due to the concentration of packets on particular routers. To address these issues, packet routing methods using machine learning have been proposed recently. Compared to these algorithms, machine learning based methods can choose a routing path intelligently by learning efficient routes. However, machine learning based methods have a disadvantage of training time overhead. We thus focus on a lightweight machine learning algorithm, OS-ELM (Online Sequential Extreme Learning Machine), to reduce the training time. Although previous work on reinforcement learning using OS-ELM exists, it has a problem of low learning accuracy. In this paper, we propose OS-ELM QN (Q-Network) with a prioritized experience replay buffer to improve the learning performance. It is compared to a deep reinforcement learning based packet routing method using a network simulator. Experimental results show that introducing the experience replay buffer improves the learning performance. OS-ELM QN achieves a 2.33 times speedup than a DQN (Deep Q-Network) in terms of learning speed. Regarding the packet transfer latency, OS-ELM QN is comparable or slightly inferior to the DQN while they are better than OSPF in most cases since they can distribute congestions.

An interconnection network is an inevitable component for constructing parallel computers. It connects computation nodes so that the nodes can communicate with each other. As a parallel computation essentially requires inter-node communication according to a parallel algorithm, the interconnection network plays an important role in terms of communication performance. This paper focuses on the collective communication that is frequently performed in parallel computation and this paper addresses the Cup-Stacking method that is proposed in our preceding work. The key issues of the method are splitting a large packet into slices, re-shaping the slice, and stacking the slices, in a genetic algorithm (GA) manner. This paper discusses extending the Cup-Stacking method by introducing additional items (genes) and proposes the extended Cup-Stacking method. Furthermore, this paper places comprehensive discussions on the drawbacks and further optimization of the method. Evaluation results reveal the effectiveness of the extended method, where the proposed method achieves at most seven percent improvement in duration time over the former Cup-Stacking method.

We assess how well the recently created MRI reconstruction technique, Multi-Resolution Convolutional Neural Network (MRCNN), performs in the core medical vision field (classification). The primary goal of MRCNN is to identify the best k-space undersampling patterns to accelerate the MRI. In this study, we use the Figshare brain tumor dataset for MRI classification with 3064 T1-weighted contrast-enhanced MRI (CE-MRI) over three categories: meningioma, glioma, and pituitary tumors. We apply MRCNN to the dataset, which is a method to reconstruct high-quality images from under-sampled k-space signals. Next, we employ the pre-trained VGG16 model, which is a Deep Neural Network (DNN) based image classifier to the MRCNN restored MRIs to classify the brain tumors. Our experiments showed that in the case of MRCNN restored data, the proposed brain tumor classifier achieved 92.79% classification accuracy for a 10% sampling rate, which is slightly higher than that of SRCNN, MoDL, and Zero-filling methods have 91.89%, 91.89%, and 90.98% respectively. Note that our classifier was trained using the dataset consisting of the images with full sampling and their labels, which can be regarded as a model of the usual human diagnostician. Hence our results would suggest MRCNN is useful for human diagnosis. In conclusion, MRCNN significantly enhances the accuracy of the brain tumor classification system based on the tumor location using under-sampled k-space signals.

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.

Modern distributed storage requires microsecond-scale tail latency, but the current coordinator-based quorum coordination causes a burdensome latency overhead. This paper presents Archon, a new quorum coordination architecture that supports low tail latency for microsecond-scale replicated storage. The key idea of Archon is to perform the quorum coordination in the network switch by leveraging the flexibility and capability of emerging programmable switch ASICs. Our in-network quorum coordination is based on the observation that the modern programmable switch provides nanosecond-scale processing delay and high flexibility simultaneously. To realize the idea, we design a custom switch data plane. We implement a Archon prototype on an Intel Tofino switch and conduct a series of testbed experiments. Our experimental results show that Archon can provide lower tail latency than the coordinator-based solution.