Variable strength orthogonal array, as a special form of variable strength covering array, plays an important role in computer software testing and cryptography. In this paper, we study the construction of variable strength orthogonal arrays with strength two containing strength greater than two by Galois field and construct some variable strength orthogonal arrays with strength l containing strength greater than l by Fan-construction.

We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.

We propose a short signature scheme under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction [Ducas and Micciancio, CRYPTO 2014], we demonstrate lattice-based signatures with improved reduction loss. As far as we know, there are no ways to use multiple tags in the signature simulation of security proof in the lattice tag-based signatures. We address the tag-collision possibility in the lattice setting, which improves reduction loss. Our scheme generates tags from messages by constructing a scheme under a mild security condition that is existentially unforgeable against random message attack with auxiliary information. Thus our scheme can reduce the signature size since it does not need to send tags with the signatures. Our scheme has short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.'s scheme. Our proposed scheme has two variants. Our scheme with one property has tighter reduction and the same verification key size of O(log n) as that of Ducas et al.'s scheme, where n is the security parameter. Our scheme with the other property achieves much tighter reduction loss of O(Q/n) and verification key size of O(n), where Q is the number of signing queries.

Ramp secret sharing is a variant of secret sharing which can achieve better information ratio than perfect schemes by allowing some partial information on a secret to leak out. Strongly secure ramp schemes can control the amount of leaked information on the components of a secret. In this paper, we reduce the construction of strongly secure ramp secret sharing for general access structures to a linear algebraic problem. As a result, we show that previous results on strongly secure network coding imply two linear transformation methods to make a given linear ramp scheme strongly secure. They are explicit or provide a deterministic algorithm while the previous methods which work for any linear ramp scheme are non-constructive. In addition, we present a novel application of strongly secure ramp schemes to symmetric PIR in a multi-user setting. Our solution is advantageous over those based on a non-strongly secure scheme in that it reduces the amount of communication between users and servers and also the amount of correlated randomness that servers generate in the setup.

Backscatter communication is an emerging wireless access technology to realize ultra-low power terminals exploiting the modulated reflection of incident radio wave. This paper proposes a method to measure the phase angle of backscatter link using principal component analysis (PCA). The phase angle measurement of backscatter link at the receiver is essential to maximize the signal quality for subsequent demodulation and to measure the distance and the angle of arrival. The drawback of popular phase angle measurement with naive phase averaging and linear regression analysis is to produce erroneous phase angle, where the phase angle is close to $pmrac{pi}{2}$ radian and the signal quality is poor. The advantage of the proposal is quantified with a computer simulation, a conducted experiment and radio propagation experiments.

This study develops a new automatic hovering control method based on just-in-time modeling for a multicopter. Especially, the main aim is to compute gains of a feedback control law such that the multicopter hovers at a desired height and at a desired time without overshoot/undershoot. First, a database that contains various hovering data is constructed, and then the proposed method computes gains for a query input from the database. From simulation results, it turns out that the multicopter achieves control purposes, and hence the new method is effective.

As a key technology of 5G, NFV has attracted much attention. In addition, monitoring plays an important role, and can be widely used for virtual network function placement and resource optimisation. The existing monitoring methods focus on the monitoring load without considering they own resources needed. This raises a unique challenge: jointly optimising the NFV monitoring systems and minimising their monitoring load at runtime. The objective is to enhance the gain in real-time monitoring metrics at minimum monitoring costs. In this context, we propose a novel NFV monitoring solution, namely, iMon (Monitoring by inferring), that jointly optimises the monitoring process and reduces resource consumption. We formalise the monitoring process into a multitarget regression problem and propose three regression models. These models are implemented by a deep neural network, and an experimental platform is built to prove their availability and effectiveness. Finally, experiments also show that monitoring resource requirements are reduced, and the monitoring load is just 0.6% of that of the monitoring tool cAdvisor on our dataset.

Electromagnetic scattering in a coaxial cable having two flanges and concentric grooves is studied. The associated Weber-Orr transform is used to represent electromagnetic fields in an infinitely long cavity, and the mode-matching method is used to enforce boundary continuity. S-parameters obtained by our approach are compared with the reference solutions, and the characteristics are discussed when geometric parameters are varied. The results show that the proposed model provides cost effective and accurate solutions to the problem.

A new method for hiding information in digital images is proposed. Our method differs from existing techniques in that the information is hidden in a mixture of colors carefully tuned on a specific device according to the device's signal-to-luminance (gamma) characteristics. Because these reproduction characteristics differ in general from device to device and even from model to model, the hidden information appears when the cover image is viewed on a different device, and hence the hiding property is device-dependent. To realize this, we modulated a cover image using two identically-looking checkerboard patterns and switched them locally depending on the hidden information. Reproducing these two patterns equally on a different device is difficult. A possible application of our method would be secure printing where an image is allowed to be viewed only on a screen but a warning message appears when it is printed.

Multiple wireless communication systems are often operated together in the same area in such manufacturing sites as factories where wideband noise may be emitted from industrial equipment over channels for wireless communication systems. To perform highly reliable wireless communication in such environments, radio wave environments must be monitored that are specific to each manufacturing site to find channels and timing that enable stable communication. The authors studied technologies using machine learning to efficiently analyze a large amount of monitoring data, including signals whose spectrum shape is undefined, such as electromagnetic noise over a wideband. In this paper, we generated common supervised data for multiple sensors by conjointly clustering features after normalizing those calculated in each sensor to recognize the signal reception timing from identical sources and eliminate the complexity of supervised data management. We confirmed our method's effectiveness through signal models and actual data sampled by sensors that we developed.

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.

For a long time, the compensation effect of hearing aid is mainly evaluated subjectively, and there are fewer studies of objective evaluation. Furthermore, a pure speech signal is generally required as a reference in the existing objective evaluation methods, which restricts the practicality in a real-world environment. Therefore, this paper presents a non-intrusive speech quality evaluation method for hearing aid, which combines the audiogram and weighted frequency information. The proposed model mainly includes an audiogram information extraction network, a frequency information extraction network, and a quality score mapping network. The audiogram is the input of the audiogram information extraction network, which helps the system capture the information related to hearing loss. In addition, the low-frequency bands of speech contain loudness information and the medium and high-frequency components contribute to semantic comprehension. The information of two frequency bands is input to the frequency information extraction network to obtain time-frequency information. When obtaining the high-level features of different frequency bands and audiograms, they are fused into two groups of tensors that distinguish the information of different frequency bands and used as the input of the attention layer to calculate the corresponding weight distribution. Finally, a dense layer is employed to predict the score of speech quality. The experimental results show that it is reasonable to combine the audiogram and the weight of the information from two frequency bands, which can effectively realize the evaluation of the speech quality of the hearing aid.

It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.

The prosperous Internet communication technologies have led to e-commerce in mobile computing and made Web of Things become popular. Electronic payment is the most important part of e-commerce, so many electronic payment schemes have been proposed. However, most of proposed schemes cannot give change. Based on proxy blind signatures, an e-cash payment system is proposed in this paper to solve this problem. This system can not only provide change divisibility through Web of Things, but also provide anonymity, verifiability, unforgeability and double-spending owner track.

End-to-end loss and delay are both fundamental metrics in network performance evaluation, and accurate measurements for these end-to-end metrics are one of the keys to keeping delay/loss-sensitive applications (e.g., audio/video conferencing, IP telephony, or telesurgery) comfortable on networks. In our previous work [1], we proposed a parallel flow monitoring method that can provide accurate active measurements of end-to-end delay. In this method, delay samples of a target flow increase by utilizing the observation results of other flows sharing the source/destination with the target flow. In this paper, to improve accuracy of loss measurements, we propose a loss measurement method by extending our delay measurement method. Additionally, we improve the loss measurement method so that it enables to fully utilize information of all flows including flows with different source and destination. We evaluate the proposed method through theoretical and simulation analyses. The evaluations show that the accuracy of the proposed method is bounded by theoretical upper/lower bounds, and it is confirmed that it reduces the error of loss rate estimations by 57.5% on average.

A searchable symmetric encryption (SSE) scheme is a method that searches encrypted data without decrypting it. In this paper, we address the substring search problem such that for a set D of documents and a pattern p, we find all occurrences of p in D. Here, a document and a pattern are defined as a string. A directed acyclic word graph (DAWG), which is a deterministic finite automaton, is known for solving a substring search problem on a plaintext. We improve a DAWG so that all transitions of a DAWG have distinct symbols. Besides, we present a space-efficient and secure substring SSE scheme using an improved DAWG. The proposed substring SSE scheme consists of an index with a simple structure, and the size is O(n) for the total size n of documents.

This paper proposes a novel random access identifier (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter filtering (TF) to the proposed method for effective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.

In wireless sensor networks (WSNs), wireless power transfer (WPT) has been studied as an energy-harvesting technique for prolonging their network lifetime. The WPT can supply power resources to sensor nodes (SNs) wirelessly, however, the reception (harvesting) power at SNs depends on their distance from a WPT equipment (WPTE), leading to the location-dependent non-uniformity in the reception power among SNs. For the fixed-located WPTE, SNs distant from the WPTE suffer from insufficient reception power. To handle this problem, this paper proposes a novel network structure introducing multiple hybrid access points (HAPs), which equip two functions of conventional cluster head function, including data collection and relay transmission, and WPT function. Then, these HAPs take terms providing both functions. By periodically rotating the HAP providing the WPT function, the location of the WPTE can be changed, which reduces the non-uniformity in the SN reception power. Also, this paper proposes a clustering scheme based on the residual power at SNs to reduce their power depletion under the proposed network structure. The evaluation results through computer simulation show that the proposed system reduces the non-uniformity in the SN reception power and the power depletion at the SNs and then improves the data collection rate, compared with the conventional systems.

SiC-MOSFETs are being increasingly implemented in power electronics systems as low-loss, fast switching devices. Despite the advantages of an SiC-MOSFET, its large dv/dt or di/dt has fear of electromagnetic interference (EMI) noise. This paper proposes and demonstrates a simple and robust gate driver that can suppress ringing oscillation and surge voltage induced by the turn-off of the SiC-MOSFET body diode. The proposed gate driver utilizes the channel leakage current methodology (CLC) to enhance the damping effect by elevating the gate-source voltage (VGS) and inducing the channel leakage current in the device. The gate driver can self-adjust the timing of initiating CLC operation, which avoids an increase in switching loss. Additionally, the output voltage of the VGS elevation circuit does not need to be actively controlled in accordance with the operating conditions. Thus, the circuit topology is simple, and ringing oscillation can be easily attenuated with fixed circuit parameters regardless of operating conditions, minimizing the increase in switching loss. The effectiveness and versatility of proposed gate driver were experimentally validated for a wide range of operating conditions by double and single pulse switching tests.