With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.

Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to efectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance.

In this paper, we developed the latest IoT honeypots to capture IoT malware currently on the loose, analyzed IoT malware with new features such as persistent infection, developed malware removal methods to be provided to IoT device users. Furthermore, as attack behaviors using IoT devices become more diverse and sophisticated every year, we conducted research related to various factors involved in understanding the overall picture of attack behaviors from the perspective of incident responders. As the final stage of countermeasures, we also conducted research and development of IoT malware disabling technology to stop only IoT malware activities in IoT devices and IoT system disabling technology to remotely control (including stopping) IoT devices themselves.

We are given a set S of n points in the Euclidean plane. We assume that S is in general position. A simple polygon P is an empty polygon of S if each vertex of P is a point in S and every point in S is either outside P or a vertex of P. In this paper, we consider the problem of enumerating all the empty polygons of a given point set. To design an efficient enumeration algorithm, we use a reverse search by Avis and Fukuda with child lists. We propose an algorithm that enumerates all the empty polygons of S in O(n2|ε(S)|)-time, where ε(S) is the set of empty polygons of S. Moreover, by applying the same idea to the problem of enumerating surrounding polygons of a given point set S, we propose an enumeration algorithm that enumerates them in O(n2)-delay, while the known algorithm enumerates in O(n2 log n)-delay, where a surroundingpolygon of S is a polygon such that each vertex of the polygon is a point in S and every point in S is either inside the polygon or a vertex of the polygon.

The bin packing problem is a problem of finding an assignment of a sequence of items to a minimum number of bins, each of capacity one. An online algorithm for the bin packing problem is an algorithm that irrevocably assigns each item one by one from the head of the sequence. Gutin, Jensen, and Yeo (2006) considered a version in which all items are only of two different sizes and the online algorithm knows the two possible sizes in advance, and gave an optimal online algorithm for the case when the larger size exceeds 1/2. In this paper we provide an optimal online algorithm for some of the cases when the larger size is at most 1/2, on the basis of a framework that facilitates the design and analysis of algorithms.

Packet classification is used to determine the behavior of incoming packets in network devices according to defined rules. As it is achieved using a linear search on a classification rule list, a large number of rules will lead to longer communication latency. To solve this, the problem of finding the order of rules minimizing the latency has been studied. Misherghi et al. and Harada et al. have proposed a problem that relaxes to policy-based constraints. In this paper, we show that the Relaxed Optimal Rule Ordering (RORO) for the allowlist is NP-hard, and by reducing from this we show that RORO for the general rule list is NP-hard. We also propose a heuristic algorithm based on the greedy method for an allowlist. Furthermore, we demonstrate the effectiveness of our method using ClassBench, which is a benchmark for packet classification algorithms.

There are two types of elliptic curves, ordinary elliptic curves and supersingular elliptic curves. In 2012, Sutherland proposed an efficient and almost deterministic algorithm for determining whether a given curve is ordinary or supersingular. Sutherland's algorithm is based on sequences of isogenies started from the input curve, and computation of each isogeny requires square root computations, which is the dominant cost of the algorithm. In this paper, we reduce this dominant cost of Sutherland's algorithm to approximately a half of the original. In contrast to Sutherland's algorithm using j-invariants and modular polynomials, our proposed algorithm is based on Legendre form of elliptic curves, which simplifies the expression of each isogeny. Moreover, by carefully selecting the type of isogenies to be computed, we succeeded in gathering square root computations at two consecutive steps of Sutherland's algorithm into just a single fourth root computation (with experimentally almost the same cost as a single square root computation). The results of our experiments using Magma are supporting our argument; for cases of characteristic p of 768-bit to 1024-bit lengths, our proposed algorithm for characteristic p≡1 (mod 4) runs in about 61.5% of the time and for characteristic p≡3 (mod 4) also runs in about 54.9% of the time compared to Sutherland's algorithm.

Authenticated Key Exchange (AKE) is a cryptographic protocol to share a common session key among multiple parties. Usually, PKI-based AKE schemes are designed to guarantee secrecy of the session key and mutual authentication. However, in practice, there are many cases where mutual authentication is undesirable such as in anonymous networks like Tor and Riffle, or difficult to achieve due to the certificate management at the user level such as the Internet. Goldberg et al. formulated a model of anonymous one-sided AKE which guarantees the anonymity of the client by allowing only the client to authenticate the server, and proposed a concrete scheme. However, existing anonymous one-sided AKE schemes are only known to be secure in the random oracle model. In this paper, we propose generic constructions of anonymous one-sided AKE in the random oracle model and in the standard model, respectively. Our constructions allow us to construct the first post-quantum anonymous one-sided AKE scheme from isogenies in the standard model.

Message franking is introduced by Facebook in end-to-end encrypted messaging services. It allows to produce verifiable reports of malicious messages by including cryptographic proofs, called reporting tags, generated by Facebook. Recently, Grubbs et al. (CRYPTO'17) proceeded with the formal study of message franking and introduced committing authenticated encryption with associated data (CAEAD) as a core primitive for obtaining message franking. In this work, we aim to enhance the security of message franking and introduce forward security and updates of reporting tags for message franking. Forward security guarantees the security associated with the past keys even if the current keys are exposed and updates of reporting tags allow for reporting malicious messages after keys are updated. To this end, we firstly propose the notion of key-evolving message franking with updatable reporting tags including additional key and reporting tag update algorithms. Then, we formalize five security requirements: confidentiality, ciphertext integrity, unforgeability, receiver binding, and sender binding. Finally, we show a construction of forward secure message franking with updatable reporting tags based on CAEAD, forward secure pseudorandom generator, and updatable message authentication code.

A fault-tolerant aggregate signature (FT-AS) scheme is a variant of an aggregate signature scheme with the additional functionality to trace signers that create invalid signatures in case an aggregate signature is invalid. Several FT-AS schemes have been proposed so far, and some of them trace such rogue signers in multi-rounds, i.e., the setting where the signers repeatedly send their individual signatures. However, it has been overlooked that there exists a potential attack on the efficiency of bandwidth consumption in a multi-round FT-AS scheme. Since one of the merits of aggregate signature schemes is the efficiency of bandwidth consumption, such an attack might be critical for multi-round FT-AS schemes. In this paper, we propose a new multi-round FT-AS scheme that is tolerant of such an attack. We implement our scheme and experimentally show that it is more efficient than the existing multi-round FT-AS scheme if rogue signers randomly create invalid signatures with low probability, which for example captures spontaneous failures of devices in IoT systems.

HDR video is possible to display the maximum 1200% luminance, however, it is limited in SDR display. In this study, we expand high luminance area considering with perceptual performance to improve a presentation performance of HDR video in the SDR display. As results of objective experiments, it is recognized that the proposed method can improve the presentation performance maximally 0.8dB in WPSNR.

In this paper, we propose a low-complexity and accurate noise suppression based on an a priori SNR (Speech to Noise Ratio) model for greater robustness w.r.t. short-term noise-fluctuation. The a priori SNR, the ratio of speech spectra and noise spectra in the spectral domain, represents the difference between speech features and noise features in the feature domain, including the mel-cepstral domain and the logarithmic power spectral domain. This is because logarithmic operations are used for domain conversions. Therefore, an a priori SNR model can easily be expressed in terms of the difference between the speech model and the noise model, which are modeled by the Gaussian mixture models, and it can be generated with low computational cost. By using a priori SNRs accurately estimated on the basis of an a priori SNR model, it is possible to calculate accurate coefficients of noise suppression filters taking into account the variance of noise, without serious increase in computational cost over that of a conventional model-based Wiener filter (MBW). We have conducted in-car speech recognition evaluation using the CENSREC-2 database, and a comparison of the proposed method with a conventional MBW showed that the recognition error rate for all noise environments was reduced by 9%, and that, notably, that for audio-noise environments was reduced by 11%. We show that the proposed method can be processed with low levels of computational and memory resources through implementation on a digital signal processor.

In this paper, the notion of 2-tuples distribution matrices of the rotation symmetric orbits is proposed, by using the properties of the 2-tuples distribution matrix, a new characterization of 2-resilient rotation symmetric Boolean functions is demonstrated. Based on the new characterization of 2-resilient rotation symmetric Boolean functions, constructions of 2-resilient rotation symmetric Boolean functions (RSBFs) are further studied, and new 2-resilient rotation symmetric Boolean functions with prime variables are constructed.

Cell-free massive MIMO (CF-mMIMO), which cooperatively utilizes a large number of antennas deployed over a communication area, has been attracting great attention as an important technology for realizing 5G-advanced and 6G systems. Recently, to ensure system scalability and mitigate inter-user interference in CF-mMIMO, a user-centric (UC) approach was investigated. In this UC approach, user-centric antenna-sets are formed by selecting appropriate antennas for each user, and postcoding is applied to reduce the strong interference from users whose antenna-sets overlap. However, in very high user density environments, since the number of interfering users increases due to increased overlapping of antenna-sets, the achievable link capacity may degrade. In this paper, we propose a user-cluster-centric (UCC) approach, which groups neighborhood users into a user-cluster and associates the predetermined number of antennas to this user-cluster for spatial multiplexing. We derive the uplink postcoding weights and explain the effectiveness of the proposed UCC approach in terms of the computational complexity of the weight computation. We also compare the uplink user capacities achievable with UC and UCC approaches by computer simulation and clarify situations where the UCC approach is effective. Furthermore, we discuss the impact of the number of interfering users considered in the zero-forcing and minimum mean square error postcoding weight computation on the user capacity.

This paper proposes two full-digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via Gaussian belief propagation (GaBP) at base stations (BSs) adopting massive multi-input multi-output (MIMO) for open radio access network (O-RAN). In addition, beyond fifth generation mobile communication (beyond 5G) systems will increase uplink capacity. In the scenarios such as O-RAN and beyond 5G, it is vital to reduce the cost of the BSs by limiting the bandwidth of fronthaul (FH) links, and the dimensionality reduction of the received signal based on the receive BF at a radio unit is a well-known strategy to reduce the amount of data transported via the FH links. In this paper, we clarify appropriate criteria for designing a BF weight considering the subsequent GaBP signal detection with the proposed methods: singular-value-decomposition-based BF and QR-decomposition-based BF with the aid of discrete-Fourier-transformation-based spreading. Both methods achieve the dimensionality reduction without compromising the desired signal power by taking advantage of a null space of channels. The proposed receive BF methods reduce correlations between the received signals in the BF domain, which improves the robustness of GaBP against spatial correlation among fading coefficients. Simulation results assuming realistic BS and user equipment arrangement show that the proposed methods improve detection capability while significantly reducing the computational cost.

A 2-D beam scanning array antenna fed by a compact 16-way 2-D beamforming network (BFN) designed in Broadside Coupled Stripline (BCS) is addressed. The proposed 16-way 2-D BFN is formed by interconnecting two groups of 4x4 Butler Matrix (BM). Each group is composed of four compact 4x4 BMs. The critical point of the design is to propose a simple and compact 4x4 BM without crossover in BCS to achieve a better transmission coefficient of the 16-way 2-D BFN with reduced size of merely 0.8λ0×0.8λ0×0.04λ0. Moreover, the complexity of the interface connection between the 2-D BFN and the 4x4 patch array antenna is reduced by using probe feeding. The 16-way 2-D BFN is able to produce the phase shift of ±45°, and ±135° in x- and y- directions. The 2-D BFN is easily integrated under the 4x4 patch array to form a 2-D phased array capable of switching 16 beams in both elevation and azimuth directions. The area of the proposed 2-D beam scanning array antenna module has been significantly reduced to 2λ0×2λ0×0.04λ0. A prototype operating in the frequency range of 4-6GHz is fabricated and measured to validate the concept. The measurement results agree well with the simulations.

Nowadays, in various wireless sensor networks, both aperiodically generated packets like event detections and periodically generated ones for environmental, machinery, vital monitoring, etc. are transferred. Thus, packet loss caused by collision should be suppressed among aperiodic and periodic packets. In addition, some packets for wireless applications such as factory IoT must be transferred within permissible end-to-end delays, in addition to improving packet loss. In this paper, we propose transmission timing control of both aperiodic and periodic packets at an upper layer of medium access control (MAC). First, to suppress packet loss caused by collision, transmission timings of aperiodic and periodic packets are distributed on the time axis. Then, transmission timings of delay-bounded packets with permissible delays are assigned within the bounded periods so that transfer within their permissible delays is possible to maximally satisfy their permissible delays. Such control at an upper layer has advantages of no modification to the MAC layer standardized by IEEE 802.11, 802.15.4, etc. and low sensor node cost, whereas existing approaches at the MAC layer rely on MAC modifications and time synchronization among all sensor nodes. Performance evaluation verifies that the proposed transmission timing control improves packet loss rate regardless of the presence or absence of packet's periodicity and permissible delay, and restricts average transfer delay of delay-bounded packets within their permissible delays comparably to a greedy approach that transmits delay-bounded packets to the MAC layer immediately when they are generated at an upper layer.

Machine learning (ML) has been used for various tasks in network operations in recent years. However, since the scale of networks has grown and the amount of data generated has increased, it has been increasingly difficult for network operators to conduct their tasks with a single server using ML. Thus, ML with edge-cloud cooperation has been attracting attention for efficiently processing and analyzing a large amount of data. In the edge-cloud cooperation setting, although transmission latency, bandwidth congestion, and accuracy of tasks using ML depend on the load balance of processing data with edge servers and a cloud server in edge-cloud cooperation, the relationship is too complex to estimate. In this paper, we focus on monitoring anomalous traffic as an example of ML tasks for network operations and formulate transmission latency, bandwidth congestion, and the accuracy of the task with edge-cloud cooperation considering the ratio of the amount of data preprocessed in edge servers to that in a cloud server. Moreover, we formulate an optimization problem under constraints for transmission latency and bandwidth congestion to select the proper ratio by using our formulation. By solving our optimization problem, the optimal load balance between edge servers and a cloud server can be selected, and the accuracy of anomalous traffic monitoring can be estimated. Our formulation and optimization framework can be used for other ML tasks by considering the generating distribution of data and the type of an ML model. In accordance with our formulation, we simulated the optimal load balance of edge-cloud cooperation in a topology that mimicked a Japanese network and conducted an anomalous traffic detection experiment by using real traffic data to compare the estimated accuracy based on our formulation and the actual accuracy based on the experiment.

To enhance safety and efficiency in the traffic environment, developing intelligent transportation systems (ITSs) is of paramount importance. In ITSs, roadside units (RSUs) are critical components that enable the environment awareness and connectivity via using radar sensing and communications. In this paper, we focus on RSUs with multiple radar systems. Specifically, we propose a parameter selection method of multiple radar systems to enhance the overall sensing performance. Furthermore, since different radars provide different sensing and tracking results, to benefit from multiple radars, we propose fusion algorithms to integrate the tracking results of different radars. We use two commercial frequency-modulated continuous wave (FMCW) radars to conduct experiments at Hsinchu city in Taiwan. The experimental results validate that our proposed approaches can improve the overall sensing performance.

We developed a self-controllable voltage level (SVL) circuit and applied this circuit to a single-power-supply, six-transistor complementary metal-oxide-semiconductor static random-access memory (SRAM) to not only improve both write and read performances but also to achieve low standby power and data retention (holding) capability. The SVL circuit comprises only three MOSFETs (i.e., pull-up, pull-down and bypass MOSFETs). The SVL circuit is able to adaptively generate both optimal memory cell voltages and word line voltages depending on which mode of operation (i.e., write, read or hold operation) was used. The write margin (VWM) and read margin (VRM) of the developed (dvlp) SRAM at a supply voltage (VDD) of 1V were 0.470 and 0.1923V, respectively. These values were 1.309 and 2.093 times VWM and VRM of the conventional (conv) SRAM, respectively. At a large threshold voltage (Vt) variability (=+6σ), the minimum power supply voltage (VMin) for the write operation of the conv SRAM was 0.37V, whereas it decreased to 0.22V for the dvlp SRAM. VMin for the read operation of the conv SRAM was 1.05V when the Vt variability (=-6σ) was large, but the dvlp SRAM lowered it to 0.41V. These results show that the SVL circuit expands the operating voltage range for both write and read operations to lower voltages. The dvlp SRAM reduces the standby power consumption (PST) while retaining data. The measured PST of the 2k-bit, 90-nm dvlp SRAM was only 0.957µW at VDD=1.0V, which was 9.46% of PST of the conv SRAM (10.12µW). The Si area overhead of the SVL circuits was only 1.383% of the dvlp SRAM.