As online social networks (OSNs) have become remarkably active, we often experience explosive user dynamics such as online flaming, which can significantly impact the real world. Since the rapidity with which online user dynamics propagates, countermeasures based on social analyses of the individuals who cause online flaming take too much time that timely measures cannot be taken. To consider immediate solutions without individuals' social analyses, a countermeasure technology for flaming phenomena based on the oscillation model, which describes online user dynamics, has been proposed. In this framework, the strength of damping to prevent online flaming was derived based on the wave equation of networks. However, the assumed damping strength was to be a constant independent of the frequency of user dynamics. Since damping strength may generally depend on frequency, it is necessary to consider such frequency dependence in user dynamics. In this paper, we derive the strength of damping required to prevent online flaming under the general condition that damping strength depends on the frequency of user dynamics. We also investigate the existence range of the Laplacian matrix's eigenvalues representing the OSN structure assumed from the real data of OSNs, and apply it to the countermeasure technology for online flaming.

TCP/IP, the foundation of the current Internet, assumes a sufficiently low packet loss rate for links in communication path. On the other hand, for communication services such as mobile and wireless communications, communication link tends to be disruptive. In this paper, we propose Layer-5 temporally-spliced path protocol (L5-TSPP), which provides disruption-tolerance in the L5 temporally-spliced path (L5-TSP), as one of the communication paths provided by Layer-5 (L5-paths). We design and implement an API for using L5-paths (L5 API). The L5 API is designed and implemented to support not only POSIX systems but also non-POSIX systems. L5 API and L5-TSPP are implemented in the user space in Go language. The measurement results show that L5-TSP achieves lower and more stable connection establishment time and better end-to-end throughput in the presence of disruption than conventional communication paths.

Liquid crystal (LC) device has high tunability with low power consumption and it is important not only in visible region but also in terahertz region. In this study, birefringence and absorption losses of hydrogen-bonded LC was estimated at 2.5 THz. Our results indicate that introduction of alkoxy chain to hydrogen-bonded LC is effective to increase birefringence in terahertz region. These results indicate that hydrogen-bonded LCs are a strong candidate for future terahertz devices because of their excellent properties in the terahertz region.

A mobile crowdsensing system (MCS) utilizes a crowd of users to collect large-scale data using their mobile devices efficiently. The collected data are usually linked with sensitive information, raising the concerns of user privacy leakage. To date, many approaches have been proposed to protect the users' privacy, with the majority relying on a centralized structure, which poses though attack and intrusion vulnerability. Some studies build a distributed platform exploiting a blockchain-type solution, which still requires a fully trusted third party (TTP) to manage a reliable reward distribution in the MCS. Spurred by the deficiencies of current methods, we propose a distributed user privacy protection structure that combines blockchain and a trusted execution environment (TEE). The proposed architecture successfully manages the users' privacy protection and an accurate reward distribution without requiring a TTP. This is because the encryption algorithms ensure data confidentiality and uncouple the correlation between the users' identity and the sensitive information in the collected data. Accordingly, the smart contract signature is used to manage the user deposit and verify the data. Extensive comparative experiments verify the efficiency and effectiveness of the proposed combined blockchain and TEE scheme.

In recent years, federated learning has attracted more and more attention as it could collaboratively train a global model without gathering the users' raw data. It has brought many challenges. In this paper, we proposed layer-based federated learning system with privacy preservation. We successfully reduced the communication cost by selecting several layers of the model to upload for global averaging and enhanced the privacy protection by applying local differential privacy. We evaluated our system in non independently and identically distributed scenario on three datasets. Compared with existing works, our solution achieved better performance in both model accuracy and training time.

In this paper, we propose a novel user selection scheme based on jointly combining channel gain (CG) and signal to interference plus noise ratio (SINR) to improve the sum-rate as well as to reduce the computation complexity of multi-user massive multi-input multi-output (MU-massive MIMO) downlink transmission through a block diagonalization (BD) precoding technique. By jointly considering CG and SINR based user sets, sum-rate performance improvement can be achieved by selecting higher gain users with better SINR conditions as well as by eliminating the users who cause low sum-rate in the system. Through this approach, the number of possible outcomes for the user selection scheme can be reduced by counting the common users for every pair of user combinations in the selection process since the common users of CG-based and SINR-based sets possess both higher channel gains and better SINR conditions. The common users set offers not only sum-rate performance improvements but also computation complexity reduction in the proposed scheme. It is shown by means of computer simulation experiments that the proposed scheme can increase the sum-rate with lower computation complexity for various numbers of users as compared to conventional schemes requiring the same or less computational complexity.

Maximum run-length limited codes are constraint codes used in communication and data storage systems. Insertion/deletion correcting codes correct insertion or deletion errors caused in transmitted sequences and are used for combating synchronization errors. This paper investigates the maximum run-length limited single insertion/deletion correcting (RLL-SIDC) codes. More precisely, we construct efficiently encodable and decodable RLL-SIDC codes. Moreover, we present its encoding and decoding algorithms and show the redundancy of the code.

A pushdown system (PDS) is known as an abstract model of recursive programs. For PDS, model checking methods have been studied and applied to various software verification such as interprocedural data flow analysis and malware detection. However, PDS cannot manipulate data values from an infinite domain. A register PDS (RPDS) is an extension of PDS by adding registers to deal with data values in a restricted way. This paper proposes algorithms for LTL model checking problems for RPDS with simple and regular valuations, which are labelings of atomic propositions to configurations with reasonable restriction. First, we introduce RPDS and related models, and then define the LTL model checking problems for RPDS. Second, we give algorithms for solving these problems and also show that the problems are EXPTIME-complete. As practical examples, we show solutions of a malware detection and an XML schema checking in the proposed framework.

Weighted Round-Robin (WRR) is often used, due to its simplicity, for scheduling packets or tasks. With WRR, a number of packets equal to the weight allocated to a flow can be served consecutively, which leads to a bursty service. Interleaved Weighted Round-Robin (IWRR) is a variant that mitigates this effect. We are interested in finding bounds on worst-case delay obtained with IWRR. To this end, we use a network calculus approach and find a strict service curve for IWRR. The result is obtained using the pseudo-inverse of a function. We show that the strict service curve is the best obtainable one, and that delay bounds derived from it are tight (i.e., worst-case) for flows of packets of constant size. Furthermore, the IWRR strict service curve dominates the strict service curve for WRR that was previously published. We provide some numerical examples to illustrate the reduction in worst-case delays caused by IWRR compared to WRR.

This paper studies using price incentives to shift bandwidth demand from peak to non-peak periods. In particular, cost discounts decrease as peak monthly usage increases. We take into account the delay sensitivity of different apps: during peak hours, the usage of hard real-time applications (HRAS) is not counted in the user's monthly data cap, while the usage of other applications (OAS) is counted in the user's monthly data cap. As a result, users may voluntarily delay or abandon OAS in order to get a higher fee discount. Then, a new data rate control algorithm is proposed. The algorithm allocates the data rate according to the priority of the source, which is determined by two factors: (I) the allocated data rate; and (II) the waiting time.

In [31], Shin et al. proposed a Leakage-Resilient and Proactive Authenticated Key Exchange (LRP-AKE) protocol for credential services which provides not only a higher level of security against leakage of stored secrets but also secrecy of private key with respect to the involving server. In this paper, we discuss a problem in the security proof of the LRP-AKE protocol, and then propose a modified LRP-AKE protocol that has a simple and effective measure to the problem. Also, we formally prove its AKE security and mutual authentication for the entire modified LRP-AKE protocol. In addition, we describe several extensions of the (modified) LRP-AKE protocol including 1) synchronization issue between the client and server's stored secrets; 2) randomized ID for the provision of client's privacy; and 3) a solution to preventing server compromise-impersonation attacks. Finally, we evaluate the performance overhead of the LRP-AKE protocol and show its test vectors. From the performance evaluation, we can confirm that the LRP-AKE protocol has almost the same efficiency as the (plain) Diffie-Hellman protocol that does not provide authentication at all.

In unlicensed spectrum, wireless communications employing carrier sense multiple access with collision avoidance (CSMA/CA) suffer from longer transmission delay time as the number of user terminals (UTs) increases, because packet collisions are more likely to occur. To cope with this problem, this paper proposes a new multiuser detection (MUD) scheme that uses both request-to-send (RTS) and enhanced clear-to-send (eCTS) for high-reliable and low-latency wireless communications. As in conventional MUD scheme, the metric-combining MUD (MC-MUD) calculates log likelihood functions called metrics and accumulates the metrics for the maximum likelihood detection (MLD). To avoid increasing the number of states for MLD, MC-MUD forces the relevant UTs to retransmit their packets until all the collided packets are correctly detected, which requires a kind of central control and reduces the system throughput. To overcome these drawbacks, the proposed scheme, which is referred to as cancelling MC-MUD (CMC-MUD), deletes replicas of some of the collided packets from the received signals, once the packets are correctly detected during the retransmission. This cancellation enables new UTs to transmit their packets and then performs MLD without increasing the number of states, which improves the system throughput without increasing the complexity. In addition, the proposed scheme adopts RTS and eCTS. One UT that suffers from packet collision transmits RTS before the retransmission. Then, the corresponding access point (AP) transmits eCTS including addresses of the other UTs, which have experienced the same packet collision. To reproduce the same packet collision, these other UTs transmit their packets once they receive the eCTS. Computer simulations under one AP conditions evaluate an average carrier-to-interference ratio (CIR) range in which the proposed scheme is effective, and clarify that the transmission delay time of the proposed scheme is shorter than that of the conventional schemes. In two APs environments that can cause the hidden terminal problem, it is demonstrated that the proposed scheme achieves shorter transmission delay times than the conventional scheme with RTS and conventional CTS.

When a failure occurs in a network element, such as switch, router, and server, network operators need to recognize the service impact, such as time to recovery from the failure or severity of the failure, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction system (DeepSIP), which predicts the service impact of network failure in a network element using a temporal multimodal convolutional neural network (CNN). More precisely, DeepSIP predicts the time to recovery from the failure and the loss of traffic volume due to the failure in a network on the basis of information from syslog messages and traffic volume. Since the time to recovery is useful information for a service level agreement (SLA) and the loss of traffic volume is directly related to the severity of the failure, we regard the time to recovery and the loss of traffic volume as the service impact. The service impact is challenging to predict, since it depends on types of network failures and traffic volume when the failure occurs. Moreover, network elements do not explicitly contain any information about the service impact. To extract the type of network failures and predict the service impact, we use syslog messages and past traffic volume. However, syslog messages and traffic volume are also challenging to analyze because these data are multimodal, are strongly correlated, and have temporal dependencies. To extract useful features for prediction, we develop a temporal multimodal CNN. We experimentally evaluated DeepSIP in terms of accuracy by comparing it with other NN-based methods by using synthetic and real datasets. For both datasets, the results show that DeepSIP outperformed the baselines.

In distributed processing for communication services, a proper server selection scheme is required to reduce delay by ensuring the event occurrence order. Although a conservative synchronization algorithm (CSA) has been used to achieve this goal, an optimistic synchronization algorithm (OSA) can be feasible for synchronizing distributed systems. In comparison with CSA, which reproduces events in occurrence order before processing applications, OSA can be feasible to realize low delay communication as the processing events arrive sequentially. This paper proposes an optimal server selection scheme that uses OSA for distributed processing systems to minimize end-to-end delay under the condition that maximum status holding time is limited. In other words, the end-to-end delay is minimized based on the allowed rollback time, which is given according to the application designing aspects and availability of computing resources. Numerical results indicate that the proposed scheme reduces the delay compared to the conventional scheme.

Service composition optimization is a classic NP-hard problem. How to quickly select high-quality services that meet user needs from a large number of candidate services is a hot topic in cloud service composition research. An efficient second-order beetle swarm optimization is proposed with a global search ability to solve the problem of cloud service composition optimization in this study. First, the beetle antennae search algorithm is introduced into the modified particle swarm optimization algorithm, initialize the population bying using a chaotic sequence, and the modified nonlinear dynamic trigonometric learning factors are adopted to control the expanding capacity of particles and global convergence capability. Second, modified secondary oscillation factors are incorporated, increasing the search precision of the algorithm and global searching ability. An adaptive step adjustment is utilized to improve the stability of the algorithm. Experimental results founded on a real data set indicated that the proposed global optimization algorithm can solve web service composition optimization problems in a cloud environment. It exhibits excellent global searching ability, has comparatively fast convergence speed, favorable stability, and requires less time cost.

We propose a generalization of the rolling guidance filter (RGF) to a similarity-based clustering (SBC) algorithm which can handle general vector data. The proposed RGF-based SBC algorithm makes the similarities between data clearer than the original similarity values computed from the original data. On the basis of the similarity values, we assign cluster labels to data by an SBC algorithm. Experimental results show that the proposed algorithm achieves better clustering result than the result by the naive application of the SBC algorithm to the original similarity values. Additionally, we study the convergence of a unimodal vector dataset to its mean vector.

Optical code-division multiple-access (CDMA) techniques provide multi-user data transmission services in optical wireless and fiber communication systems. Several signature codes, such as modified prime sequence codes (MPSCs), generalized MPSCs (GMPSCs) and modified pseudo-orthogonal M-sequence sets, have been proposed for synchronous optical CDMA systems. In this paper, a new scheme is proposed for synchronous optical CDMA to increase the number of users and, consequently, to increase the total data rate without increasing the chip rate. The proposed scheme employs a GMPSC and an extended bi-orthogonal code which is a unipolar code generated from a bipolar Walsh code. Comprehensive comparisons between the proposed scheme and several conventional schemes are shown. Moreover, bit error rate performance and energy efficiency of the proposed scheme are evaluated comparing with those of the conventional optical CDMA schemes under atmospheric propagation environment.

In order to upgrade the refresh rate about High-Resolution (1280×1024) OLED-on-Silicon (OLEDoS) microdisplay, this paper discusses one compression scan strategy by reducing scan time redundancy. This scan strategy firstly compresses the low-bit gray level scan serial as one unit; second, the scan unit is embedded into the high-bit gray level serial and new scan sequence is generated. Furthermore, micro-display platform is designed to verify the scan strategy performance. The experiment shows that this scan strategy can deal with 144Hz refresh rate, which is obviously faster than the traditional scan strategy.

We investigate an interactive evolutionary computation (IEC) using multiple users' gaze information when users partially participate in each design evaluation. Many previous IEC systems have a problem that user evaluation loads are too large. Hence, we proposed to employ user gaze information for evaluating designs generated by IEC systems in order to solve this problem. In this proposed system, users just view the presented designs, not assess, then the system automatically creates users' favorite designs. With the user's gaze information, the proposed system generates coordination that can satisfy many users. In our previous study, we verified the effectiveness of the proposed system from a real system operation viewpoint. However, we did not consider the fluctuation of the users during a solution candidate evaluation. In the actual operation of the proposed system, users may change during the process due to the user interchange. Therefore, in this study, we verify the effectiveness of the proposed system when varying the users participating in each evaluation for each generation. In the experiment, we employ two types of situations as assumed in real environments. The first situation changes the number of users evaluating the designs for each generation. The second situation employs various users from the predefined population to evaluate the designs for each generation. From the experimental results in the first situation, we confirm that, despite the change in the number of users during the solution candidate evaluation, the proposed system can generate coordination to satisfy many users. Also, from the results in the second situation, we verify that the proposed system can also generate coordination which both users who participate in the coordination evaluation can more satisfy.

In order to realize the higher bit rates compared for the fifth-generation (5G) mobile communication system, massive MIMO technologies in higher frequency bands with wider bandwidth are being investigated for 5G evolution and 6G. One of practical method to realize massive MIMO in the high frequency bands is hybrid beamforming (BF). With this approach, user selection is an important function because its performance is highly affected by inter-user interference. However, the computational complexity of user selection in multi-user massive MIMO is high because MIMO channel matrix size excessive. Furthermore, satisfying user fairness by proportional fairness (PF) criteria leads to further increase of the complexity because re-calculation of precoding and postcoding matrices is required for each combination of selected users. To realize a fair and low-complexity user selection algorithm for multi-user massive MIMO employing hybrid BF, this paper proposes a two-step user selection algorithm that combines PF based user selection and chordal distance user selection. Computer simulations show that the proposed two-step user selection algorithm with higher user fairness and lower computational complexity can achieve higher system performance than the conventional user selection algorithms.