Various types of synchronization phenomena have been reported in coupled chaotic systems. In recent years, the applications of these phenomena have been advancing for utilization in sensor network systems, secure communication systems, and biomedical systems. Specifically, chaos-chaos intermittency (CCI) synchronization is a characterized synchronization phenomenon. Previously, we proposed a new chaos control method, termed as the “reduced region of orbit (RRO) method,” to achieve CCI synchronization using external feedback signals. This method has been gathering research attention because of its ability to induce CCI synchronization; this can be achieved even if internal system parameters cannot be adjusted by external factors. Further, additive stochastic noise is known to have a similar effect. The objective of this study was to compare the performance of the RRO method and the method that applies stochastic noise, both of which are capable of inducing CCI synchronization. The results showed that even though CCI synchronization can be realized using both control methods under the induced attractor merging condition, the RRO method possesses higher adoptability and accomplishes a higher degree of CCI synchronization compared to additive stochastic noise. This advantage might facilitate the application of synchronization in coupled chaotic systems.

This paper proposes a computational complexity-reduced algorithm for an adaptive peak-to-average power ratio (PAPR) reduction method previously developed by members of our research group that uses the null space in a multiple-input multiple-output (MIMO) channel for MIMO-orthogonal frequency division multiplexing (OFDM) signals. The proposed algorithm is an extension of the peak cancellation (PC) signal-based method that has been mainly investigated for per-antenna PAPR reduction. This method adds the PC signal, which is designed so that the out-of-band radiation is removed/reduced, directly to the time-domain transmission signal at each antenna. The proposed method, referred to as PCCNC (PC with channel-null constraint), performs vector-level signal processing in the PC signal generation so that the PC signal is transmitted only to the null space in the MIMO channel. We investigate three methods to control the beamforming (BF) vector in the PC signal, which is a key factor in determining the achievable PAPR performance of the algorithm. Computer simulation results show that the proposed PCCNC achieves approximately the same throughput-vs.-PAPR performance as the previous method while dramatically reducing the required computational cost.

In this paper, we study a radio frequency (RF)-powered backscatter assisted cognitive radio network (CRN), where an eavesdropper exists. This network includes a primary transmitter, a pair of secondary transmitter and receiver, a friendly jammer and an eavesdropper. We assume that the secondary transmitter works in ambient backscatter (AmBack) mode and the friendly jammer works in harvest-then-transmit (HTT) mode, where the primary transmitter serves as energy source. To enhance the physical layer security of the secondary user, the friendly jammer uses its harvested energy from the primary transmitter to transmit jamming noise to the eavesdropper. Furthermore, for maximizing the secrecy rate of secondary user, the optimal time allocation including the energy harvesting and jamming noise transmission phases is obtained. Simulation results verify the superiority of the proposed scheme.

Link prediction, the computational problem of determining whether there is a link between two objects, is important in machine learning and data mining. Feature-based link prediction, in which the feature vectors of the two objects are given, is of particular interest because it can also be used for various identification-related problems. Although the factorization machine and the higher-order factorization machine (HOFM) are widely used for feature-based link prediction, they use feature combinations not only across the two objects but also from the same object. Feature combinations from the same object are irrelevant to major link prediction problems such as predicting identity because using them increases computational cost and degrades accuracy. In this paper, we present novel models that use higher-order feature combinations only across the two objects. Since there were no algorithms for efficiently computing higher-order feature combinations only across two objects, we derive one by leveraging reported and newly obtained results of calculating the ANOVA kernel. We present an efficient coordinate descent algorithm for proposed models. We also improve the effectiveness of the existing one for the HOFM. Furthermore, we extend proposed models to a deep neural network. Experimental results demonstrated the effectiveness of our proposed models.

We study wireless networked control systems (WNCSs), where controllers (CLs), controlled objects (COs), and other devices are connected through wireless networks. In WNCSs, COs can become unstable due to bursty packet losses and random delays on wireless networks. To reduce these network-induced effects, we utilize the packetized predictive control (PPC) method, where future control vectors to compensate bursty packet losses are generated in the receiving horizon manner, and they are packed into packets and transferred to a CO unit. In this paper, we extend the PPC method so as to compensate random delays as well as bursty packet losses. In the extended PPC method, generating many control vectors improves the robustness against both problems while it increases traffic on wireless networks. Therefore, we consider control vector selection to improve the robustness effectively under the constraint of single packet transmission. We first reconsider the input strategy of control vectors received by COs and propose a control vector selection scheme suitable for the strategy. In our selection scheme, control vectors are selected based on the estimated average and variance of round-trip delays. Moreover, we solve the problem that the CL may misconceive the CO's state due to insufficient information for state estimation. Simulation results show that our selection scheme achieves the higher robustness against both bursty packet losses and delays in terms of the 2-norm of the CO's state.

We propose to use clustered interference alignment for the situation where the backhaul link capacity is limited and the base station is cache-enabled given MIMO interference channels, when the number of Tx-Rx pairs exceeds the feasibility constraint of interference alignment. We optimize clustering with the soft cluster size constraint algorithm by adding a cluster size balancing process. In addition, the CSI overhead is quantified as a system performance indicator along with the average throughput. Simulation results show that cluster size balancing algorithm generates clusters that are more balanced as well as attaining higher long-term throughput than the soft cluster size constraint algorithm. The long-term throughput is further improved under high SNR by reallocating the capacity of the backhaul links based on the clustering results.

This paper proposes a novel flexible receiver with virtual channels for overloaded multiple-input multiple-output (MIMO) channels. The receiver applies extended rotation matrices proposed in the paper for the flexibility. In addition, adaptive selection of the extended rotation matrices is proposed for further performance improvement. We propose two techniques to reduce the computational complexity of the adaptive selection. As a result, the proposed receiver gives us an option to reduce the complexity with a slight decrease in the transmission performance by changing receiver configuration parameters. A computer simulation reveals that the adaptive selection attains a gain of about 3dB at the BER of 10-3.

This paper demonstrates how to achieve simulation-based strong attribute hiding against adaptive adversaries for predicate encryption (PE) schemes supporting expressive predicate families under standard computational assumptions in bilinear groups. Our main result is a simulation-based adaptively strongly partially-hiding PE (PHPE) scheme for predicates computing arithmetic branching programs (ABP) on public attributes, followed by an inner-product predicate on private attributes. This simultaneously generalizes attribute-based encryption (ABE) for boolean formulas and ABP's as well as strongly attribute-hiding PE schemes for inner products. The proposed scheme is proven secure for any a priori bounded number of ciphertexts and an unbounded (polynomial) number of decryption keys, which is the best possible in the simulation-based adaptive security framework. This directly implies that our construction also achieves indistinguishability-based strongly partially-hiding security against adversaries requesting an unbounded (polynomial) number of ciphertexts and decryption keys. The security of the proposed scheme is derived under (asymmetric version of) the well-studied decisional linear (DLIN) assumption. Our work resolves an open problem posed by Wee in TCC 2017, where his result was limited to the semi-adaptive setting. Moreover, our result advances the current state of the art in both the fields of simulation-based and indistinguishability-based strongly attribute-hiding PE schemes. Our main technical contribution lies in extending the strong attribute hiding methodology of Okamoto and Takashima [EUROCRYPT 2012, ASIACRYPT 2012] to the framework of simulation-based security and beyond inner products.

To enhance the prediction accuracy of the number of faults, many studies proposed various prediction models. The model is built using a dataset collected in past projects, and the number of faults is predicted using the model and the data of the current project. Datasets sometimes have many data points where the dependent variable, i.e., the number of faults is zero. When a multiple linear regression model is made using the dataset, the model may not be built properly. To avoid the problem, the Tobit model is considered to be effective when predicting software faults. The model assumes that the range of a dependent variable is limited and the model is built based on the assumption. Similar to the Tobit model, the Poisson regression model assumes there are many data points whose value is zero on the dependent variable. Also, log-transformation is sometimes applied to enhance the accuracy of the model. Additionally, ensemble methods are effective to enhance prediction accuracy of the models. We evaluated the prediction accuracy of the methods separately, when the number of faults is zero and not zero. In the experiment, our proposed ensemble method showed the highest accuracy, and Pred25 was 21% when the number of faults was not zero, and it was 45% when the number was zero.

In machine translation (MT) mediated human-to-human communication, it is not an easy task to select the languages and translation services to be used as the users have various language backgrounds and skills. Our previous work introduced the best-balanced machine translation mechanism (BBMT) to automatically select the languages and translation services so as to equalize the language barriers of participants and to guarantee their equal opportunities in joining conversations. To assign proper languages to be used, however, the mechanism needs information of the participants' language skills, typically participants' language test scores. Since it is important to keep test score confidential, as well as other sensitive information, this paper introduces agents, which exchange encrypted information, and secure computation to ensure that agents can select the languages and translation services without destroying privacy. Our contribution is to introduce a multi-agent system with secure computation that can protect the privacy of users in multilingual communication. To our best knowledge, it is the first attempt to introduce multi-agent systems and secure computing to this area. The key idea is to model interactions among agents who deal with user's sensitive data, and to distribute calculation tasks to three different types of agents, together with data encryption, so no agent is able to access or recover participants' score.

Simultaneous multithreading (SMT) technology improves CPU throughput, but also causes unpredictable performance fluctuations for co-running workloads. Although recent major SMT processors have adopted some techniques to promote hardware support for quality-of-service (QoS), achieving both precise performance guarantees and high throughput on SMT architectures is still a challenging open problem. In this paper, we demonstrate through some comprehensive investigations on a cycle-accurate simulator that not only almost all in-core resources suffer from severe contention as workloads vary but also there is a non-linear relationship between performance and available quotas of resources. We consider these observations as the fundamental reason leading to the challenging problem above. Thus, we introduce QoSMT, a novel hardware scheme that leverages a closed-loop controlling mechanism consisting of detection, prediction and adjustment to enforce precise performance guarantees for specific targets, e.g. achieving 85%, 90% or 95% of the performance of a workload running alone respectively. We implement a prototype on GEM5 simulator. Experimental results show that the average control error is only 1.4%, 0.5% and 3.6%.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

Massive open online course (MOOC) is an online course aimed at unlimited participation and open access via the web. Although there are many MOOC providers, they typically focus on the online course providing and typically do not link with traditional education and business sector requirements. This paper presents a MOOC service framework that focuses on adopting MOOC to provide additional services to support students in traditional education and to provide credit bank consisting of student academic credentials for business sector demand. Particularly, it extends typical MOOC to support academic/ credential record and transcript issuance. The MOOC service framework consists of five layers: authentication, resources, learning, assessment and credential layers. We discuss the adoption of the framework in Thai MOOC, the national MOOC system for Thai universities. Several main issues related to the framework adoption are discussed, including the service strategy and model as well as infrastructure design for large-scale MOOC service.

The interval in ℕ composed of finite states of the stream version of asymmetric binary systems (ABS) is irreducible if it admits an irreducible finite-state Markov chain. We say that the stream version of ABS is irreducible if its interval is irreducible. Duda gave a necessary condition for the interval to be irreducible. For a probability vector (p,1-p), we assume that p is irrational. Then, we give a necessary and sufficient condition for the interval to be irreducible. The obtained conditions imply that, for a sufficiently small ε, if p∈(1/2,1/2+ε), then the stream version of ABS could not be practically irreducible.

Software defect prediction (SDP) plays a vital role in allocating testing resources reasonably and ensuring software quality. When there are not enough labeled historical modules, considerable semi-supervised SDP methods have been proposed, and these methods utilize limited labeled modules and abundant unlabeled modules simultaneously. Nevertheless, most of them make use of traditional features rather than the powerful deep feature representations. Besides, the cost of the misclassification of the defective modules is higher than that of defect-free ones, and the number of the defective modules for training is small. Taking the above issues into account, we propose a cost-sensitive and sparse ladder network (CSLN) for SDP. We firstly introduce the semi-supervised ladder network to extract the deep feature representations. Besides, we introduce the cost-sensitive learning to set different misclassification costs for defective-prone and defect-free-prone instances to alleviate the class imbalance problem. A sparse constraint is added on the hidden nodes in ladder network when the number of hidden nodes is large, which enables the model to find robust structures of the data. Extensive experiments on the AEEEM dataset show that the CSLN outperforms several state-of-the-art semi-supervised SDP methods.

This paper considers a velocity control problem for merging and splitting maneuvers of vehicle platoons. In this paper, an external device sends velocity commands to some vehicles in the platoon, and the others adjust their velocities autonomously. The former is pinning control, and the latter is consensus control in multi-agent control. We propose a switched pinning control algorithm. Our algorithm consists of three sub-methods. The first is an optimal switching method of pinning agents based on an MLD (Mixed Logical Dynamical) system model and MPC (Model Predictive Control). The second is a representation method for dynamical platoon formation with merging and splitting maneuver. The platoon formation follows the positional relation between vehicles or the formation demand from the external device. The third is a switching reduction method by setting a cost function that penalizes the switching of the pinning agents in the steady-state. Our proposed algorithm enables us to improve the consensus speed. Moreover, our algorithm can regroup the platoons to the arbitrary platoons and control the velocities of the multiple vehicle platoons to each target value.

This paper proposes a traveling maintenance method based on the resource pool concept, as a new network maintenance model. For failure recovery, the proposed method utilizes permissible time that is ensured by shared resource pools. In the proposed method, even if a failure occurs in a communication facility, maintenance staff wait for occurrence of successive failures in other communication facilities during the permissible time instead of immediately tackling the failure. Then, the maintenance staff successively visit the communication facilities that have faulty devices and collectively repair them. Therefore, the proposed method can reduce the amount of time that the maintenance staff take for fault recovery. Furthermore, this paper provides a system design that optimizes the proposed traveling maintenance according to system requirements determined by the design philosophy of telecommunication networks. Through simulation experiments, we show the effectiveness of the proposed method.

In order not to disrupt a team member concentrating on his/her own task, the interrupter needs to wait for a proper time. In this research, we examined the feasibility of predicting prospective interruptible times of office workers who use PCs. An analysis of actual working data collected from 13 participants revealed the relationship between uninterruptible durations and four features, i.e. type of application software, rate of PC operation activity, activity ratio between keystrokes and mouse clicks, and switching frequency of application software. On the basis of these results, we developed a probabilistic work continuance model whose probability changes according to the four features. The leave-one-out cross-validation indicated positive correlations between the actual and the predicted durations. The medians of the actual and the predicted durations were 539 s and 519 s. The main contribution of this study is the demonstration of the feasibility to predict uninterruptible durations in an actual working scenario.

In this paper, hierarchical interference coordination is proposed that suppresses both intra- and inter-cluster interference (ICI) in clustered wireless networks. Assuming transmitters and receivers are equipped with multiple antennas and complete channel state information is shared among all transmitters within the same cluster, interference alignment (IA) is performed that uses nulls to suppress intra-cluster interference. For ICI mitigation, we propose a null-steering precoder designed on the nullspace of a principal eigenvector of the correlated ICI channels, which eliminates a significant amount of ICI power given the exchange of cluster geometry between neighboring clusters. However, as ICI is negligible for the system in which the distance between clusters are large enough, the proposed scheme may not improve the system performance compared with the pure IA scheme that exploits all spatial degrees of freedom (DoF) to increase multiplexing gain without ICI mitigation. For the efficient interference management between intra- and inter-cluster, we analyze the decision criterion that provides an adaptive transmission mode selection between pure IA and proposed ICI reduction in given network environments. Moreover, a low computational complexity based transmission mode switching algorithm is proposed for irregularly distributed networks.

The advancement of multicore technology has made hundreds or even thousands of cores processor on a single chip possible. However, on a larger scale multicore, a hardware-based cache coherency mechanism becomes overwhelmingly complicated, hot, and expensive. Therefore, we propose a software coherence scheme managed by a parallelizing compiler for shared-memory multicore systems without a hardware cache coherence mechanism. Our proposed method is simple and efficient. It is built into OSCAR automatic parallelizing compiler. The OSCAR compiler parallelizes the coarse grain task, analyzes stale data and line sharing in the program, then solves those problems by simple program restructuring and data synchronization. Using our proposed method, we compiled 10 benchmark programs from SPEC2000, SPEC2006, NAS Parallel Benchmark (NPB), and MediaBench II. The compiled binaries then are run on Renesas RP2, an 8 cores SH-4A processor, and a custom 8-core Altera Nios II system on Altera Arria 10 FPGA. The cache coherence hardware on the RP2 processor is only available for up to 4 cores. The RP2's cache coherence hardware can also be turned off for non-coherence cache mode. The Nios II multicore system does not have any hardware cache coherence mechanism; therefore, running a parallel program is difficult without any compiler support. The proposed method performed as good as or better than the hardware cache coherence scheme while still provided the correct result as the hardware coherence mechanism. This method allows a massive array of shared memory CPU cores in an HPC setting or a simple non-coherent multicore embedded CPU to be easily programmed. For example, on the RP2 processor, the proposed software-controlled non-coherent-cache (NCC) method gave us 2.6 times speedup for SPEC 2000 “equake” with 4 cores against sequential execution while got only 2.5 times speedup for 4 cores MESI hardware coherent control. Also, the software coherence control gave us 4.4 times speedup for 8 cores with no hardware coherence mechanism available.