This paper focuses on the “data collection period” for training a better Just-In-Time (JIT) defect prediction model — the early commit data vs. the recent one —, and conducts a large-scale comparative study to explore an appropriate data collection period. Since there are many possible machine learning algorithms for training defect prediction models, the selection of machine learning algorithms can become a threat to validity. Hence, this study adopts the automatic machine learning method to mitigate the selection bias in the comparative study. The empirical results using 122 open-source software projects prove the trend that the dataset composed of the recent commits would become a better training set for JIT defect prediction models.

In this letter, we consider a global stabilization problem for a class of feedforward systems by an event-triggered control. This is an extended work of [10] in a way that there are uncertain feedforward nonlinearity and time-varying input delay in the system. First, we show that the considered system is globally asymptotically stabilized by a proposed event-triggered controller with a gain-scaling factor. Then, we also show that the interexecution times can be enlarged by adjusting a gain-scaling factor. A simulation example is given for illustration.

Recently, Linux Container has been the de-facto standard for a cloud system, enabling cloud providers to create a virtual environment in a much more scaled manner. However, configuring container networks remains immature and requires automatic verification for efficient cloud management. We propose Verikube, which utilizes a novel graph structure representing policies to reduce memory consumption and accelerate verification. Moreover, unlike existing works, Verikube is compatible with the complex semantics of Cilium Policy which a cloud adopts from its advantage of performance. Our evaluation results show that Verikube performs at least seven times better for memory efficiency, at least 1.5 times faster for data structure management, and 20K times better for verification.

Multilayer perceptron (MLP) is a basic neural network model that is used in practical industrial applications, such as network intrusion detection (NID) systems. It is also used as a building block in newer models, such as gMLP. Currently, there is a demand for fast training in NID and other areas. However, in training with numerous GPUs, the problems of power consumption and long training times arise. Many of the latest deep neural network (DNN) models and MLPs are trained using a backpropagation algorithm which transmits an error gradient from the output layer to the input layer such that in the sequential computation, the next input cannot be processed until the weights of all layers are updated from the last layer. This is known as backward locking. In this study, a weight parameter update mechanism is proposed with time delays that can accommodate the weight update delay to allow simultaneous forward and backward computation. To this end, a one-dimensional systolic array structure was designed on a Xilinx U50 Alveo FPGA card in which each layer of the MLP is assigned to a processing element (PE). The time-delay backpropagation algorithm executes all layers in parallel, and transfers data between layers in a pipeline. Compared to the Intel Core i9 CPU and NVIDIA RTX 3090 GPU, it is 3 times faster than the CPU and 2.5 times faster than the GPU. The processing speed per power consumption is 11.5 times better than that of the CPU and 21.4 times better than that of the GPU. From these results, it is concluded that a training accelerator on an FPGA can achieve high speed and energy efficiency.

In this paper, we propose a multi-domain learning model for action recognition. The proposed method inserts domain-specific adapters between layers of domain-independent layers of a backbone network. Unlike a multi-head network that switches classification heads only, our model switches not only the heads, but also the adapters for facilitating to learn feature representations universal to multiple domains. Unlike prior works, the proposed method is model-agnostic and doesn't assume model structures unlike prior works. Experimental results on three popular action recognition datasets (HMDB51, UCF101, and Kinetics-400) demonstrate that the proposed method is more effective than a multi-head architecture and more efficient than separately training models for each domain.

Recently, robust speech recognition for real-world applications has attracted much attention. This paper proposes a robust speech recognition method based on the teacher-student learning framework for domain adaptation. In particular, the student network will be trained based on a novel optimization criterion defined by the encoder outputs of both teacher and student networks rather than the final output posterior probabilities, which aims to make the noisy audio map to the same embedding space as clean audio, so that the student network is adaptive in the noise domain. Comparative experiments demonstrate that the proposed method obtained good robustness against noise.

To the best of our knowledge, there have been very few work on computational algorithms for the core or its variants in MC-nets games. One exception is the work by [Hirayama, et.al., 2014], where a constraint generation algorithm has been proposed to compute a payoff vector belonging to the least core. In this paper, we generalize this algorithm into the one for finding a payoff vector belonging to ϵ-core with pre-specified bound guarantee. The underlying idea behind this algorithm is basically the same as the previous one, but one key contribution is to give a clearer view on the pricing problem leading to the development of our new general algorithm. We showed that this new algorithm was correct and never be trapped in an infinite loop. Furthermore, we empirically demonstrated that this algorithm really presented a trade-off between solution quality and computational costs on some benchmark instances.

This paper shows structural optimal design of optical waveguide components utilizing an efficient 3D frequency-domain and 2D time-domain beam propagation method (BPM) with an alternating direction implicit (ADI) scheme. Usual optimal design procedure is based on iteration of numerical simulation, and total computational cost of the optimal design mainly depends on the efficiency of numerical analysis method. Since the system matrices are tridiagonal in the ADI-based BPM, efficient analysis and optimal design are available. Shape and topology optimal design shown in this paper is based on optimization of density distribution and sensitivity analysis to the density parameters. Computational methods of the sensitivity are shown in the case of using the 3D semi-vectorial and 2D time-domain BPM based on ADI scheme. The validity of this design approach is shown by design of optical waveguide components: mode converters, and a polarization beam splitter.

Network Function Virtualization (NFV) enables the embedding of Virtualized Network Function (VNF) into commodity servers. A sequence of VNFs can be chained in a particular order to form a service chain (SC). This paper considers placing multiple SCs in a geo-distributed edge system owned by multiple service providers (SPs). For a pair of SC and SP, minimizing the placement cost while meeting a latency constraint is formulated as an integer programming problem. As SC clients and SPs are self-interested, we study the matching between SCs and SPs that respects individual's interests yet maximizes social welfare. The proposed matching approach excludes any blocking individual and block pair which may jeopardize the stability of the result. Simulation results show that the proposed approach performs well in terms of social welfare but is suboptimal concerning the number of placed SCs.

Enterprises have paid attention to consortium blockchains like Hyperledger Fabric, which is one of the most promising platforms, for efficient decentralized transactions without depending on any particular organization. A consortium blockchain-based system will be typically built across multiple organizations. In such blockchain-based systems, system operations across multiple organizations in a decentralized manner are essential to maintain the value of introducing consortium blockchains. Decentralized system operations have recently been becoming realistic with the evolution of consortium blockchains. For instance, the release of Hyperledger Fabric v2.x, in which individual operational tasks for a blockchain network, such as command execution of configuration change of channels (Fabric's sub-networks) and upgrade of chaincodes (Fabric's smart contracts), can be partially executed in a decentralized manner. However, the operations workflows also include the preceding procedure of pre-sharing, coordinating, and pre-agreeing the operational information (e.g., configuration parameters) among organizations, after which operation executions can be conducted, and this preceding procedure relies on costly manual tasks. To realize efficient decentralized operations workflows for consortium blockchain-based systems in general, we propose a decentralized inter-organizational operations method that we call Operations Smart Contract (OpsSC), which defines an operations workflow as a smart contract. Furthermore, we design and implement OpsSC for blockchain network operations with Hyperledger Fabric v2.x. This paper presents OpsSC for operating channels and chaincodes, which are essential for managing the blockchain networks, through clarifying detailed workflows of those operations. A cost evaluation based on an estimation model shows that the total operational cost for executing a typical operational scenario to add an organization to a blockchain network having ten organizations could be reduced by 54 percent compared with a conventional script-based method. The implementation of OpsSC has been open-sourced and registered as one of Hyperledger Labs projects, which hosts experimental projects approved by Hyperledger.

Service chaining is attracting attention as a promising technology for providing a variety of network services by applying virtual network functions (VNFs) that can be instantiated on commercial off-the-shelf servers. The data transmission for each service chain has to satisfy the quality of service (QoS) requirements in terms of the loss probability and transmission delay, and hence the amount of resources for each VNF is expected to be sufficient for satisfying the QoS. However, the increase in the amount of VNF resources results in a high cost for improving the QoS. To reduce the cost of utilizing a VNF, sharing VNF instances through multiple service chains is an effective approach. However, the number of packets arriving at the VNF instance is increased, resulting in a degradation of the QoS. It is therefore important to select VNF instances shared by multiple service chains and to determine the amount of resources for the selected VNFs. In this paper, we propose a cost-effective service chain construction with a VNF sharing model. In the proposed method, each VNF is modeled as an M/M/1/K queueing model to evaluate the relationship between the amount of resources and the loss probability. The proposed method determines the VNF sharing, the VNF placement, the amount of resources for each VNF, and the transmission route of each service chain. For the optimization problem, these are applied according to our proposed heuristic algorithm. We evaluate the performance of the proposed method through a simulation. From the numerical examples, we show the effectiveness of the proposed method under certain network topologies.

In this study, a computational method is proposed for acoustic field analysis tasks that require lengthy observation times. The acoustic fields at a given observation time are obtained using a fast inverse Laplace transform with a finite-difference complex-frequency-domain. The transient acoustic field can be evaluated at arbitrary sampling intervals by obtaining the instantaneous acoustic field at the desired observation time using the proposed method.

Design of distributed energy management systems composed of several agents such as factories and buildings is important for realizing smart cities. In addition, demand response for saving the power consumption is also important. In this paper, we propose a design method of distributed energy management systems with real-time demand response, in which both electrical energy and thermal energy are considered. Here, we use ADMM (Alternating Direction Method of Multipliers), which is well known as one of the powerful methods in distributed optimization. In the proposed method, demand response is performed in real-time, based on the difference between the planned demand and the actual value. Furthermore, utilizing a blockchain is also discussed. The effectiveness of the proposed method is presented by a numerical example. The importance of introducing a blockchain is pointed out by presenting the adverse effect of tampering the actual value.

Recently, the adoption of the industrial Internet of things (IIoT) has optimized many industrial sectors and promoted industry “smartization.” Smart factories and smart industries connect the real and virtual worlds through cyber-physical systems (CPS). However, these linkages will increase the cyber security danger surface to new levels, putting millions of dollars' worth of assets at risk if communications in big network systems like IIoT settings are left unsecured. To solve these problems, the fundamental method is security, such as authentication and confidentiality, and it should require the encryption key. However, it is challenging the security performance with the limited performance of the sensor. Blockchain-based identity management is emerging for lightweight, integrity and persistence. However, the key generation and management issues of blockchain face the same security performance issues. First, through blockchain smart contracts and hierarchical deterministic (HD) wallets, hierarchical key derivation efficiently distributes and manages keys by line and group in the IIoT environment. Second, the pairing verification value based on an elliptic curve single point called Root Signature performs efficient public key certificate registration and verification and improves the key storage space. Third, the identity log recorded through the blockchain is the global transparency of the key lifecycle, providing system reliability from various security attacks. Keyless Signature Infrastructure (KSI) is adopted to perform efficiently via hash-based scheme (hash calendar, hash tree etc.). We analyze our framework compared to hash-based state commitment methods. Accordingly, our method achieves a calculation efficiency of O(nlog N) and a storage space saving of 60% compared to the existing schemes.

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop an optimization algorithm for minimizing the empirical risk under the sign constraints. The algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd+d2) computational cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an ε-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Non-orthogonal multiple access (NOMA) allows several users to multiplex in the power-domain to improve spectral efficiency. To further improve its performance, it is desirable to reduce inter-user interference (IUI). In this paper, we propose a downlink asynchronous NOMA (ANOMA) scheme applicable to frequency-selective channels. The proposed scheme introduces an intentional symbol offset between the multiplexed signals to reduce IUI, and it employs cyclic-prefixed single-carrier transmission with frequency-domain equalization (FDE) to reduce inter-symbol interference. We show that the mean square error for the FDE of the proposed ANOMA scheme is smaller than that of a conventional NOMA scheme. Simulation results show that the proposed ANOMA with appropriate power allocation achieves a better sum rate compared to the conventional NOMA.

We proposed a new method for evaluating the deterioration of messenger wires by using terahertz waves. We use terahertz time-domain spectroscopy to measure several twisted wire samples with different levels of deterioration. We find that each twisted wire sample had a different distribution of reflection intensity which was due to the wires' twist structure. We show that it is possible to assess the degradation from the straight lines present in the reflection intensity distribution image. Furthermore, it was confirmed that our method can be applied to wire covered with resin.

This letter focuses on the cross-corpus speech emotion recognition (SER) task, in which the training and testing speech signals in cross-corpus SER belong to different speech corpora. Existing algorithms are incapable of effectively extracting common sentiment information between different corpora to facilitate knowledge transfer. To address this challenging problem, a novel convolutional auto-encoder and adversarial domain adaptation (CAEADA) framework for cross-corpus SER is proposed. The framework first constructs a one-dimensional convolutional auto-encoder (1D-CAE) for feature processing, which can explore the correlation among adjacent one-dimensional statistic features and the feature representation can be enhanced by the architecture based on encoder-decoder-style. Subsequently the adversarial domain adaptation (ADA) module alleviates the feature distributions discrepancy between the source and target domains by confusing domain discriminator, and specifically employs maximum mean discrepancy (MMD) to better accomplish feature transformation. To evaluate the proposed CAEADA, extensive experiments were conducted on EmoDB, eNTERFACE, and CASIA speech corpora, and the results show that the proposed method outperformed other approaches.

This paper aims to design multiple-input multiple-output (MIMO) radar receiving weights and transmitting waveforms, in order to obtain better spatial filtering performance and enhance the robustness in the case of signal-dependent interference and jointly inaccurate estimated angles of target and interference. Generally, an alternate iterative optimization algorithm is proposed for the joint design problem. Specifically, the receiving weights are designed by the generalized eigenvalue decomposition of the matrix which contains the estimated information of the target and interference. As the cost function of the transmitting waveform design is fractional, the fractional optimization problem is first converted into a secondary optimization problem. Based on the proposed algorithm, a closed-form solution of the waveform is given using the alternating projection. At the analysis stage, in the presence of estimated errors under the environment of signal-dependent interference, a robust signal-to-interference and noise ratio (SINR) performance is obtained using a small amount of calculation with an iterative procedure. Numerical examples verify the effectiveness of the performances of the designed waveform in terms of the SINR, beampattern and pulse compression.

The road space rationing (RSR) method regulates a period in which a user group can make telephone calls in order to decrease the call attempt rate and induce calling parties to shorten their calls during disaster congestion. This paper investigates what settings of this indirect control induce more self-restraint and how the settings change calling parties' behavior using experimental psychology. Our experiments revealed that the length of the regulated period differently affected calling parties' behavior (call duration and call attempt rate) and indicated that the 60-min RSR method (i.e., 10 six-min periods) is the most effective setting against disaster congestion.