Modern high-level programming languages have a wide variety of grammar and can implement the required functionality in different ways. The authors believe that a large amount of code that implements the same functionality in different ways exists even in open source software where the source code is publicly available, and that by collecting such code, a useful data set can be constructed for various studies in software engineering. In this study, we construct a dataset of pairs of Java methods that have the same functionality but different structures from approximately 314 million lines of source code. To construct this dataset, the authors used an automated test generation technique, EvoSuite. Test cases generated by automated test generation techniques have the property that the test cases always succeed. In constructing the dataset, using this property, test cases generated from two methods were executed against each other to automatically determine whether the behavior of the two methods is the same to some extent. Pairs of methods for which all test cases succeeded in cross-running test cases are manually investigated to be functionally equivalent. This paper also reports the results of an accuracy evaluation of code clone detection tools using the constructed dataset. The purpose of this evaluation is assessing how accurately code clone detection tools could find the functionally equivalent methods, not assessing the accuracy of detecting ordinary clones. The constructed dataset is available at github (https://github.com/YoshikiHigo/FEMPDataset).

The purpose of Facial Beauty Prediction (FBP) is to automatically assess facial attractiveness based on human aesthetics. Most neural network-based prediction methods do not consider the ranking information in the task. For scoring tasks like facial beauty prediction, there is abundant ranking information both between images and within images. Reasonable utilization of these information during training can greatly improve the performance of the model. In this paper, we propose a novel end-to-end Convolutional Neural Network (CNN) model based on ranking information of images, incorporating a Rank Module and an Adaptive Weight Module. We also design pairwise ranking loss functions to fully leverage the ranking information of images. Considering training efficiency and model inference capability, we choose ResNet-50 as the backbone network. We conduct experiments on the SCUT-FBP5500 dataset and the results show that our model achieves a new state-of-the-art performance. Furthermore, ablation experiments show that our approach greatly contributes to improving the model performance. Finally, the Rank Module with the corresponding ranking loss is plug-and-play and can be extended to any CNN model and any task with ranking information. Code is available at https://github.com/nehcoah/Rank-Info-Net.

Wide deployment of artificial intelligence (AI) is inducing exponentially growing energy consumption. Traditional digital platforms are becoming difficult to fulfill such ever-growing demands on energy efficiency as well as computing latency, which necessitates the development of high efficiency analog hardware platforms for AI. Recently, optical and electrooptic hybrid computing is reactivated as a promising analog hardware alternative because it can accelerate the information processing in an energy-efficient way. Integrated photonic circuits offer such an analog hardware solution for implementing photonic AI and machine learning. For this purpose, we proposed a photonic analog of support vector machine and experimentally demonstrated low-latency and low-energy classification computing, which evidences the latency and energy advantages of optical analog computing over traditional digital computing. We also proposed an electrooptic Hopfield network for classifying and recognizing time-series data. This paper will review our work on implementing classification computing and Hopfield network by leveraging silicon photonic circuits.

L-type LC/CL matching circuits are well known for their simple analytical solutions and have been applied to many radio-frequency (RF) circuits. When actually constructing a circuit, parasitic elements are added to inductors and capacitors. Therefore, each L and C element has a self-resonant frequency, which affects the characteristics of the matching circuit. In this paper, the parallel parasitic capacitance to the inductor and the series parasitic inductor to the capacitance are taken up as parasitic elements, and the details of the effects of the self-resonant frequency of each element on the S11, voltage standing wave ratio (VSWR) and S21 characteristics are reported. When a parasitic element is added, each characteristic basically tends to deteriorate as the self-resonant frequency decreases. However, as an interesting feature, we found that the combination of resonant frequencies determines the VSWR and passband characteristics, regardless of whether it is the inductor or the capacitor.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

A 150 GHz fundamental oscillator employing an inter-stage matching network based on a transmission line is presented in this letter. The proposed oscillator consists of a two-stage common-emitter amplifier loop, whose inter-stage connections are optimized to meet the oscillation condition. The oscillator is designed in a 130-nm SiGe BiCMOS process that offers fT and fMAX of 350 GHz and 450 GHz. According to simulation results, an output power of 3.17 dBm is achieved at 147.6 GHz with phase noise of -115 dBc/Hz at 10 MHz offset and figure-of-merit (FoM) of -180 dBc/Hz.

Self-dithered digital delta-sigma modulators (DDSMs) are commonly used in fractional-N frequency synthesizers due to their ability to eliminate unwanted spurs from the synthesizer’s spectra without requiring additional hardware. However, when operating with a low-bit input, self-dithered DDSMs can still suffer from spurious tones at certain inputs. In this paper, we propose a self-dithered MASH 1-1-1-1 structure to mitigate the spur issue in the self-dithered MASH DDSMs. The proposed self-dithered MASH 1-1-1-1 suppresses the spurs with shaped dithering and achieves 4th order noise shaping.

In cyber-physical systems (CPSs) that interact between physical and information components, there are many sensors that are connected through a communication network. In such cases, the reduction of communication costs is important. Event-triggered control that the control input is updated only when the measured value is widely changed is well known as one of the control methods of CPSs. In this paper, we propose a design method of output feedback controllers with decentralized event-triggering mechanisms, where the notion of uniformly ultimate boundedness is utilized as a control specification. Using this notion, we can guarantee that the state stays within a certain set containing the origin after a certain time, which depends on the initial state. As a result, the number of times that the event occurs can be decreased. First, the design problem is formulated. Next, this problem is reduced to a BMI (bilinear matrix inequality) optimization problem, which can be solved by solving multiple LMI (linear matrix inequality) optimization problems. Finally, the effectiveness of the proposed method is presented by a numerical example.

A PBN is well known as a mathematical model of complex network systems such as gene regulatory networks. In Boolean networks, interactions between nodes (e.g., genes) are modeled by Boolean functions. In PBNs, Boolean functions are switched probabilistically. In this paper, for a PBN, a simplified representation that is effective in analysis and control is proposed. First, after a polynomial representation of a PBN is briefly explained, a simplified representation is derived. Here, the steady-state value of the expected value of the state is focused, and is characterized by a minimum feedback vertex set of an interaction graph expressing interactions between nodes. Next, using this representation, input selection and stabilization are discussed. Finally, the proposed method is demonstrated by a biological example.

Control of complex networks such as gene regulatory networks is one of the fundamental problems in control theory. A Boolean network (BN) is one of the mathematical models in complex networks, and represents the dynamic behavior by Boolean functions. In this paper, a solution method for the finite-time control problem of BNs is proposed using a BDD (binary decision diagram). In this problem, we find all combinations of the initial state and the control input sequence such that a certain control specification is satisfied. The use of BDDs enables us to solve this problem for BNs such that the conventional method cannot be applied. First, after the outline of BNs and BDDs is explained, the problem studied in this paper is given. Next, a solution method using BDDs is proposed. Finally, a numerical example on a 67-node BN is presented.

In this paper, we consider distributed decision-making over directed time-varying multi-agent systems. We consider an adversarial bandit problem in which a group of agents chooses an option from among multiple arms to maximize the total reward. In the proposed method, each agent cooperatively searches for the optimal arm with the highest reward by a consensus-based distributed Exp3 policy. To this end, each agent exchanges the estimation of the reward of each arm and the weight for exploitation with the nearby agents on the network. To unify the explored information of arms, each agent mixes the estimation and the weight of the nearby agents with their own values by a consensus dynamics. Then, each agent updates the probability distribution of arms by combining the Hedge algorithm and the uniform search. We show that the sublinearity of a pseudo-regret can be achieved by appropriately setting the parameters of the distributed Exp3 policy.

Automatic detection of prohibited items is vital in helping security staff be more efficient while improving the public safety index. However, prohibited item detection within X-ray security inspection images is limited by various factors, including the imbalance distribution of categories, diversity of prohibited item scales, and overlap between items. In this paper, we propose to leverage the Poisson blending algorithm with the Canny edge operator to alleviate the imbalance distribution of categories maximally in the X-ray images dataset. Based on this, we improve the cascade network to deal with the other two difficulties. To address the prohibited scale diversity problem, we propose the Re-BiFPN feature fusion method, which includes a coordinate attention atrous spatial pyramid pooling (CA-ASPP) module and a recursive connection. The CA-ASPP module can implicitly extract direction-aware and position-aware information from the feature map. The recursive connection feeds the CA-ASPP module processed multi-scale feature map to the bottom-up backbone layer for further multi-scale feature extraction. In addition, a Rep-CIoU loss function is designed to address the overlapping problem in X-ray images. Extensive experimental results demonstrate that our method can successfully identify ten types of prohibited items, such as Knives, Scissors, Pressure, etc. and achieves 83.4% of mAP, which is 3.8% superior to the original cascade network. Moreover, our method outperforms other mainstream methods by a significant margin.

The detection of hardware trojans has been extensively studied in the past. In this article, we propose a side-channel analysis technique that uses a wrapper-based feature selection technique for hardware trojan detection. The whale optimization algorithm is modified to carefully extract the best feature subset. The aim of the proposed technique is multiobjective: improve the accuracy and minimize the number of features. The power consumption traces measured from AES-128 trojan circuits are used as features in this experiment. The stabilizing property of the feature selection method helps to bring a mutual trade-off between the precision and recall parameters thereby minimizing the number of false negatives. The proposed hardware trojan detection scheme produces a maximum of 10.3% improvement in accuracy and reduction up to a single feature by employing the modified whale optimization technique. Thus the evaluation results conducted on various trust-hub cryptographic benchmark circuits prove to be efficient from the existing state-of-art methods.

We present a novel adversarial, end-to-end framework based on Creative-GAN to generate artistic music conditioned on dance videos. Our proposed framework takes the visual and motion posture data as input, and then adopts a quantized vector as the audio representation to generate complex music corresponding to input. However, the GAN algorithm just imitate and reproduce works what humans have created, instead of generating something new and creative. Therefore, we newly introduce Creative-GAN, which extends the original GAN framework to two discriminators, one is to determine whether it is real music, and the other is to classify music style. The paper shows that our proposed Creative-GAN can generate novel and interesting music which is not found in the training dataset. To evaluate our model, a comprehensive evaluation scheme is introduced to make subjective and objective evaluation. Compared with the advanced methods, our experimental results performs better in measureing the music rhythm, generation diversity, dance-music correlation and overall quality of generated music.

In software defined network (SDN), packet processing is commonly implemented using match-action model, where packets are processed based on matched actions in match action table. Due to the limited FPGA on-board resources, it is an important challenge to achieve large-scale high throughput based on exact matching (EM), while solving hash conflicts and out-of-order problems. To address these issues, this study proposed an FPGA-based EM table that leverages shared rule tables across multiple pipelines to eliminate memory replication and enhance overall throughput. An out-of-order reordering function is used to ensure packet sequencing within the pipelines. Moreover, to handle collisions and increase load factor of hash table, multiple hash table blocks are combined and an auxiliary CAM-based EM table is integrated in each pipeline. To the best of our knowledge, this is the first time that the proposed design considers the recovery of out-of-order operations in multi-channel EM table for high-speed network packets processing application. Furthermore, it is implemented on Xilinx Alveo U250 field programmable gate arrays, which has a million rules and achieves a processing speed of 200 million operations per second, theoretically enabling throughput exceeding 100 Gbps for 64-Byte size packets.

Cloud gaming systems allow users to play games that require high-performance computational capability on their mobile devices at any location. However, playing games through cloud gaming systems increases the Round-Trip Time (RTT) due to increased network delay. To simulate a local gaming experience for cloud users, we must minimize RTTs, which include network delays. The speculative video transmission pre-generates and encodes video frames corresponding to all possible user inputs and sends them to the user before the user’s input. The speculative video transmission mitigates the network, whereas a simple solution significantly increases the video traffic. This paper proposes tile-wise delta detection for traffic reduction of speculative video transmission. More specifically, the proposed method determines a reference video frame from the generated video frames and divides the reference video frame into multiple tiles. We calculate the similarity between each tile of the reference video frame and other video frames based on a hash function. Based on calculated similarity, we determine redundant tiles and do not transmit them to reduce traffic volume in minimal processing time without implementing a high compression ratio video compression technique. Evaluations using commercial games showed that the proposed method reduced 40-50% in traffic volume when the SSIM index was around 0.98 in certain genres, compared with the speculative video transmission method. Furthermore, to evaluate the feasibility of the proposed method, we investigated the effectiveness of network delay reduction with existing computational capability and the requirements in the future. As a result, we found that the proposed scheme may mitigate network delay by one to two frames, even with existing computational capability under limited conditions.

In this study, the edge diffraction of a TM-polarized electromagnetic plane wave by two-dimensional dielectric wedges has been analyzed. An asymptotic solution for the radiation field has been derived from equivalent electric and magnetic currents which can be determined by the geometrical optics (GO) rays. This method may be regarded as an extended version of physical optics (PO). The diffracted field has been represented in terms of cotangent functions whose singularity behaviors are closely related to GO shadow boundaries. Numerical calculations are performed to compare the results with those by other reference solutions, such as the hidden rays of diffraction (HRD) and a numerical finite-difference time-domain (FDTD) simulation. Comparisons of the diffraction effect among these results have been made to propose additional lateral waves in the denser media.

To reduce the common mode voltage (CMV), suppress the CMV spikes, and improve the steady-state performance, a simplified reactive torque model predictive control (RT-MPC) for induction motors (IMs) is proposed. The proposed prediction model can effectively reduce the complexity of the control algorithm with the direct torque control (DTC) based voltage vector (VV) preselection approach. In addition, the proposed CMV suppression strategy can restrict the CMV within ±Vdc/6, and does not require the exclusion of non-adjacent non-opposite VVs, thus resulting in the system showing good steady-state performance. The effectiveness of the proposed design has been tested and verified by the practical experiment. The proposed algorithm can reduce the execution time by an average of 26.33% compared to the major competitors.

An efficient optical power splitter constructed by a metal-dielectric-metal plasmonic waveguide with a resonator structure has been analyzed. The method of solution is the finite difference time domain (FD-TD) method with the piecewise linear recursive convolution (PLRC) method. The resonator structure consists of input/output waveguides and a narrow waveguide with a T-junction. The power splitter with the resonator structure is expressed by an equivalent transmission-line circuit. We can find that the transmittance and reflectance calculated by the FD-TD method and the equivalent circuit are matched when the difference in width between the input/output waveguides and the narrow waveguide is small. It is also shown that the transmission wavelength can be adjusted by changing the narrow waveguide lengths that satisfy the impedance matching condition in the equivalent circuit.

Webpage texts are often emphasized by decorations such as bold, italic, underline, and text color using HTML (HyperText Markup Language) tags and CSS (Cascading Style Sheets). However, users with visual impairment often struggle to recognize decorations appropriately because most screen readers do not read decorations appropriately. To overcome this limitation, we propose a method to read emphasized texts by changing the reading voice parameters of a screen reader and adding sound effects. First, the strong emphasis types and reading voices are investigated. Second, the intensity of the emphasis type is used to calculate a score. Then the score is used to assign the reading method for the emphasized text. Finally, the proposed method is evaluated by users with and without visual impairment. The proposed method can convey emphasized texts, but future improvements are necessary.