Reinforcement learning methods achieve performance superior to humans in a wide range of complex tasks and uncertain environments. However, high performance is not the sole metric for practical use such as in a game AI or autonomous driving. A highly efficient agent performs greedily and selfishly, and is thus inconvenient for surrounding users, hence a demand for human-like agents. Imitation learning reproduces the behavior of a human expert and builds a human-like agent. However, its performance is limited to the expert's. In this study, we propose a training scheme to construct a human-like and efficient agent via mixing reinforcement and imitation learning for discrete and continuous action space problems. The proposed hybrid agent achieves a higher performance than a strict imitation learning agent and exhibits more human-like behavior, which is measured via a human sensitivity test.

This paper describes designing a new pedestrian navigation system using a comprehensive framework called the pedestrian navigation concept reference model (PNCRM). We implement this system as a publicly-available smartphone application and evaluate its positioning performance near Omiya station's western entrance. We also evaluate users' subjective impressions of the system using a questionnaire. In both cases, promising results are obtained, showing that the PNCRM can be used as a tool for designing pedestrian navigation systems, allowing such systems to be created systematically.

This paper describes graph-based Wave Function Collapse algorithm for procedural content generation. The goal of this system is to enable a game designer to procedurally create key content elements in the game level through simple association rule input. To do this, we propose a graph-based data structure that can be easily integrated with a navigation mesh data structure in a three-dimensional world. With our system, if the user inputs the minimum association rule, it is possible to effectively perform procedural content generation in the three-dimensional world. The experimental results show that the Wave Function Collapse algorithm, which is a texture synthesis algorithm, can be extended to non-grid shape content with high controllability and scalability.

We combine the siamese network and the recurrent regression network, proposing a two-stage tracking framework termed as SiamReg. Our method solves the problem that the classic siamese network can not judge the target size precisely and simplifies the procedures of regression in the training and testing process. We perform experiments on three challenging tracking datasets: VOT2016, OTB100, and VOT2018. The results indicate that, after offline trained, SiamReg can obtain a higher expected average overlap measure.

In this paper, an analysis method for calculating balanced and unbalanced modes of a small antenna is summarized. Modal condactances which relate dissipated power of the antenna are directly obtained from standard S-parameters that we can measure by a 2-port network analyzer. We demonstrate the validity and effectiveness of the proposed method by simulation and measurement for a dipole antenna with unbalaned feed. The ratio of unbalanced-mode power to the total power (unbalanced-mode power ratio) calculated by the proposed method agrees precisely with that yielded by the conventional method using measured radiation patterns. Furthermore, we analyze a small loop antenna with unbalanced feed by the proposed method and show that the self-balancing characteristic appears when the loop is set in resonant state by loading capacitances or the whole length of the loop is less than 1/20th the wavelength.

Internationalized domain names (IDNs) are abused to create domain names that are visually similar to those of legitimate/popular brands. In this work, we systematize such domain names, which we call deceptive IDNs, and analyze the risks associated with them. In particular, we propose a new system called DomainScouter to detect various deceptive IDNs and calculate a deceptive IDN score, a new metric indicating the number of users that are likely to be misled by a deceptive IDN. We perform a comprehensive measurement study on the identified deceptive IDNs using over 4.4 million registered IDNs under 570 top-level domains (TLDs). The measurement results demonstrate that there are many previously unexplored deceptive IDNs targeting non-English brands or combining other domain squatting methods. Furthermore, we conduct online surveys to examine and highlight vulnerabilities in user perceptions when encountering such IDNs. Finally, we discuss the practical countermeasures that stakeholders can take against deceptive IDNs.

Named Entity Recognition (NER) systems are often realized by supervised methods such as CRF and neural network methods, which require large annotated data. In some domains that small annotated training data is available, multi-domain or multi-task learning methods are often used. In this paper, we explore the methods that use news domain and Chinese Word Segmentation (CWS) task to improve the performance of Chinese named entity recognition in weibo domain. We first propose two baseline models combining multi-domain and multi-task information. The two baseline models share information between different domains and tasks through sharing parameters simply. Then, we propose a Double ADVersarial model (DoubADV model). The model uses two adversarial networks considering the shared and private features in different domains and tasks. Experimental results show that our DoubADV model outperforms other baseline models and achieves state-of-the-art performance compared with previous works in multi-domain and multi-task situation.

As one of the popular topics in the field of human-computer interaction, the Speech Emotion Recognition (SER) aims to classify the emotional tendency from the speakers' utterances. Using the existing deep learning methods, and with a large amount of training data, we can achieve a highly accurate performance result. Unfortunately, it's time consuming and difficult job to build such a huge emotional speech database that can be applicable universally. However, the Siamese Neural Network (SNN), which we discuss in this paper, can yield extremely precise results with just a limited amount of training data through pairwise training which mitigates the impacts of sample deficiency and provides enough iterations. To obtain enough SER training, this study proposes a novel method which uses Siamese Attention-based Long Short-Term Memory Networks. In this framework, we designed two Attention-based Long Short-Term Memory Networks which shares the same weights, and we input frame level acoustic emotional features to the Siamese network rather than utterance level emotional features. The proposed solution has been evaluated on EMODB, ABC and UYGSEDB corpora, and showed significant improvement on SER results, compared to conventional deep learning methods.

In radio channel measurements and modeling, directional scanning via highly directive antennas is the most popular method to obtain angular channel characteristics to develop and evaluate advanced wireless systems for high frequency band use. However, it is often insufficient for ray-/cluster-level characterizations because the angular resolution of the measured data is limited by the angular sampling interval over a given scanning angle range and antenna half power beamwidth. This study proposes the sub-grid CLEAN algorithm, a novel technique for high-resolution multipath component (MPC) extraction from the multi-dimensional power image, so called double-directional angular delay power spectrum. This technique can successfully extract the MPCs by using the multi-dimensional power image. Simulation and measurements showed that the proposed technique could extract MPCs for ray-/cluster-level characterizations and channel modeling. Further, applying the proposed method to the data captured at 58.5GHz in an atrium entrance hall environment which is an indoor hotspot access scenario in the fifth generation mobile system, the multipath clusters and corresponding scattering processes were identified.

Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.

High frame rate and ultra-low delay are the most essential requirements for building excellent human-machine-interaction systems. As a state-of-the-art local keypoint detection and feature extraction algorithm, A-KAZE shows high accuracy and robustness. Nonlinear scale space is one of the most important modules in A-KAZE, but it not only has at least one frame delay and but also is not hardware friendly. This paper proposes a hardware oriented nonlinear scale space for high frame rate and ultra-low delay A-KAZE matching system. In the proposed matching system, one part of nonlinear scale space is temporally forward and calculated in the previous frame (proposal #1), so that the processing delay is reduced to be less than 1 ms. To improve the matching accuracy affected by proposal #1, pre-adjustment of nonlinear scale (proposal #2) is proposed. Previous two frames are used to do motion estimation to predict the motion vector between previous frame and current frame. For further improvement of matching accuracy, pixel-level pre-adjustment (proposal #3) is proposed. The pre-adjustment changes from block-level to pixel-level, each pixel is assigned an unique motion vector. Experimental results prove that the proposed matching system shows average matching accuracy higher than 95% which is 5.88% higher than the existing high frame rate and ultra-low delay matching system. As for hardware performance, the proposed matching system processes VGA videos (640×480 pixels/frame) at the speed of 784 frame/second (fps) with a delay of 0.978 ms/frame.

High frame rate and ultra-low delay matching system plays an increasingly important role in human-machine interactions, because it guarantees high-quality experiences for users. Existing image matching algorithms always generate mismatches which heavily weaken the performance the human-machine-interactive systems. Although many mismatch removal algorithms have been proposed, few of them achieve real-time speed with high frame rate and low delay, because of complicated arithmetic operations and iterations. This paper proposes a temporal constraints and block weighting judgement based high frame rate and ultra-low delay mismatch removal system. The proposed method is based on two temporal constraints (proposal #1 and proposal #2) to firstly find some true matches, and uses these true matches to generate block weighting (proposal #3). Proposal #1 finds out some correct matches through checking a triangle route formed by three adjacent frames. Proposal #2 further reduces mismatch risk by adding one more time of matching with opposite matching direction. Finally, proposal #3 distinguishes the unverified matches to be correct or incorrect through weighting of each block. Software experiments show that the proposed mismatch removal system achieves state-of-the-art accuracy in mismatch removal. Hardware experiments indicate that the designed image processing core successfully achieves real-time processing of 784fps VGA (640×480 pixels/frame) video on field programmable gate array (FPGA), with a delay of 0.858 ms/frame.

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.

A new design approach of broadband RF power amplifier (PA) is introduced in this work with combination of large signal X-parameter and Real-Frequency Technique (RFT). A theoretical analysis of large signal X-parameter is revisited, and a simplification method is introduced to determine the optimum large signal impedances of a Gallium Nitride HEMT (GaN HEMT) device. With the optimum impedance extraction over the wide frequency range (0.3 to 2.0 GHz), a wideband matching network is constructed employing RFT and the final design is implemented with practical mixed-lumped elements. The prototype broadband RF PA demonstrates an output power of 40 dBm. The average drain efficiency of the PA is found to be more than 60%; while exhibiting acceptable flat gain performance (12±0.25 dB) over the frequency band of (0.3-2.0 GHz). The PA designed using the proposed approach yields in small form factor and relatively lower production cost over those of similar PAs designed with the classical methods. It is expected that the newly proposed design method will be utilized to construct power amplifiers for radio communications applications.

This paper presents a three-dimensional (3D) spatial localization algorithm by using multiple one-dimensional uniform linear arrays (ULA). We first discuss geometric features of the angle-of-arrival (AOA) measurements of the array and present the corresponding principle of spatial cone angle intersection positioning with an angular measurement model. Then, we propose a new positioning method with an analytic study on the geometric dilution of precision (GDOP) of target location in different cases. The results of simulation show that the estimation accuracy of this method can attain the Cramér-Rao Bound (CRB) under low measurement noise.

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.

Since deep learning was introduced, a series of achievements has been published in the field of automatic machine translation (MT). However, Korean-Vietnamese MT systems face many challenges because of a lack of data, multiple meanings of individual words, and grammatical diversity that depends on context. Therefore, the quality of Korean-Vietnamese MT systems is still sub-optimal. This paper discusses a method for applying Named Entity Recognition (NER) and Part-of-Speech (POS) tagging to Vietnamese sentences to improve the performance of Korean-Vietnamese MT systems. In terms of implementation, we used a tool to tag NER and POS in Vietnamese sentences. In addition, we had access to a Korean-Vietnamese parallel corpus with more than 450K paired sentences from our previous research paper. The experimental results indicate that tagging NER and POS in Vietnamese sentences can improve the quality of Korean-Vietnamese Neural MT (NMT) in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score. On average, our MT system improved by 1.21 BLEU points or 2.33 TER scores after applying both NER and POS tagging to the Vietnamese corpus. Due to the structural features of language, the MT systems in the Korean to Vietnamese direction always give better BLEU and TER results than translation machines in the reverse direction.

In this paper, we propose a method to reduce the labeling cost while acquiring training data for a malicious domain name detection system using supervised machine learning. In the conventional systems, to train a classifier with high classification accuracy, large quantities of benign and malicious domain names need to be prepared as training data. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by using approximately 1% of the training data used by the conventional systems. Another disadvantage of the conventional system is that if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved. The combination of the two methods proposed here allows us to develop a new system for malicious domain name detection with high classification accuracy and generalization ability by labeling a small amount of training data.

The modern radar signals are in a wide frequency space. The receiving bandwidth of the radar reconnaissance receiver should be wide enough to intercept the modern radar signals. The Nyquist folding receiver (NYFR) is a novel wideband receiving architecture and it has a high intercept probability. Chirp signals are widely used in modern radar system. Because of the wideband receiving ability, the NYFR will receive the concurrent multiple chirp signals. In this letter, we propose a novel parameter estimation algorithm for the multiple chirp signals intercepted by single channel NYFR. Compared with the composite NYFR, the proposed method can save receiving resources. In addition, the proposed approach can estimate the parameters of the chirp signals even the NYFR outputs are under frequency aliasing circumstance. Simulation results show the efficacy of the proposed method.

Inspired by the efficient proof procedures discussed in Computability logic [3],[5],[6], we describe a heuristic proof procedure for first-order logic. This is a variant of Gentzen sequent system [2] and has the following features: (a) it views sequents as games between the machine and the environment, and (b) it views proofs as a winning strategy of the machine. From this game-based viewpoint, a poweful heuristic can be extracted and a fair degree of determinism in proof search can be obtained. This article proposes a new deductive system LKg with respect to first-order logic and proves its soundness and completeness.