Tạp chí tin học và điều khiển học

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Along with the explosion of user reviews on the Internet, sentiment analysis has becomeone of the trending research topics in the field of natural language processing. In the last five years,many shared tasks were organized to keep track of the progress of sentiment analysis for various lan-guages. In the Fifth International Workshop on Vietnamese Language and Speech Processing (VLSP2018), the Sentiment Analysis shared task was the first evaluation campaign for the Vietnamese lan-guage. In this paper, we describe our system for this shared task. We employ a supervised learningmethod based on the Support Vector Machine classifiers combined with a variety of features. Weobtained the F1-score of 61% for both domains, which was ranked highest in the shared task. For theaspect detection subtask, our method achieved 77% and 69% in F1-score for the restaurant domainand the hotel domain respectively.

This paper introduces an optimal tracking controller for robot manipulators with saturation torques. The robot model is presented as a strict-feedback nonlinear system. Firstly, the position tracking control problem is transformed into the optimal tracking control problem. Subsequently, the saturated optimal control law is designed. The optimal control law is determined through the solution of the Hamilton-Jacobi-Bellman (HJB) equation. We use a reinforcement learning algorithm with only one neural network (NN) to approximate the solution of the equation HJB. The technique of experience replay is used to relax a persistent citation condition. By Lyapunov analysis, the tracking and the approximation errors are uniformly ultimately bounded (UUB). Finally, the simulation on a robot manipulator with saturation torques is performed to verify the efficiency of the proposed controller.

Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, entity linking, coreference resolution, schema/ontology matching, semantic text similarity, textual entailment, question answering, tagging, etc. Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R\&D directions.

In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used.

This paper deals with personal scheduling problem in available time-windows with split-min and setup-time constraints. The jobs are splitable into sub-jobs and a common lower bound on the size of each sub-job is imposed. The objective function aims to find a feasible schedule that minimizes the maximum completion time of all jobs. The proposed scheduling problem was proved to be strongly NP-hard by a reduction to 3-SAT problem in the preliminary results. We propose in this paper an exact method based on MILP model to find optimal solution, some heuristics to find feasible solution and a meta-heuristic based on tabu search algorithm to find good solution. The computational results show the performance of proposed exact method, some heuristics and tabu search algorithm.

Drones are increasingly used in various application domains including surveillance, agriculture, delivery, search and rescue missions. Object detection in aerial images (captured by drones) gradually gains more interest in computer vision community. However, research activities are still very few in this area due to numerous challenges such as top-view angle, small-scale object, diverse directions, and data imbalance. In this paper, we investigate different data augmentation techniques. Furthermore, we propose combining data augmentation methods to further enhance the performance of the state-of-the-art object detection methods. Extensive experiments on two datasets, namely, AERIAU, and XDUAV, demonstrate that the combination of random cropped and vertical flipped data boosts the performance of object detectors on aerial images.

In this paper, we propose an efficient distributed denial-of-Service (DDoS) Attack deFEnse solution, namely SAFE, which utilizes an elastic traffic flow inspection mechanism, for Software-Defined Networking (SDN) based data centers. In particular, we first examine a leaf-spine SDN-based data center network, which is highly vulnerable to volumetric DDoS attacks. Next, we develop a rank-based anomaly detection algorithm to recognize anomalies in the amount of incoming traffic. Then, for the traffic flow inspection, we introduce a component called DFI (Deep Flow Inspection) running an Open vSwitch (OvS) that can be dynamically initiated (as a virtual machine) on-demand to collect traffic flow statistics. By utilizing deep reinforcement learning-based traffic monitoring from our previous study, the DFIs can be protected from the flow-table overflow problem while providing more detailed traffic flow information. Afterward, a machine learning-based attack detector analyzes the gathered flow rule statistics to identify the attack, and appropriate policies are implemented if an attack is recognized. The experiment results show that the SAFE can effectively defend against volumetric DDoS attacks while assuring a reliable Quality-of-Service level for benign traffic flows in SDN-based data center networks. Specifically, for TCP SYN and UDP floods, the SAFE attack detection performance is improved by approximately 40% and 30%, respectively, compared to the existing SATA solution. Furthermore, the attack mitigation performance, the ratio of dropped malicious packets obtained by the SAFE is superior by approximately 48% (for TCP SYN flood) and 52% (for UDP flood) to the SATA.

Bài báo này giới thiệumột phương pháp đơn giản để điều khiển cho một robot 2 chân 10 bậc tự do với một dáng đi ổn định và giống người sử dụng một cấu hình phần cứng đơn giản. Robot 2 chân được mô hình như một con lắc ngược 3 chiều. Dáng đi của robot được tạo bởi hệ thống điều khiển bám điểm mô men không (ZMP) của robot 2 chân theo quỹ đạo là đường zigzac theo lòng bàn chân của robot. Một bộ điều khiển tối ưu được thiết kế để cho hệ thống điều khiển bám điểm ZMP. Một quỹ đạo của khối tâm của robot trong vùng ổn định được tạo ra khi ZMP của robot bám theo quỹ đạo theophương x và y luôn luôn nằm trong lòng bàn chân của robot. Dựa vào quỹ đạo ổn định của khối tâm, bước đi của robot được đề xuất bằng cách giải bài toán động học ngược và được tích hợp trên phần cứng dùng PIC18F4431 và DSPIC30F6014. Phương pháp đề xuất được kiểm chứng thông qua mô phỏng và thực nghiệm.

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