Reviewerly/exHarmony huggingface dataset:
A Multi-label Feature Selection Method Combining Graph and Matrix Coupling Decomposition

Selecting representative features and avoiding redundant features are crux of constructing learning methods in knowledge discovery, which is a more challenging work in multilabel data sets. The relevant information and the inherent key information in both the feature matrix and the label matrix need to be fully considered to improve the performance of the feature selection method. In this study, a novel multi-label feature selection method (GMFS) combining graph based method and matrix coupling decomposition is proposed. By introducing the matrix coupling decomposition and acting on the two original matrices, their shared latent semantic matrix can be extracted. The latent semantic matrix has less redundant information and irrelevant information. Simultaneously, a manifold regularizaiton term is used to preserve the local manifold structure of the sample space and helps training a more informative mapping matrix. Moreover, an improved method of the mapping matrix is given to further retain the feature-label dependency information. The effectiveness of the proposed GMFS method is compared with several algorithms.
Fusion of Complex Networks-Based Global and Local Features for Feature Representation

To realize effective feature representation, this paper proposes a new complex networks (CNs)-based feature fusion scheme to recognize texture images. Specifically, we propose two feature extractors to detect the global and local features of texture images respectively. To capture the global features, we first map a texture image as an undirected graph based on pixel location and intensity, and three feature measurements are selected to further decipher the image features, which retains as much image information as possible. Next, given the original band images (BIs) and the generated feature images, we encode them using local binary patterns (LBPs). Thus, the global feature vector is obtained by concatenating four spatial histograms. To decipher the local features, we jointly transfer and fine-tune a pre-trained VGGNet-16 model. After then, we fuse the middle outputs of max-pooling layers (MPs) and generate the local feature vector based on a global average pooling layer (GAP). Finally, the global and local feature vectors are connected to form the final feature representation of texture images. Experiment results show that the proposed scheme outperforms the state-of-the-art statistical descriptors and several deep convolutional neural network (CNN) models.
Application and Development Prospect of Artificial Intelligence in Quality Education

The integration of education and artificial intelligence has become an important trend of educational reform. With the continuous deepening of the application of artificial intelligence in the field of education, the popularization of artificial intelligence in primary and secondary schools is facing many problems, such as curriculum system and curriculum standards, teaching modes and methods, teachers, class hours, teaching venues and teaching aids. Therefore, a collection and analysis platform for education big data is built. The platform can collect and integrate student data and teaching data. Focusing on the modern development direction of intelligence, starting with the construction of intelligent system, build an intelligent education management system with advanced technology, cultivate the intelligent literacy of educatees, and promote the further integration and development of education and artificial intelligence.
Q-Learning Based Robot Path Planning with Improved Dynamic Window Approach

Robot path planning is the basis for robot to perform various tasks successfully. Dynamic window approach (DWA) is a flexible way to solve this task, however its evaluation functions are not comprehensive enough and the weight of these functions will not be adjusted with the change of environment in real time, which makes the robot tend to circumnavigate the outside of the dense obstacle area, and it is easy to fall into the failure of the evaluation function when it meets the combination of “C” shaped obstacles. In this paper, an improved DWA based on Q-learning is proposed. Firstly, we add a new evaluation function to prevent the robot from circling in place. Then, on the basis of defining the state space, action space and reward function in the robot motion planning, Q-learning is used to adjust the weight of the evaluation function adaptively, so that it can adapt to the changing environment. Finally, the simulation results show that the new evaluation function not only solves the above problems, but also improves the overall motion efficiency of mobile robots.
Sign Language Video Translation Based on BERT-SLSTM and Reinforcement Learning

This paper proposes a novel method based on BERT-Stacked LSTM and Reinforcement Learning (BSRL) model for joint sign language recognition and sign language translation. The proposed model applies the BERT model as the encoder to process the features of multi-channel communication of sign language videos. The stacked LSTM is applied as the decoder to effectively extract the text representation. A reinforcement learning algorithm based on self-critical sequence training is applied to model parameters based on evaluation indicators so that the generated texts have a high similarity to the ground truth texts. Experiment results on Phoenix2014T dataset demonstrate the effectiveness of the proposed method.
An Automated ICD-10 Code Assigning System using A Classification Method

At present, some hospitals in Thailand have to manually analyze patient treatment data for assigning the disease diagnostic code, or ICD-10 (International Classification of Diseases and Related Health Problem 10th Revision) code. The ICD-10 codes are collected and submitted to the Ministry of Public Health to collect Thailand’s disease incidence statistics and allocate a budget for the development of the country’s health system. These hospitals have difficulty recruiting personnel with expertise in analyzed and assigned ICD-10 codes, causing a long working time and a problem with the accuracy of the analyzed and assigned ICD-10 codes, due to many patients daily. This paper presents the automated ICD-10 code assigning system developed for solving the problem of analyzing and assigning the ICD-10 code manually by a human expert in the hospitals. The system uses a classification method with a decision tree diagram as the model to classify the ICD-10 codes from patient treatment data, i.e., medicine and laboratory results. The system can be used as a tool to support a medical staff who is the expertise that analyzes and assigns the ICD-10 code in a more accurate and rapid manner. The evaluation of the classification result with the decision tree model is found to be 91.67 percent accurate in performance for the ICD-10 codes assigned.
Research on the Policy of Occupying Attack Position Based on Fuzzy Comprehensive Evaluation for Autonomous Underwater Vehicles

Since of the uncertain situation when autonomous underwater vehicles occupy attack positions, it is difficult to make optimal policy to occupy attack positions. This paper proposes a policy evaluation method to choose the optimal policy, the proposed method combines the generalized simulated annealing algorithm and fuzzy comprehensive evaluation. It firstly establishes a fuzzy evaluation model for AUV to occupy attack positions and quantitatively evaluates the occupying policy based on the evaluation score. The parameters of the fuzzy evaluation model are set according to the experts’ experience. Facing different policies, it uses a generalized simulated annealing algorithm to search the optimal one that has the biggest evaluation score. This modified method changes the energy transition mode which improves the search performance. The simulation experiments show that the proposed method is effective to get the optimal policy for AUV to occupy attack positions.
Model-Based Multi-agent Policy Optimization with Dynamic Dependence Modeling

This paper explores the combination of model-based methods and multi-agent reinforcement learning (MARL) for more efficient coordination among multiple agents. A decentralized model-based MARL method, Policy Optimization with Dynamic Dependence Modeling (POD2M), is proposed to dynamically determine the importance of other agents’ information during the model building process. In POD2M, the agents adapt their mutual dependence during building their own dynamic models in order to make a trade-off between an individual-learning process and a coordinated-learning process. Once the dynamic models have been built, the policies are then trained based on one-step model predictive rollouts. Empirical experiments on both cooperative and competitive scenarios indicate that our method can achieve higher sample efficiency against the compared model-free MARL algorithms, and outperforms the centralized method in large domains.
A measurable refinement method of design and verification for micro-kernel operating systems in communication network

A secure operating system in the communication network can provide the stable working environment, which ensures that the user information is not stolen. The micro-kernel operating system in the communication network retains the core functions in the kernel, and unnecessary tasks are implemented by calling external processes. Due to the small amount of code, the micro-kernel architecture has high reliability and scalability. Taking the micro-kernel operating system in the communication network prototype VSOS as an example, we employ the objdump tool to disassemble the system source code and get the assembly layer code. On this basis, we apply the Isabelle/HOL, a formal verification tool, to model the system prototype. By referring to the mathematical model of finite automata and taking the process scheduling module as an example, the security verification based on the assembly language layer is developed. Based on the Hoare logic theory, each assembly statement of the module is verified in turn. The verification results show that the scheduling module of VSOS has good functional security, and also show the feasibility of the refinement framework.
Hierarchical Discrepancy Learning for Image Restoration Quality Assessment

With the unremitting efforts of the community, great progress has been made in image restoration (IR). However, how to objectively benchmark these algorithms is much less investigated and remains a challenging problem to date, which may hinder the rapid development of IR technologies. Most of the current IR quality metrics are scenario-special and cannot be generalized to diversified scenarios. Besides, they make predict solely on basis of restored versions, without using any information from the original degraded images. Therefore, they are blind to the relative perceptual discrepancy (RPD) between the degraded-restored image (DRI) pairs, which is much more useful when comparing the performance of IR algorithms. Inspired by this, we propose a hierarchical discrepancy learning (HIDI) model for benchmarking IR algorithms. To address the small sample problem, a quality evaluation database containing a variety of simulation distortions is built to pre-train a prior evaluator. Then, the DRI pairs are constructed and used to train the fully end-to-end HIDI model. The proposed HIDI model is extensively evaluated on several public databases and experimental results demonstrate the HIDI model has higher performance than the state-of-the-arts. Besides, compared with the competitors, the HIDI model shows the best generalization ability in cross-database experiments.
Encrypted Operator Design with Domain Aware Practical Performance Improvement

Cloud has established itself as an essential part of the new-age computation. However, using a public platform like cloud for data storage and computing, raises severe data security concerns. Storing and uploading data in encrypted form to the cloud may conform data confidentiality. However, data encrypted with traditional encryption schemes do not support processing on encrypted data. Hence, this diminishes the computing possibility in the cloud domain, and each time data need to be taken back and forth. Homomorphic Encryption (HE) is a possible solution to this problem, supporting direct processing of encrypted data. However, realizing any algorithm in the encrypted domain is not straightforward and requires algorithms to be represented in circuit-based form which is a challenging task considering underlying existing processors are unencrypted. Further, performance issues of HE techniques pose a real bottleneck to practical applications. In this work, we focus on a domain aware approach to realize homomorphic computations in practical time which are otherwise too slow or infeasible to implement in encrypted domain. In this work we discussed exponent, logarithm, and sigmoid operators; highlighted why exponent computation on encrypted data with traditional multiple multiplications incur huge performance overhead and logarithm, sigmoid computations on encrypted values are impossible considering existing underlying unencrypted processors. To the best of our knowledge, this work is first in literature to handle these operators fully in the encrypted domain without any cloud domain intermediate decryption.
A Novel Reduced Reference Image Quality Assessment Based on Formal Concept Analysis

Abstract The assessment of image quality provides the confidentiality, safety, quality and transparency of the obtained images. At the same time, there are many existing quality metric approaches that belong to the group of modification alteration procedures (pixel-based metric). These techniques have not been well-matched with perceptual image quality. The Perceptual Human Visual System (HVS) aspect drives our approach; The human visual system (HVS) is the best judge of image quality. This paper presents a new reduced reference image quality metric in the spatial domain that reduces the complexity of the image quality assessment. From the reference and distortion images, we extract four intrinsic features, namely, contrast, entropy, histogram and standard deviation. Then, we build the formal concept analysis matrix for reference and distortion images. Finally, we compare the obtained matrixes to evaluate the image quality. The performance of the proposed technique is assessed using LIVE, TID2013 and CSIQ datasets, and the obtained results are compared in terms of PSNR, SSIM and NCC metrics. Also, a comparison with more recent and relevant approaches is performed to highlight the superior performance of our proposed approach, the experimental results indicated that the proposed approach provides efficient performance among the compression, Gaussian blur, Contrast and add noise distortion types.
Cooperative Learning System for English Majors and Its Practical Application in the Mobile Information Age

With tne rapid development of computer technology and communication technology, human society has entered the mobile information age. Mobile communication devices, represented by smartphones, have changed people's lifestyles, and so has the way university students learn. They can browse all kinds of information anytime and anywhere, and build a learning environment based on social networking platforms, with easy access to information and innovative learning modes. Smart phones are not only communication tools, but also a way to obtain knowledge. It has become a trend to address the problems of teaching English as a foreign language and to make full use of the conveniences of the mobile information age for collaborative learning. This paper constructs a mobile collaborative learning model, carries out a requirement analysis and summary design based on systems engineering thinking, and proposes practical application strategies to improve the effectiveness of English learning, meet students' psycho-emotional needs and promote codevelopment.
Efficient Data Clustering Techniques for Software-Defined Network Centres

In a smart system, a software-defined network (SDN) is frequently used to monitor and manage the communication organisation. Large-scale data analysis for SDN-based bright networks is gaining popularity. It's a potential technique to deal with a large amount of data created in an SDN-based shrewd lattice using AI advancements. Nonetheless, the disclosure of personal security information must be considered. Client power conduct examination, for example, may result in the disclosure of personal security information due to information bunching. Clustering is an approach for displaying models' observations, data items, or feature vectors in groups. Batching addresses has been catered to in various interesting circumstances and by masters in distinct requests; it gleams far-reaching attractiveness and assistance as one of the ways in exploratory data examination and moreover increases the genuine assessment of data. In this chapter, the authors conduct a study of packing and its various types and examine the computation. Finally, they use it to create an outline model.
Improved Hate Speech Detection System Using Multi-layers Hybrid Machine Learning Model

Hate speech and cyber-harassment have been a major concern on the Internet for a long time. Furthermore, social media platforms, particularly Twitter and Facebook, have elevated it to a worldwide platform on which hate speeches can spread much faster. Manually detecting hate speech from social media is a time-consuming process. Hence, several studies are still being conducted in this field. We build a two-layer hybrid machine learning model using existing machine learning algorithms. This hybrid machine learning algorithm is capable of efficiently detecting hate speech from social media texts. The hybrid approach combines nine different machine learning algorithms to make one hybrid machine learning model. Additionally, we used the bag-of-words and TF-IDF techniques with the two-gram approach to extract the features. Significant experiments are carried out on the hate speech dataset. The accuracy gained by the hybrid machine learning model is much higher than that of available conventional machine learning models.
A Novel Technique to Identify View-Invariant Hand Postures

It’s been interesting to replace the mouse or keywords with merely gestures in a virtual environment to allow users to engage with computers in a more natural way. This takes the user experience to the next level by allowing them to engage with a 3D world without having to touch the computers. In order to recognize hand gestures, one must require solving various challenging tasks like hand detection, segmentation, 3D hand pose estimation, hand shape classification, and overall hand configuration parameters. Furthermore, early vision-based hand gesture identification systems do not deliver sufficient performance in a real-time scenario when certain aspects are taken into account, such as a crowded background and sensitivity to lighting conditions. In this regard, the information gathered from the shape and texture of the hands is utilized to create a breakthrough technique of personal identification and verification scheme for hand postures is presented in this paper. The system is made up of two primary components. In the first part, a threshold method based on Otsu’s algorithm and a color-based detection is used to differentiate the hand from its background. The palm and fingers are then classified using a set of completely autonomous techniques. The hand contours and fingers are assessed and employed in these algorithms to establish the global position of the hand as well as the pose of each finger. Finally, the palms and fingers are clipped, the stance is rectified, and the image is normalized. The recognition system takes many structure and texture-based criteria and uses them for matching purposes. For shape and textural properties of the fingers, the Independent-Component Analysis (ICA), Iterative Closest Point (ICP), and modified Hausdorff are applied. Moreover, directional line features and Discrete Cosine Transform (DCT) are employed to create palm prints and better results were achieved by combining a new robust posture invariance hand estimating strategy with an optimized feature selection, feature matching, and integration methodology. Simulation results are demonstrated, with a collection of 570 different subjects, and compared with the existing systems which show 97% efficiency for hand detection and recognition.
Simple Illustration and Gentle Description of ECC Process

With the advance of cryptography, not only do a large number of devices have to maintain security but they should also minimize the key size, which is a great concern. In turn, Elliptical Curve Cryptography (ECC), introduced by Victor Miller and Neal Koblitz have gained tremendous attention in recent years, which might be referred to as an "improvement" of RSA. While the security of RSA and ECC are both based on discrete logarithm problem, which is also called the hard problem, ECC is based on a more complex one, the elliptic curve discrete logarithm problem (ECDLP). Simultaneously, having equivalent levels of security with the RSA, ECC’s operation depends on the combination of elliptic curves and modular arithmetic, meaning a much smaller key size. On the other hand, even though ECC is of great significance, only a small number of people are familiar with them. Accordingly, this article will illustrate the operation of elliptic curves with some pictures, such as the addition and doubling. Furthermore, some description of them will also be discussed in detail, demonstrating why ECC generates a much smaller key size. To be specific, some math will be included such as the group law and the arithmetic modular, but most of them are only simple ones and sets no barrier to understand, comparing to the long and boring proof.
Substantiation of the Periodicity of Multi-Scanning Radar Monitoring of Moving Objects

In this paper we analyze the issues of detecting trajectories of radar objects. We consider implementation of "track-before-detect" (TBD) methods of sequential and block type, implementing procedures for detecting trajectories and preliminary evaluation of their parameters. We analyze the influence of radar survey frequency on the effectiveness of the implementation of multi-scanning observation methods (TBD methods). As indicators of comparative analysis, we consider: the time of detection of trajectories, the quality of the obtained trajectory information, the features of the implementation of TBD methods at high and low frequency of survey (observations). We analyze the variants of multi-scanning observations of different periodicity to solve the detection-measurement problem based on the available a priori information. We substantiate the rational choice of radar observations frequency for the implementation of sequential and block-type TBD methods.
An Overview of Head’s Up Display for Low-Medium Segment Cars

According to the World Health Organisation (WHO), road crashes cause deaths of 1.2 million people and permanently disable another 50 million every year, So our intended/proposed system aims at reducing the distraction of the driver and therefore minimizing the chance of accidents by employing an interactive car infotainment system which helps to display the necessary information such as Speed, RPM, Engine Load, etc. on the windshield. As a further enhancement to the distraction-free driving experience, we have integrated a voice control system. According to the survey conducted by The Economic Times, 40% of the overall consumer cars on the road are of budget/economic class which miss out on this feature. With the evolution of machine learning and artificial intelligence, Additional features - lane detection, object tracking, etc can be added to our proposed system to make it much more efficient and cost-friendly.