Reviewerly/exHarmony huggingface dataset:
Partial multi-label learning with cross-validation

Partial multi-label learning (PML) tackles the problem that each example is assigned a candidate label set, of which only a subset is the ground-truth labels. By decomposing the problem using the first-order strategy, we found that PML is similar to the problem of learning with label noise. Motivated by this observation, we proposed a novel method, PML-CV, which tackles the PML problem with a cross-validation approach. To be specific, PML-CV enhances potentially correct labels by using cross-validation. And then use an example refining scheme to weaken the impact of noisy labels further. We also provide some theoretical analysis to explain the effectiveness of our proposed method. Finally, we conduct extensive experiments on different datasets to verify the effectiveness of our method. The experimental results verify that our method is comparable to current state-of-the-art methods.
Q-learning for single-agent and multi-agent and its application

Q-learning is a reinforcement learning method for solving Markov decision problems with incomplete information proposed by Watkins. With the development of reinforcement learning, more and more Q-learning related algorithms have been proposed, and their application range has become wider. In this paper, we discussed single agent algorithms including basic Q learning, deep Q learning and double Q learning. In addition, we discussed multi-agent algorithms including modular Q learning, ant Q learning and Nash Q learning with prominent characteristics. This paper will compare their advantages and disadvantages, and put forward our own views on the current application of Q-learning and the future trend of Q-learning.
Researches advanced in credit assignment in reinforcement learning

The credit assignment has always been one of the hot tasks in the reinforcement learning community, which aims to estimating and deducing the reward for each agent during the decision-making process. In this paper, based on extensive literature research, we first review the existing solutions to the credit assignment issue, and compare the impact of different credit assignment strategies on algorithms’ final performance. In addition, we also summarize the main challenges of credit assignment and give some suggestions for the future research directions.
Improvement of predictive recurrent neural network based on feature fusion

Video can be regarded as a sequence composed of several related images. To predict video, we should not only grasp the characteristics of a single image, but also combine the temporal logic information between images. The Predictive Recurrent Neural Network (PredRNN) is a video frame prediction network using spatiotemporal memory stream structure in spatiotemporal LSTM network. This paper introduces the improved method of PredRNN based on feature fusion. The spatiotemporal memory flow structure of PredRNN will bring the problem of gradient disappearance with the increase of depth. This paper proposes to perform feature fusion on the spatiotemporal memory information and increase the gradient of deep network to improve the long-term video prediction effect of the network. Finally, the moving MNIST dataset and the KTH dataset are used to prove our network. The experimental results show that our method has a certain improvement over the PredRNN.
Path planning for 6-DOF manipulator

Manipulator plays an important role in industrial production and can complete a number of tasks, such as handling, picking, sorting. However, its working environment is not necessarily very spacious, there are other equipment or objects in the working environment, such as shelves, railings, or computers, and the manipulator will be blocked by them when moving. Therefore, it is necessary to use algorithms to ensure the ideal path and reduce the path deviation of motion and obstacle avoidance. Thus, this paper aims to prove that whether the manipulator can find a feasible motion path under many obstacles with RRT * algorithm. Meanwhile, the D-H method is used to establish the manipulator model, then the RRT* algorithm is studied for path planning, and finally verified by MATLAB simulation. The results show that the manipulator can quickly avoid obstacles and reach the target point under the control of path planning algorithm.
Research on feature matching algorithm of outdoor complex scenes

An essential part of image processing is feature matching. The commonly used algorithms for feature matching include the scale-invariant feature transform (SIFT) algorithm, accelerated up robust features (surf), and oriented FAST and rotated BRIEF(ORB) algorithm. So far, most of the research on these algorithms at home and abroad mainly focuses on speed, accuracy and robustness, lacking specific analysis for specific scenes. To aim at three classical algorithms, this paper first uses the HPSequences Dataset to test speed and accuracy in general. Then, test the accuracy and speed with the light, angle, and ambiguity changing for a specific outdoor scene. The experimental results show that SURF can still maintain high matching accuracy when the object ambiguity and angle change. SIFT can still maintain high matching accuracy when the object's light changes. The robustness of ORB is the worst. In any case, the matching accuracy decreases the fastest.
Researches advanced in data-to-text generation based on neural networks

Data-to-text generation has always been a research hotspot in multimodal machine learning. Given the structured data such as a list of characteristics of a person or statistical data from an exam, the data-to-text generation aims to automatically generate smooth, true and accurate text which can describe the input data. Thanks to the rapid development of convolutional neural networks, the text generation models based on deep learning have made great breakthroughs in performance, but still face many challenges which mainly include confusion of the output location of different information, generation of non-existent content, duplication of information and data sparseness. In this paper, based on in-depth literature survey, we introduce the representative algorithms from the aspects of improving text evaluation quality, combining deep learning and knowledge, adapting to various expressions and aligning text and structured data. We also summarize the existing problems and prospect the possible future development direction in the data-to-text generation research field.
Researches advanced in small object detection based on deep learning

Object detection has always been one of the hot tasks in the computer vision community, whose goal is to locate the instances from the image and predict instances’ category. In recent years, with the development of deep learning technology, both the accuracy and speed of object detection have made great progress. However, limited by the low resolution and little feature information of the small objects, detecting the small object is still facing many difficulties and attracting more and more researchers’ attention. In this paper, we first introduce the mainstream object detection algorithms, and then detail the development of small object detection algorithms from the perspective of the data enhancement, context learning, adversarial learning, feature fusion, and other aspects. Also, we analyze the performance of these representative algorithms on the common datasets. Finally, we summarize the existing problems and prospect the possible future development direction in the small object detection research field.
Handwritten character recognition combining Random Forest kernel and multiclass SVM

Handwritten character recognition can be applied in education, finance, social good, and many other fields. Varieties of kernelized methods are applied to this field. However, the fact is that many relative kernelized algorithms’ performances depend greatly on the kernels, and the number of current choices is quite limited. This paper uses Random Forest to generate a kernel and constructs a multiclass SVM classifier based on the kernel for handwritten character recognition. The proposed method can generate a targeted kernel by analyzing the dataset, which improves the prediction accuracy of the final model. At first, all the images are compressed by grayscale transformation, binary conversion, and redundancy elimination, and their sizes are uniform to 𝟑𝟐&times; 𝟑𝟐. Then, the data selected for training and validation is used to build a Random Forest model responsible for generating the Random Forest kernel. Finally, based on the kernel constructed before, multiclass SVM is used to classify the dataset into 36 kinds of handwritten characters. The proposed method is compared with a Linear-kernel SVM model, a Polynomial-kernel SVM model, and a Random Forest model to evaluate the model's efficiency. The experiment results demonstrate that SVM with Random Forest kernel can achieve relatively high accuracy while recognizing handwritten characters.
Graph neural network based agent in Google Research Football

Deep neural networks (DNN) can approximate value functions or policies for reinforcement learning, which makes the reinforcement learning algorithms more powerful. However, some DNNs, such as convolutional neural networks (CNN), cannot extract enough information or take too long to obtain enough features from the inputs under specific circumstances of reinforcement learning. For example, the input data of Google Research Football, a reinforcement learning environment which trains agents to play football, is the small map of players’ locations. The information is contained not only in the coordinates of players, but also in the relationships between different players. CNNs can neither extract enough information nor take too long to train. To address this issue, this paper proposes a deep q-learning network (DQN) with a graph neural network (GNN) as its model. The GNN transforms the input data into a graph which better represents the football players' locations so that it extracts more information of the interactions between different players. With two GNNs to approximate its local and target value functions, this DQN allows players to learn from their experience by using value functions to see the prospective value of each intended action. The proposed model demonstrated the power of GNN in the football game by outperforming other DRL models with significantly fewer steps.
Research on operation safety monitoring system of road transport trucks based on vehicle networking

With the development of Automotive Intelligence and networking technology, vehicle-borne intelligent devices and systems have been widely used, and it can collect operation data in real time. Data has the characteristics of large amount of data, many types and wide coverage. Through the analysis and identification of these operation data, it is of great significance for monitoring vehicle operation status and evaluating vehicle technical performance. Especially the road transport vehicles, which have high running intensity and high safety risk, need to carry out targeted research. This paper collects and combs the operation data parameters of road transport trucks, refines and extracts various signal parameters by using principal component analysis theory, so as to form the data set of on-line monitoring of vehicle operation safety and provide basic data support for subsequent vehicle safety management.
Do registers have different functions for text length?

Abstract Similar to lexical and grammatical choices, the length of a text is also guided by situational constraints and functional needs. Consequently, texts of different lengths are associated with different communicative functions. This study explores the role of register in the functions which are associated with comment lengths on the social media platform Reddit. Since registers differ in their functional and situational makeup, the same text length may also have different functions in different registers. By analyzing variation in the frequencies of register features across comment lengths in a number of popular subreddits in a large-scale dataset of Reddit comments, I show that the functional associations of text length can differ greatly between subreddits, and that comments of the same length can even have virtually opposite functions in different subreddits. Furthermore, some subregisters are clearly differentiated not only by their feature makeup but also by the length of their comments.
Integrating facies analysis and geostatistical methods in an onshore oil field using petrophysical groups

Reservoir facies studies are of great importance in different stages of exploration and development of hydrocarbon fields. We have aimed to generate a reservoir facies model for the Asmari Formation in an onshore oil field located in southwest Iran. Input data for electrofacies (EF) clustering algorithms are used, which include gamma-ray (GR), density (RHOB), porosity, and sonic logs from four wells. We obtain the petrophysical group (PG) and EF class using core drill data (mercury injection capillary pressure) and well-logs analysis. The integration of PGs and log EF significantly decreases the uncertainty in reservoir modeling, which alternatively enhances field development decisions. We compare the multiresolution graph-based clustering (MRGC) and k-means clustering methods. EF clustering results find nine EF classes. We delineate high-quality reservoirs based on lower GR, RHOB, and high-porosity logs. Next, we use the clustering results in the static reservoir modeling process, using the sequential index simulation and indicator kriging methods. The comparison between the facies obtained models and existing drilling core data finds that the absolute percentage error of the MRGC algorithm is less than that of the k-means algorithm. The results obtained by this study can provide useful information for the development of hydrocarbon exploration plans in the studied oil field.
A Hybrid Optimization Framework with Dynamic Transition Scheme for Large-Scale Portfolio Management

Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems. However, they may fail to solve other problems. Among the various approaches, hybridizing meta-heuristic algorithms may possibly help to enrich their search behaviors while promoting the search adaptability. Accordingly, an efficient hybrid population-based optimization framework, namely the HYPO, is proposed in this study in which two meta-heuristic algorithms with different search ideas are connected by a dynamic contribution-based state transition scheme. Specifically, the dynamic transition scheme determines the directions of information transitions after considering the current contribution and system state at each iteration so that useful information can be shared and learnt between the concerned meta-heuristic algorithms throughout the search process. To carefully examine the effectiveness of the dynamic transition scheme, the proposed HYPO framework is compared against various well-known meta-heuristic algorithms on a set of large-scale benchmark functions and portfolio management problems of different scales in which the HYPO attains outstanding performances on the problems with complex features. Last but not least, the hybrid framework sheds lights on many possible directions for further improvements and investigations.
Research on anomaly detection algorithm of time series data in cloud environment

Cloud environment is a large-scale, distributed and complex system. Due to the complex interdependence and call relationship between its functional layers, the efficient operation and maintenance of cloud environment has become a major problem. The main form of daily monitoring KPI data in cloud environment is time series data. The prediction and anomaly detection of these data have always been two hot spots at home and abroad. The algorithm with high prediction accuracy and high anomaly detection accuracy can help us find the potential problems in the cloud environment and stop the loss in time to avoid large losses. Based on the previous relevant research results, this paper takes the data in the cloud environment as the research object, and takes improving the prediction accuracy and anomaly detection accuracy of the data as the research goal, and puts forward efficient and accurate prediction algorithms and anomaly detection algorithms suitable for the data characteristics in the cloud environment.
Error-Reject Tradeoff Analysis on Two-Stage Classifier Design with a Reject Option

When a human is unsure of the answer to a difficult question, a natural response is "I don’t know." It is often useful to allow classification systems this same discretion; the so-called "reject option" allows classifiers to reject an input instead of attempting classification. In this paper, we introduce a simple ensemble design for a two-stage classifier with a reject option. The choice of classification algorithms used for each stage is unrestricted, but directly affects the error and reject rates. We center our analysis around the error-reject tradeoff produced by the user’s choice of algorithms.
Posture Estimation System for Excavator Manipulator Using Deep Learning and Inverse Kinematics

This study aims to construct a simple system for estimating and measuring the front manipulator posture by attaching a camera to an excavator. We propose a method to extract the feature points of the arms and buckets tip by deep learning and to calculate the posture of the front part using camera geometry and inverse kinematics from the obtained feature points. The effectiveness of the proposed method was verified experimentally using a scale model.
Image Classification Based on a Weighted CNN Model

This paper explores a deeper network architecture diagram, which can be viewed as an enhanced version of AlexNet. In the design of the winder, a smaller winder core is used, which verifies that the small size of the winder core in the deep network can not only reduce the parameters, but also achieve better results. The concept of Inception was introduced and the idea of "widening" was designed under the prevailing model of "deepening" at that time. Moreover, two weighted Softmax branches can be conducted, which consist of two functions. One is to avoid the disappearance of the gradient, and is used for conducting the gradient forward. If one of the layers is 0 in the back propagation, the chain is 0. The second is to utilize the output layer of the middle layer as a classification to analyze the significant role in the model fusion. They decompose the convolution of the filter size NXN into a combination of 1xn and nx1 convolution. For example, 3 by 3 convolution is equivalent to first performing 1 by 3 convolution, and then performing 3 by 1 convolution on its output. They found that this method is 33% cheaper than a single 3x3 convolution.
A dual speckle method for eliminating air distortion in high temperature optical measurement experiments

High-temperature deformation measurements based on optical measurement techniques are important for studying material properties. Due to the existence of temperature gradients between the high-temperature subject and the imaging system, the uneven air density distribution leads to random changes in the refractive index of air along the beam transmission path, which triggers random jitter in the images captured by the imaging system, resulting in large errors in the measured displacement field. In this paper, a dual speckle method is developed for eliminating air distortion in high-temperature experiments. The main experimental setup consists of two cameras, where one camera is focused on the speckles sprayed on the specimen surface that can measure the coupled displacement field, and the other camera is focused on the semipermeable speckles, which can measure the pure displacement caused by the change in the refraction index of air. Then, the true displacement of the tested specimen can be obtained by an arithmetic operation after the calculating point meets the one-to-one relationship. A simplified yet effective test was conducted and verified the feasibility of the method proposed, as the error analysis was analyzed in detail.
Automated Security Assessments of Amazon Web Services Environments

Migrating enterprises and business capabilities to cloud platforms like Amazon Web Services (AWS) has become increasingly common. However, securing cloud operations, especially at large scales, can quickly become intractable. Customer-side issues such as service misconfigurations, data breaches, and insecure changes are prevalent. Furthermore, cloud-specific tactics and techniques paired with application vulnerabilities create a large and complex search space. Various solutions and modeling languages for cloud security assessments exist. However, no single one appeared sufficiently cloud-centered and holistic. Many also did not account for tactical security dimensions. This article, therefore, presents a domain-specific modeling language for AWS environments. When used to model AWS environments, manually or automatically, the language automatically constructs and traverses attack graphs to assess security. Assessments, therefore, require minimal security expertise from the user. The modeling language was primarily tested on four third-party AWS environments through securiCAD Vanguard, a commercial tool built around the AWS modeling language. The language was validated further by measuring performance on models provided by anonymous end users and a comparison with a similar open source assessment tool. As of March 2020, the modeling language could represent essential AWS structures, cloud tactics, and threats. However, the tests highlighted certain shortcomings. Data collection steps, such as planted credentials, and some missing tactics were obvious. Nevertheless, the issues covered by the DSL were already reminiscent of common issues with real-world precedents. Future additions to attacker tactics and addressing data collection should yield considerable improvements.
Auto-DenseNet: DenseNet Optimization Method Based on Evolutionary Algorithm

To address the issue of time-consuming and laborious design of network structure parameters and connection methods, in this paper we propose an evolutionary algorithm-based DenseNet network optimization method (Auto Dense Convolutional Network, Auto-DenseNet). Firstly, the internal parameters of DenseNet are encoded to generate an initial population, and crossover and variation operators are designed to ensure the effectiveness of the evolution of offspring. Secondly, the dataset is imported into the decoded individual evaluation fitness. And then, the superior individuals are selected to enter the next iteration. Finally, the optimized network individuals are selected for the image classification task. After tuning validation on the MNIST-RD dataset, tests were conducted on three widely used benchmark image classification datasets. The experimental results demonstrate that the network structure searched by the Auto-DenseNet algorithm outperforms most existing structures in terms of classification performance, and the number of network parameters.
Night Patrol Robot for Detecting and Tracking of Human Motions using Proximity Sensor

Every human being needs to feel secure. The need for security has grown as the population has increased. This paper proposes the concept of creating a robot that can walk independently, recognize human motions, and distinguish between a legitimate employee and a robber in light of the expanding industries and growing need for security in this field. With the aid of the Raspberry Pi, the four- wheeled robot has been automated.