Reviewerly/exHarmony huggingface dataset:
P‐3.10: Current status and prospects of simulation training equipment based on virtual reality and augmented reality technology

Virtual simulation technology is an effective means to construct “real” environment and experimental conditions. By analyzing the advantages and characteristics of virtual simulation technology and applying it to the field of aviation rescue medicine, it is used to study the medical support work of operators in the aviation environment such as injury and illness treatment. The mode characteristics of the aviation rescue medical virtual simulation experiment of “data restoration‐scenario simulation‐platform simulation‐experimental research” have important significance for simulation training. This article mainly starts from the advantages of AR/VR technology and the application equipment of the current mixed reality technology in the simulation training system. Analyze and prospect the current situation of simulation training equipment. Through gesture recognition and intelligent evaluation technologies, Further prospects for the future intelligent development of simulation training.
A Novel Feature Importance Based Layer to Improve Neural Networks

The normalized cross-correlation coefficient is a standard metric that can be used to evaluate the relation of one vector with another. In machine learning and as part of feature selection, the coefficient can be used to evaluate the importance of each feature in relation to the required output. This work aims to utilize this coefficient or similar feature importance indicators to improve the accuracy of a neural network by first computing the corresponding coefficient of each of the input features with the required output and then by creating a specialized network layer that can be used to improve the accuracy of the network. The specialized and novel layer requires adding a number of parameters that depends on the number of input features to the network. These parameters are optimized through the learning algorithm utilized by the network. The proposed layer guides the network to perform better with little effect on the computational cost by initially weighing each feature according to its importance such as the correlation. It is possible to use meaningful coefficients such as the Pearson correlation, the Spearman coefficient or others. The results indicate a consistently enhanced performance with the accuracy gain often exceeding 1% to more than 5% in some cases. The work considers several types of data including regression datasets, multi-output and convolutional neural networks related problems with concentration on binary classification datasets.
Peer-reviewing as a tool of scientific communication

The main types of peer review are considered. The interaction of reviewers is described. Controversial issues of peer review are touched upon. The principle of meta-reviewing is explained. The issues of taxonomy of scientific reviewing are highlighted.
Learning Observation-Based Certifiable Safe Policy for Decentralized Multi-Robot Navigation

Safety is of great importance in multi-robot navigation problems. In this paper, we propose a control barrier function (CBF) based optimizer that ensures robot safety with both high probability and flexibility, using only sensor measurement. The optimizer takes action commands from the policy network as initial values and provides refinement to drive the potentially dangerous ones back into safe regions. With the help of a deep world model that predicts the evolution of surrounding dynamics and the consequences of different actions, the CBF module can guide the optimization within a reasonable time horizon. We also present a novel joint training framework that improves the cooperation between the Reinforcement Learning (RL) based policy and the CBF-based optimizer by utilizing reward feedback from the CBF module. We observe that our policy can achieve a higher success rate while maintaining the safety of multiple robots in significantly fewer episodes. Experiments are conducted in multiple scenarios both in simulation and the real world, the results demonstrate the effectiveness of our method in maintaining the safety of multiple robots. Code is available at https://github.com/YuxiangCui/MARL-OCBF.
BigyaPAn: Deep Analysis of Old Paper Advertisement

In this paper, we work on analyzing old paper advertisement (Ad). An Ad usually contains various types of textual and non-textual objects, which may also be in different orientations. We attempt to detect such objects from an early Indian-print paper Ad database comprising 1500 Ad images. The past major object detectors did not perform well on this database. We propose a deep reinforcement learning-based orientation-aware object detector. Our system learns by itself where to look and what to look of an Ad image. Therefore, it can bypass the impeding zone due to degraded image quality. To find the looking spot, we come up with a foveal transformation. In reinforcement learning, we present a scheme for shaping an internal reward with a top-up. For oriented object detection, we also propose a generic loss function. Our system obtained encouraging results from the experiments performed on the Ad database.
Enriching the 1758 Portuguese Parish Memories (Alentejo) with Named Entities

This work presents an enriched version of the Parish Memories (1758–1761), an essential Portuguese historical source manually transcribed. It is enriched with annotations of named entities of the types PERSON, LOCATION, and ORGANIZATION. The annotation was done automatically for the whole collection where two researchers annotated a portion of it manually for evaluation purposes. In this dataset, we provide the tagged texts, the lists of extracted entities, and frequency counts. The corpus is useful for historians, allowing, for instance, comparative analyses between parishes and regions or to calculate the area of influence of a locality. The paper describes the creation and evaluation of the corpus, discusses its applications and limitations. This first release may be improved by other researchers interested in the historical source itself or in the technology employed in its annotation.
Mass Detection in Digital Mammogram Image using Convolutional Neural Network (CNN)

The implementation of convolutional neural networks (CNN) in medical imaging has become very favorable nowadays especially in mammography. CNN is capable of evolving an automatic mass detection system that plays an important role in aiding the radiologist to makes an accurate diagnosis as well as increase recalls back the patient to further being investigated. Thus, in this paper, a revolutionary computer-aided system with entirely automated detection scheme in digital mammogram is proposed. This proposed CAD framework consist of three fundamental phases such as preprocessing of mammogram images, mass detection, as well as classification of mass into three category such as benign, malignant, and normal. We utilized the authentic version of 322 mammograms images from MIAS database and its augmented mammograms image in testing and training the proposed system using CNN. At first, the CNN is trained using the large augmented database. After that, the model is transferred and tested onto the smaller database which is the original database. Three usually used CNNs such as VGG19, InceptionV3, and MatConvNet is evaluated in this study. As a result, the proposed CAD system able to detects the mass position with overall accuracy of 97.04%. This proved that the use of CNN in this study is applicable and feasible to be used by the radiologist in helping them detecting and classifying breast mass in digital mammogram image.
A Latent Variable Model with Hierarchical Structure and GPT-2 for Long Text Generation

Variational AutoEncoder (VAE) has made great achievements in the field of text generation. However, the current research mainly focuses on short texts, with little attention paid to long texts (more than 20 words). In this paper, we first propose a hidden-variable model based on the GPT-2 and hierarchical structure to generate long text. We use hierarchical GRU to encode long text to get hidden variables. At the same time, to generate the text better, we combine the hierarchical structure and GPT-2 in the decoder for the first time. Our model improves Perplexity (PPL), Kullback Leibler (KL) divergence, Bilingual Evaluation Understudy (BLEU) score, and Self-BLEU. The experiment indicates that the coherence and diversity of sentences generated by our model are better than the baseline model.
Loop Transformations using Clang’s Abstract Syntax Tree

OpenMP 5.1 introduced the first loop nest transformation directives unroll and tile, and more are expected to be included in OpenMP 6.0. We discuss the two Abstract Syntax Tree (AST) representations used by Clang’s implementation that is currently under development. The first representation is designed for compatibility with the existing implementation and stores the transformed loop nest in a shadow AST next to the syntactical AST. The second representation introduces a new meta AST-node OMPCanonicalLoop that guarantees that the semantic requirements of an OpenMP loop are met, and a CanonicalLoopInfo type that the OpenMPIRBuilder uses to represent literal and transformed loops. This second approach provides a better abstraction of loop semantics, removes the need for shadow AST nodes that are only relevant for code generation, allows sharing the implementation with other front-ends such as flang, but depends on the OpenMPIRBuilder which is currently under development.
Lightness Enhancement by Fuzzy Logic Depending on Power Membership Function

Lightness problem considered one of the challenging fields in the image enhancement category. The power membership function based on a fuzzy logic algorithm is developed within this paper to enhance lightness in the color images. The suggested algorithm depends on nonlinear power function to enhance image contrast. The suggested algorithm compared with other algorithms such as fuzzy logic (square method), fuzzy logic (cubic method), Histogram Equalization and Multi-Scale Retinex. Different criteria used to evaluate the performance of the presented algorithm like Mean Square Error in the Saturation (MSES), Mean Square Error in the Hue (MSEH), nature image quality Evaluator and entropy. The evaluated methods show that the suggested algorithm improved the lightness and contrast batter than the other methods.
Revisiting Edge Detection in Convolutional Neural Networks

The ability to detect edges is a fundamental attribute necessary to truly capture visual concepts. In this paper, we show that edges cannot be represented properly in the first layer of a conventional convolutional neural network, and further show that they are poorly captured in two popular neural network architectures VGG-16 and ResNet. The neural networks are found to rely on color information, which might vary in unexpected ways outside of the datasets used for their evaluation. To improve their robustness, we propose edge-detection units and show that they reduce performance loss and generate qualitatively different representations. By comparing various models, we show that the robustness of edge detection is an important factor contributing to the robustness of models against color noise.
Application of 6-Dof Robot Motion Planning in Fabrication

Abstract In practical robotic construction work, such as laying bricks and painting walls, obstructing objects are encountered and motion planning needs to be done to prevent collisions. This paper first introduces the background and results of existing work on motion planning and describes two of the most mainstream methods, the potential field method, and the sampling-based method. How to use the probabilistic route approach for motion planning on a 6-axis robot is presented. An example of a real bricklaying job is presented to show how to obtain point clouds and increase the speed of computation by customizing collision and ignore calculations. Several methods of smoothing paths are presented and the paths are re-detected to ensure the validity of the paths. Finally, the flow of the whole work is presented and some possible directions for future work are suggested. The significance of this paper is to confirm that a relatively fast motion planning can be achieved by an improved algorithmic process in grasshopper.
Facial Target Detection and Keypoints Location Study Using MTCNN Model

Abstract With the extremely rapid development of AI, facial recognition technology in the field of computer vision, which has the advantages of non-contact and easier to collect information, has become a hot topic in all walks of life. Facial target detection and keypoints location are two core parts of facial recognition technology. According to the analysis of the existing facial detection and keypoints location methods, by comparing their advantages and disadvantages, we find the existing problems. Based on the existing research methods, this paper proposes a high-precision facial detection and keypoints location method based on MTCNN model, which highlights the good accuracy of this method compared with the basic CNN or traditional algorithms, so as to improve the accuracy and effectiveness of facial recognition. It has more prominent advantages in this field, and is worthy of scientific research.
Improving reinforcement learning with human assistance: an argument for human subject studies with HIPPO Gym

Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learning times with high data requirements because they begin by acting randomly. In order to better learn in complex tasks, we argue that an external teacher can often significantly help the RL agent learn. OpenAI Gym is a common framework for RL research, including a large number of standard environments and agents, making RL research significantly more accessible. This article introduces our new open-source RL framework, the Human Input Parsing Platform for Openai Gym (HIPPO Gym), and the design decisions that went into its creation. The goal of this platform is to facilitate human-RL research, making human-in-the-loop RL more accessible, including learning from demonstrations, learning from feedback, or curriculum learning. In addition, all experiments can be conducted over the internet without any additional software needed on the client’s computer, making experiments at scale significantly easier.
Comparison of SMOTE Sampling Based Algorithm on Imbalanced Data for Classification of New Student Admissions

One of the efforts to get quality students is through selection. The selection process must be balanced with a strategy so that the selected students are truly qualified. Classification techniques can be used to see the history of new student admissions who are accepted with the student’s lecture history. There are many classification algorithms that can be used, so comparisons need to be made to see the best performance of the algorithm. The classification algorithm used is Decision Tree C4.5, K-Nearest Neighbor, Naïve Bayes and Neural Network. The data used are 546 records in the imbalanced data category. So we need the Smote algorithm to make the data balanced so as not to result in misclassification. The classification results were tested using the Confusion Matrix, ROC and Geometric Mean (G-Mean) as well as a T-Test. The comparison results show that the best performance is on the K-Nearest Neighbor algorithm with an accuracy value of 84.99%, AUC of 0.700, G-Mean 62.95% and the T-test produces a significant different from other algorithms.
eCSCDA: An efficient system for analyzing contents of Computer Science Courses

This paper aims to introduce a new efficient system, called eCSCDA, to efficient analyze Computer Science (CS) course description. The primary task of the system is to identify similar (and dissimilar) contents amount two (or a group of) CS course descriptions which can help to know similar and different focuses on important contents to teach to students. Moreover, it can help to check for integrity and quality and to set up a standard of teaching contents of the course. In eCSCDA, text processing procedure is newly rearranged and developed. Besides, the linguistic rules and their derivation are newly updated and applied to extracted important keywords. Moreover, two synonym corpuses, terminology and word synonyms, are recently designed and collected to consider synonyms of the keywords hidden in the course descriptions. Last, to efficiently identify similar contents, two new matching techniques, sub-keyword and semantic matching techniques, are designed and applied together with exact and subset (superset) matching methods. Experiments were conducted on CS course contents gathered from nine Thai Universities to examine the effectiveness of our proposed system in comparison with previous system and related methodologies. From the results, it shows that eCSCDA is efficient to analyze the course contents and outperforms other related systems in various terms e.g. percentage of similar contents, precision, recall and F-measure, respectively.
Identification of TLS Communications Using Randomness Testing

In recent years, the use of encryption in Internet communications, such as HTTPS, has become more widespread. While encrypted communication technology has been popular and improved the security of communications, there is a concern that the information available from the communications will be reduced, making it difficult to distinguish between normal and malicious communications. As for SSL/TLS, there is an existing measurement called TLS fingerprinting which tries to identify a server or client based on surface-level information such as headers and handshake parameters. However, by randomizing parameters or modifying handshakes, some attacks have already bypassed the detection. Our goal is to identify encrypted communications in a way that is more robust against such circumvention. Therefore, we propose a method that can identify encryption algorithms and cryptographic libraries used in a communication. We focus on the randomness of encrypted communications and use the statistical characteristics of randomness. Our experiment on HTTPS shows that by using only the encrypted application data from TLS communications, we can identify encryption algorithms (without considering the key length), used in the communication, with 89.6% accuracy.
Aspect-Based Sentiment Analysis in Drug Reviews Based on Hybrid Feature Learning

This paper aims to develop a novel hybrid feature learning approach for aspect-based sentiment analysis to detect and classify the widely available unlabeled social data. The proposed approach employs Bidirectional Encoder Representations from Transformers (BERT) technique with Latent Dirichlet Allocation (LDA) model to predict the context words and learn the corresponding sentence and document vectors. Also, the proposed approach utilizes Bi-directional Long Short-Term Memory (Bi-LSTM) to classify the extracted sentiment. Various experiments are conducted on several social media datasets about adverse drug reactions (ADRs) reviews as a case study to evaluate the effectiveness of the proposed approach. Obtained experimental results show that the proposed feature learning approach outperforms other tested state-of-the-art feature learning approaches and improves the feature and topic extraction for unstructured social media text and sentiment classification. The proposed approach achieved significant values of 95.4% for average accuracy, 0.935 for AUC score, and 94% for F-measure.
3D Object Detection on large-scale dataset

3D object detection based on LiDAR point cloud has gained more and more attention from industry and academia. Most of the previous works are carried out on the KITTI dataset, which has the gap with real-world scenes in terms of data size and distribution. Faced with large-scale datasets, existing methods have encountered with some problems, especially in the detection of difficult targets and heading prediction. In this paper, we design an integrated framework to improve 3D object detection accuracy targeting on large-scale datasets. We explore data augmentation methods from the perspective of data-rich scenes. Two well-designed auxiliary losses are introduced into our framework to relieve the problem caused by the diversity of points density and distribution. To promote the performance of heading prediction, the existing heading classifier is replaced by our novel dual heading classifier. Experiments on the largest and most realistic 3D object detection benchmark of Waymo Open Dataset show that our integrated framework boosts the detection accuracy for a large margin.
A Tristage Adaptive Biased Learning for Artificial Bee Colony

In recent ten years, artificial bee colony (ABC) has attracted more and more attention, and many state-of-the-art ABC variants (ABCs) have been developed by introducing different biased information to the search equations. However, the same biased information is employed in employed bee and onlooker bee phases, which will cause over exploitation and lead to premature convergence. To overcome this limit, an effective framework with tristage adaptive biased learning is proposed for existing ABCs (TABL + ABCs). In TABL + ABCs, the search direction in the employed bee stage is guided by learning the ranking biased information of the parent food sources, while in the onlooker bee stage, the search direction is determined by extracting the biased information of population distribution. Moreover, a deletion-restart learning strategy is designed in scout bee stage to prevent the potential risk of population stagnation. Systematic experiment results conducted on CEC2014 competition benchmark suite show that proposed TABL + ABCs perform better than recently published AEL + ABCs and ACoS + ABCs.
CoEmoCause: A Chinese Fine-Grained Emotional Cause Extraction Dataset

Emotion Cause Extraction (ECE) is an emerging hot topic in the field of sentiment analysis. The purpose of the ECE task is to extract the causes of emotions in the text according to the text and given emotions. Emotional Cause Pair Extraction (ECPE) is a brand-new research problem on ECE, whose main purpose is to extract the emotion clauses and emotion cause clauses in texts at the same time. At present, the ECPE task has received extensive attentions from both academia and industry communities. The existing ECPE researches are mainly carried out on online news report corpus. However, this kind of corpus has several limitations, such as the small scale and rough tagging granularity. In this paper, we construct an emotion cause extraction dataset containing 5,195 COVID-19 pandemic-related discussion posts collected from Sina Weibo, and leverage 10 state-of-the-art models to perform ECE and ECPE tasks on this new dataset. We find that the performances of most existing models on our newly constructed dataset decrease dramatically compared with the reported results in the online news dataset. We further analyze the causes of this phenomenon.