Reviewerly/exHarmony huggingface dataset:
Individual differences in the temporal progression of motion sickness and anxiety: the role of passengers’ trait anxiety and motion sickness history

The objective of the study is to show that trait anxiety and motion sickness history are responsible for different temporal progressions of sickness in passengers. The level of inflight anxiety and inflight sickness severity was monitored for 124 passengers in a full-motion cabin simulator during a short-haul flight with four different flight segments. Four groups with different characteristics in trait anxiety and motion sickness susceptibility showed different profiles of inflight sickness development. High trait anxiety was responsible for high inflight anxiety and a constantly high level of motion sickness, while passengers with just a motion sickness history showed an increase in motion sickness severity over time. We suggest that trait anxiety and motion sickness susceptibility interact and have an impact on the temporal progression of inflight sickness severity. The analysis of temporal developments of anxiety and sickness are fruitful for understanding the origins of motion sickness, research and individual treatments. Practitioner summary: In a full-motion cabin simulator study with 124 passengers the level of inflight anxiety and inflight sickness severity was monitored. Trait anxiety and motion sickness history were found to have different impacts on the temporal progression of individual sickness severity. Abbreviations: ANOVA: analysis of variance; AVES: air vehicle simulator; hiA/hiM: group with high anxiety and high motion sickness susceptibility; hiA/loM: group with high anxiety and low motion sickness susceptibility;MSSQ: motion sickness susceptibility scale; loA/hiM: group with low anxiety and high motion sickness susceptibility; loA/loM: group with low anxiety and low motion sickness susceptibility; SPSS: statistical package for the social sciences; SSQ-TS: total score from the simulator sickness questionaire; STAI: state trait anxiety inventory
Learning Monocular Face Reconstruction using Multi-View Supervision

We present a method to reconstruct faces from a single portrait image. While traditional face reconstruction methods fit low-dimensional 3D morphable models to images, we train a deep network to regress depth from a single image directly. We do so by combining supervised losses on synthetic data with indirect supervision on real data using a novel multi-view photo-consistency loss. Furthermore, we regularize the depth estimation using a 3D morphable model (3DMM). We demonstrate that this leads to results that preserve facial features, capture facial geometry that goes beyond 3DMMs, and is also robust to viewpoint conditions. We evaluate our method on various datasets and via ablation studies, and demonstrate that it outperforms previous work significantly.
CrossFeat: Multi-scale Cross Feature Aggregation Network for Hand Gesture Recognition

Hand gestures are considered as an effective means of communication in the field of Human-computer interaction. However, the design of an efficient hand gesture recognition (HGR) system is still a challenging task owing to a plethora of complexities such as cluttered background, illumination changes, and occlusion in a real-world environment. The paper proposes a lightweight CNN based network named CrossFeat: Multi-scale Cross Feature Aggregation network for explicit hand gesture recognition (HGR). CrossFeat employs multi-scale convolutional layers and preserves the spatial features from the hand gesture region. The use of multi-scale filters: 1 × 1, 3 × 3, 5 × 5 and 7 × 7 allow the network to learn granular and coarse edges from the different regions of the hand gestures. These complementary features enhance the learning ability of the network. Moreover, the cross-layer connectivity enables the gradient information to reach the top layers and prevent it from diminishing in the upstream layers. The proposed network is investigated on three benchmark datasets: ASL Finger Spelling, NUS-I and NUS-II. The experimental results and analysis show that the aggregation of multi-scale and cross features enhances the performance of the HGR system compared to the existing networks.
Electronic Certificate Sharing Scheme with Searchable Attribute-Based Encryption on Blockchain

Abstract With the promotion of “Internet +” and smart cities, as an important data voucher, electronic certificate brings great convenience to people’s life. However, the phenomenon of “data island” based on the electronic certificate database of each city hinders the sharing of an electronic certificate. In addition, problems such as centralization of data storage, poor security, and tamper-proof modification are common in electronic certificate libraries. To solve these problems, we present a blockchain-based electronic certificate sharing scheme, which uses the InterPlanetary File System (IPFS) and Ethereum smart contract to achieve secure storage and data sharing of electronic certificate by taking advantage of the non-tampering and decentralization of blockchain. At the same time, we achieve fine-grained access control by using attribute-based encryption of ciphertext policy and assigning attribute keys to users without affecting retrieval efficiency. Finally, through the simulation and performance analysis of the Ethereum test network, the analysis results show that our scheme is effective and feasible.
Detection of Blades Damages in Aero Engine

The health condition of engine which is the key part of modern aircraft determines whether the aircraft can safely carry out the flight mission. As for the detection of the mainstream technology, borescope inspection technology can effectively diagnose the fault and provide a strong guarantee for the next maintenance work. Recently the hardware of borescope inspection technology develops rapidly, but the grade of automation and intelligence is relatively inferior. It is necessary to develop a model detecting flaw parts for borescope equipment because of the lack of the corresponding software system. However, complex conditions inside the engine and various injures types will affect the image quality and increase the difficulty of recognition. In this paper, the YOLOv3 algorithm is applied to the detection task of aero-engine blade damage. Compared with other algorithms, YOLOv3 could have a good tradeoff between detection accuracy and detection speed. In this paper, a network model which could detect the aeroengine fans faults images automatically is trained by train data set. Then, test set is fed into it as follow to verify the validity of detection results, visual analysis is also performed on these results. Finally, our research method demonstrates us a satisfied recognition results of blade damages.
Speakers extrapolate community-level knowledge from individual linguistic encounters

Speakers' lexical choices are affected by interpersonal-level influences, like a tendency to reuse an interlocutor's words. Here, we examined how those choices are additionally affected by community-level factors, like whether the interlocutor is from their own or another speech community (in-community vs. out-community partner), and how such interpersonal experiences contribute to the acquisition of community-level linguistic knowledge. Our three experiments tested (i) how speakers' lexical choices varied depending on their partner's choices and speech community, and (ii) how speakers' extrapolation of these choices to a subsequent partner was influenced by their partners' speech communities. In Experiment 1, Spanish participants played two sessions of an online picture-matching-and-naming task, encountering the same pictures but different confederates in each session. The first confederate was either an in-community partner (Spanish) or an out-community partner (Latin American); the second confederate was either from the same community as the first confederate or not. Participants' referential choices in Session 1 were influenced by their partner's choices, but not by their community. However, participants' likelihood to subsequently maintain these choices was affected by their partners' communities. Experiment 2 replicated this pattern in Mexicans, and Experiment 3 confirmed that these results were driven by confederates' communities, rather than perceived linguistic status. Our results suggest that speakers encode speech community information during dialogue and store it to inform future contexts of language use, even when it has not affected their choices during that particular encounter. Thus, speakers learn community-level knowledge by extrapolating linguistic information from interpersonal-level experiences.
Imbalanced data classification based on DB-SLSMOTE and random forest

The classification problem of imbalanced data is a popular issue in the field of machine learning in recent years. For imbalanced data, traditional classification algorithms tend to classify minority class samples into majority class, which result in the misclassification of many minority samples by the classifier. For imbalanced data classification problems, this paper proposes a Density Based Safe Level Synthetic Minority Oversampling TEchnique (DB-SLSMOTE). First, the algorithm clusters minority samples through Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Then, the Safe Level Synthetic Minority Oversampling TEchnique (Safe-Level- SMOTE) is utilized for clusters of any shape discovered by DBSCAN. It is followed that the processed data is classified by Random Forest (RF). The experimental results show that the DB- SLSMOTE algorithm can effectively improve the classification performance of RF for minority samples in imbalanced data.
The Multidimensional Motion Features of Spatial Depth Feature Maps: An Effective Motion Information Representation Method for Video-Based Action Recognition

In video action recognition based on deep learning, the design of the neural network is focused on how to acquire effective spatial information and motion information quickly. This paper proposes a kind of deep network that can obtain both spatial information and motion information in video classification. It is called MDFs (the multidimensional motion features of deep feature map net). This method can be used to obtain spatial information and motion information in videos only by importing image frame data into a neural network. MDFs originate from the definition of 3D convolution. Multiple 3D convolution kernels with different information focuses are used to act on depth feature maps so as to obtain effective motion information at both spatial and temporal. On the other hand, we split the 3D convolution at space dimension and time dimension, and the spatial network feature map has reduced the dimensions of the original frame image data, which realizes the mitigation of computing resources of the multichannel grouped 3D convolutional network. In order to realize the region weight differentiation of spatial features, a spatial feature weighted pooling layer based on the spatial-temporal motion information guide is introduced to realize the attention to high recognition information. By means of multilevel LSTM, we realize the fusion between global semantic information acquisition and depth features at different levels so that the fully connected layers with rich classification information can provide frame attention mechanism for the spatial information layer. MDFs need only to act on RGB images. Through experiments on three universal experimental datasets of action recognition, UCF10, UCF11, and HMDB51, it is concluded that the MDF network can achieve an accuracy comparable to two streams (RGB and optical flow) that requires the import of both frame data and optical flow data in video classification tasks.
Proposing a High-Robust Approach for Detecting the Tampering Attacks on English Text Transmitted via Internet

In this paper, a robust approach INLPETWA (an Intelligent Natural Language Processing and English Text Watermarking Approach) is proposed to tampering detection of English text by integrating zero text watermarking and hidden Markov model as a soft computing and natural language processing techniques. In the INLPETWA approach, embedding and detecting the watermark key logically conducted without altering the plain text. Second-gram and word mechanism of hidden Markov model is used as a natural text analysis technique to extracts English text features and use them as a watermark key and embed them logically and validates them during detection process to detect any tampering. INLPETWA approach has been implemented by self-developed program using PHP with VS code IDE. INLPETWA approach has been proved with various experiments and simulation scenarios. Comparison results with baseline approaches also show that the proposed approach is appropriate to detect all types of tampering attacks. The paper includes implications for integrating natural language processing and text-watermarking to propose an intelligent solution. This paper fulfils an identified need to study how we can use a robust text information via various Internet applications.
Research on Fault Diagnosis Method of Power Grid Based on Artificial Intelligence

With the development of social science and technology and the rapid development of China's economy, people's living standards have been greatly improved, and there is an increasing demand for power resources in all walks of life and among the people. In the fault diagnosis of power grid, the scientific application of artificial intelligence technology can not only ensure the effective solution of power system operation faults, but also significantly improve the safety performance of power system. Electric power system is an important part to ensure the normal operation of people and enterprises. Because the power system needs to adapt to various working environments, once the power system fails, it will produce serious consequences. All kinds of protection measures in power system can timely generate alarm and abnormal changes of power state when problems occur. Based on this, this paper discusses the application of artificial intelligence technology in power grid fault diagnosis, and studies the power grid fault diagnosis method based on artificial intelligence.
Fast Unsupervised Projection for Large-Scale Data

Dimensionality reduction (DR) technique has been frequently used to alleviate information redundancy and reduce computational complexity. Traditional DR methods generally are inability to deal with nonlinear data and have high computational complexity. To cope with the problems, we propose a fast unsupervised projection (FUP) method. The simplified graph of FUP is constructed by samples and representative points, where the number of the representative points selected through iterative optimization is less than that of samples. By generating the presented graph, it is proved that large-scale data can be projected faster in numerous scenarios. Thereafter, the orthogonality FUP (OFUP) method is proposed to ensure the orthogonality of projection matrix. Specifically, the OFUP method is proved to be equivalent to PCA upon certain parameter setting. Experimental results on benchmark data sets show the effectiveness in retaining the essential information.
Aspect Based Sentiment Oriented Hotel Recommendation Model Exploiting User Preference Learning

Due to the advancement of the technology, people tend to focus on the online content related to products and services available through websites and the opinions of others which are provided in the form of reviews and comments. In the tourism domain, travelers are more concerned with the place of accommodation, facilities provided by a hotel, the location or the environment that the hotel is situated and tend to find the hotels that fulfill their requirements. They have to go through each and every review or comment in order to get a clear idea about a particular hotel, according to the opinion of the previous reviewers, which is a difficult and time-consuming task. Therefore, through this research, the users are provided with a system which analyses hotel reviews and provides aspect based personalized hotel recommendations that help users to easily find the best hotel according to their preferences, without having to go through a lot of reviews. For that, the proposed system was implemented with four steps, namely data gathering, data pre-processing, aspect extraction and sentiment analysis, and visualization of the output. In the implemented system, hotel reviews were analyzed and extracted the overall opinion of the reviews as opinion units with their related aspects. Based on the sentiment of those opinion units and the preferences of the user, the best hotels were suggested enabling the users to get an insight about the hotels. For this approach, aspect-based sentiment analysis was used. In addition to that, a weighted average calculation method was used for the final recommendations of the hotels, which suit users' preferences in an accurate manner.
Comparative Study of Machine Learning Approaches on Diagnosing Breast Cancer for Two Different Dataset

Over 50 years in the world Breast Cancer (BC) has become a major health issue. According to the World Health Organization (WHO) in each year, approximately 1.38M new breast cancer cases arise. Among women, it is the leading cause of cancer death and also the most second in common. However, we can diagnose, manage breast cancer effectively, and also improve the surviving rate of breast cancer by correctly and early detection. Because if we take a long time for diagnosing breast cancer it may increase the chances of cancer spreading rate. Lots of different Machine Learning technique is used nowadays to diagnosis BC. In this paper, we apply different classification algorithm such as Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN), on two different datasets of BC (Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC). By using the confusion matrix on 10-fold cross-validation, classification accuracy, precision, sensitivity and f-score we compare the performance of different classifiers. Here ROC curve is also used to show the comparison of the different classifiers on different datasets after extracting key features. The prime objective of this paper is to use key features to detect breast cancer with high accuracy and a low error rate in a short time. To find objective and systematic prognostic it will highly help the pathologists.
Bangla Handwritten Character Recognition Using Deep Convolutional Autoencoder Neural Network

Handwritten Character Recognition (HCR) is one of the most challenging tasks in the field of pattern recognition and computer vision. In image classification, performance can be improved by representing data from a high feature space to a low feature space. The Convolutional Neural Network (CNN) aims to reduce the images into a form without losing any features. In this paper, we introduce an Autoencoder with a Deep CNN, which we call DConvAENNet for recognizing Bangla Handwritten Character (BHC). A total of 22 experiments were performed on the three-character datasets (BanglaLekha-Isolated, CMATERdb 3.1, Ekush). All attempts acquire satisfying results up to 90% accuracy for the recognition of Bangla handwritten numerals, vowels, consonants, compound characters, modifiers, and all characters set unitedly. Using this supervised and unsupervised learning technique, our proposed DConvAENNet model achieved 95.21% on BanglaLekha-Isolated for 84 classes, 92.40% on CMATERdb 3.1 for 238 classes, and 95.53% on Ekush for 122 classes. Most errors in our model were caused due to the similarity and high curvature nature of the BHC sets.
Learning from Incomplete Labeled Data via Adversarial Data Generation

Positive and unlabeled (PU) learning aims to obtain a well-performed classifier via an incomplete binary training set, in which only a part of labels of one category is known while the rest are unknown. However, in many real-world applications such as image recognition, the collected data samples often involve more than two categories. Moreover, only a small portion of the collected samples might have associated labels due to some practical reasons, and these labeled samples cannot always cover all the categories. We refer to this type of data as incomplete labeled data. In this paper, we first formally define the incomplete labeled data learning problem and then aim to tackle it via adversarial data generation. Specifically, we propose a novel generative framework LILA, which can produce synthetic labeled samples for both partially labeled categories and unlabeled categories. To enforce that the generated samples for unlabeled categories can associate with correct labels, we integrate two active learning processes into the LILA framework for selecting unlabeled samples in the collected sample set to query their labels effectively. After LILA has been well trained, a classifier can be trained on the balanced augmented data set consisting of both generated and original labeled samples. Extensive experiments on real image data demonstrate the effectiveness of our proposed framework. We release the implementation of the proposed framework via https://github.com/wentao-repo/LILA.
Convolutional Neural Network Based Kannada-MNIST Classification

Before machine learning emerged, tasks that involve the recognition of handwritten characters, which include postcode recognition, document digitalization, and ancient character recognition, all require extensive human effort. Nowadays, various methods have been developed so that such tasks can be handled efficiently and accurately by computers. In 2018, the School of Computer and Information Science of Southwest University and the Research Institute of Yi Nationality at Guizhou University of Engineering Science jointly took lead in the research of ancient Yi language classification by using artificial intelligence technology. A model based on Convolutional Neural Networks (CNN) was developed and achieved a rather high accuracy. This triggered my great curiosity about handwritten character classification using machine learning methods. In 2019, the Kannada-MNIST (K-MNIST) dataset, a well-processed dataset of numerals in the Kannada language, was disseminated. We decided to make use of this dataset for our research in handwritten character classification. A CNN model was primarily developed due to its special architecture that makes it well suited for image classification tasks. This paper focuses on the establishment and experimentation of this CNN model and makes a detailed analysis. We also considered other methodologies such as Logistic Regression and Support Vector Machine for making comparisons, so that we can gain an insight into the performance of our model when compared to other machine learning methods. Through our experiment, the model achieved an accuracy of 98.77% over the testing set of K-MNIST, surpassing all the baselines, and it is effective for the classification of all categories of the dataset (0-9). Thus, we eventually concluded the strong capability of CNN models when performing classification tasks of handwritten characters.
Multi-Agent Programming Contest 2019 FIT BUT Team Solution

During our participation in MAPC 2019, we have developed two multi-agent systems that have been designed specifically for this competition. The first of the systems is a proactive system that works with pre-specified scenarios and tasks agents with generated goals designed for individual agents according to assigned role. The second system is designed as more reactive and employs layered architecture with highly dynamic behaviour, where agents select their own action based on their perception of usefulness of said action.