Reviewerly/exHarmony huggingface dataset:
Research on Negative Obstacle Detection Method Based on Image Enhancement and Improved Anchor Box YOLO

The ability of environmental awareness is the premise for unmanned ground vehicles to navigate autonomously and avoid obstacles. Obstacle detection is an important part of environmental awareness technology. Compared with the positive obstacles above the ground, the negative obstacles located below the ground are more difficult to detect and cause more damage to the unmanned vehicle. Consequently, research on various types of negative obstacle detection technology has great significance and value. Aiming at the problem that the texture features of negative obstacles are not obvious, the features are easily blocked by shadows, and the negative obstacles are relatively small in the camera’s field of vision. This paper presents a negative obstacle detection method based on image enhancement and the improved anchor box YOLOv3. Firstly, AutoMSRCR is used to enhance the texture features of negative obstacles in the image to obtain the image enhancement data set. Then, k-means++ clustering and threshold scaling are performed on the data set to get the anchor boxes of the training network. The enhanced images are brought into the improved anchor box YOLOv3 as a training set. Finally, three sets of experiments are designed to verify the detection effect of the negative obstacle detection method, and the experimental results show that the network can quickly detect negative obstacles with high accuracy and a low missed detection rate.
Redundancy and coverage aware enriched dragonfly-FL single document summarization

Due to the massive amount of information accessible on the internet, it has become a challenging task for users to discover the desired information. Automatic document summarization has become an emerging technology to address these issues. This allows the users to get the relevant information in a shortened version. However, the summary should have high content coverage and low redundancy to generate a good quality summary. Therefore, an enriched Dragonfly-Fuzzy Logic (FL) Single Document summarization is presented in this paper. Initially, the web document is preprocessed in which some functions such as segmentation, stop word removal, URL removal, stemming, etc. are performed. After preprocessing, the important features such as sentence location, proper nouns, numeric data, cue phrases, etc. are extracted from the web document. Here, the significance of the extracted features is decided by providing weights to each of the features using the Enriched Dragonfly Optimization Algorithm (EDOA). Once the weights are allotted for the features, the importance of the sentence is determined by using the FL system to form a summary. Finally, the sentence similarity in the generated summary is calculated, and then the similar sentences are eliminated from the summary to avoid redundancy issues. The performance of the proposed Dragonfly-FL summarization is tested in the CNN/Daily Mail dataset, and finally, the results are compared with the existing techniques such as MAMHOA, ExDoS, Karci summarization, and regression-based technique, DSN, Semantic approach, and BERTSUMEXT in terms of ROUGE-1, ROUGE-2, and ROUGE-L measures. The observation demonstrates that the proposed technique performs better than the existing techniques with precision, recall, and an F-score of 0.11, 0.05, and 0.01 respectively.
Monocular Depth Estimation: Lightweight Convolutional and Matrix Capsule Feature-Fusion Network

This paper reports a study that aims to solve the problem of the weak adaptability to angle transformation of current monocular depth estimation algorithms. These algorithms are based on convolutional neural networks (CNNs) but produce results lacking in estimation accuracy and robustness. The paper proposes a lightweight network based on convolution and capsule feature fusion (CNNapsule). First, the paper introduces a fusion block module that integrates CNN features and matrix capsule features to improve the adaptability of the network to perspective transformations. The fusion and deconvolution features are fused through skip connections to generate a depth image. In addition, the corresponding loss function is designed according to the long-tail distribution, gradient similarity, and structural similarity of the datasets. Finally, the results are compared with the methods applied to the NYU Depth V2 and KITTI datasets and show that our proposed method has better accuracy on the C1 and C2 indices and a better visual effect than traditional methods and deep learning methods without transfer learning. The number of trainable parameters required by this method is 65% lower than that required by methods presented in the literature. The generalization of this method is verified via the comparative testing of the data collected from the internet and mobile phones.
Real-Time Monitoring of Video Quality in a DASH-based Digital Video Broadcasting using Deep Learning

Digital video processing and transmission can introduce numerous distortions while capturing signals from broadcasting stations. These distortions become a nightmare for multimedia companies, especially terrestrial broadcasting companies that have fully adopted the online video streaming service. While terrestrial broadcasting benefits from online streaming through over-the-top (OTT) channels, there is a potential setback to reducing the video quality due to preprocessing of signals. Video quality assessment (VQA) algorithms have been developed for analyzing the quality of videos in a database, but little attention has been paid to implementing such algorithms in a real-time situation. This paper develops a novel real-time VQA framework by integrating a deep learning technology into the broadcasting pipeline. Previous studies used objective metrics augmented with subjective values to validate techniques. However, this approach is not appropriate for real-time video evaluation. Our proposed framework uses objective metrics (devoid of subjective scores like mean opinion scores) but rather introduced a new metric to validate the framework. The whole framework is validated using compressed/uncompressed signals and varying devices to show the signal differences. Results show that the framework is a step toward feasible incorporation of a VQA tool in a digital terrestrial television model. Using 100 epochs for our simulated video stream, the restricted Boltzmann machine yields a root mean square and mean absolute of 3.6903 and 2.3861 respectively.
Reference Electrode Placement in EOG-based Systems Design

The recording of eye movement biopotential is called electrooculogram (EOG). This technique is applied as a diagnostic method in ophthalmology to investigate the human oculomotor system and to control different assistive systems. The first step for developing an EOG-based system is to know in depth the possible sources of interference and the placement of the electrode, especially the reference electrode. They affect the recording of the EOG and pose a challenge to analog signal acquisition. This paper presents a novel study conducted to analyze the influence of these issues in the design of an EOG-based system. To estimate the noise level at each of the reference electrode placements, signal-to-noise-ratio (SNR) and percent root-mean-square difference (PRD) parameters were calculated.
Image Colorization with Dense Feature Fusion

We propose a new model for colorizing grayscale images with a U-Net-like network structure that focus on the connection between global and local features. A novel skip connection method is adopted to change the way information flows, which incorporating multi-scale feature information. This enables us to obtain more common features of encoding and decoding layers. Low-level detail features and high-level location features are exactly the semantic information we need. We argue that these semantic information plays an important role in the model’s learning of colorization tasks. When there is as much similar semantic information as possible from the decoder and encoder networks, the network will handle easier learning tasks. The proposed model architecture is evaluated on a large dataset for gray image colorization. Experimental results show that our model improve the coloring performance.
A Multi-Criteria Decision-Making Approach to Ontology Ranking with ELECTRE II and IV

The field of big data and artificial intelligence is growing at a tremendous rate, generating massive amounts of data and information in the process. It is of utmost importance that methods of managing these large amounts of data and knowledge be developed, such as ontology engineering. Ontologies, however, are highly complex and accordingly ontology engineers emphasize the reuse of existing ontologies as opposed to developing ontologies de novo. Unfortunately, given the vast range of ontologies available online, users are faced with the arduous problem of deciding which ontologies to select for reuse. This study attempts to solve the ontology selection problem by employing a multi-criteria decision-making approach. The ELECTRE II and IV methods are implemented and applied to rank a set of 200 biomedical ontologies obtained from the BioPortal repository. The statistical correlation between the methods is quantified with the use of the Spearman’s Rho and Kendall’s Tau correlation coefficients. All methods of analysis illustrated that the ELECTRE II and IV methods are suitable for ranking ontologies and depicts comparable results.
Camera Calibration Algorithm for Long Distance Binocular Measurement

Binocular vision has important applications in long distance measurement fields such as aircraft surface contour reconstruction. To improve the measurement accuracy of binocular vision system under long distance condition, a new binocular camera calibration method is proposed in this paper. The infrared LED point light source with constant brightness is fixed on the cantilever of the CMM(coordinate measuring machine). Through the movement of the mechanical mechanism of the CMM, the LED point light source is accurately moved to the fixed position and the two cameras are used to collect images. Through the spatial movement of LED point light source, a virtual lattice composed of discrete points is formed. Because the distance between two points does not change due to the selection of coordinate system, an optimization function with binocular camera parameters as independent variables is designed and each camera parameter is determined by high-dimensional space function optimization. The experimental results show that without considering the distortion factors, the measurement error of the camera parameters obtained by the algorithm in this paper is much higher than that of Zhang Zhengyou algorithm. In the field of view with the axial depth of 2.5 m-4.5 m, the single point measurement error is less than 2.4 mm.
RGBD image based human detection for electromechanical equipment in underground coal mine

Human detection within the operating area of electromechanical equipment is essential to ensure safe production and avoid accidents in the underground coal mine. Low light intensity and uneven light distribution in its environment surrenders the traditional color image based methods for human detection. In this paper, we focus on accurate detection of human in the operating area of electromechanical mining equipment using RGBD image. A novel network framework for miner detection based on YOLOv3 is proposed to fuse color image and depth image with enhanced attention mechanism. In the Pre-Backbone, feature extraction of both Depth and RGB branches are developed as the preliminary feature extractor with convolutional layer and residual block. Then the Convolutional Block Attention Module (CBAM) is improved to select and fuse RGB and Depth features by defining channel weights. Finally, the features are further inputted to Post-Backbone and used for multi-scale prediction in Head. The experiments demonstrate the superiority of the proposed method over some classical methods on miner detection with different light intensities and distributions.
Blockchain-Enabled decentralized Attribute-Based access control with policy hiding for smart healthcare

With the rapid development of edge computing technologies, smart healthcare significantly improves people's lives by collecting and analyzing health data in real time. However, security and privacy issues impede the wide deployment of smart healthcare systems (SHS). Most of existing solutions still have drawbacks with respect to computation efficiency and users' privacy. In this paper, a blockchain-enabled attribute-based access control scheme with hidden policies is proposed for SHS. The scheme introduces multiple authorities to avoid single point failure. Especially, the mode of online-offline encryption relieves users' online computation burden by transferring computation tasks to the idle time of users, and policy hiding protects users' sensitive information. Furthermore, fair payments are realized based on blockchain and smart contracts to support the outsourcing of decryption between users and mobile edge computing servers. Finally, the proposed scheme is proven secure in the random oracle model, and experimental results show that it is computationally efficient and hence can be used in the edge computing environment.
Secure and Privacy Issues in Telemedicine: Issues, Solutions, and Standards

IT sector plays a vital role in the field of telemedicine over the last years. Many countries have passed the laws to standardize the technology and administration in order to protect patient privacy and safety which is most important. Various privacy and security concerns in healthcare information systems are discussed in this chapter. Improving data security requires more rules and regulations, consolidation reporting, and a growing need for communication among patients, providers, and payers. This chapter deals with healthcare data security and privacy concerns both in information systems and non-information systems fields like public health, health information, law, and medicine.
Using Integrated Hands-on and Virtual Reality (VR) or Augmented Reality (AR) Approaches in Construction Management Education

Traditional learning methods may not sufficiently engage students and many have studied VR and AR's impact on student learning. Many construction management (CM) students have difficulty visualizing a building structure and understanding the materials and methods used to build it. This research investigated the use of a physical hands-on construction exercise integrated with VR and AR as a way to improve student visualization ability and learning of the course material. In this study, students built a physical model of a wood frame structure using K'NEX viewing either 2D drawings, 3D drawings, VR or AR models. Students perceived the VR, AR, and hands-on activity, as beneficial for improving their visualization of building structures. They also noted improvement in their understanding of construction materials and sequencing compared to using 2D drawings. In addition, the use of VR and AR improved student communications skills. This research is significant because it adds information on integrating hands-on and VR or AR approaches, in the same activity, to further improve CM student visualization skills and their learning of the course material. The integrated hands-on and VR/AR activity could be used by other CM programs that are interested in applying an active, learning-by-doing, approach in their curriculum.
Prefatory data analysis approach to synthetically generated pneumatic actuator data set

This article illustrates the importance of exploratory data analysis as the key step in the data validation process and also for further model development. Descriptive statistics is performed by applying the exploratory data analysis approach to a data set that is derived from the simulink library of a benchmark industrial actuator process provided by the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems research group. In this work, the data set is synthetically generated from an actuator model by simulating it under different fault conditions. The exploratory data analysis is performed as an initial investigation on the data set to reveal the anomalies and patterns for making suitable assumptions and test hypothesis to develop more accurate models. The raw data are visualised using different visualisation techniques to find patterns and behaviours. The data distribution, class distribution, feature correlation and presence of outliers are revealed through data visualisation. Data processing is performed to transform the data, and treat outliers and missing values in the data set. The treated data set after performing data processing is visually confirmed using appropriate visualisation techniques. The inferences from the visualisation methods are validated quantitatively with statistical results to support the exploratory data analysis. Data profiling is dealt with by collecting the metadata on the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems data set and aids in enhancing the data set quality and content to procure accurate predictive models employed to foresee the actuator performance.
“Meanspo Please, I Want to Lose Weight”: A Characterization Study of Meanspiration Content on Tumblr Based on Images and Texts

Past research has demonstrated a linkage between social media usage and disordered eating habits and body dissatisfaction. Trends relating to eating disorders develop around specific hashtags in communities in social networking sites such as Tumblr. One of these trends is #meanspiration, a tag that is used to request and give mean messages from/to social media users to inspire them to lose weight. In this study, images and texts of Meanspiration posts are automatically analyzed based on colorfulness, the images' emotional measures pleasure, arousal and dominance, whereas the textual information of the posts is evaluated based on sentiments, emotions and readability. These characteristics are used in a classification task to distinguish Meanspiration from regular content on Tumblr with 81% accuracy.
An Analysis of Logic Rule Dissemination in Sentiment Classifiers

Disseminating and incorporating logic rules in deep neural networks has been extensively explored for sentiment classification. Methods that are proposed for that goal rely on a component that aims to capture and model logic rules, followed by a sequence model to process the input sequence. While these methods claim to effectively capture syntactic structures that affect sentiment, they only show improvement in terms of accuracy to support their claims with no further analysis. Focusing on the A-but-B rule, we use the PERCY metric (a recently developed Post-hoc Explanation-based score for logic Rule dissemination ConsistencY assessment) to analyze and study the ability of these methods to identify the A-but-B structure, and to make their classification decision based on the B conjunct. PERCY proceeds by estimating feature attribution scores using LIME, a model-agnostic framework that aims to explain the predictions of any classifier in an interpretable and faithful manner. Our experiments show that (a) accuracy is misleading in assessing these methods, (b) not all these methods are effectively capturing the A-but-B structure, (c) often, the underlying sequence model is what captures the syntactic structure, and (d) the best method classifies less than 25% of test examples based on the B conjunct.
Noise-Reduction for Automatically Transferred Relevance Judgments

The TREC Deep Learning tracks used MS MARCO Version 1 as their official training data until 2020 and switched to Version 2 in 2021. For Version 2, all previously judged documents were re-crawled. Interestingly, in the track's 2021 edition, models trained on the new data were less effective than models trained on the old data. To investigate this phenomenon, we compare the predicted relevance probabilities of monoT5 for the two versions of the judged documents and find substantial differences. A further manual inspection reveals major content changes for some documents (e.g., the new version being off-topic). To analyze whether these changes may have contributed to the observed effectiveness drop, we conduct experiments with different document version selection strategies. Our results show that training a retrieval model on the "wrong" version can reduce the nDCG@10 by up to 75%.
The Effect of Prolonged Exposure to Online Education on a Classroom Search Companion

Exposure to technology impacts children's perception and conceptualisation of the way devices they regularly use work. This prompts us to study if almost two years of online teaching, enabled by a broad range of technologies, have influenced the way children imagine a search companion would look and behave when helping them perform school-related search tasks. We conducted a 2-stage study during which children ages 9 to 11 drew and described their imaginary search companion; they also chose a few desirable and non-necessary traits. By following the protocol of a study conducted pre-pandemic, we contextualise salient altered expectations that we attribute to exposure to technology prompted by the COVID-19 pandemic. We highlight and discuss emerging trends observed from the analysis of data gathered before and after the extensive online experience and how these will guide the design of functionality of a search companion for the classroom.
Combining Specialized Word Embeddings and Subword Semantic Features for Lexical Entailment Recognition

The challenge of Lexical Entailment Recognition (LER) aims to identify the is-a relation between words. This problem has recently received attention from researchers in the field of natural language processing because of its application to varied downstream tasks. However, almost all prior studies have only focused on datasets that include single words; thus, how to handle compound words effectively is still a challenge. In this study, we propose a novel method called LERC (Lexical Entailment Recognition Combination) to solve this problem by combining embedding representations and subword semantic features. For this aim, firstly a specialized word embedding model for the LER tasks is trained. Secondly, subword semantic information of word pairs is exploited to compute another feature vector. This feature vector is combined with embedding vectors for supervised classification. We considered three LER tasks, including Lexical Entailment Detection, Lexical Entailment Directionality, and Lexical Entailment Determination. Experimental results conducted on several benchmark datasets in English and Vietnamese languages demonstrated that the subword semantic feature is useful for these tasks. Moreover, LERC outperformed several methods published recently.