Reviewerly/exHarmony huggingface dataset:
Superimposing holograms on real world objects using HoloLens 2 and its depth camera

Abstract Augmented Reality glasses such as HoloLens 2 may provide visual guidance during surgical interventions. To superimpose the holograms on real world objects (RWO), for instance a patient, spatial registration is required. In this work, we propose an approach to automatically register a hologram to the according RWO. To this end, the framework utilizes the depth camera of HoloLens 2 to acquire the point cloud (PC) of the RWO. A novel and recently published PC registration algorithm allows to register the PC of the RWO and the hologram after a rough initial placement without any need for pre-processing or outlier removal. The approach is evaluated by measuring displacements between certain known positions of the hologram and the RWO. The first metric relies on measuring points using an optically tracked stylus while the second is based on visually perceived positions. The median displacements were 22.3 mm, 35.6 mm, and 13.3 mm for the x-, y-, and z-axes in the first metric and 8.1 mm, 4.3 mm, and 11.9 mm for the second metric. Even though the accuracy is not yet adequate for many surgical interventions, the framework provides an initial step for a convenient marker less registration of holograms to an RWO.
Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign used to address an airplane is an essential part of all ATCo-pilot communications. We propose a two-steps approach to add contextual knowledge during semi-supervised training to reduce the ASR system error rates at recognizing the part of the utterance that contains the callsign. Initially, we represent in a WFST the contextual knowledge (i.e. air-surveillance data) of an ATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the contextual knowledge is added by second-pass decoding (i.e. lattice re-scoring). Results show that `unseen domains' (e.g. data from airports not present in the supervised training data) are further aided by contextual SSL when compared to standalone SSL. For this task, we introduce the Callsign Word Error Rate (CA-WER) as an evaluation metric, which only assesses ASR performance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER relative improvement applying SSL with an additional 17.5% CA-WER improvement by adding contextual knowledge during SSL on a challenging ATC-based test set gathered from LiveATC.
Overview of EXIST 2021: sEXism Identification in Social neTworks

The paper describes the organization, goals, and results of the sEXism Identiﬁcation in Social neTworks (EXIST) challenge, a shared task proposed for the ﬁrst time at IberLEF 2021. EXIST 2021 proposes two challenges: sexism identiﬁ-cation and sexism categorization of tweets and gabs, both in Spanish and English. We have received a total of 70 runs for the sexism identiﬁcation task and 61 for the sexism categorization challenge, submitted by 31 diﬀerent teams from 11 countries. We present the dataset, the evaluation methodology, an overview of the proposed systems, and the results obtained. The ﬁnal dataset consists of more than 11,000 annotated texts from two social networks (Twitter and Gab) and its development has been supervised and monitored by experts in gender issues.
Deep Integration of Physical Health Education Based on Intelligent Communication Technology

In recent years, intelligent medical communication technology has developed rapidly, and the advancement of hardware technology has accelerated the development of software technology. Physical exercise breaks the limits of all aspects of the human body and requires scientific and reliable information. Industrial cameras realize collection and high-resolution image processing functions and use artificial intelligence to create conditions for collecting scientific data. With the powerful driving force of artificial intelligence, it is believed that physical education will develop to a new height. The intellectualization of physical education makes people expect that it will also bring some inevitable problems, but as long as we continue to overcome and improve, we believe that artificial intelligence will better serve physical education and benefit mankind. Artificial intelligence is the current trend of social development. This paper takes physical education as a breakthrough point to discuss the application of artificial intelligence in physical education. This paper uses the grey GM prediction model and questionnaire survey method to build an online education platform and develop an effective education model, which can integrate the country's best educational resources and reduce time and space constraints. Providing a relaxing learning environment for education, one can learn anytime, anywhere. In a typical physical education system, the average self-efficacy score is converted from 20.48 points to 68.37 points, and the average self-score of physical education in the integrated artificial intelligence environment is converted from 30.25 points to 75.54 points. The data show that the quality of education has greatly improved.
Controllable Medical Image Generation via Generative Adversarial Networks

Radiologists and pathologists frequently make highly consequential perceptual decisions. For example, visually searching for a tumor and recognizing whether it is malignant can have a life-changing impact on a patient. Unfortunately, all human perceivers-even radiologists-have perceptual biases. Because human perceivers (medical doctors) will, for the foreseeable future, be the final judges of whether a tumor is malignant, understanding and mitigating human perceptual biases is important. While there has been research on perceptual biases in medical image perception tasks, the stimuli used for these studies were highly artificial and often critiqued. Realistic stimuli have not been used because it has not been possible to generate or control them for psychophysical experiments. Here, we propose to use Generative Adversarial Networks (GAN) to create vivid and realistic medical image stimuli that can be used in psychophysical and computer vision studies of medical image perception. Our model can generate tumor-like stimuli with specified shapes and realistic textures in a controlled manner. Various experiments showed the authenticity of our GAN-generated stimuli and the controllability of our model.
Sustainable Data Governance for Cooperative, Connected and Automated Mobility in the European Union

The article focuses on the issue of data governance in connected vehicles through a novel analysis of current legal frameworks in the European Union. The analysis of relevant legislation, judicial decisions, and doctrines is supplemented by discussions relating to associated sustainability issues. Relevant notions of autonomous vehicles are analyzed, and a respective legal framework is introduced. Although fully automated vehicles are a matter for the future, the time to regulate is now. The European Union aims to create cooperative, connected, and automated mobility based on cooperation between different interconnected types of machinery. The essence of the system is data flow, as data governance in connected vehicles is one of the most intensively discussed themes nowadays. This triggers a need to analyze relevant legal frameworks in connection with fundamental rights and freedoms. Replacing human decision-making with artificial intelligence has the capacity to erode long-held and protected social and cultural values, such as the autonomy of individuals as has already been in evidence in legislation. Finally, the article deals with the issue of responsibility and liability of different actors involved in processing personal data according to the General Data Protection Regulation (GDPR) applied to the environment of connected and automated vehicle (CAV) smart infrastructure. Based on a definition and analysis of three model situations, we point out that in several cases of processing personal data within the CAV, it proves extremely demanding to determine the liable entity, due to the functional and relatively broad interpretation of the concept of joint controllers, in terms of the possibility of converging decisions on the purposes and means of processing within the vehicles discussed.
Perceptual quality assessment of nighttime video

The quality of nighttime videos is important for consumer photography and monitoring of video clearness. However, little work has been done on the study of nighttime video quality assessment. In this paper, to the best of our knowledge, we explore the study on the nighttime video quality assessment for the first time. First, we build a real-world nighttime video quality assessment database (NVQA) containing 200 videos with abundant content and diverse distortion. Additionally, we carry out subjective tests to rate all nighttime videos in the NVQA database. Thereafter, we proposed a blind nighttime video quality assessment model based on feature fusion and conducted experiments to evaluate the performance and efficiency of our proposed model. The experiment results demonstrate that our model outperforms most traditional methods.
Data Preprocessing: The Techniques for Preparing Clean and Quality Data for Data Analytics Process

The model and pattern for real time data mining have an important role for decision making. The meaningful real time data mining is basically depends on the quality of data while row or rough data available at warehouse. The data available at warehouse can be in any format, it may huge or it may unstructured. These kinds of data require some process to enhance the efficiency of data analysis. The process to make it ready to use is called data preprocessing. There can be many activities for data preprocessing such as data transformation, data cleaning, data integration, data optimization and data conversion which are use to converting the rough data to quality data. The data preprocessing techniques are the vital step for the data mining. The analyzed result will be good as far as data quality is good. This paper is about the different data preprocessing techniques which can be use for preparing the quality data for the data analysis for the available rough data.
Length matters: Scalable fast encrypted internet traffic service classification based on multiple protocol data unit length sequence with composite deep learning

As an essential function of encrypted Internet traffic analysis, encrypted traffic service classification can support both coarse-grained network service traffic management and security supervision. However, the traditional plaintext-based Deep Packet Inspection (DPI) method cannot be applied to such a classification. Moreover, machine learning-based existing methods encounter two problems during feature selection: complex feature overcost processing and Transport Layer Security (TLS) version discrepancy. In this paper, we consider differences between encryption network protocol stacks and propose a composite deep learning-based method in multiprotocol environments using a sliding multiple Protocol Data Unit (multiPDU) length sequence as features by fully utilizing the Markov property in a multiPDU length sequence and maintaining suitability with a TLS-1.3 environment. Control experiments show that both Length-Sensitive (LS) composite deep learning model using a capsule neural network and LS-long short time memory achieve satisfactory effectiveness in F1-score and performance. Owing to faster feature extraction, our method is suitable for actual network environments and superior to state-of-the-art methods.
Semi/Fully-Automated Segmentation of Gastric-Polyp Using Aquila-Optimization-Algorithm Enhanced Images

The incident rate of the Gastrointestinal-Disease (GD) in humans is gradually rising due to a variety of reasons and the Endoscopic/Colonoscopic-Image (EI/CI) supported evaluation of the GD is an approved practice. Extraction and evaluation of the suspicious section of the EI/CI is essential to diagnose the disease and its severity. The proposed research aims to implement a joint thresholding and segmentation framework to extract the Gastric-Polyp (GP) with better accuracy. The proposed GP detection system consist; (i) Enhancement of GP region using Aquila-Optimization-Algorithm supported tri-level thresholding with entropy (Fuzzy/Shannon/Kapur) and between-class-variance (Otsu) technique, (ii) Automated (Watershed/Markov-Random-Field) and semi-automated (Chan-Vese/Level-Set/Active-Contour) segmentation of GP fragment, and (iii) Performance evaluation and validation of the proposed scheme. The experimental investigation was performed using four benchmark EI dataset (CVC-ClinicDB, ETIS-Larib, EndoCV2020 and Kvasir). The similarity measures, such as Jaccard, Dice, accuracy, precision, sensitivity and specificity are computed to confirm the clinical significance of the proposed work. The outcome of this research confirms that the fuzzy-entropy thresholding combined with Chan-Vese helps to achieve a better similarity measures compared to the alternative schemes considered in this research.
Learning from Deep Stereoscopic Attention for Simulator Sickness Prediction

Simulator sickness induced by 360° stereoscopic video contents is a prolonged challenging issue in Virtual Reality (VR) system. Current machine learning models for simulator sickness prediction ignore the underlying interdependencies and correlations across multiple visual features which may lead to simulator sickness. We propose a model for sickness prediction by automatic learning and adaptive integrating multi-level mappings from stereoscopic video features to simulator sickness scores. Firstly, saliency, optical flow and disparity features are extracted from videos to reflect the factors causing simulator sickness, including human attention area, motion velocity and depth information. Then, these features are embedded and fed into a 3-dimensional convolutional neural network (3D CNN) to extract the underlying multi-level knowledge which includes low-level and higher-order visual concepts, and global image descriptor. Finally, an attentional mechanism is exploited to adaptively fuse multi-level information with attentional weights for sickness score estimation. The proposed model is trained by an end-to-end approach and validated over a public dataset. Comparison results with state-of-the-art models and ablation studies demonstrated improved performance in terms of Root Mean Square Error (RMSE) and Pearson Linear Correlation Coefficient.
Detection of Covert Timing Channel Based on Time Series Symbolization

Covert Timing Channels(CTCs) is a technique to leak information. CTCs only modify inter-arrival time sequence(IATs) between packets, consequently, traditional network security mechanisms, such as firewalls and proxies, can not effectively detect CTCs. If CTCs are maliciously utilized by criminals, will pose a great threat to network security. Classic CTCs detection methods, such as KS-test, Entropy-test, etc, not only have less universality and robustness, but also require more sampled IATs to detect CTCs, therefore, how to improve performance of detection methods against CTCs, has became a popular research in recent years. In this paper, a new CTCs detection method based on time series symbolization is proposed. It firstly converts the sampled IATs to symbolic time series, and regards each discrete value as a status. Then counts the times of transition for each status to status, and calculates the status transition probability matrix(STPM). Finally, it differentiates the label(overt or covert) of sampled IATs, by calculating similarity score. Experimental results about detection accuracy show that, in an ideal network environment, compared with classic methods, our method has better performance, with average accuracy of about 96%. Besides, our method has better performance as well, with the existence of network interference.
Multi-camera stereo-DIC methods and application in full-field deformation analysis of reinforced Coral-SWSSC beams

Multi-camera stereo-digital image correlation (stereo-DIC) methods have gained popularity as non-contact methods for measuring high-spatial-resolution deformation of large engineering structures. Common multi-camera stereo-DIC methods can be classified into two types: target-based and calibration-based methods. Considering the wide application prospects of multi-camera stereo-DIC methods, it is important to study and compare the performances of these two types of multi-camera stereo-DIC methods. A four-point bending test of reinforced seawater sea-sand coral concrete (Coral-SWSSC) beams was conducted in this study to investigate the performance of multi-camera stereo-DIC in deformation measurement of slender components, and the measurement accuracy, robustness, and applicability of the two types of multi-camera stereo-DIC methods were compared and discussed. Furthermore, deformation analysis of reinforced Coral-SWSSC beams based on high-resolution multi-camera stereo-DIC methods was performed. The analysis showed that beams strengthened with basalt fiber-reinforced polymer wrapped steel tube and ultra high performance concrete (UHPC) can fully exploit the properties of Coral-SWSSC; the two strengthening materials demonstrated a higher compatibility, which compensates for the low strength defect of the Coral-SWSSC, making it a viable strengthening method. This study may promote the application of multi-camera stereo-DIC methods to measuring high-resolution deformation of large engineering structures.
Construction and optimization of inventory management system via cloud-edge collaborative computing in supply chain environment in the Internet of Things era

The present work aims to strengthen the core competitiveness of industrial enterprises in the supply chain environment, and enhance the efficiency of inventory management and the utilization rate of inventory resources. First, an analysis is performed on the supply and demand relationship between suppliers and manufacturers in the supply chain environment and the production mode of intelligent plant based on cloud manufacturing. It is found that the efficient management of spare parts inventory can effectively reduce costs and improve service levels. On this basis, different prediction methods are proposed for different data types of spare parts demand, which are all verified. Finally, the inventory management system based on cloud-edge collaborative computing is constructed, and the genetic algorithm is selected as a comparison to validate the performance of the system reported here. The experimental results indicate that prediction method based on weighted summation of eigenvalues and fitting proposed here has the smallest error and the best fitting effect in the demand prediction of machine spare parts, and the minimum error after fitting is only 2.2%. Besides, the spare parts demand prediction method can well complete the prediction in the face of three different types of time series of spare parts demand data, and the relative error of prediction is maintained at about 10%. This prediction system can meet the basic requirements of spare parts demand prediction and achieve higher prediction accuracy than the periodic prediction method. Moreover, the inventory management system based on cloud-edge collaborative computing has shorter processing time, higher efficiency, better stability, and better overall performance than genetic algorithm. The research results provide reference and ideas for the application of edge computing in inventory management, which have certain reference significance and application value.
Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Abstract Single image rain removal is a challenging ill‐posed problem due to various shapes and densities of rain streaks. We present a novel incremental randomly wired network (IRWN) for single image deraining. Different from previous methods, most structures of modules in IRWN are generated by a stochastic network generator based on the random graph theory, which ease the burden of manual design and further help to characterize more complex rain streaks. To decrease network parameters and extract more details efficiently, the image pyramid is fused via the multi‐scale network structure. An incremental rectified loss is proposed to better remove rain streaks in different rain conditions and recover the texture information of target objects. Extensive experiments on synthetic and real‐world datasets demonstrate that the proposed method outperforms the state‐of‐the‐art methods significantly. In addition, an ablation study is conducted to illustrate the improvements obtained by different modules and loss items in IRWN.
CE-Net: A Coordinate Embedding Network for Mismatching Removal

Mismatching removal is at the core yet still a challenging problem in the photogrammetry and computer vision field.In this paper, we propose a coordinate embedding network (named CE-Net).We consider the mismatching problem as a graph node classification problem, and generate node descriptors by embedding point coordinates and aggregating geometric information from neighboring nodes based on selfattention and cross-attention mechanism.Finally, a binary classifier is used to separate node descriptors into two classes, namely matching inliers and outliers.Benefiting from the attention mechanism, firstly the node descriptors can get geometric information from "good neighbors" (i.e., matching inliers) and keep away from "bad neighbors" (i.e., matching outliers), improving the exactness of the descriptors; secondly the node descriptors can contain the information from both intra-graph and inter-graph, improving their distinctiveness.Experiments in testing datasets show that our proposed CE-Net achieves the state-of-the-art performance with a precision of 0.972, an outlier recall of 0.984, and an inlier recall of 0.963.Furthermore, CE-Net also outperforms the compared methods in real mismatching removal tasks in terms of positional accuracy, dispersion, and number of remaining point pairs, showing great potentials in practical applications.Our codes and data will be available on https://github.com/csyhy1986if the paper is accepted.