Reviewerly/exHarmony huggingface dataset:
Separable Reversible Data Hiding Based on Integer Mapping and Multi-MSB Prediction for Encrypted 3D Mesh Models

Extensive research has been conducted on image-based reversible data hiding in encrypted domain (RDH-ED) methods, but these methods cannot be directly applied to other cover medium, such as text, audio, video, and 3D mesh. With the widespread use of 3D mesh on the Internet, the use of 3D mesh as cover medium for RDH has gradually become a research topic. The main challenge of studying RDH based on 3D mesh is that the data structure of 3D mesh is complex and the geometric structure is irregular. In this paper, we propose a separable RDH-ED method based on integer mapping and multiple most significant bit (Multi-MSB) prediction. Firstly, all vertices of 3D mesh are divided into "embedded" set and "reference" set, and floating-point vertex values are mapped to integers. Then, sender calculates prediction error of the "embedded" set. Data hider embeds additional data by replacing the Multi-MSB of the encrypted vertex coordinates of the "embedded" set without prediction error. According to different permissions, legal recipients can obtain the original mesh, the additional data or both of them by using the proposed separable method. Experimental results prove that the proposed method outperforms state-of-the-art methods.
A hierarchical and multi-view registration of serial histopathological images

Serial histopathological images sliced from the same biopsy section exhibit quite similar topological structures. They are usually stained with different dyes and inspected individually for different clinical purposes in the routine workflow of pathology. Recently there is increasing demand for browsing them jointly, e.g., viewing the colocalization of biomarkers. Image registration is recognized as a feasible means to bridge this requirement. However, it is challenging to register the images due to their natural content difference, variation and distortion introduced from slice preparation, the extremely large image size, etc. This letter proposes a novel Hierarchical and Multi-View Registration (HMVReg) method to alleviate the difficulties. It is a three-stage coarse-to-fine registration consisting of global rigid registration, multi-view affine transformation and multi-view elastic registration, respectively for eliminating the translational and rotational differences, globally isotropic deformation and locally anisotropic distortion. The stages are complementary and jointly contribute to a stable registration. Experimental results on both private and public data, which cover histopathological images from multiple organ tissues, demonstrate the effectiveness of the proposed HMVReg. It not only well balance the registration accuracy, robustness and speed, where serial but differently stained images are accurately aligned in a reasonable time cost. Moreover, it also shows satisfactory user experience in applying the alignment to biomarker colocalization browsing.
An Interactive Neural Network Approach to Keyphrase Extraction in Talent Recruitment

As a fundamental task of document content analysis, keyphrase extraction (KE) aims at predicting a set of lexical units that conveys the core information of the document. In this paper, we study the problem of KE in the talent recruitment. This problem is critical for the development of a variety of intelligent recruitment services, such as person-job fit, market trend analysis and course recommendation. However, unlike traditional textual data, the texts from the recruitment domain, such as resume and job postings, often have unique characteristics of abbreviation and succinctness, resulting in massive keyphrases consisting of inconsecutive words that are hard to be fully captured by existing KE methods. To this end, we propose an interactive neural network approach, INKE, for facilitating KE in the talent recruitment. To be specific, we first introduce a novel keyphrase indicator that captures the explicit hint information for each keyphrase. Then, we design a dynamically-initialized decoder which can generate keyphrases in an interactive manner. Moreover, we propose a hierarchical reinforcement learning algorithm to enhance the interaction between the hint information capture and keyphrase generation. Finally, extensive experiments on real-world data clearly validate the effectiveness and interpretability of INKE compared with state-of-the-art baselines.
Deep learning network for medical volume data segmentation based on multi axial plane fusion

High-dimensional data generally contains more accurate information for medical image, e.g., computerized tomography (CT) data can depict the three dimensional structure of organs more precisely. However, the data in high-dimension often needs enormous computation and has high memory requirements in the deep learning convolution networks, while dimensional reduction usually leads to performance degradation.In this paper, a two-dimensional deep learning segmentation network was proposed for medical volume data based on multi-pinacoidal plane fusion to cover more information under the control of computation.This approach has conducive compatibility while using the model proposed to extract the global information between different inputs layers.Our approach has worked in different backbone network. Using the approach, DeepUnet's Dice coefficient (Dice) and Positive Predictive Value (PPV) are 0.883 and 0.982 showing the satisfied progress. Various backbones can enjoy the profit of the method.Through the comparison of different backbones, it can be found that the proposed network with multi-pinacoidal plane fusion can achieve better results both quantitively and qualitatively.
Neural Network Analysis for Microplastic Segmentation

It is necessary to locate microplastic particles mixed with beach sand to be able to separate them. This paper illustrates a kernel weight histogram-based analytical process to determine an appropriate neural network to perform tiny object segmentation on photos of sand with a few microplastic particles. U-net and MultiResUNet are explored as target networks. However, based on our observation of kernel weight histograms, visualized using TensorBoard, the initial encoder stages of U-net and MultiResUNet are useful for capturing small features, whereas the later encoder stages are not useful for capturing small features. Therefore, we derived reduced versions of U-net and MultiResUNet, such as Half U-net, Half MultiResUNet, and Quarter MultiResUNet. From the experiment, we observed that Half MultiResUNet displayed the best average recall-weighted F1 score (40%) and recall-weighted mIoU (26%) and Quarter MultiResUNet the second best average recall-weighted F1 score and recall-weighted mIoU for our microplastic dataset. They also require 1/5 or less floating point operations and 1/50 or a smaller number of parameters over U-net and MultiResUNet.
Fault Resilience Analysis of Quantized Deep Neural Networks

Because of the ground-breaking results in many different fields, Deep Neural Networks (DNNs) have also been widely adopted for safety-critical applications i.e. autonomous driving and space exploration. These applications are not only resource-constrained but also require a high level of reliability. DNNs quantization is viewed as a viable method to minimize the implementation cost and maximize the resiliency of the DNNs while preserving the inference accuracy. We perform a comprehensive layer-wise fault analysis of homogeneous and heterogeneous quantized DNNs and study the impact of faults (e.g., soft errors modeled as bit flips) in the DNNs' weights. The findings of this study suggest that quantizing the DNN model to fewer bits helps to increase the resiliency of the model i.e., quantizing the model from a fixed point (FxP) 32-bit to FxP 4-bit can increase the resiliency of the model by 20.7 % at 10 % Bit Error Rate (BER). But more vigorous quantization could sacrifice the resiliency and accuracy.
Learning the dynamics of coupled oscillators from transients

Whereas the importance of transient dynamics to the functionality and management of complex systems has been increasingly recognized, most of the studies are based on models. Yet in realistic situations the mathematical models are often unknown and what is available are only measured time series. Meanwhile, many real-world systems are dynamically stable, in the sense that the systems return to their equilibria in a short time after perturbations. This increases further the difficulty of dynamics analysis, as many information of the system dynamics are lost once the system is settled onto the equilibrium states. The question we ask is: Given the transient time series of a complex dynamical system measured in the stable regime, can we infer from the data some properties of the system dynamics and make predictions, e.g., predicting the critical point where the equilibrium state becomes unstable? We show that for the typical transitions in system of coupled oscillators, including quorum sensing, amplitude death, and complete synchronization, this question can be addressed by the technique of reservoir computing in machine learning. More specifically, by the transient series acquired at several states in the stable regime, we demonstrate that the trained machine is able to predict accurately not only the transient behaviors of the system in the stable regime, but also the critical point where the stable state becomes unstable. Considering the ubiquitous existence of transient activities in natural and man-made systems, the findings may have broad applications.
A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation

Electronic medical records (EMRs) are usually stored in relational databases that require SQL queries to retrieve information of interest. Effectively completing such queries can be a challenging task for medical experts due to the barriers in expertise. Existing text-to-SQL generation studies have not been fully embraced in the medical domain.The objective of this study was to propose a neural generation model that can jointly consider the characteristics of medical text and the SQL structure to automatically transform medical texts to SQL queries for EMRs.We proposed a medical text-to-SQL model (MedTS), which employed a pretrained Bidirectional Encoder Representations From Transformers model as the encoder and leveraged a grammar-based long short-term memory network as the decoder to predict the intermediate representation that can easily be transformed into the final SQL query. We adopted the syntax tree as the intermediate representation rather than directly regarding the SQL query as an ordinary word sequence, which is more in line with the tree-structure nature of SQL and can also effectively reduce the search space during generation. Experiments were conducted on the MIMICSQL dataset, and 5 competitor methods were compared.Experimental results demonstrated that MedTS achieved the accuracy of 0.784 and 0.899 on the test set in terms of logic form and execution, respectively, which significantly outperformed the existing state-of-the-art methods. Further analyses proved that the performance on each component of the generated SQL was relatively balanced and offered substantial improvements.The proposed MedTS was effective and robust for improving the performance of medical text-to-SQL generation, indicating strong potential to be applied in the real medical scenario.
RVDet: Feature-level Fusion of Radar and Camera for Object Detection

Obstacle perception based on radar sensor has drawn wide attentions in autonomous driving due to robust performance and low cost. It is significant to utilize fusion, e.g., camera information, to further enhance the radar perception ability. Although much progress has been made, we still observe two problems: First, the spatial alignment among multi-modal data is intractable when involving multiple radar and camera sensors. Second, most existing works are based on object-level fusion, which inevitably has information loss leading to a performance degradation. To this end, we propose a feature-level fusion detection framework based on multiple radars and cameras, termed as the RVDet. We first establish an occupancy grid map by using 4 corner radars and extract radar features in the bird&#x0027;s eye view(BEV). Meantime, the image features of 4 fish-eye cameras are obtained using a pretraining vision detection model. Then, an adaptive projection network is employed to transform all the 4 image features to the BEV domain and integrate them to a dense spatial feature map aligned with the radar feature. Last, the carefully aligned multi-modal feature maps are jointly sent to a deep fusion network to predict final fused detection results. Experiments show that both object detection and positioning performance achieve significant gains by the proposed method in a custom dataset.
t-SNE and PCA in Ensemble Learning based Human Activity Recognition with Smartwatch

Smartwatch based Human Activity Recognition (HAR) is gaining popularity due to habitual unhealthy behavior of the population and the rich in-built sensors of smartwatch. Raw sensor data is not well suited for the classifiers to identify similar activity patterns. According to the HAR literature handcrafted features are beneficial to properly identify the activities, which is time consuming and need expert domain knowledge. Automatic feature extraction libraries give high-dimensional feature sets that increase the computation and memory cost. In this work, we present an Ensemble Learning framework that exploit dimensional reduction and visualization to improve performance specification. Specifically, using Time Series Feature Extraction Library (TSFEL), the high dimensional features are extracted automatically. Then, to reduce the dimension of the feature set and proper visualization, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used respectively. The relevant extracted features using PCA are fed to an ensemble of three different Machine Learning (ML) classifiers to identify six different human physical activities. We also compare the proposed method with three popularly used shallow ML methods. Self collected human activity smartwatch sensor signal is used to establish the feasibility of the proposed framework. We observe that the proposed framework outper-forms the existing state-of-the-art benchmark frameworks, with an accuracy of 96%.
Artificial Intelligence and Automation

Transformation is being observed in almost every sphere of life. Industries accept technological advancements by accepting artificial intelligence and automation to improve the quality of products and services and to have errorless processes. The industry has a large number of human resources, and it functions on the concept of human touch, also known as the essence of hospitality; however, with the advent of artificial intelligence, fear of losing the human resources and human touch in the industry is paramount. The authors of the chapter detail the significance of the human touch in the tourism and hospitality industry. The chapter also highlights the usage of artificial intelligence in tourism through predictive analysis, travel experiences through virtual assistance, and the digital transformation tourism and hospitality have observed mainly in the coronavirus pandemic. It ends with a discussion on artificial intelligence in tourism and hospitality as support system for human resources or enhanced service quality and customer experience.
FTNet: Feature Transverse Network for Thermal Image Semantic Segmentation

Thermal imaging is a process of using infrared radiation and thermal energy to collect information about objects. It is superior to visible imaging for its ability to operate in darkness and tolerate illumination variations. In addition, it has potential to penetrate smoke, aerosol, dust, and mist, which are critical inhibitors for visible imaging applications, including semantic segmentation. Unfortunately, current state-of-the-art image semantic segmentation methods (i) mainly concentrate on visible spectrum images, which do not adequately capture the context of corresponding pixels, particularly edge details in thermal images, and (ii) accept a trade-off between higher accuracy and lower speed, or vice-versa. Here, a novel end-to-end trainable convolutional neural network architecture, feature transverse network (FTNet), has been proposed to solve the aforementioned problems. FTNet captures and optimizes feature representation at the multi-scale resolution, thereby improving the capability to process high-resolution images and producing quality output with a lower computational cost. Extensive computer experimentations were conducted on publicly available benchmarking thermal datasets, including SODA, MFNet, and SCUT-Seg, to demonstrate the effectiveness of the proposed FTNet compared to state-of-the-art methods. This comparison includes multiple aspects, including the quantitative accuracy and speed of the various approaches. The source code is available at https://github.com/shreyaskamathkm/FTNet.
A Residual Correction Approach for Semi-supervised Semantic Segmentation

Fully-supervised deep learning models have achieved a great success in complex semantic segmentation tasks. However, the segmentation annotations are prohibitively expensive, which causes a growing interest in the methods that require lower annotating cost but still achieve a competitive performance. This paper proposes a residual correction approach based on self-training for semi-supervised semantic segmentation. We train a residual correction network built on top of the segmentation network with labeled data to predict a residual of the original segmentation. For unlabeled data, the output of the residual correction network is combined with the original segmentation to form the pseudo label used to train the segmentation network. Extensive experimental results on the PASCAL VOC 2012 and the Cityscapes datasets demonstrate the effectiveness of the proposed approach.
Neural Cameras: Learning Camera Characteristics for Coherent Mixed Reality Rendering

Coherent rendering is important for generating plausible Mixed Reality presentations of virtual objects within a user’s real-world environment. Besides photo-realistic rendering and correct lighting, visual coherence requires simulating the imaging system that is used to capture the real environment. While existing approaches either focus on a specific camera or a specific component of the imaging system, we introduce Neural Cameras, the first approach that jointly simulates all major components of an arbitrary modern camera using neural networks. Our system allows for adding new cameras to the framework by learning the visual properties from a database of images that has been captured using the physical camera. We present qualitative and quantitative results and discuss future direction for research that emerge from using Neural Cameras.
A Survey of Transfer Learning Applied in Medical Image Recognition

Transfer learning has made great progress and has been applied to computer vision, especially image recognition. In this paper, the concept and basic method of transfer learning is studied. Some representative methods for solving the recognition problems is studied, especially the transfer learning method applied in images recognition, the paper highlighted the research status and technical characteristics of transfer learning in the medical image recognition. Finally, the possible future directions of transfer learning applied in medical image recognition are briefly discussed.
The representation of argumentation in scientific papers: A comparative analysis of two research areas

Abstract Scientific papers are essential manifestations of evolving scientific knowledge, and arguments are an important avenue to communicate research results. This study aims to understand how the argumentation process is represented in scientific papers, which is important for knowledge representation, discovery, and retrieval. First, based on fundamental argument theory and scientific discourse ontologies, a coding schema, including 17 categories was constructed. Thereafter, annotation experiments were conducted with 40 scientific articles randomly selected from two different research areas (library and information science and biomedical sciences). Statistical analysis and the sequential pattern mining method were then employed; the ratio of different argumentation units and evidence types were calculated, the argumentation semantics of figures and tables analyzed, and the argumentation structures extracted. A correlation analysis between argumentation and rhetorical structures was also performed to further reveal how argumentation was represented within scientific discourses. The results indicated a difference in the proportion of the argumentation units in the two types of scientific papers, as well as a similar linear construction with differences in the specific argument structures of each knowledge domain and a clear correlation between argumentation and rhetorical structure.
Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach

The advances in deep neural networks (DNN) have significantly enhanced real-time detection of anomalous data in IoT applications. However, the complexity-accuracy-delay dilemma persists: Complex DNN models offer higher accuracy, but typical IoT devices can barely afford the computation load, and the remedy of offloading the load to the cloud incurs long delay. In this article, we address this challenge by proposing an adaptive anomaly detection scheme with hierarchical edge computing (HEC). Specifically, we first construct multiple anomaly detection DNN models with increasing complexity and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network . We also incorporate a parallelism policy training method to accelerate the training process by taking advantage of distributed models. We build an HEC testbed using real IoT devices and implement and evaluate our contextual-bandit approach with both univariate and multivariate IoT datasets. In comparison with both baseline and state-of-the-art schemes, our adaptive approach strikes the best accuracy-delay tradeoff on the univariate dataset and achieves the best accuracy and F1-score on the multivariate dataset with only negligibly longer delay than the best (but inflexible) scheme.
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning

It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions. While deep reinforcement learning methods have excelled in recent years and been applied to automated vehicle driving policy, there are concerns about their capability to quickly adapt to unseen traffic with new environment dynamics. We formulate this challenge as a multi-Markov Decision Processes (MDPs) adaptation problem and developed Meta Reinforcement Learning (MRL) driving policies to showcase their quick learning capability. Two types of distribution variation in environments were designed and simulated to validate the fast adaptation capability of resulting MRL driving policies which significantly outperform a baseline RL.
Adventures in Supersingularland

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