Reviewerly/exHarmony huggingface dataset:
Robust Steganography for High Quality Images

With the prosperity of online social networks (OSNs), people usually share photos taken with their mobile phones to OSN, which is suitable for covert communication. However, these images have high quality factors (QFs) and will be JPEG recompressed with low QFs by OSNs, which precludes existing robust steganography methods using low QF images. Therefore, we propose a Postprocessing and precise dither Modulation based robust Adaptive Steganography method (PMAS) for high quality images. Precise dither modulation ensures the robustness of the modified coefficients and improves security by reducing the magnitude of modifications. Postprocessing amends the coefficients that changed after recompression to ensure the robustness of unmodified coefficients. Additionally, we devise heuristics to mitigate post-processing and explore the relationship between the modification magnitude and distortion assignment with novel scaling functions, which all contribute to security. The experimental results demonstrate that PMAS is competent for high quality images, and the effect of every module in PMAS is verified.
A Secure Design of Healthcare System with Blockchain and Internet of Things (IoT)

Health is one of the most important aspects of human life. For a healthy society, the healthcare sector must be reliable and efficient as much as possible. Blockchain networks can be used to provide innovative solutions to store and share patient data among hospitals, pharmaceutical companies, diagnostic labs, and physicians. Blockchain applications are capable of building trust and detecting fatal errors in the sharing of data. So blockchain can help in the security, effectiveness, and transparency of sharing medical data in the healthcare industry. Using this technology, medical institutions can get quality and trustworthy knowledge which they can use to improve the analysis of patient data and help in the good diagnosis of health issues. A thorough study and analysis have been done to explore the opportunities that blockchain technology can provide to improve the healthcare industry. The various characteristics, methods, and smooth workflow processes of blockchain technology are discussed in diagrams as a potential means of improving global healthcare. The article concludes by listing and analyzing various significant blockchain applications for the healthcare industry. It can help in implementing the smart contract, nonce concepts, and P2P distributed ledgers which ensure the immutability of the shared data. The methods discussed in this paper improve the security of Electronic Health Records (EHRs). The data of patients are taken directly via IoT devices which are used as medical instruments for patients which increases the reliability of data as there is no human interference in between hence human error is eliminated. The proposed technique implementation results show that the security of data has increased and it is more efficient.
Role of Hybrid Optimization in Improving Performance of Sentiment Classification System

Researchers are employing sentiment analysis on user comments and tweets to get a feel for popular opinion. The rate at which the world is changing as a consequence of technological progress is unprecedented. In today's world, having access to the Internet is a need. To share their thoughts on current events, more and more people are turning to social networking apps. Collecting and analysing client feedback is crucial whether marketing a product or attempting to enhance a government service. Opinion mining, also known as sentiment analysis, is often done in advance of a discussion in which the attitudes behind different points of view are to be found. Sentiment analysis's popularity as a tool for understanding the public's mood has skyrocketed in recent years. In the newest research, neural networks are used to analyse the tweets of Twitter users. Researchers are increasingly interested in "tweets" (comments) and the substance of these expressions, and the use of Twitter data for survey research has increased in latest years. Purpose of this research is to assess performance of several sentiment analysis methods when applied to Twitter data. Present research has used compression mechanism to reduce image size and noise removal mechanism to improve the quality of image before applying CNN model. Thus integration of compression, noise removal and CNN results in hybrid model that is providing more accurate solution with better performance.
Federated Learning for Tabular Data using TabNet: A Vehicular Use-Case

In this paper, we show how Federated Learning (FL) can be applied to vehicular use-cases in which we seek to classify obstacles, irregularities and pavement types on roads. Our proposed framework utilizes FL and TabNet, a state-of-the-art neural network for tabular data. We are the first to demonstrate how TabNet can be integrated with FL. Moreover, we achieve a maximum test accuracy of 93.6%. Finally, we reason why FL is a suitable concept for this data set.
Personalized hypertension treatment recommendations by a data-driven model

Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics.Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP.The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians.Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.
DeAF: A multimodal deep learning framework for disease prediction

Multimodal deep learning models have been applied for disease prediction tasks, but difficulties exist in training due to the conflict between sub-models and fusion modules. To alleviate this issue, we propose a framework for decoupling feature alignment and fusion (DeAF), which separates the multimodal model training into two stages. In the first stage, unsupervised representation learning is conducted, and the modality adaptation (MA) module is used to align the features from various modalities. In the second stage, the self-attention fusion (SAF) module combines the medical image features and clinical data using supervised learning. Moreover, we apply the DeAF framework to predict the postoperative efficacy of CRS for colorectal cancer and whether the MCI patients change to Alzheimer's disease. The DeAF framework achieves a significant improvement in comparison to the previous methods. Furthermore, extensive ablation experiments are conducted to demonstrate the rationality and effectiveness of our framework. In conclusion, our framework enhances the interaction between the local medical image features and clinical data, and derive more discriminative multimodal features for disease prediction. The framework implementation is available at https://github.com/cchencan/DeAF.
Immersive experience framework: a Delphi approach

The concept of immersion has been widely used for the design and evaluation of user experiences. Augmented, virtual and mixed-reality environments have further sparked the discussion of immersive user experiences and underlying requirements. However, a clear definition and agreement on design criteria of immersive experiences remains debated, creating challenges to advancing our understanding of immersive experiences and how these can be designed. Based on a multidisciplinary Delphi approach, this study provides a uniform definition of immersive experiences and identifies key criteria for the design and staging thereof. Thematic analysis revealed five key themes – transition into/out of the environment, in-experience user control, environment design, user context relatedness, and user openness and motivation, that emphasise the coherency in the user-environment interaction in the immersive experience. The study proposes an immersive experience framework as a guideline for industry practitioners, outlining key design criteria for four distinct facilitators of immersive experiences – systems, spatial, empathic/social, and narrative/sequential immersion. Further research is proposed using the immersive experience framework to investigate the hierarchy of user senses to optimise experiences that blend physical and digital environments and to study triggered, desired and undesired effects on user attitude and behaviour.
Crop Disease Diagnosis with Deep Learning-Based Image Captioning and Object Detection

The number of people participating in urban farming and its market size have been increasing recently. However, the technologies that assist the novice farmers are still limited. There are several previously researched deep learning-based crop disease diagnosis solutions. However, these techniques only focus on CNN-based disease detection and do not explain the characteristics of disease symptoms based on severity. In order to prevent the spread of diseases in crops, it is important to identify the characteristics of these disease symptoms in advance and cope with them as soon as possible. Therefore, we propose an improved crop disease diagnosis solution which can give practical help to novice farmers. The proposed solution consists of two representative deep learning-based methods: Image Captioning and Object Detection. The Image Captioning model describes prominent symptoms of the disease, according to severity in detail, by generating diagnostic sentences which are grammatically correct and semantically comprehensible, along with presenting the accurate name of it. Meanwhile, the Object Detection model detects the infected area to help farmers recognize which part is damaged and assure them of the accuracy of the diagnosis sentence generated by the Image Captioning model. The Image Captioning model in the proposed solution employs the InceptionV3 model as an encoder and the Transformer model as a decoder, while the Object Detection model of the proposed solution employs the YOLOv5 model. The average BLEU score of the Image Captioning model is 64.96%, which can be considered to have high performance of sentence generation and, meanwhile, the mAP50 for the Object Detection model is 0.382, which requires further improvement. Those results indicate that the proposed solution allows the precise and elaborate information of the crop diseases, thereby increasing the overall reliability of the diagnosis.
Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-ray Contraband Detection

X-ray contraband detection plays an important role in the field of public safety. To solve the multi-scale and obscuration problem in X-ray contraband detection, we propose a material-aware path aggregation network to detect and classify contraband in X-ray baggage images. Based on YoloX, our network integrates two new modules: multi-scale smoothed atrous convolution (SCA) and material-aware coordinate attention modules (MCA). In SAC, an improved receptive field-enhanced network structure is proposed by combining smoothed atrous convolution, using separate shared convolution, with a parallel branching structure, which allows for the acquisition of multi-scale receptive fields while reducing grid effects. In the MCA, we incorporate a spatial coordinate separation material perception module with a coordinated attention mechanism. A material perception module can extract the material information features in X and Y dimensions, respectively, which alleviates the obscuring problem by focusing on the distinctive material characteristics. Finally, we design the shape-decoupled SIoU loss function (SD-SIoU) for the shape characteristics of the X-ray contraband. The category decoupling module and the long–short side decoupling module are integrated to the shape loss. It can effectively balance the effect of the long–short side. We evaluate our approach on the public X-ray contraband SIXray and OPIXray datasets, and the results show that our approach is competitive with other X-ray baggage inspection approaches.
DeepActsNet: A deep ensemble framework combining features from face, hands, and body for action recognition

Human action recognition from videos has gained substantial focus due to its wide applications in the field of video understanding. Most of the existing approaches extract human skeleton data from videos to encode actions because of the invariance nature of the skeleton information with respect to lightning conditions and background changes. Despite their success in achieving high recognition accuracy, methods based on limited body joints fail to capture the nuances of subtle body parts which are highly relevant for discriminating similar actions. In this paper, we overcome this limitation by presenting a holistic framework for combining spatial and motion features from the body, face, and hands to develop a novel data representation termed “Deep Actions Stamps (DeepActs)” for video-based action recognition. Compared to the skeleton sequences based on limited body joints, DeepActs encode more effective spatio-temporal features that provide robustness against pose estimation noises and improve action recognition accuracy. We also present “DeepActsNet”, a deep learning based ensemble model which learns convolutional and structural features from Deep Action Stamps for highly accurate action recognition. Experiments on three challenging action recognition datasets (NTU60, NTU120, and SYSU) show that the proposed model produces significant improvements in the action recognition accuracy with less computational cost compared to the state-of-the-art methods.
Knowledge structure construction and skill reduction methods based on multi-scale context

ABSTRACTABSTRACTThe conjunctive model of skill map reflects a way to solve the items in a knowledge domain. Currently, a skill map has been transformed into a formal context to construct a knowledge structure. In fact, the conjunctive model of skill map can be regarded as a special case of skill function. In this paper, we consider a way to solve the items in a knowledge domain as a scale. As a result, a skill function can be decomposed into multiple formal contexts, which correspond to a multi-scale context. Based on this, a method for constructing a knowledge structure delineated via competency model by skill function is proposed. In addition, a method for finding minimal skill function is proposed to reduce the complexity. Last but not least, the experimental analysis of five data sets on the UCI Repository shows that the improved method of constructing knowledge structure is effective and feasible and that it is necessary to perform skill reduction on skill function.KEYWORDS: Skill functionknowledge structuremulti-scale contextcompetency modelminimal skill function AcknowledgementsWe would like to thank the editors and anonymous reviewers for their constructive comments and suggestions which have helped to improve this paper. This work was supported by the National Natural Science Foundation of China (No. 11871259, 62076221, 12271191 and 12171293) and the Natural Science Foundation of Fujian Province (No. 2020J02043 and 2022J01306).Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the National Natural Science Foundation of China [11871259, 12171293, 62076221, 12271191]; Natural Science Foundation of Fujian Province [2020J02043, 2022J01306]
Uncertainty-Based Thin Cloud Removal Network via Conditional Variational Autoencoders

Existing thin cloud removal methods treat this image restoration task as a point estimation problem, and produce a single cloud-free image following a deterministic pipeline. In this paper, we propose a novel thin cloud removal network via Conditional Variational Autoencoders (CVAE) to generate multiple reasonable cloud-free images for each input cloud image. We analyze the image degradation process with a probabilistic graphical model and design the network in an encoder-decoder fashion. Since the diversity in sampling from the latent space, the proposed method can avoid the shortcoming caused by the inaccuracy of a single estimation. With the uncertainty analysis, we can generate a more accurate clear image based on these multiple predictions. Furthermore, we create a new benchmark dataset with cloud and clear image pairs from real-world scenes, overcoming the problem of poor generalization performance caused by training on synthetic datasets. Quantitative and qualitative experiments show that the proposed method significantly outperforms state-of-the-art methods on real-world cloud images. The source code and dataset are available at https://github.com/haidong-Ding/Cloud-Removal .
End-to-End Image-Based Fashion Recommendation

In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated deep neural networks, comparisons to attribute-aware models are often disregarded despite their ability to be easily extended to leverage items' image features. In this paper, we propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning in item recommendation tasks. The proposed model utilizes items' image features extracted by a calibrated ResNet50 component. We present an ablation study to compare incorporating the image features using three different techniques into the recommender system component that can seamlessly leverage any available items' attributes. Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models.
Image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods.
Fourier-based augmentation with applications to domain generalization

When deployed on a new domain different from the training set, deep learning often suffers from severe performance degradation. To combat domain shift, domain adaptation and domain generalization are proposed, where the former aims at transferring knowledge from related source domains to a known target domain, while the latter is more challenging by requiring the model to generalize to unknown target domains. This paper focuses on domain generalization and introduce a novel Fourier-based perspective for it. The main idea comes from the fact that Fourier amplitude component contains low-level statistics while phase component preserves high-level semantics. We thus propose a novel Fourier-based data augmentation strategy called AmpMix by linearly interpolating the amplitudes of two images while keeping their phases unchanged, to highlight the generalizable semantics contained in phase. To make full use of Fourier-augmented samples, we further incorporate consistency training between different augmentation views and devise a Fourier-based framework for three different domain generalization settings. Extensive experiments demonstrate the effectiveness of our Fourier-based method.
Feature similarity rank-based information distillation network for lightweight image superresolution

To explore a fast and accurate image superresolution (SR) algorithm for real-world scenarios, information distillation is used to compress the number of feature channels, which has become one of the most commonly used tools in the field of lightweight image SR. However, existing information distillation-based image SR methods simply distill the first (distilled rate) channels in which many channels with unique features, i.e., low-redundancy features, are distilled as well. Hence, these methods lead to suboptimal SR performance since low-redundancy features are indispensable for image SR reconstruction. To alleviate this issue, this paper proposes a feature similarity ranking algorithm, which sorts the extracted feature channels according to the degree of channel redundancy. Then, it utilizes the ranked information distillation block (RIDB) for distilling high-redundancy features. In addition, considering that feature channels have already been sorted in the channel dimension, it involves a gated nonlocal attention module (GNLM) to capture the long-distance feature correlation in the spatial dimension. GNLM greatly reduces the computational consumption through the partitioned nonlocal module as well as the gate unit for evaluating the importance of each module, which is more suitable for lightweight image SR. Extensive experiments demonstrate that the proposed method performs favorably in terms of SR performance compared with not only information distillation-based image SR methods but also other state-of-the-art lightweight SR methods.