Reviewerly/exHarmony huggingface dataset:
Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review

Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.
PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation

In the last few years, convolutional neural networks (CNNs) have demonstrated increasing success at learning many computer vision tasks including dense estimation problems such as optical flow and stereo matching. However, the joint prediction of these tasks, called scene flow, has traditionally been tackled using slow classical methods based on primitive assumptions which fail to generalize. The work presented in this paper overcomes these drawbacks efficiently (in terms of speed and accuracy) by proposing PWOC-3D, a compact CNN architecture to predict scene flow from stereo image sequences in an end-to-end supervised setting. Further, large motion and occlusions are well-known problems in scene flow estimation. PWOC-3D employs specialized design decisions to explicitly model these challenges. In this regard, we propose a novel self-supervised strategy to predict occlusions from images (learned without any labeled occlusion data). Leveraging several such constructs, our network achieves competitive results on the KITTI benchmark and the challenging FlyingThings3D dataset. Especially on KITTI, PWOC-3D achieves the second place among end-to-end deep learning methods with 48 times fewer parameters than the top-performing method.
Capture and 3D Video Processing of Volumetric Video

Volumetric video is regarded worldwide as the next important development step in the field of media production. Especially in the context of the extremely rapid development of the Virtual Reality (VR) and Augmented Reality (AR) markets, volumetric video is becoming a key technology. In this paper, a new capture and processing system for volumetric video is presented, called 3D Human Body Reconstruction (3DHBR). The system is based on 16 stereo pairs of high-resolution cameras capturing a moving person in 360 degree. A novel stereo approach provides depth information from all perspectives, which is then fused to a single consistent 3D point cloud. A meshing and mesh reduction algorithm finally produces a sequence of meshes that can be integrated into common render engines. Given that, an integration of realistic dynamic 3D reconstructions of moving persons in VR and AR applications is possible.
A Neural Citation Count Prediction Model based on Peer Review Text

Citation count prediction (CCP) has been an important research task for automatically estimating the future impact of a scholarly paper.Previous studies mainly focus on extracting or mining useful features from the paper itself or the associated authors.An important kind of data signals, i.e., peer review text, has not been utilized for the CCP task.In this paper, we take the initiative to utilize peer review data for the CCP task with a neural prediction model.Our focus is to learn a comprehensive semantic representation for peer review text for improving the prediction performance.To achieve this goal, we incorporate the abstractreview match mechanism and the cross-review match mechanism to learn deep features from peer review text.We also consider integrating hand-crafted features via a wide component.The deep and wide components jointly make the prediction.Extensive experiments have demonstrated the usefulness of the peer review data and the effectiveness of the proposed model.Our dataset has been released online.
Exact Transform-Domain Noise Variance for Collaborative Filtering of Stationary Correlated Noise

Collaborative filters perform denoising through transform-domain shrinkage of a group of similar blocks extracted from an image. Existing methods for collaborative filtering of stationary correlated noise have all used simple approximations of the transform noise power spectrum adopted from methods which do not employ block grouping. We note the inaccuracies of these approximations and introduce a method for the exact computation and effective approximations of the noise power spectrum. Unlike earlier methods, the calculated noise variances are exact even when noise in one block is correlated with noise in any of the other blocks. We discuss the adoption of the exact noise power spectrum within shrinkage, in similarity testing (block matching), and in aggregation. Extensive experiments support the proposed method over earlier crude approximations used by image denoising filters such as BM3D, demonstrating dramatic improvement in many challenging conditions.
Detection of Fake Images Via The Ensemble of Deep Representations from Multi Color Spaces

Recently, the success of generating fake images by Generative Adversarial Network (GAN) has threatened the authentication of digital images. To address this issue, several automated fake image detectors have been proposed. However, current methods remain vulnerable when testing samples undergo post-processing attacks. In this work, we employed residual signals of chrominance components from multi color spaces, including YCbCr, HSV and Lab, to learn robust deep representations via the well-designed shallow convolutional neural network (CNN). Then, the learned deep representations from different color spaces are concatenated and then fed into the Random Forest (RF), which is the widely used ensemble classifier, to obtain final detection results. Extensive experiments are conducted on the fake image dataset generated by the advanced GAN technique. Experimental results demonstrate the proposed scheme outperforms state-of-the-art methods and achieves the promising average detection accuracy (above 99%) under several post-processing attacks, such as Gaussian blurring and so on.
Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance.
Handling annotation uncertainty in human activity recognition

Developing systems for Human Activity Recognition (HAR) using wearables typically relies on datasets that were manually annotated by human experts with regards to precise timings of instances of relevant activities. However, obtaining such data annotations is often very challenging in the predominantly mobile scenarios of Human Activity Recognition. As a result, labels often carry a degree of uncertainty-label jitter-with regards to: i) correct temporal alignments of activity boundaries; and ii) correctness of the actual label provided by the human annotator. In this work, we present a scheme that explicitly incorporates label jitter into the model training process. We demonstrate the effectiveness of the proposed method through a systematic experimental evaluation on standard recognition tasks for which our method leads to significant increases of mean F1 scores.
Some New Constructions of Quantum MDS Codes

It is an important task to construct quantum maximum-distance-separable (MDS) codes with good parameters. In the present paper, we provide six new classes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -ary quantum MDS codes by using generalized Reed–Solomon (GRS) codes and Hermitian construction. The minimum distances of our quantum MDS codes can be larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {q}{2}+1$ </tex-math></inline-formula> . Three of these six classes of quantum MDS codes have longer lengths than the ones constructed in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> , hence some of their results can be easily derived from ours via the propagation rule. Moreover, some known quantum MDS codes of specific lengths can be seen as special cases of ours and the minimum distances of some known quantum MDS codes are also improved as well.
Image-to-Image Translation Using Generative Adversarial Network

Now a days, Generative Adversarial Networks (GANs) are an arising technology for both supervised and unsupervised learning which have capability to generate data of high standard. Image to Image translation is one of the application of GANs as a data augmentation which we have used in this proposed framework. Generative Networks makes the mapping between source image and target image easier and it calculates the loss function also to improve the quality of generated target image. In this paper, Conditional GANs are used which translates the images based upon some conditions. The performance is also analyzed of the model by doing hyper-parameter tuning.
JPEG XL next-generation image compression architecture and coding tools

An update on the JPEG XL standardization effort: JPEG XL is a practical approach focused on scalable web distribution and efficient compression of high-quality images. It will provide various benefits compared to existing image formats: significantly smaller size at equivalent subjective quality; fast, parallelizable decoding and encoding configurations; features such as progressive, lossless, animation, and reversible transcoding of existing JPEG; support for high-quality applications including wide gamut, higher resolution/bit depth/dynamic range, and visually lossless coding. Additionally, a royalty-free baseline is an important goal. The JPEG XL architecture is traditional block-transform coding with upgrades to each component. We describe these components and analyze decoded image quality.
An improved Simulated Annealing algorithm based on ancient metallurgy techniques

Simulated Annealing (SA) is a single-solution-based metaheuristic technique based on the annealing process in metallurgy. It is also one of the best-known metaheuristic algorithms due to its simplicity and good performance. Despite its interesting characteristics, SA suffers from several limitations such as premature convergence. On the other hand, Japanese swordsmithing refers to the manual-intensive process for producing high-quality bladed weapons from impure raw metals. During this process, Japanese smiths fold and reheat pieces of metal multiple times in order to eliminate impurities and defects. In this paper, an improved version of the SA algorithm is presented. In the new approach, a population of agents is considered. Each agent conducts a search strategy based on a modification of the SA scheme. The proposed algorithm modifies the original SA incorporating two new operators, folding and reheating, inspired by the ancient Japanese Swordsmithing technique. Under the new approach, the process of folding is conceived as a compression of the search space, while the reheating mechanism considers a reinitialization of the cooling process in the original SA scheme. With this inclusion, the new algorithm maintains the computational structure of the SA method but improving its search capacities. In order to evaluate its performance, the proposed algorithm is tested in a set of 28 benchmark functions, which include multimodal, unimodal, composite and shifted functions, and 3 real world optimization problems. The results demonstrate the high performance of the proposed method when compared to the original SA and other popular state-of-the-art algorithms.
Pushing revisited: Differential flatness, trajectory planning, and stabilization

We prove that quasi-static pushing with a sticking contact and ellipsoid approximation of the limit surface is differential flat. Both graphical and algebraic derivations are given. A major conclusion is that the pusher–slider system is reducible to the Dubins car problem where the sticking contact constraints translate to bounded curvature. Planning is as easy as computing a Dubins curve with the additional benefit of time-optimality. For trajectory stabilization, we design closed-loop control using dynamic feedback linearization or open-loop control using two contact points as a form of mechanical feedback. We conduct robotic experiments using objects with different pressure distributions, shape, and contact materials placed at different initial poses that require difficult switching action maneuvers to the goal pose. The average error is within 1.67 mm in translation and 0.5° in orientation over 60 experimental trials. We also show an example of pushing among obstacles using a RRT planner with exact steering.
Algebraic Cryptanalysis of STARK-Friendly Designs: Application to MARVELlous and MiMC

The block cipher Jarvis and the hash function Friday, both members of the MARVELlous family of cryptographic primitives, are among the first proposed solutions to the problem of designing symmetric-key algorithms suitable for transparent, post-quantum secure zero-knowledge proof systems such as ZK-STARKs. In this paper we describe an algebraic cryptanalysis of Jarvis and Friday and show that the proposed number of rounds is not sufficient to provide adequate security. In Jarvis, the round function is obtained by combining a finite field inversion, a full-degree affine permutation polynomial and a key addition. Yet we show that even though the high degree of the affine polynomial may prevent some algebraic attacks (as claimed by the designers), the particular algebraic properties of the round function make both Jarvis and Friday vulnerable to Gröbner basis attacks. We also consider MiMC, a block cipher similar in structure to Jarvis. However, this cipher proves to be resistant against our proposed attack strategy. Still, our successful cryptanalysis of Jarvis and Friday does illustrate that block cipher designs for "algebraic platforms" such as STARKs, FHE or MPC may be particularly vulnerable to algebraic attacks.
High-capacity reversible data hiding in encrypted image based on specific encryption process

This paper proposes a novel reversible data hiding method in encrypted images based on specific encryption process. In the proposed specific encryption algorithm, the stream cipher and prediction error are combined to vacate room for data embedding. After that, a permutation operation is performed on the encrypted image to improve the security. In the embedding process, we can embed a large amount of secret data in the encrypted image by pixel value expansion because most of the pixel values are less than 128 by the specific encryption process. At the receiver end, the encrypted image can be recovered from the marked encrypted image without knowing the secret data. Therefore, even if the recipient only has the encryption key, the original image will be perfectly recovered. If the recipient only has the data-hiding key, the secret data will be extracted. And if the recipient has both keys, the original image and the secret data are both available. The proposed method achieves a higher embedding capacity than that of methods based on vacating room after encryption. It does not require the image owner to perform reversible data hiding techniques on the original image, which is more convenient than methods based on reserving room before encryption. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.
Blockchain based consensus checking in decentralized cloud storage

As a popular application of cloud computing, cloud storage has been widely used. In distributed computing system, blockchain as a secure mechanism is often used to store data, such as account information, trading record, and others. Once any data is saved in blockchain, it is sealed and cannot be changed any more. All the data copies must be consistent in blockchain. In cloud computing, data is always duplicated to prevent the data loss. However, the consensus issue of different data copies may appear in various data operations. Sometimes the results of data operations may be seriously affected. As a useful technique, blockchain can effectively maintain the data consistency in a distributed computing system, including the cloud-based applications, such as cloud storage. Based on a specific type of blockchain called practical Byzantine fault tolerance (PBFT), this paper proposes a synchronous Byzantine fault tolerance (SBFT) algorithm that not only maintains the data consistency, but also has much higher efficiency than other general blockchain algorithms. In a small cloud environment, SBFT is simulated to compare with both Byzantine fault tolerance (BFT) and PBFT. The experiment results demonstrate the better performance of SBFT in data consistency, efficiency, and reliability.
Compositional Attention Networks With Two-Stream Fusion for Video Question Answering

Given a video, Video Question Answering (VideoQA) aims at answering arbitrary free-form questions about the video content in natural language. A successful VideoQA framework usually has the following two key components: 1) a discriminative video encoder that learns the effective video representation to maintain as much information as possible about the video and 2) a question-guided decoder that learns to select the most related features to perform spatiotemporal reasoning, as well as outputs the correct answer. We propose compositional attention networks (CAN) with two-stream fusion for VideoQA tasks. For the encoder, we sample video snippets using a two-stream mechanism (i.e., a uniform sampling stream and an action pooling stream) and extract a sequence of visual features for each stream to represent the video semantics with implementation. For the decoder, we propose a compositional attention module to integrate the two-stream features with the attention mechanism. The compositional attention module is the core of CAN and can be seen as a modular combination of a unified attention block. With different fusion strategies, we devise five compositional attention module variants. We evaluate our approach on one long-term VideoQA dataset, ActivityNet-QA, and two short-term VideoQA datasets, MSRVTT-QA and MSVD-QA. Our CAN model achieves new state-of-the-art results on all the datasets.
How Does CSMA/CA Affect the Performance and Security in Wireless Blockchain Networks

The impact of communication transmission delay on the original blockchain, has not been well considered and studied since it is primarily designed in stable wired communication environment with high communication capacity. However, in a wireless scenario, due to the scarcity of spectrum resource, a blockchain user may have to compete for wireless channel to broadcast transactions following media access control (MAC) mechanism. As a result, the communication transmission delay may be significant and pose a bottleneck on the blockchain system performance and security. To facilitate blockchain applications in wireless industrial Internet of Things (IIoTs), this article aims to investigate whether the widely used MAC mechanism, carrier sense multiple access/collision avoidance (CSMA/CA), is suitable for wireless blockchain networks or not. Based on tangle, as an example to analyze the system performance in term of confirmation delay, transaction per second and transaction loss probability by considering the impact of queueing and transmission delay caused by CSMA/CA. Next, a stochastic model is proposed to analyze the security issue taking into account the malicious double-spending attack. Simulation results provide valuable insights when running blockchain in wireless network, the performance would be limited by the traditional CSMA/CA protocol. Meanwhile, we demonstrate that the probability of launching a successful double-spending attack would be affected by CSMA/CA as well.