Reviewerly/exHarmony huggingface dataset:
Colour Variation Minimization Retinex Decomposition and Enhancement with a Multi-Branch Decomposition Network

This paper proposes a colour variation minimization retinex decomposition and enhancement with a multi-branch decomposition network (CvmD-net) to remove single image darkness. The network overcomes the problem that retinex deep learning model relies on matching bright images to process dark images. Specifically, our method takes two stages to light up the darkness in initial images: image decomposition and brightness optimization. We propose an input constant feature prior mechanism (ICFP) based on reflection constant features. The mechanism extracts structure and colour from the input images and constrains the reflected images output from the decomposition model to reduce color distortion and artifacts. The noise amplification during decomposition is addressed by a multi-branch decomposition network. Sub-networks with different structures are employed to focus on different prediction tasks. This paper proposes a reference mechanism for input brightness. This mechanism optimizes the output brightness distribution by calculating the reference brightness of the dark images. Experimental results on two benchmark datasets, namely, LOL and ZeroDCE, demonstrate that the proposed method can better balance dense noise interference and colour restoration. For the evaluation on real images, we collect Skynet images at night to verify the performance of the proposed approach. Compared with the state-of-the-art non-reference retinex decomposition-enhancement models, this paper has the best brightness optimization.
RESS: A Reliable and Effcient Storage Scheme for Bitcoin Blockchain Based on Raptor Code

The Bitcoin system uses a fully replicated data storage mechanism in which each node keeps a full copy of the blockchain. As the number of nodes in the system increases and transactions get more complex, more and more storage space are needed to store block data. The scalability of storage has become a bottleneck, limiting the practical application of blockchain. This paper proposes a node storage scheme, called RESS, to integrate erasure coding technology into the blockchain to encode multiple blocks. Under the proposed block grouping method, nodes can reduce the times of coded block decoding. In addition, the coding scheme based on Raptor codes proposed in this paper has linear coding and decoding complexity. The rateless feature of Raptor code helps to achieve high decentralization and scalability of the Bitcoin network. RESS ensures data availability, efficiency and blockchain robustness based on achieving storage space scalability. Experimental results show that the proposed scheme reduces the storage requirements of nodes by nearly an order of magnitude.
What if we could travel without passport? First sight to blockchain-based identity management in tourism

Blockchain technology, as a distributed digital ledger, enables users to control their credentials without being breached by third parties. From a tourism perspective, it allows tourists to pass through checkpoints and/or bookings without waiting and having to go through third-party transactions. Hence, this paper aims to discuss traditional identity management (IdM) system challenges and what blockchain might offer as a counterpoint to conventional travel experiences within the tourism domain. We have tried to identify challenges, issues, and implementation areas of IdM in the tourism industry domain.
Code-Based Conjunction Obfuscation

The pattern-matching problem with wildcards can be formulated as a conjunction where an accepting string is same as the pattern for all non-wild-cards. A scheme of conjunction obfuscation is a algorithm that “encrypt” the pattern to prevent some adversary from forging any accepting string. Since 2013, there are abundant works about conjunction obfuscation which discussed with weak/strong functionality preservation and distributed black-box security. These works are based on generic group model, learning with error assumption, learning with noise assumption, etc. Our work proposes the first conjunction obfuscation with strong functionality preservation and distributed black-box security from a standard assumption. Our scheme with some parameter constraints can also resist some related attacks such as the information set decoding attack and the structured error arrack.
Semantic Comprehension Method for Chinese Sentences Based on Minimal Semantic Structures and Its Application

The importance of small Chinese sentences is no less than that of sentences, which is an inherent feature of Chinese itself. According to this characteristic, this paper proposes a sentence semantic understanding method for Chinese scientific and technological abstracts based on the minimum semantic structure. Firstly, a conceptual model was established for identifying the minimum semantic structure of a sentence based on a corpus of verbs, relative words, prepositions and markers based on Language Technology Planform (LTP) tools. Secondly, the model was used to extract the minimum semantic structure of abstract sentence. Finally, three experiments were carried out, namely, the classification of the abstract sentences, knowledge graph generation and automatic semantic inference discovery. Our study confirmed the practical value of the small Chinese sentence. The experimental results show that the effect of using small sentences to understand the semantics of Chinese text is better than that of the full stop sentence, and the minimum semantic structure can be used as the basic unit of the Chinese sentence semantic comprehension. This method is conducive in the automatic understanding of the basic semantics of sentences in unstructured Chinese science and technology text sentences.
Joint Source-Channel Coding for a Multivariate Gaussian Over a Gaussian MAC Using Variational Domain Adaptation

With the development of the distributed learning and edge computing, servers must often receive information from multiple terminal devices; thus, the importance of source-channel coding for distributed sources over multiple access channels (MACs) becomes more and more significant. This paper presents a deep joint source-channel coding (JSCC) design for a multivariate Gaussian source over a Gaussian MAC. The widely used autoencoder based deep-JSCC cannot perform stably under such conditions due to their easiness to fall into local optimum. Therefore we propose the variational domain adaptation (VDA)-JSCC scheme. Firstly, the loss function with an additional regularization term is introduced through variational analysis. The crucial prior distribution related to this item is obtained by domain adaptation, which is a transfer learning method. The proposed fine-tuning technique during the training process yields further performance improvement. Experiment results show that VDA-JSCC can always learn reasonable coding structures without artificial design and outperforms other state-of-the-art methods under different channel signal-to-noise ratios (CSNRs). We have also analyzed the reason why the performance of VDA-JSCC deteriorates in high CSNR range and then replace the encoder of VDA-JSCC with Mixture-of-Experts to improve its performance in high CSNR range. Finally, VDA-JSCC exhibits considerable robustness when the channel quality or correlation coefficient varies.
Quaternion Quasi-Chebyshev Non-local Means for Color Image Denoising

Quaternion non-local means (QNLM) denoising algorithm makes full use of high degree self-similarities inside images to suppress the noise, so the similarity metric plays a key role in its denoising performance. In this study, two improvements have been made for the QNLM: 1) For low level noise, the use of quaternion quasi-Chebyshev distance is proposed to measure the similarity of image patches and it has been used to replace the Euclidean distance in the QNLM algorithm. Since the quasi-Chebyshev distance measures the maximal distance in all color channels, the similarity of color images measured by quasi-Chebyshev distance can capture the structural similarity uniformly for each color channel; 2) For high level noise, quaternion bilateral filtering has been proposed as the preprocessing step in the QNLM algorithm. Denoising simulations were performed on 110 images of landscape, people, and architecture at different noise levels. Compared with QNLM, quaternion non-local total variation (QNLTV), and non-local means (NLM) variants (NLTV, NLM after wavelet threshold preprocessing, and the color adaptation of NLM), our novel algorithm not only improved PSNR/SSIM (peak signal to noise rate/structural similarity) and figure of merit values by an average of 2.77 dB/8.96% and 0.0491 respectively, but also reduced processing time.
Unsupervised Video Object Segmentation via Weak User Interaction and Temporal Modulation

In unsupervised video object segmentation (UVOS), the whole video might segment the wrong target due to the lack of initial prior information. Also, in semi-supervised video object segmentation (SVOS), the initial video frame with a fine-grained pixel-level mask is essential to good segmentation accuracy. It is expensive and laborious to provide the accurate pixel-level masks for each training sequence. To address this issue, We present a weak user interactive UVOS approach guided by a simple human-made rectangle annotation in the initial frame. We first interactively draw the region of interest by a rectangle, and then we leverage the mask RCNN (region-based convolutional neural networks) method to generate a set of coarse reference labels for subsequent mask propagations. To establish the temporal correspondence between the coherent frames, we further design two novel temporal modulation modules to enhance the target representations. We compute the earth mover's distance (EMD)-based similarity between coherent frames to mine the co-occurrent objects in the two images, which is used to modulate the target representation to highlight the foreground target. We design a cross-squeeze temporal modulation module to emphasize the co-occurrent features across frames, which further helps to enhance the foreground target representation. We augment the temporally modulated representations with the original representation and obtain the compositive spatio-temporal information, producing a more accurate video object segmentation (VOS) model. The experimental results on both UVOS and SVOS datasets including Davis2016, FBMS, Youtube-VOS, and Davis2017, show that our method yields favorable accuracy and complexity. The related code is available.
On the Linear Complexity of a Class of Periodic Sequences Derived from Euler Quotients

In this paper, a family of binary sequences derived from Euler quotients with RSA modulus <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$pq$</tex> is introduced. Here two primes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> are distinct and satisfy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{gcd}(\pmb{pq},(p-1)(q-1))=1$</tex> . The linear complexities and minimal polynomials of the proposed sequences are determined. Besides, this kind of sequences is shown not to have correlation of order four although there exist some special relations by the properties of Euler quotients.
Recover the Secret Components in a ForkCipher

Recently, a new cryptographic primitive has been proposed called ForkCiphers. This paper aims at proposing new generic cryptanalysis against such constructions. We give a generic method to apply existing decompositions againt the underlying block cipher <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{E}^{r}$</tex> on the forking variant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{Fork}\mathcal{E}-(r-1)-r_{0}-(r+1-r_{0})$</tex> . As application, we consider the security of ForkSPN and ForkFN with secret inner functions. We provide a generic attack against <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ForkSPN}-2-r_{0}-(4-r_{0})$</tex> based on the decomposition of SASAS. And also we extend the decomposition of Biryukov et al. against Feistel networks in SAC 2015 to get all the unknown round functions in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{ForkFN}-r-r_{0}-r_{1}$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r\leq 6$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$r_{0}+r_{1}\leq 8$</tex> . Therefore, compared with the original block cipher, the forking version requires more iteration rounds to resist the recovery attack.
Rating Prediction Model Based on Causal Inference Debiasing Method in Recommendation

The rating prediction task plays an important role in the recommendation model. Most existing methods predict ratings by extracting user and items characteristics from historical review data. However, the recommended strategies in historical review data are often based on partial observational data, which having the problems of unbalanced distribution, lack of robustness, and inability to obtain unbiased prediction results. Therefore, a novel rating prediction model based on causal inference debiasing (CID) method is proposed. The model can mitigate the negative effects of context bias and improve the robustness by studying the causal relationship between review information and user ratings. The proposed CID rating prediction model is plug-and-play and is not limited to one baseline prediction method. The proposed method is tested on four open datasets. The results show that the proposed method is feasible. Compared with the most advanced models, the prediction accuracy of the CID rating prediction model has been further improved. The experimental results show the debiasing effectiveness of the CID rating prediction model.
Forensic integrity verification of video recordings based on <scp>MTS</scp> files

Digital video is used in criminal trials as evidence with legal responsibility because video content vividly depicts events occurring at a crime scene. However, using sophisticated video editing software, assailants can easily manipulate visible clues for their own benefit. Therefore, the integrity of digital video files acquired or submitted as evidence must be ensured. Forensic analysis of digital video is key to ensuring the integrity of links with individual cameras. In this study, we analyzed whether it is possible to ensure the integrity of MTS video files. Herein, we propose a method to verify the integrity of MTS files encoded by advanced video coding high definition (AVCHD), which is frequently used for video recording. To verify MTS file integrity, we propose five features. Codec information, picture timing, and camera manufacture/model are modified AVI and MP4-like format video verification features. Group of pictures and Universally Unique Identifier patterns were specifically developed for MTS streams. We analyzed the features of 44 standard files recorded using all recording options of seven cameras. We checked whether integrity can be validated on unmanipulated videos recorded in various environments. In addition, we considered whether manipulated MTS files edited in video editing software could be validated. Experimental results show that all unmanipulated and manipulated MTS files with known recording devices were discriminated only when all five features were checked. These results show that the proposed method verifies the integrity of MTS files, strengthening the validity of MTS file-based evidence in trials.
A multiple-image encryption algorithm based on orthogonal arrays with strength 3

In recent years, remote working and studying lead to numerous images being transmitted on the Internet. Many of these images need protection from illegal obtaining or malicious peeping. Under the circumstances, the demand for image encryption algorithms with sufficient capacity and high efficiency is increasing. Multiple-image encryption algorithms can suit the demand well. This paper proposes a multiple-image encryption algorithm based on orthogonal arrays with strength 3. Orthogonal array is a typical configuration in combinatorial design theory, but the application research of orthogonal arrays with strength 3 in image encryption is still a blank. The proposed algorithm combines orthogonal arrays and chaotic maps perfectly to generate multiple 3D permutation maps and substitution maps, then multiple images can be encrypted efficiently. In simulation experiments, the average value of information entropy is 7.9993, the average value of PSNR is 8.884, the average value of MSD is 50, the average values of NPCR and UACI are 99.6127% and 33.4765% respectively. The numerical results demonstrate that the algorithm has enough ability to resist common attacks.
Dictionary-based histogram packing technique for lossless image compression

This paper proposes a dictionary-based histogram packing technique for lossless image compression. It is used to improve the performance of the state-of-the-art lossless image compression standards and methods when compressing sparse and locally sparse histogram images. The proposed method leverages inter-block correlations and similarities not only within the neighborhood but also across the entire image, thereby effectively reducing the block boundary artifacts commonly observed in block-based histogram packing techniques. To achieve this, a dictionary is employed to represent highly correlated blocks using a key that captures the union of their active symbol sets. Experimental results have demonstrated that the proposed method, when applied to sparse and locally sparse histogram images, enhances the performance of various state-of-the-art lossless image compression techniques. Notably, improvements were observed in standards and methods such as JPEG-2000, JPEG-LS, JPEG-XL, PNG, and CALIC.
Robust adaptive enhancement algorithm for multi-modal high grayscale image displaying on low-bit monitors based on HIS and priori knowledge

Gray-scale images are widely used in many fields, including satellite remote sensing, autonomous driving, medical CT, and X-ray detection, but gray-scale images have many shortcomings, such as high bit, low contrast, and high dynamic ranges. The visual analysis of these images is complicated and limited by people's psychology and physiology, and pseudo-color technology is an effective way to solve this problem. To address the weak adaptive capability, poor robustness, and insufficient generalization of existing pseudo-color enhancement methods, an adaptive enhancement algorithm for high grayscale images based on prior knowledge and HIS space, called APKHHIS, was proposed. Firstly, an adaptive gray-scale image correction mapping algorithm based on original prior knowledge was designed, and the quantization of images with different bit depths is realized. Secondly, to ensure the effectiveness of the enhanced data, an adaptive power compensation method was designed to compensate for the brightness of the gray-scale corrected images. Finally, an adaptive brightness and power correction algorithm was innovatively designed for the first time, which can adaptively adjust the color saturation and spatial brightness of the image more reasonably. To verify the effectiveness of the algorithm proposed in this paper, adaptive enhancement experiments were carried out on multi-modal data sets. The subjective and objective experimental results show that the APKHHIS algorithm can achieve the best performance on various data sets, and has strong robustness and universality, which provides an effective solution for the adaptive enhancement of multi-modal low-quality images.
Norm-based reinforcement learning for QoS-driven service composition

QoS-aware service composition is challenging due to a high number of QoS attributes, component services, and candidate services. Realistic service composition applications operate in uncertain environments where QoS values may change dynamically. Moreover, user requirements on QoS attributes should be considered, and their different nature can make it difficult to express them by adopting relative weights. Reinforcement Learning is proposed as a viable approach in order to deal with the complexity and variability of the environment. In this paper, we propose a novel approach that integrates traditional reinforcement learning with a norm-based paradigm to consider cases where component services may have a different number and types of QoS attributes. In such a way, it is possible to consider additional local requirements that may hold only for specific service components still pursuing a global optimization. Norms allow using a uniform formalism to express qualitative and quantitative as well as hard and soft user requirements. The approach has been tested on a real dataset of 2500 web services showing its performance, scalability, and adaptability properties.
Intelligent path planning algorithm of Autonomous Underwater Vehicle based on vision under ocean current

Abstract Autonomous Underwater Vehicle (AUV) is an important tool for intelligent ocean applications, which can be applied to detect underwater environment and search target. Path planning is the key technology to realize AUV intelligence, which has important research significance. Aiming at the dynamic path planning problem, the Regional Ocean Modeling System (ROMS) was first applied to the AUV three‐dimensional (3D) dynamic path planning, and a Gate Recurrent Unit Proximal Policy Optimization with Local Vision (GPPO‐LV) model based on local vision is proposed. The 3D ocean current environment is constructed based on the ROMS simulation data. The local vision matrix is constructed based on underwater images, and the features of local vision are extracted using convolutional neural network. Furthermore, Gate Recurrent Unit (GRU) network is used to mine the hidden information between observation states. Finally, the Actor network for strategy output and the Critical network for action value evaluation are constructed, and strategies are optimized in the process of interaction with the environment. The experiment shows that under various unknown environments, AUV can carry out real‐time path planning under real ocean current data, and has good obstacle avoidance ability.
Centralized multi-sensor labeled multi-Bernoulli filter with partially overlapping fields of view

In this paper, the labeled multi-Bernoulli (LMB) filter is extended to the centralized multi-sensor (MS) case, resulting in a multi-sensor multi-object tracking (MS-MOT) algorithm having the ability to cope with partially overlapping fields-of-view (FoVs). Specifically, a joint prediction and update scheme for the MS-LMB filter is presented and the inherent NP-hard multi-dimensional assignment problem is solved via Gibbs sampling. We propose a suboptimal stationary distribution for the Gibbs sampling which admits conditionally independent sampling among multiple sensors thus dramatically decreasing the computational complexity. In addition, in order to tune object initialization when there is no background knowledge of the birth locations, a novel MS measurement-driven birth process is proposed. The proposed birth process has superior ability in terms of suppressing false tracks originating from clutter measurements of multiple sensors. The effectiveness of the proposed algorithm and birth process is demonstrated in simulation experiments concerning MS surveillance scenarios with partially overlapping sensor FoVs.