Reviewerly/exHarmony huggingface dataset:
Realistic Nighttime Haze Image Generation with Glow Effect

Abstract: Haze rendering aims to generate realistic nighttime haze images from clear images. The results can be applied to various practical applications, such as nighttime image dehazing algorithms, game scene rendering, shooting filters, etc. We investigate two smaller but challenging problems in nighttime haze rendering, namely 1) how to accurately estimate the transmission map and air light from clear and haze images, respectively, in the absence of paired datasets? 2) How to render the characteristics of a realistic nighttime haze image: glow effect? For this purpose, we propose an unsupervised nighttime haze image rendering method called NHRM (Nighttime Haze Rendering Module). Precisely, the NHRM consists of three modules: 1) an illumination retention module based on Retinex theory. 2) a transmission map estimation network using multi-scale feature fusion and attention mechanism. 3) a glow generation module using tone mapping and convolution bloom technique. Compared with existing haze generation methods, NHRM can render realistic and controllable nighttime haze images by simulating the main features of nighttime haze scenes in an unsupervised manner. Numerous experimental results show that our method outperforms existing rendering methods with better visual effects and quantitative metrics.
Low-light Image Enhancement via Zero-Reference Dynamic Curve Estimation

Abstract: Most low-light enhancement methods based on curve estimation do not have the ability of multi-illumination processing. They cannot use one model to process images with different exposure levels. This paper proposes to process multi-illumination images by dynamic curve estimation. The curve is a new dynamic light enhancement curve that allows appropriate enhancement, suppression, and hold operations depending on the light level of the input image. Therefore, the model can process images in different lighting environments. In addition, to further improve the model's ability to process different illumination images, the illumination discriminator is designed. It can further adjust the image to avoid overexposure and underexposure problems. Many qualitative and quantitative experiments show that this method is superior to the existing methods.
Detail Recovery and Color Enhancement for Single Image Dehazing

Due to the complexity of taking photos on foggy days, images captured in the real world are prone to problems of detail loss, color dimness, and low saturation. Considering image dehazing as a problem of detail recovery and color enhancement, a two-stage progressive fusion method of single image dehazing is proposed, which consists of a detail recovery branch, a color enhancement branch, and a wavelet fusion module. Firstly, to solve the problem of hazy image details and detail loss after image defogging, we introduce gamma transform, block homomorphic filtering, and multi-scale exposure fusion in the detail recovery branch for the first fusion stage to restore image details. Secondly, aiming at the problem that the overall color of the image is dark and the image saturation is poor after defogging, the color-preserving CLAHE(limited contrast adaptive histogram equalization) of contrast enhancement method is applied in the color enhancement branch to enhance the color. Finally, to fully use the complementary information between the two enhanced images, a wavelet fusion module is designed for pixel-level fusion in the second stage, which can recover the details and improve color information while removing the fog to the greatest extent. The experimental results show that the image's color can be restored naturally, and the details can be preserved completely. The experimental results show that the image's color can be restored naturally and the details can be fully preserved.
Weakly Supervised Semantic Segmentation Based on Image-level Class Labels with Deep Learning: A Survey

The training of fully supervised semantic segmentation (FSSS) networks relies on a large amount of data with pixel-level class labels, which limits semantic segmentation's application in practical scenarios. Weakly supervised semantic segmentation (WSSS) based on image-level class labels has become a new research hotspot in the field of image semantic segmentation. In this paper, the WSSS algorithms with image-level class labels are classified and sorted out according to two critical steps of the generic pipeline: seed generation and mask refinement. Different ways of seed generation can be divided into three categories: Multiple-Instance Learning-based methods, Class Activation Maps-based methods, and other generation methods. Different seed mask refinement methods can be divided into affinity-based mask refinement methods, additional supervision-based mask refinement methods, and other mask refinement methods. In addition, this paper analyzes the principles, key ideas, primary contributions, and advantages and disadvantages of various methods. Then, the segmentation performance of different WSSS algorithms on different datasets are compared, and the current state-of-the-art segmentation algorithms are labeled. Finally, the main challenges currently faced in the field and possible future directions have been prospected.
Method of Webpage Entity Extraction Based on Mixed Attribute Measurement and DOM Tree

Most mainstream web information extraction models adopt multidimensional analysis and then fusion evaluation, which has issues such as the high cost of vectorization and unreasonable fusion. For this reason, a text mining method of webpages based on the mixed attribute measure and DOM tree segmentation is proposed, which uses the K-Nearest Neighbor algorithm to measure the similarity of the mixed attributes of nodes in the DOM tree to alleviate the shortcomings of previous algorithms that focus solely on categorical or numeric attributes, and improves extraction quality, in addition, the web page segmentation algorithm is used to find the set of target DOM tree nodes, and ultimately information extraction is completed and effectively reduces extraction costs. Experimental results show that, compared to multiple baseline models, the method has over 6.5% and 11.6% improvement in the accuracy and recall rate evaluation indicators across numerous scenarios and clear speed benefits.
Real-time semantic segmentation network based on improved BiSeNet V1

Aiming at the problems of unclear boundaries and low segmentation accuracy in general real-time semantic segmentation networks, a real-time semantic segmentation network based on improved BiSeNet V1 was proposed. Based on the BiSeNetV1 network, a spatial enhancement module (SRM) is introduced into the spatial path to enhance the spatial information and improve the detection ability of target boundaries and small targets. At the same time, when the spatial path and context path feature information are fused, the Feature Aggregation Module (FAM) is proposed to solve the difference in feature representation between the two paths in feature fusion and improve the fusion efficiency. We experiment on Cityscapes that reach 69.6% MIoU and 100.4 FPS. The experimental results show that the proposed algorithm can improve the efficiency of segmentation.
A Real-Time Voice Conversion Method Based on A Non-Parallel Corpus for Training

Voice conversion (VC) is a modification of a person's voice to imitate a certain target person. Real-time VC can be widely used in confidential communication, voice enhancement, speech synthesis, and other fields, and has important research value. According to current research, high-quality VC methods tend to be computationally complex, and it is difficult to achieve real-time processing on computing platforms with different performances, which limits the usefulness of these methods. In this study, the authors proposed a VC method that can simplify computation based on different hardware processing capabilities to achieve real-time processing. The method also uses the vowel /a/ for speech feature alignment during training, which is a method with a non-parallel corpus and has stronger practicality. The authors designed and implemented a real-time VC system by using this method. Through a series of experiments, it was demonstrated that the system has good VC quality while ensuring real-time processing.
Linguistic variation analysis in Veterans’ tweets

Compatriots revere soldiers for their willingness to put their life on the line for serving the nation. They deserve to be admired, given their power to endure the hardships of battlegrounds. Experts say that war veterans often show signs of traumatic disorders. It can be seen in the messages expressed on social media by these individuals. In this research, we leveraged tweets to gain insights into the sentiments of war veterans. We examined the elements of the tweets to study the lexical and non-lexical features. We counted adjectives with top 100 frequencies in soldiers and civilians’ corpora and used EmoLex to perform emotion analysis on 10 emotions. Our objective was to compare tweets of war survivors with the civilians. We performed this comparison to find cues in the tweets of war veterans that indicate psychological distress.
A Comparative Study of CNN and Bi-LSTM in Text-Based Sentiment Analysis

Sentiment analysis is an important natural language processing task that seeks to extract contextual subjective information and perform classification. CNN and Bi-LSTM are two of the most popular models in text-based sentiment analysis. However, with intense training computations on large datasets, it is costly to decide among , Bi-LSTM and many of their composite models by simple trial-and-error. This paper conducts a comparison between CNN and Bi-LSTM across different datasets, analyzing the causes of differences and drawing conclusions assisting the selection of models. Also, the paper finds that certain disparities within input may impact performance, and proposes standards to pre-stratify dataset and configure different CNN/Bi-LSTM based models on different sub-datasets.
Modified Butterfly Optimization Algorithm based on Convergence Factor and Disturbance Strategy

The butterfly optimization algorithm (BOA) is a relatively new optimization technology with strong competitiveness compared with other meta-heuristic algorithms. However, BOA has shortcomings in convergence accuracy, convergence speed, and jumping out of local optimum. This paper proposes an improved Butterfly Optimization Algorithm based on convergence factor and disturbance strategy (LCD-BOA) to solve these shortcomings. Based on the BOA algorithm, the Levy flight strategy and disturbance factor strategy are added to improve the exploration ability of the algorithm. In addition, the convergence factor strategy is added to improve the exploitation ability of the algorithm further so that the exploration and exploitation of the algorithm are balanced as far as possible. Finally, the experimental results on 11 benchmark functions prove the effectiveness of the improved algorithm. The experimental results show that the improved algorithm significantly improves the performance of BOA compared with other meta-heuristic algorithms.
Blockchain-based Transaction Scheme for Massive MIMO Channel Estimation

In wireless communication systems, accurate channel estimation is essential to ensure the performance of wireless communication systems. Massive Multiple Input Multiple Output (M-MIMO) systems have a dramatic increase in the number of channels to be estimated due to the large number of antennas, which poses a great challenge to the computing resources of the base station, and one solution is offloading these computing tasks of channel estimation to other computing nodes with more computing resources (such as servers). However, in real life, computing nodes are not willing to participate in channel estimation because of their equipment and operation cost. In order to solve this problem, this paper introduces blockchain technology and designs a blockchain-based transaction scheme for channel estimation (BTS-CE) to solve the problem of computing resources transaction for massive MIMO channel estimation, and designs the specific process of the scheme. In order to solve the problem that computing nodes cannot complete the computation task due to failure or malicious disconnection, a task allocation model is designed to assign a computation task to multiple computation nodes. The security of the transactions is guaranteed through the blockchain. Practical Byzantine Fault Tolerance (PBFT) consensus mechanism cannot be applied to the public chain due to its low performance caused by a large number of nodes participating in consensus, this problem is solved by selecting some active nodes as consensus nodes. Simulation results show that improved PBFT(IPBFT) is lower in transaction delay and higher in throughput than PoW consensus mechanism.
Traffic Classification Using an Efficient Lightweight Convolutional Network

Traffic classification is playing a key role in network security domain with rapid growth of current Internet network, since traffic characterization is an important step for network management and network anomaly detection. Numerous researches have been done on this topic which have led to many different methods. Most of them use predefined features extracted by an expert to classify network traffic, which is costly and time consuming. In contrast, in the work, we propose a deep learning (DL) based approach, and it can automatically extract and select features through training, which has made DL-based method a highly desirable approach for traffic classification. Especially, inspired by the ConvNeXt, we believe that, compared with other DL-based method, 2D ConvNet can achieve a better performance by training techniques, while maintaining the simplicity and efficiency of standard ConvNets. Experimental results have verified this. After an initial pre-processing phase on data, the data are fed into DL framework to classify network traffic. Experiments have demonstrated that the proposed method enhanced the initial DL architecture (99.24% accuracy), and achieved accuracy of 99.47% in traffic categorization on UNB ISCX VPN-nonVPN dataset.
GShuttle: Optimizing Memory Access Efficiency for Graph Convolutional Neural Network Accelerators

Graph convolutional neural networks (GCNs) have emerged as an effective approach to extending deep learning for graph data analytics, but they are computationally challenging given the irregular graphs and the large number of nodes in a graph. GCNs involve chain sparse-dense matrix multiplications with six loops, which results in a large design space for GCN accelerators. Prior work on GCN acceleration either employs limited loop optimization techniques, or determines the design variables based on random sampling, which can hardly exploit data reuse efficiently, thus degrading system efficiency. To overcome this limitation, this paper proposes GShuttle, a GCN acceleration scheme that maximizes memory access efficiency to achieve high performance and energy efficiency. GShuttle systematically explores loop optimization techniques for GCN acceleration, and quantitatively analyzes the design objectives (e.g., required DRAM accesses and SRAM accesses) by analytical calculation based on multiple design variables. GShuttle further employs two approaches, pruned search space sweeping and greedy search, to find the optimal design variables under certain design constraints. We demonstrated the efficacy of GShuttle by evaluation on five widely used graph datasets. The experimental simulations show that GShuttle reduces the number of DRAM accesses by a factor of 1.5 and saves energy by a factor of 1.7 compared with the state-of-the-art approaches.
Baselines for Automatic Medical Image Reporting

Despite the high number of machine learning models presented in the last few years for automatically annotating medical images with deep learning models, clear baselines to compare methods upon are still missing. We present an initial set of experimentations of a standard encoder-decoder architecture with the Indiana University Chest X-ray dataset. The experiments include different convolutional architectures and decoding strategies for the recurrent decoder module. The results here presented could potentially benefit those tackling the same task in languages with fewer linguistic resources than those available in English.
Identification, collection, and investigation of electronic imagery as sources of evidence

Given the rapid pace of informatization of society, the number of criminal offences involving the use of computers, their software, as well as telecommunications systems is continuously growing. Such illegal actions are characterized by leaving traces, including electronic imagery. They can be evidence of the commission of criminal offences, which explains the development and improvement of methods for their detection, collection, and investigation by law enforcement agencies. However, today such methods of detecting, collecting, and investigating electronic imagery of evidence are separately contained in several scientific papers of Ukrainian and foreign scientists, which allowed comprehensively covering them in this study. The purpose of this study was to review the theory and practice of the activities of authorized entities for the detection, collection, and investigation of electronic imagery of evidence. The study uses a set of various methods, namely scientific cognition of real phenomena and their connections with the practical activities of authorized bodies for the detection, collection, and investigation of electronic imagery (dialectical method), as well as special and general scientific methods of legal science. The study showed as follows: usually, investigators and operational officers detect electronic imagery independently, or as part of an investigative task force during the investigation of criminal offences, or before their commission; the collection of electronic imagery occurs during procedural actions (usually law enforcement intelligence actions) both from technical devices with which a criminal offence was committed, and from those that were attacked. When extracting electronic imagery, it is advisable to involve a suitable specialist (if possible, a cyberpolice officer); an authorized investigator, specialist, and expert are authorized to examine electronic imagery. Expert research of electronic imagery belongs only to experts and is carried out using the following examinations: computer equipment and software products, telecommunications systems and tools, as well as technical and forensic examination of documents. The conducted review will help authorized practitioners restore the memory of knowledge about information about the tools for detecting, collecting, and investigating electronic imagery, which will ensure the effective implementation of the tasks of criminal proceedings
A framework for deep neural network multiuser authorization based on channel pruning

Summary Various deep neural network (DNN) model watermarks have been proposed by researchers to verify copyrights for deep neural networks DNN. However, most DNN watermarking methods cannot prevent attackers from stealing and using the model. Unlike many existing approaches, this paper uses a channel pruning algorithm to protect DNN models, which verifies DNN models copyrights but also prevents the illegal use of DNN models. In this work, the pruning threshold or pruning rate is used as the secret key of a DNN model. After the secret key is distributed to multiple users, they prune the DNN model with the secret key, and the pruned and fine‐tuned model is provided to the users. The users can verify ownership of the model according to the pruning accuracy and fine‐tuning accuracy. If the secret key is incorrect, the accuracy of the model after fine‐tuning will be very low, and users will be unable to use the reasoning function of the fine‐tuned model. Based on the CIFAR‐10 and CIFAR‐100 datasets, we conducted experiments on five popular DNN models. The experimental results show that we can authorize multiple users by pruning very few channels in the convolution layers of the DNN model.
Species diversity of herpetofauna in bat cave area, indarung village, lubuk kilangan district, padang city

Bat Cave is a new tourist attraction located in Indarung Village, Lubuk Kilangan District, Padang City. Changes in habitat will affect the fauna in it, especially the herpetofauna, because herpetofauna is very sensitive to environmental changes. Herpetofauna research in this area has never been done. The research was conducted in December 2022, the observation method used was the transect method combined with the VES (Visual Encounter Survey). The purpose of this study was to determine the diversity of herpetofauna species in the Bat Cave area. Based on the research, nine types of herpetofauna were found, consisting of six types of amphibians and three types of reptiles. Total number of individuals obtained was 57 individuals. The most common type found was Limnonectes blythii, namely 23 individuals. In this study, one individual was found endemic to West Sumatra, namely Ichthyophis elongatus. Based on the research conducted, the diversity of herpetofauna species in the Bat Cave area is low.
Reduction between Aristotelian Modal Syllogisms Based on the Syllogism &amp;amp;#9671;I&amp;amp;#9633;A&amp;amp;#9671;I-3

In order to provide a consistent explanation for Aristotelian modal syllogistic, this paper reveals the reductions between the Aristotelian modal syllogism IAI-3 and the other valid modal syllogisms.Specifically, on the basis of formalizing Aristotelian modal syllogisms, this paper proves the validity of IAI-3 by means of the truth value definition of (modal) categorical propositions.Then in line with classical propositional logic and modal logic, generalized quantifier theory and set theory, this paper deduces the other 47 valid Aristotelian modal syllogisms from the modal syllogism IAI-3.This study shows that the reasons why these syllogisms are reducible are: 1) any of Aristotelian quantifier can be defined by the other three Aristotelian quantifiers; 2) the Aristotelian quantifiers some and no have symmetry; 3) the possible modality  and necessary modality  can be mutually defined.This formal study of Aristotelian modal syllogistic not only conforms to the needs of formalization transformation of various information in the era of artificial intelligence, but also provides a unified mathematical research paradigm for other kinds of syllogistic.