Reviewerly/exHarmony huggingface dataset:
A new X-ray images enhancement method using a class of fractional differential equation

Many image-processing applications heavily depend on the quality of medical images. Due to the unpredictable variation in the captured images, medical images frequently have problems with noise or low contrast; therefore, improving medical imaging is a challenging task. For better treatment, physicians need images with good contrast to provide the most detailed picture of the disease. The generalized k-differential equation based on the k-Caputo fractional differential operator (K-CFDO) is used in this study to determine the energy of the image pixels to improve the visual quality and provide a clearly defined problem. The logic behind using the K-CFDO approach in image enhancement is the ability of K-CFDO to efficiently capture high-frequency details using the probability of pixels as well as preserve the fine image details. Moreover, the visual quality of X-ray images is improved by performing a low-contrast X-ray image enhancement.•Determine the energy of the image pixels for better pixel intensity enhancement.•Capture high frequency image details using the image probability of pixels. The findings of this study indicate that the average Brisque, Niqe, and Piqe values for the provided chest X-ray were found to be (Brisque=23.25, Niqe=2.8, Piqe21.58), and for the dental X-ray, they were (Brisque=21.12, Niqe=3.77, Piqe=23.49). The results of this study show potential improvements with the proposed enhancement methods that may contribute to increasing efficiency in healthcare processes at rural clinics. Generally, this model improves the details of medical images, which may aid medical staff throughout the diagnostic process by increasing the efficiency and accuracy of clinical decisions. Due to the improper setting of the suggested enhancing parameters, the current study included a limitation on image over-enhancement.
SSS-Net: A shadowed-sets-based semi-supervised sample selection network for classification on noise labeled images

Sample selection is a fundamental technique utilized in image classification with noisy labels. A plethora of sample selection approaches published in the literature are based on a small-loss strategy, in which division thresholds are set manually and the correlation between sample losses is ignored. Furthermore, one of the most evident shortcomings of these approaches is that noisy samples with low-quality pseudo-labels can negatively impact the model resulting in poor performance. In this study, a shadowed-sets-based semi-supervised sample selection network called SSS-Net is developed to address these limitations. Our approach leverages a novel technique that combines a loss-similarity-based-clustering method (LSCM) with the shadowed-sets theory to adaptively select clean samples. We then introduce an original high-quality pseudo-label sample reselection (HPSR) strategy, which is designed through the co-training of two networks, to pick the samples with high-quality pseudo-labels. Finally, the selected samples are utilized to further train the network and complete classification. This study presents an automated approach that determines optimal division thresholds to select clean samples adaptively. Furthermore, it improves the current semi-supervised sample selection method by effectively utilizing noisy samples. The suitability and promising performance of the proposed approach are supported through experimental studies using five real-world datasets. Comparative studies involving several state-of-the-art methods are also reported.
SiamNet: Exploiting source camera noise discrepancies using Siamese Network for Deepfake Detection

Recent advancements in deep neural networks especially GAN (Generative Adversarial Network) have resulted in the creation of more realistic deepfake media. This technology can swap the source person's face or alter facial expressions in an image or video; Media manipulated in such a way is termed deepfake. This type of manipulated media poses potential risks to journalism, politics, court proceedings and various social aspects. While existing approaches concentrate on the use of deep neural networks to directly extract facial artefacts for deepfake detection, they do not examine subtle inconsistencies in/across frame/frames. Moreover, state-of-the-art deepfake detection networks appear more complex and tend to overfit on specific artefacts which limits their generalizability on unseen data. This paper proposed a novel technique that tackles the problem of manipulated face detection in videos and images by exploiting the noise pattern inconsistency amongst face region and rest of the frame. To enable a comparison between the noise patterns of these two regions, we propose a two-stream Siamese-like network called SiamNet. This network can extract the noise patterns of the face region and patch through separate streams without increasing the number of parameters, thereby enhancing its efficiency and effectiveness. Each branch consists of pretrained Inception-v3 architecture for camera noise extraction. Siamese training is utilized to compare both noise patterns computed through different base models. The proposed two-branch network, SiamNet is found efficient for several large-scale deepfake datasets such as FF++, Celeb-DF, DFD and DFDC achieving accuracy rates of 99.7%, 98.3%, 96.08% and 89.2% respectively. Furthermore, the proposed technique exhibits greater generalizability and outperforms state-of-the-art of deepfake detection methods. Performance of the proposed model is also evaluated on FaceForensics benchmark dataset against different approaches.
Gas-expensive patterns detection to optimize smart contracts

Smart contracts are programmable protocols that run on Ethereum and require gas to be deployed and used. Gas-expensive operations in some smart contracts can cause users to consume extra gas in transactions. There are already several methods to detect gas-expensive patterns in smart contracts. However, some problems still need to be solved: most static analysis methods are based on specialist knowledge, and the patterns used to detect gas-expensive patterns must be manually summarized before the detection methods can be applied. Furthermore, due to the explosive growth of smart contracts, which generate a large amount of data, it is challenging to reuse these methods across different patterns. To address these issues, this work first proposes a new learning-based method, ExpenGas, based on the idea of evolutionary computation-based machine learning to detect Expensive Operation patterns of smart contracts through pre-trained techniques and multi-crucial data flow graphs. The low complexity of the multi-crucial data flow graph enables the model to focus on key features. Finally, by testing on 21981 smart contract files, ExpenGas has 83.05% accuracy and 91.96% recall in detecting the Expensive Operation patterns of gas-expensive patterns, which is significantly more optimal than the current state-of-the-art methods.
Pollock: A Data Loading Benchmark

Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps. We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic "pollution" process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.
Efficient Multi-Source Self-Attention Data Fusion for FDIA Detection in Smart Grid

As a new cyber-attack method in power cyber physical systems, false-data-injection attacks (FDIAs) mainly disturb the operating state of power systems by tampering with the measurement data of sensors, thereby avoiding bad-data detection by the power grid and threatening the security of power systems. However, existing FDIA detection methods usually only focus on the detection feature extraction between false data and normal data, ignoring the feature correlation that easily produces diverse data redundancy, resulting in the significant difficulty of detecting false-data-injection attacks. To address the above problem, we propose a multi-source self-attention data fusion model for designing an efficient FDIA detection method. The proposed data fusing model firstly employs a temporal alignment technique to integrate the collected multi-source sensing data to the identical time dimension. Subsequently, a symmetric hybrid deep network model is built by symmetrically combining long short-term memory (LSTM) and a convolution neural network (CNN), which can effectively extract hybrid features for different multi-source sensing data. Furthermore, we design a self-attention module to further eliminate hybrid feature redundancy and aggregate the differences between attack-data features and normal-data features. Finally, the extracted features and their weights are integrated to implement false-data-injection attack detection using a single convolution operation. Extensive simulations are performed over IEEE14 node test systems and IEEE118 node test systems; the experimental results demonstrate that our model can achieve better data fusion effects and presents a superior detection performance compared with the state-of-the-art.
Creating learning personas for collaborative learning in higher education: A Q methodology approach

Although collaborative learning is acknowledged as important in higher education, there are many challenges when designing and implementing collaborative learning and research has found that many college students were frustrated with group work. This study was aimed at investigating students’ perspectives on various sources of frustrations in group learning and creating learner personas that can be helpful for faculty and instructional designers to design positive collaborative learning experiences. Using Q methodology, five learner personas with rich narratives were identified: goal-oriented collaborator, fairness-oriented independent learner, learning-oriented collaborator, instruction-dependent learner, and adapted collaborator. This paper discusses the implications, benefits, and limitations for instructional design.
Computationally Efficient Neural Rendering for Generator Adversarial Networks Using a Multi-GPU Cluster in a Cloud Environment

Due to its fantastic performance in the quality of the images created, Generator Adversarial Networks have recently become a viable option for image reconstruction. The main problem with employing GAN is how expensive the computations are. Researchers have developed techniques for distributing GANs across multiple nodes. However, these techniques typically do not scale because they frequently separate the components (Discriminator and Generator), leading to high communication overhead or encountering distribution-related problems unique to GAN training. In this study, the training procedure for the GAN is parallelized and carried out over many Graphical Processing Units (GPUs). TensorFlow’s built-in logic and a custom loop were tweaked for more control over the resources allotted to each GPU worker. In this study, GPU image processing improvements and multi-GPU learning are used. The GAN model is accelerated using Distributed TensorFlow with synchronous data-parallel training on a single system and several GPUs. Acceleration was accomplished using the Genesis Cloud Platform and the NVIDIA Ⓡ GeForceTM GTX 108 GPU accelerator. The speed-up of 1.322 for two GPUs, 1.688 for three GPUs, and 1.7792 for four GPUs using multi-GPU acceleration. The parameter server model’s data initialization and image production bottlenecks are removed, but the results’ speed-up is not linear. Increasing the number of GPUs and removing the connectivity constraint will accelerate things even more. The bottlenecks are detected using new network lines and resources, and solutions are suggested. Recomputation and quantization are the two techniques to reduce the amount of GPU acceleration in memory. Deployment and versioning are essential for successfully operating multi-node GAN models in MLflow. Properly deploying and versioning these models can improve scalability, reproducibility, and collaboration across teams working on the same model. MLflow provides built-in tools for versioning and tracking model performance, making it easier to manage multiple versions of the model and reproduce it in different environments.
High-Dynamic-Range Tone Mapping in Intelligent Automotive Systems

Intelligent driver assistance systems are becoming increasingly popular in modern passenger vehicles. A crucial component of intelligent vehicles is the ability to detect vulnerable road users (VRUs) for an early and safe response. However, standard imaging sensors perform poorly in conditions of strong illumination contrast, such as approaching a tunnel or at night, due to their dynamic range limitations. In this paper, we focus on the use of high-dynamic-range (HDR) imaging sensors in vehicle perception systems and the subsequent need for tone mapping of the acquired data into a standard 8-bit representation. To our knowledge, no previous studies have evaluated the impact of tone mapping on object detection performance. We investigate the potential for optimizing HDR tone mapping to achieve a natural image appearance while facilitating object detection of state-of-the-art detectors designed for standard dynamic range (SDR) images. Our proposed approach relies on a lightweight convolutional neural network (CNN) that tone maps HDR video frames into a standard 8-bit representation. We introduce a novel training approach called detection-informed tone mapping (DI-TM) and evaluate its performance with respect to its effectiveness and robustness in various scene conditions, as well as its performance relative to an existing state-of-the-art tone mapping method. The results show that the proposed DI-TM method achieves the best results in terms of detection performance metrics in challenging dynamic range conditions, while both methods perform well in typical, non-challenging conditions. In challenging conditions, our method improves the detection F2 score by 13%. Compared to SDR images, the increase in F2 score is 49%.
Deep-Learning-Based Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulants

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper outperformed alternative approaches for classifying digitally modulated signals that included conventional classifiers that employed CSP-based techniques, as well as alternative DL-based classifiers that used convolutional neural networks (CNNs) or residual networks (RESNETs) with the in-phase/quadrature (I/Q) data used for training and classification.
General intelligence requires rethinking exploration

We are at the cusp of a transition from 'learning from data' to 'learning what data to learn from' as a central focus of artificial intelligence (AI) research. While the first-order learning problem is not completely solved, large models under unified architectures, such as transformers, have shifted the learning bottleneck from how to effectively train models to how to effectively acquire and use task-relevant data. This problem, which we frame as exploration, is a universal aspect of learning in open-ended domains like the real world. Although the study of exploration in AI is largely limited to the field of reinforcement learning, we argue that exploration is essential to all learning systems, including supervised learning. We propose the problem of generalized exploration to conceptually unify exploration-driven learning between supervised learning and reinforcement learning, allowing us to highlight key similarities across learning settings and open research challenges. Importantly, generalized exploration is a necessary objective for maintaining open-ended learning processes, which in continually learning to discover and solve new problems, provides a promising path to more general intelligence.
Determination of a class of permutation quadrinomials

Abstract We determine all permutation polynomials over of the form where, for some that is a power of the characteristic of , we have and all terms of have degrees in . We use this classification to resolve eight conjectures and open problems from the literature, and we list 77 recent results from the literature that follow immediately from the simplest special cases of our result. Our proof makes a novel use of geometric techniques in a situation where they previously did not seem applicable, namely to understand the arithmetic of high‐degree rational functions over small finite fields, despite the fact that in this situation the Weil bounds do not provide useful information.
CSN: Component supervised network for few-shot classification

The few-shot classification (FSC) task aims to classify data with limited labeled examples across different categories. Typically, researchers pre-train a feature extractor using base data and use it to extract features from novel data for classification Notably, the novel set only has a few annotated samples and has non-overlapped categories from the base set, which leads to the fact that the pre-trained feature extractor cannot adapt to the novel data flawlessly. We dub this problem as Feature-Extractor-Maladaptive (FEM) problem. Starting from the root cause of this problem, this paper presents a new scheme called Component Supervised Network (CSN), to improve the performance of FSC. We believe that even though the categories in the base and novel sets are different, their sample components share similarities. For example, both cats and dogs contain leg and head components. These entity components are intra-class stable and have cross-category versatility, making them useful for generalization to new categories. However, finding common information among different categories is difficult in real-world scenarios, hindering the possibility of modeling based on this assumption. To overcome this, we first design a Dictionary-based Implicit-Component Generator (DICG) to mine common information of different sets; then construct an implicit-component-based auxiliary task to improve the adaptability of the feature extractor. We evaluated our CSN on three benchmark datasets (mini-ImageNet, tiered-ImageNet, and FC100) and achieved improvements of at least 0.9% compared to classical methods, demonstrating the efficiency of our approach.
Multi-Label Classification of Chinese Rural Poverty Governance Texts Based on XLNet and Bi-LSTM Fused Hierarchical Attention Mechanism

Hierarchical multi-label text classification (HMTC) is a highly relevant and widely discussed topic in the era of big data, particularly for efficiently classifying extensive amounts of text data. This study proposes the HTMC-PGT framework for poverty governance’s single-path hierarchical multi-label classification problem. The framework simplifies the HMTC problem into training and combination problems of multi-class classifiers in the classifier tree. Each independent classifier in this framework uses an XLNet pretrained model to extract char-level semantic embeddings of text and employs a hierarchical attention mechanism integrated with Bi-LSTM (BiLSTM + HA) to extract semantic embeddings at the document level for classification purposes. Simultaneously, this study proposes that the structure uses transfer learning (TL) between classifiers in the classifier tree. The experimental results show that the proposed XLNet + BiLSTM + HA + FC + TL model achieves micro-P, micro-R, and micro-F1 values of 96.1%, which is 7.5~38.1% higher than those of other baseline models. The HTMC-PGT framework based on XLNet, BiLSTM + HA, and transfer learning (TL) between classifier tree nodes proposed in this study solves the hierarchical multi-label classification problem of poverty governance text (PGT). It provides a new idea for solving the traditional HMTC problem.
Elephant Flows Detection Using Deep Neural Network, Convolutional Neural Network, Long Short-Term Memory, and Autoencoder

Currently, the wide spreading of real-time applications such as VoIP and videos-based applications require more data rates and reduced latency to ensure better quality of service (QoS). A well-designed traffic classification mechanism plays a major role for good QoS provision and network security verification. Port-based approaches and deep packet inspections (DPI) techniques have been used to classify and analyze network traffic flows. However, none of these methods can cope with the rapid growth of network traffic due to the increasing number of Internet users and the growth of real time applications. As a result, these methods lead to network congestion, resulting in packet loss, delay and inadequate QoS delivery. Recently, a deep learning approach has been explored to address the time-consumption and impracticality gaps of the above methods and maintain existing and future traffics of real-time applications. The aim of this research is then to design a dynamic traffic classifier that can detect elephant flows to prevent network congestion. Thus, we are motivated to provide efficient bandwidth and fast transmision requirements to many Internet users using SDN capability and the potential of Deep Learning. Specifically, DNN, CNN, LSTM and Deep autoencoder are used to build elephant detection models that achieve an average accuracy of 99.12%, 98.17%, and 98.78%, respectively. Deep autoencoder is also one of the promising algorithms that does not require human class labeler. It achieves an accuracy of 97.95% with a loss of 0.13 . Since the loss value is closer to zero, the performance of the model is good. Therefore, the study has a great importance to Internet service providers, Internet subscribers, as well as for future researchers in this area.
Automated digital color restitution of mural paintings using minimal art historian input

Digital color restitution aims to digitally restore the original colors of a painting. Existing image editing applications can be used for this purpose, but they require a select-and-edit workflow and thus they do not scale well to large collections of paintings or different regions of the same painting. To address this issue, we propose an automated workflow that requires only a few representative source colors and associated target colors as input from art historians. The system then creates a control grid to model a deformation of the CIELAB color space. Such deformation can be applied to arbitrary images of the same painting. The proposed approach is suitable for restituting the color of images from a large photographic campaign, as well as for the textures of 3D reconstructions of a monument. We demonstrate the benefits of our method on a collection of mural paintings from a medieval monument.