Reviewerly/exHarmony huggingface dataset:
SFGDO: Smart flower gradient descent optimization enabled generative adversarial network for recognition of Tamil handwritten character

The Tamil language is complicated to identify, and thus more efforts are devised in the literary works. The objective is to develop a model called the Smart Flower Gradient Descent optimization-based Generative Adversarial Network (SFGDO-based GAN) for recognising Tamil handwriting. The dataset is first used to acquire the input image. The undesired noises are then pre-processed using a bilateral filter, and the binarization procedure is carried out using gradient-based thresholding. The necessary characteristics are then extracted for further categorization of Tamil characters, including character length, character width, statistical features, Local Binary Pattern (LBP), Convolutional Neural Network (CNN), density features, and Histogram of Oriented Gradients (HOG) features. Finally, utilising the proposed SFGDO enabled GAN, Tamil handwritten characters are recognised. The proposed Smart Flower Gradient Descent optimization (SFGDO) algorithm is developed by integrating Smart Flower Optimization Algorithm SFOA) and Gradient Descent Optimization (GDO).
Fast Progressive Differentiable Architecture Search based on adaptive task granularity reorganization

Shrinkage methods reduce the search space of a Differentiable Architecture Search (DARTS) by progressively discarding candidates, which accelerates the search speed. However, their shrinkage strategy suffers from the vulnerability of too fine task granularity. In other words, they drop only one of the least promising candidates per round of shrinkage, which is suboptimal in terms of performance and efficiency. In this study, we introduce the concept of Granular Computing (GrC) into the shrinkage method and present a Fast Progressive Differentiable Architecture Search (FP-DARTS) method. This method effectively reduces the computational complexity of each round of shrinkage, thereby improving the efficiency and performance of the algorithm. FP-DARTS can be divided into three stages: adaptive granularity division and selection, granular-channel performance evaluation, and progressive shrinkage. In the first stage, to reorganize the task granularity, we cluster the candidate operations into granular-channels and adaptively select the appropriate task granularity. We also propose a dynamic clustering strategy to avoid introducing additional computation. In the second stage, we train the architecture parameters to measure the potential of the granular-channels. In the third stage, to improve the stability of the shrinkage results, we introduce a channel annealing mechanism to smoothly discard unpromising granular-channels. We conducted systematic experiments on CIFAR-10 and ImageNet and achieved a test accuracy of 97.56% on CIFAR-10 with 0.04 GPU-days, and a test accuracy of 75.5% on ImageNet with 1.2 GPU-days. We also conducted experiments on the search space of NAS-Bench-201, and obtained test accuracies of 94.22, 73.07, and 46.23% for CIFAR-10, CIFAR-100 and ImageNet16-120, respectively. The above experimental results demonstrate that FP-DARTS achieves higher search speed and competitive performance compared to other state-of-the-art shrinkage methods and non-shrinkage methods.
Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP). In the first stage, we segment the invasive tumor over the user-indicated Region of Interest (ROI). Then, in the second stage, we classify the tumor tissue into four HER2 classes. For the classification stage, we use weakly supervised, constrained optimization to find a model that classifies cancerous patches such that the tumor surface percentage meets the guidelines specification of each HER2 class. We end the second stage by freezing the model and refining its output logits in a supervised way to all slide labels in the training set. To ensure the quality of our dataset's labels, we conducted a multi-pathologist HER2 scoring consensus. For the assessment of doubtful cases where no consensus was found, our model can help by interpreting its HER2 class percentages output. We achieve a performance of 0.78 in F1-score on the test set while keeping our model interpretable for the pathologist, hopefully contributing to interpretable AI models in digital pathology.
Flexible Adaptive Graph Embedding for Semi-supervised Dimension Reduction

Graph-based semi-supervised dimension reduction can use the inherent graph structure of samples to propagate label information, and has become a hot research field in machine learning. However, most current methods have strict linear constraints and cannot handle data sampled from nonlinear manifolds; and rely on predefined graphs, which cannot capture the local structure information of data and cannot handle complex non-Gaussian data. To address these issues, this paper proposes a new locality-preserved flexible dimension reduction framework, called Semi-supervised Flexible Adaptive Graph Embedding (SFAG), which learns the embedding space that can preserve the local neighborhood structure by constructing a k1-nearest neighbor graph over labeled samples. Then, another k2-nearest neighbor graph is constructed on all samples to adaptively construct the optimal graph, clustering labeled and unlabeled embedding sample points with neighborhood relations into the same sub-manifold sharing the same label information. Last but not least, the hard linear projection constraint is relaxed by adding residual terms to obtain not only the nonlinear embedding of the training samples, but also the linear projection matrix applied directly to the out-of-sample. In addition, two different semi-supervised dimension reduction methods for adaptive construction of optimal graphs are proposed based on the SFAG framework. Several evaluation experiments validate the effectiveness of our method in exploring manifold structures and classification tasks.
A new collective anomaly detection approach using pitch frequency and dissimilarity: Pitchy anomaly detection (PAD)

Anomaly detection in time series is an important process that can aid in both preprocessing and postprocessing, particularly in biomedical data modalities where anomalies often signify the presence of disorders that require identification. However, quasi-periodic data behavior frequently poses a challenge in detecting collective anomalies, especially when they fall within the normal data range. This difficulty makes detecting collective anomalies particularly challenging. This paper proposes a new method for detecting collective anomalies using pitch frequency information from the frequency domain and dissimilarity metric scores in a sliding window approach. The proposed method called pitchy anomaly detection (PAD) is expected to be effective in the analysis of quasi-periodic time series data. To evaluate the effectiveness of the PAD approach, it is compared to three other benchmark time series anomaly detection methods. Due to the particular interest in quasi-periodic data modalities, the comparative analysis is conducted under five different anomalous conditions on anomaly-added benchmark electrocardiography (ECG) datasets. The results show that the PAD approach yields promising classification performance results for detecting collective anomalies.
KEIC: A tag recommendation framework with knowledge enhancement and interclass correlation

Tag recommendation is critical in organizing and managing resources on social media platforms. The incessant deluge of new content and terms and the challenge of creating new tags significantly complicates this task. To address these challenges, we present KEIC, a tag recommendation framework that unifies Knowledge Enhancement and Interclass Correlation. KEIC enriches the deep semantic understanding of the text by incorporating commonsense knowledge and identifies interclass correlations to migrate the long-tail effect of tags. Experiments conducted on three large-scale datasets have demonstrated that KEIC can seamlessly integrate with existing classification-based tag recommendation models, delivering outstanding performance without the need for excessive parameter augmentation, while effectively mitigating the long-tail effects in the data. The ablation experiments further confirm the effectiveness of different components of our proposed framework.
Unpaired Image-To-Image Translation Using Generative Adversarial Networks With Coordinate Attention Loss

Image stylization is an important research direction in image processing, graphics, and computer vision. At present, methods based on deep learning, especially generative adversarial network, have made great progress in image stylization migration. However, there are several limitations to the current mainstream methods, the biggest of which is the inability to perform geometry changes, remove large objects, or ignore irrelevant textures in unpaired scenarios. This paper proposes a style transfer algorithm CAGAN based on Adversarial Consistency Loss Generative Adversarial Network and Coordinate Attention. The stylized transfer of high perceptual quality in mismatched scenes is achieved by combating consistency loss and attention mechanism, and the Laplacian noise module is added to generate multi-modal output. Through a lot of experiments, it is verified that the algorithm can achieve high quality stylization effect.
Waste Recycling to Save Energy by Using AI

Our study aims to use artificial intelligence in recycling waste and using it to produce electric energy instead of fuel. The more the city is populated with residents, commercial activity and other public facilities, the more it produces a large amount of household and institutional waste by its residents and other types by visitors, and this is what the capital is going through. Baghdad at the present time is densely populated and has many visitors from other provinces inside or outside the country. Which results in an environmental defect, distortion of the general view of the city, not to mention the health and psychological danger to individuals. Because of poor management and a lack of human and material resources, as it must be reconsidered by those responsible for the capital in order to improve living and other services; As well as intensifying the efforts of citizens and their participation among them in order to improve their environment; Also, our neighborhoods lack artificial intelligence in managing waste, which has caused a reduction in the accumulation of waste through the sorting feature, which is the basic stage and the first step in organizing waste management from the house to the ocean and then recycling it to produce an electric power plant. When using artificial intelligence in waste recycling, the waste collection process will achieve an unprecedented level of sustainability. By using AI to automate and scale the operations of waste disposal trucks, another important step in the waste management cycle can be improved, sustainably.
Vulnerability Detection by Learning from Syntax-Based Execution Paths of Code

Vulnerability detection is essential to protect software systems. Various approaches based on deep learning have been proposed to learn the pattern of vulnerabilities and identify them. Although these approaches have shown vast potential in this task, they still suffer from the following issues: (1) It is difficult for them to distinguish vulnerability-related information from a large amount of irrelevant information, which hinders their effectiveness in capturing vulnerability features. (2) They are less effective in handling long code because many neural models would limit the input length, which hinders their ability to represent the long vulnerable code snippets. To mitigate these two issues, in this work, we proposed to decompose the syntax-based Control Flow Graph (CFG) of the code snippet into multiple execution paths to detect the vulnerability. Specifically, given a code snippet, we first build its CFG based on its Abstract Syntax Tree (AST), refer to such CFG as syntax-based CFG, and decompose the CFG into multiple paths from an entry node to its exit node. Next, we adopt a pre-trained code model and a convolutional neural network to learn the path representations with intra- and inter-path attention. The feature vectors of the paths are combined as the representation of the code snippet and fed into the classifier to detect the vulnerability. Decomposing the code snippet into multiple paths can filter out some redundant information unrelated to the vulnerability and help the model focus on the vulnerability features. Besides, since the decomposed paths are usually shorter than the code snippet, the information located in the tail of the long code is more likely to be processed and learned. To evaluate the effectiveness of our model, we build a dataset with over 231k code snippets, in which there are 24k vulnerabilities. Experimental results demonstrate that the proposed approach outperforms state-of-the-art baselines by at least 22.30%, 42.92%, and 32.58% in terms of Precision, Recall, and F1-Score, respectively. Our further analysis investigates the reason for the proposed approach's superiority.
CSR-SVM: Compositional semantic representation for intelligent identification of engineering change documents based on SVM

Timely and accurate identification of engineering change documents plays an important role in the document management system of the Architecture, Engineering, and Construction (AEC) industry, which facilitates decision making. The current way of manual review and analysis may cause a delay or omission in information transmission, which may severely affect the project schedule and cost. This paper adopts text classification to explore the intelligent identification of unstructured engineering change documents. However, the paucity of available corpus and the limited number of engineering change texts are challenges in practice. Additionally, the semantic information of texts cannot be elegantly exploited. Especially there are unregistered words that interfere with semantics in engineering change texts. To tackle these problems, we propose a compositional semantic representation (CSR) and develop an SVM-based method named CSR-SVM. We introduce a language model to produce word embeddings; here, a domain dictionary is established for unregistered words. The embeddings are then exploited in CSR so that CSR can incorporate both key and global semantic representations. The former is obtained based on dependency parsing and word embeddings, and the latter is obtained according to all the word representations of a text. The CSR provides sufficient semantic representation for SVM in an efficient way. The advantages of CSR-SVM have been validated by experiments on a real-world dataset.
New code-based cryptosystems via the IKKR framework

One main construct for code-based public key cryptosystems is the McEliece framework that hedges upon the hardness of decoding arbitrary linear codes. Based on Goppa codes, the original McEliece cryptosystem however, suffers from having very large public keys. To alleviate this problem, we define a new IKKR problem that is NP-complete and use this assumption of the intractability of the decisional IKKR problem to construct a IND-CCA2-secure code-based public key encryption scheme. We consider generalized Reed–Solomon codes in our public-key cryptosystem and show that it resists Sidelnikov and Shestakov’s key recovery attack. Our generalized Reed–Solomon code encryption scheme achieves optimal public key size when compared with other PKE or key encapsulation mechanisms with deterministic decryption or decapsulation, as it requires only 88.1 kilobytes to store public key for schemes achieving 128-bit security level and 399.69 kilobytes to store public key for schemes achieving 256-bit security level. A public key size reduction of nearly 92% is obtained as compared to the classic McEliece PKE, and nearly 53% compared to the Reed–Solomon code-based PKE.
Video quality metrics toolkit: An open source software to assess video quality

a b s t r a c tVideo content on the Internet continues to grow.As a result, streaming platforms must ensure a certain level of quality when preparing their content.To this end, several metrics have been developed by the research community to evaluate video quality.This work integrates 14 video metrics and the SI-TI indicators into a container image to create a cross-platform tool, VQMTK.The tool offers a web interface and a Bash script that combines all metrics into a single tool.Performance tests have demonstrated that the tool is capable of handling all the integrated metrics using 4K video samples.The tool can be used in scientific and educational environments.
Attention and DCT Based Global Context Modeling for Text-Independent Speaker Recognition

Learning an effective speaker representation is crucial for achieving reliable performance in speaker verification tasks. Speech signals are high-dimensional, long, and variable-length sequences that entail a complex hierarchical structure. Signals may contain diverse information at each time-frequency (TF) location. The standard convolutional layer that operates on neighboring local regions often fails to capture the complex TF global information. Our motivation stems from the need to alleviate these challenges by increasing the modeling capacity, emphasizing significant information, and suppressing possible redundancies in the speaker representation. We aim to design a more robust and efficient speaker recognition system by incorporating the benefits of attention mechanisms and Discrete Cosine Transform (DCT) based signal processing techniques, to effectively represent the global information in speech signals. To achieve this, we propose a general global time-frequency context modeling block for speaker modeling. First, an attention-based context model is introduced to capture the long-range and non-local relationship across different time-frequency locations. Second, a 2D-DCT based context model is proposed to improve model efficiency and examine the benefits of signal modeling. A multi-DCT attention mechanism is presented to improve modeling power with alternate DCT base forms. Finally, the global context information is used to recalibrate salient time-frequency locations by computing the similarity between the global context and local features. The proposed lightweight blocks can be easily incorporated into a speaker model with little additional computational costs. This effectively improves the speaker verification performance compared to the standard ResNet model and Squeeze&Excitation block by a large margin. Detailed ablation studies are also performed to analyze various factors that may impact performance of the proposed individual modules. Our experimental results show that the proposed global context modeling method can efficiently improve the learned speaker representations by achieving channel-wise and time-frequency feature recalibration.
CFSL: A Credible Federated Self-Learning Framework

Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, non-independent and identically distributed (Non-IID) characteristics of data affect the effectiveness of federated learning. Personalized federated learning can help resolve this, but it cannot adapt to unknown data. In addition, practical applications also call for trusted training environment and remain stable when there are security threats. In this article, we propose a credible federated self-learning (CFSL), based on the idea of hypernetwork supported by blockchain to achieve secured, credible, personalized federated self-learning, especially, for unknown data in Non-IID environment. Extensive experiments on three Non-IID data sets demonstrate the capabilities on adaptive resilience for security attacks and on accuracy of recognizing unknown objects, with good performance at the same time. CFSL outperforms the existing personalized federated learning methods, with an increase in average accuracy by 4.11%.
LKAT-GAN: A GAN for Thermal Infrared Image Colorization Based on Large Kernel and AttentionUNet-Transformer

Because thermal infrared (TIR) images are not affected by light and foggy environments, which are widely used in various night traffic scenarios. Especially, thermal infrared images also play an important role in autonomous vehicles. However, low contrast and lack of chromaticity have always been their problems. Image colorization is a vital technique to improve the quality of TIR images, which is beneficial to human interpretation and downstream tasks. Despite thermal infrared image colorization methods have been rapidly improved, the detail blurriness and color distortion in colorized images remain under-addressed. Mostly because these methods cannot effectively extract the ambiguous feature information of TIR images. Hence, we propose a large kernel (LK) U-Net and Attention_U-Net-Transformer (ViT-Based) based generative adversarial network. An LK_U-Net is designed to extract the feature of TIR images. Then, a branch structure composed of Attention_U-Net and ViT-Based can provide the network with semantic information from different perspectives to decode features. In addition, a composite loss function is employed to ensure the network generates a high-quality colorized image. The proposed method is evaluated on KAIST and IRVI datasets. Experimental results demonstrate the superiority of the proposed LKAT-GAN over other methods for the task of thermal infrared image colorization. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jinxinhuo/LKAT-GAN</uri> .
Probing correlations in two-mode bosonic fields via Gaussian noise channels

Gaussian noise channels arise naturally in many physical situations and play an important role in information transmission involving continuous-variable quantum systems. In this work we employ Gaussian noise channels to probe and characterize correlations in two-mode bosonic fields. Based on the fact that local channels often cause more decoherence for global states than for local states due to correlations, we introduce a notion of conditional coherence relative to a local channel, which is defined as the difference between the global and local decoherence caused by the channel. We discuss its connections with quantum discord and relative quantum Fisher information and propose a class of correlation quantifiers for two-mode bosonic states in terms of conditional coherence relative to local Gaussian noise channels. We prove that any nonproduct state exhibits such correlations and show that these correlation quantifiers are measures of total correlations, which may include both classical and quantum parts. We further illustrate these correlation quantifiers through several typical two-mode bosonic states and make a comparative study between these correlation quantifiers and other known ones such as entanglement and discord.