Reviewerly/exHarmony huggingface dataset:
Non-iterative high-precision RNS scaling based on Core function

The Residue Number System (RNS) is known for fast, parallel, and hardware-efficient arithmetic (addition and multiplication) due to independent channels operating with small numbers. This makes RNS appealing for the hardware accelerators based on matrix multiplication or polynomial computation operations. However, non-modular operations (e.g. scaling, comparison, overflow detection) are inefficient in RNS, especially for arbitrary moduli sets. This limits RNS applicability in computing hardware accelerators due to a complexity of floating-point support. Published efficient division algorithms are imprecise or impose restrictions on moduli sets with a small number of moduli (e.g. {2k, 2k + 1, 2k − 1}), demonstrating inefficiency in multiply and accumulate operations, comparing with the moduli sets with a high number of moduli. This article proposes a novel scaling algorithm by arbitrary integer based on Core function for the numbers represented in RNS without any strict dependency on moduli set (algorithm is suitable for arbitrary moduli sets). It is proven that the absolute error of RNS number division is less than 1. The new algorithm allows to save up to 41% of hardware area and consume up to 31% less power, and it is also up to 15% faster than precise scaling method involving conversion to Binary Number System (BNS).
Fault Diagnosis of Avionics System Based on Improved CNN and BiLSTM

This paper presents a novel approach for diagnosing faults in aerospace systems by leveraging the power of Word2vec to transform text data into word vectors. Using improved Convolutional Neural Network(CNN) and bi-directional LSTM models to extract fault feature information from the input data, introducing an attention mechanism to focus more on key feature information in the data, and employing a dropout regularization method to prevent the model from being overfitted. Our model considers local features, as well as contextual information, which fully extracts the feature information of the text to classify the fault data. Compared with other methods, the proposed method outperforms other techniques and significantly reduces the manual dependence of fault classification, saves the troubleshooting time of maintenance personnel, and achieves the optimum in accuracy, recall and f1-score performance indicators, which demonstrates the effectiveness of our approach.
MOFC: Network Embedding Based on Multi-Orders Friends Contribution for User Identification

Different social networks are independent of each other, and identifying the same person in different social networks is of great significance for cross-network information dissemination and the construction of a complete portrait of users. Existing methods for user identification via network embedding ignore the different contributions of neighbors and only consider low-order friend relationships. Aiming at these shortcomings, an algorithm of Network Embedding based on Multi-Orders Friends Contribution for User Identification (MOFC) is proposed. Firstly, the difference contribution degree of nodes to their friends is used to assign different weights to the edges between the nodes and their friends. Secondly, the embedding representation vectors of multi-orders friend contribution are used to describe the different orders friend relations of the nodes, and then the representation vectors of different orders are merged to more comprehensively represent network features. Finally, the fusion representation vectors are used as input sample of a neural network to train and build a user identification model. Experiments show that the evaluation indicators of the model have been significantly improved compared with several advanced methods. Compared with the newer and better the CSN_LINE, the accuracy rate of MOFC can be increased by 4.83%, the precision rate can be increased by 6.77%, the recall rate can be increased by 5.49%, the F1-score can be increased by 5.43%.
Neural Future-Dependent Online Mirror Descent

In recent years, many neural-network-based iterative Nash Equilibrium Finding (NEF) algorithms have been proposed for solving large-scale Extensive-Form Imperfect-Information Games (EF-IIGs). These algorithms use neural networks to approximate the values and strategies that change at every iteration, which enables the application of the NEF algorithms in large-scale games. However, previous algorithms usually have to approximate some kind of values that accumulated at every iteration and suffer from the problem of accumulating approximation error as the iteration progresses. As a result, the algorithms are prone to premature convergence and resulting high-exploitability strategies. Additionally, due to the highly dynamic of the approximation target, it may require many training steps for a good approximation in every iteration. In this paper, we propose a new algorithm named Neural FD-OMD. Neural FD-OMD does not approximate cumulative variables, but the strategy at every iteration. So the approximation error is controlled in each iteration and will not propagate to the future iterations. More importantly, Neural FD-OMD limits strategy deviation between iterations. As a result, Neural FD-OMD can use much fewer training steps than previous algorithms to track strategy by fine-tuning the neural network. Experiments on multiple poker games show that Neural FD-OMD outperforms previous NEF algorithms significantly, especially in large-scale games.
LAGNet: Lightweight Attention Guided Network for Real-time Semantic Segmentation

In recent years, semantic segmentation has been used extensively in a variety of scenarios. It is essential for most practical applications that predictions are accurate and efficient. Toward this end, we present a novel lightweight attention guided semantic segmentation network (LAGNet) aiming for a balance between prediction accuracy and running efficiency. As a first step, we developed the Efficient Inter-attention Bottleneck Module (EIRM) in order to obtain local and contextual information at a lower cost of computation. We then present a novel Image Decomposition Attention Mechanism (IDAM) that refines the feature maps at various stage. Furthermore, we present a novel Decoder named Mutual-Attention Guided Decoder to promote the accuracy of the prediction results, which utilizes attention mechanism to recover the detailed information effectively. The results of extensive experiments on Cityscapes and Camvid datasets show that our model achieves 73.3% and 71.4% mIoU along with 110 and 105 frames per second on the Cityscapes and Camvid datasets, respectively.
Lightweight Power Engineering Audit Event Recognition Model Based on BERT

Given the limitations of mobile hardware resources for on-site power audits, it is often impractical to deploy large-parameter audit event recognition models. In response, a lightweight power engineering audit event recognition model based on BERT (Bidirectional Encoder Representation from Transformers) has been proposed. The dynamic vector representation capability of the model is utilized to optimize the initial vector representation of power engineering audit text. To mitigate the model's demand for mobile resources, a low-rank matrix is employed to optimize the feedforward network layer. Additionally, a sparse attention normalization layer is introduced to compress the Transformer Encoder layers of BERT. Finally, to enhance the model's recognition accuracy for different power audit events, the LoRA (Low Rank Adaptation) method is applied to fine-tune the feedforward neural network for various categories of power audit event samples. The process constructs recognition modules tailored to different power engineering audit events. These modules are combined with the compressed BERT, forming a lightweight power engineering audit event recognition model. The model can be deployed on mobile devices such as smartphones from the experimental results. The recognition accuracy reaches 96%, and there is a significant reduction in the model's storage space occupancy, coupled with an improvement in response speed. These findings indicate the feasibility and effectiveness of the proposed method.
I2PN: Improved Image Projection Network for OCTA image segmentation

Optical coherence tomography (OCT) is one of the most significant advances in medical images, and OCT segmentation is an important task in medical-assisted diagnostics. However, existing 3D processing methods require large computational resources. To alleviate this problem, we present an improved image projection network (I2PN), which is an end-to-end OCT image segmentation model. Our key insight is to build a simple projection module (SPM) that uses several group convolutions to conduct effective feature extraction and dimension reduction concurrently. By stacking multiple SPMs, the proposed network can process 3D OCTA data into a high-level semantic feature map. Afterward, the Down-Up modules (DUM) that contain down-sampling and up-sampling are applied to align the SPM features to the OCT image segmentation target. Finally, with a category-dependent segmentation head (CDS), which can balance region selection among hierarchical feature maps, the I2PN model can handle the OCTA image segmentation tasks simultaneously, such as retinal vessel and FAZ segmentation. I2PN provides a succinct idea for the quantification of retinal indicators. Extensive experiments on the OCTA-500 dataset validate the effectiveness of the proposed method I2PN.
A retina-inspired neural model for single image haze removal

The mammalian retina, a complex and highly specialized structure, serves a crucial role in transforming light into meaningful visual information. Drawing inspiration from the hierarchical processing of information in the retina’s neurons, we propose a multilayer retina-based convolutional neural model and apply this model to image dehazing. This model simulates the progression of information and functions from photoreceptor cells to bipolar cells in the retina. In the photoreceptor layer, we simulate cone neurons for color sensitivity and rod cells for brightness. Horizontal cells extract features from photoreceptor neurons and inhibit bipolar cells. Bipolar cells include on and off types, responding to increased and decreased light intensity, respectively. The amacrine cell layer excites on-bipolar cells while inhibiting off-bipolar cells. The ganglion cell layer simulates color-opponent mechanisms and disinhibitory effects. Finally, the combination of brightness and color information, along with spikes encoding, produces a haze-free image. This approach effectively reduces haze without estimating atmospheric light sources. The model’s validity is verified on two image de-hazing datasets: SOTS and D-Hazy, which encompass varying degrees of haze. Experimental results demonstrate that our proposed model has a comparable performance by comparing with the classical methods in de-hazing tasks.
Strengthening Emotion Recognition Algorithms: A Defense Mechanism against FGSM White-Box Attacks

Emotion recognition algorithms have been widely used in various domains. However, the security of these algorithms has not received sufficient attention for a considerable time. In this paper, we propose a more attack-resistant emotion recognition model that can better withstand FGSM white-box attacks. The proposed model utilizes SE-ResNet for image feature extraction and integrates the extracted information into transformer modules and global depth convolutional layers. The anti-interference ability of the model is further enhanced through knowledge distillation, resulting in smoother convergence. Experimental results demonstrate that our proposed model achieves 74.95% accuracy on the FER2013 dataset and exhibits a certain level of resistance against FGSM interference.
Leveraging ResNet CNN and XGBoost for Enhanced Bitcoin Price Forecasting

This research proposes a novel approach for forecasting cryptocurrency prices, specifically Bitcoin which dominates the market. Accurately predicting cryptocurrency values is challenging due to their highly volatile nature. The proposed hybrid model uses ResNet Convolutional Neural Network to encode Bitcoin price time series data into discriminative representations. These representations capture long-range dependencies using XGBoost regression. Additionally, wavelet denoising is applied to filter noise from the price data. The combined ResNet-XGBoost-Wavelet model achieves satisfactory results for Bitcoin price forecasting and has practical applications for developing quantitative trading strategies. While incorporating sentiment analysis and additional influencing factors could further improve predictions, this work presents a competitive approach for minimizing investment risks and maximizing profits in the complex domain of cryptocurrency markets.
Lightweight Infrared Object Detection Network based on Improved SSD

Object detection involves the identification and localization of objects and serves as the foundation for accurately perceiving various targets. Infrared imaging, known for its ability to perceive differences in the heat distribution of environments, has garnered attention in many fields. Similar to general object detection tasks, the goal of infrared image object detection is to identify objects of interest within the image. However, infrared images often present challenges due to the small size of the targets, limited features, and the ease that target information being confused with background information. To address this issue, this paper proposes an improved algorithm based on the SSD (Single Shot MultiBox Detector) algorithm. The algorithm modifies the size and quantity of anchor boxes at different levels within the SSD framework, making the model more inclined to select smaller targets. Additionally, we implement a series of lightweight modifications to the SSD model, reducing computational resources required for processing lower-resolution infrared images. Experimental results on two datasets demonstrate that this algorithm’s predictions are precise, with an average prediction accuracy reaching 80% and classification confidence level at 79%, surpassing the performance of existing object detection algorithms.
Distance Transform Guided Medical Image Segmentation With Channel Information Exchange

Medical image segmentation plays an important role in the computer-aided screening of many diseases. Different from popular deep learning-based segmentation methods that mainly focus on the whole target, recent studies pay more attention to target contour by making use of the distance transform of the object boundary. In this paper, to learn both the object content and the boundary information, we incorporate a regression task that estimates a distance map of target contour into a CNN-based segmentation network. The segmentation task and the regression task share most parts of the backbone network. Moreover, we introduce a channel information exchange module that dynamically exchanges feature maps between the segmentation and the regression tasks. The proposed information exchange module is parameter-free and controlled by the batch normalization scaling factor. In addition, the output feature maps of the two tasks are further integrated to provide fused segmentation results.We evaluate our method on three popular datasets, including the 2D ultrasound thyroid nodule segmentation dataset, the 3D MRI left atrial segmentation dataset and 2017 MM-WHS whole heart segmentation Challenge datasets. Experimental results show that our method achieves promising performance compared to the state-of-the-art approaches.
Anomaly detection method combining sample subspace distribution and generative models

In the high-dimensional space, data sparsity has great impacts on the effect of anomaly detection. The generative model can generate potential samples by receiving noise and learning the distribution of current samples, which provides a novel idea to address problems caused by data sparsity. However, the samples of a high-dimensional dataset seldom derive from a unique distribution, but usually a hybrid distribution with various components. This brings a challenge to address the problem of data sparsity by data generation. Therefore, an anomaly detection method that combines subspace distribution and generative models is to be explored. The Dirichlet Process Mixture Model (DPMM) is introduced to partition the original data space into subspaces, each of which has a unique distribution. Then, potential samples are generated for each subspace, depending on the confrontation of the generator and the discriminator of a Generative Adversarial Network (GAN). Such confrontation can help the discriminator to learn the boundary between the normal data and the abnormal. The outputs of the discriminators of GANs for different subspaces are selectively integrated to complete the anomaly detection, which can avoid mode collapse and improve the adaptability and accuracy of the model. The proposed method is evaluated on public datasets with different dimensions and sizes for anomaly detection. Experiments show that the proposed method improves the average accuracy by 1.15% to 5.664%, the average recall by 1.127% to 8.576%, and the average F1 score by 0.65% to 3.368% compared with existing methods.
A reference constructicon as a database

Abstract This article explores the importance of database architecture in building a reference constructicon. Several reference constructicons have been implemented for various languages (FrameNet Constructicon, English Constructicon, Japanese FrameNet Constructicon, German Constructicon, Swedish Constructicon, FrameNet Brasil Constructicon, Russian Constructicon). However, most constructicon projects do not provide explicit information about their implementation or the underlying data model. A recent reference constructicon project dealing mainly with the English language is CASA (Construction Grammar Approach to Syntactic Analysis). In this paper, we present the data model underlying CASA in detail and its current implementation as a relational database and web interface. Relational databases are widely used for many different applications and allow large amounts of data to be stored and managed in a structured and efficient way.
Unlocking Insights

The social media analytics, which relate to huge amount of data from various social media platforms, are used to understand an opinion from the written language such as tweets, chats, comments. In existing methods, the sentiment analysis on Xcorp (Twitter) was mostly used for emotion detection for the polling methods; and star ratings are used to see the response from people. In this model, using Twitter API to fetch the data and Naive Bayes model for classifying them. The tweets, retweets and comments are collected and processed with the positive, neutral, and negative responses from the user will be reflected for ethical considerations. The information obtained from this system is used in various applications like analysis of social media support for politicians, safety technology in social media, a review based on user response for a product, response from people or government for social or political issues (Hashtags), movies, etc. The system will help to visualize statistics to analyse people's responses to provide the most effective statistical tool for various industries.
AI-Based Smart Surveillance System

Due to the expanding requirement for security, intelligence-based savvy observation was taken into consideration. Observation frameworks are outlined to detect videos, pictures, sound, or any sort of reconnaissance information without human intercession. Later progress in computer vision, mechanical autonomy, and machine learning have played a noteworthy part in the selection of shrewd frameworks. The framework proposed here will automatically react based on what comes about amid preparation in different modes. This chapter aims to supply an outline of AI-based insights and their capabilities. This chapter also incorporates data around most forms of intelligence-based surveillance frameworks such as following, location, and dissemination of objects and perception behavior.
Analysis of Crime Against Bharat Girls and Women

With increase in the technology there is increase in the crimes against girls and women. No matter how much Bharat is growing in terms of economy, there is vast growth in sexual assault. So, there is a necessity to analyse this aspect also. The crime against girls and women reported from 2017 to 2021 for various states of Bharat country has been retrieved from the government port and used for the analyses of this research chapter. The data is represented keeping view in every criterion which includes rape, kidnapping, abduction, death due to dowry, insult, and many more. Graphical representation of the data becomes easy to under real time situation of the country. Using this chapter, the authors want to attract the Government of Bharat to understand the situation in the country and take appropriate action in this field.
Mobile Apps for Personal Safety of Women Using Blockchain Technology

The safety and security of women in today's society is of paramount concern, given the myriad of challenges and threats they face. In response to these pressing issues, this chapter explores the convergence of technology and social welfare through the lens of “Mobile Apps Using Blockchain for Women's Safety.” The chapter commences by illuminating the gravity of the problem, emphasizing the need for innovative solutions. It delves into the fundamental aspects of blockchain technology, elucidating its decentralized, transparent, and secure nature, highlighting why it is aptly suited to address women's safety concerns.
Object Detection Using Scalable Feature Maps in Remote Sensing Images

In recent years, as deep learning-based general object detection has undergone continuous advancement, remote sensing object detection has become a highly concerned computer vision task. Unlike ordinary images, remote sensing images are characterized by the prevalence of small objects and complex background. Therefore, directly applying general object detection frameworks to remote sensing images often fails to achieve satisfactory detection performance. In this paper, we introduce an object detector using scalable feature maps (SCFDet) for remote sensing object detection, which comprises two integral modules: the feature refusion enhancement module (FREM) and the feature resolution rebuilding module (FRBM). The FREM utilizes different feature fusion approaches to generate enhanced features tailored to the specific requirements of classification and localization tasks. The enhanced feature map fed into the classification branch (with a size reduced to one-sixteenth of the original) leverages the larger receptive field of higher-level features to obtain relevant contextual information, assisting in classification. Meanwhile, the enhanced feature map fed into the localization branch (with the same size as the original) fuses lower-level features to acquire more fine-grained details, assisting in regression. The FRBM enlarges the low-resolution feature map in the localization branch back to its original size before obtaining the final classification results. The experiments demonstrate that compared to other remote sensing object detectors, our proposed SFMDet exhibits excellent performance on the DOTA-v1.0 dataset.