Reviewerly/exHarmony huggingface dataset:
Hybrid approach to implement multi‐robotic navigation system using neural network, fuzzy logic, and bio‐inspired optimization methodologies

Abstract Mobile robots have been increasingly popular in a variety of industries in recent years due to their ability to move in variable situations and perform routine jobs effectively. Path planning, without a dispute, performs a crucial part in multi‐robot navigation, making it one of the very foremost investigated issues in robotics. In recent times, meta‐heuristic strategies have been intensively investigated to tackle path planning issues in the similar way that optimizing issues were handled, or to design the optimal path for such multi‐robotics to travel from the initial point to such goal. The fundamental purpose of portable multi‐robot guidance is to navigate a mobile robot across a crowded area from initial point to target position while maintaining a safe route and creating optimum length for the path. Various strategies for robot navigational path planning were investigated by scientists in this field. This work seeks to discuss bio‐inspired methods that are exploited to optimize hybrid neuro‐fuzzy analysis which is the combination of neural network and fuzzy logic is optimized using the particle swarm optimization technique in real‐time scenarios. Several optimization approaches of bio‐inspired techniques are explained briefly. Its simulation findings, which are displayed for two simulated scenarios reveal that hybridization increases multi‐robot navigation accuracy in terms of navigation duration and length of the path.
Dual attention interactive fine-grained classification network based on data augmentation

The key to fine-grained image classification is to find discriminative regions. Most existing methods only use simple baseline networks or low-recognition attention modules to discover object differences, which will limit the model to finding discriminative regions hidden in images. This article proposes an effective method to solve this problem. The first is a novel layered training method, which uses a new training method to enhance the feature extraction ability of the baseline model. The second step focuses on key regions of the image based on improved long short-term memory (LSTM) and multi-head attention. In the third step, based on the feature map obtained by the dual attention network, spatial mapping is performed by a multi-layer perceptron (MLP). Then the element-by-element mutual multiplication calculation of the channel is performed to obtain a feature map with finer granularity. Finally, the CUB-200-2011, FGVC Aircraft, Stanford Cars, and MedMNIST v2 datasets achieved good performance. • Our method is convenient to obtain rich global image overall attributes. • We use the global attention of dual attention mechanism to lock the target area. • We employ interactive fusion of channels to distinguish similar images quickly.
Research on an Algorithm of Express Parcel Sorting Based on Deeper Learning and Multi-Information Recognition

With the development of smart logistics, current small distribution centers have begun to use intelligent equipment to indirectly read bar code information on courier sheets to carry out express sorting. However, limited by the cost, most of them choose relatively low-end sorting equipment in a warehouse environment that is complex. This single information identification method leads to a decline in the identification rate of sorting, affecting efficiency of the entire express sorting. Aimed at the above problems, an express recognition method based on deeper learning and multi-information fusion is proposed. The method is mainly aimed at bar code information and three segments of code information on the courier sheet, which is divided into two parts: target information detection and recognition. For the detection of target information, we used a method of deeper learning to detect the target, and to improve speed and precision we designed a target detection network based on the existing YOLOv4 network, Experiments show that the detection accuracy and speed of the redesigned target detection network were much improved. Next for recognition of two kinds of target information we first intercepted the image after positioning and used a ZBAR algorithm to decode the barcode image after interception. The we used Tesseract-OCR technology to identify the intercepted three segments code picture information, and finally output the information in the form of strings. This deeper learning-based multi-information identification method can help logistics centers to accurately obtain express sorting information from the database. The experimental results show that the time to detect a picture was 0.31 s, and the recognition accuracy was 98.5%, which has better robustness and accuracy than single barcode information positioning and recognition alone.
A novel deep learning approach for diagnosing Alzheimer's disease based on eye-tracking data

Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks.
Privacy-aware genetic algorithm based data security framework for distributed cloud storage

An increased use of data driven applications and integrated systems have caused an accelerating expansion in data volumes and increase in the number of digital records, over the past few decades. Exponentially growing data volumes being processed by large-scale distributed data-intensive applications have placed an increasing pressure on the underlying storage services for timely and efficient storage and retrieval of the data. The use of cloud storage is among the best strategies to efficiently store growing volumes of data. However, outsourcing data to public cloud storage leads to the challenge of data confidentiality preservation. Data Confidentiality is among the top challenges associated with cloud storage which have contributed substantially as an inhibitor for cloud computing adoption all over the world and is considered a serious concern, especially in case of big data, where securing data in a timely and accurate manner is an arduous task. Our study aims to contribute in anti-cybercrime by protecting the confidentiality of sensitive growing data. We enunciate an optimized confidentially preserving framework on distributed cloud storage, that works for growing data with time-efficiency and minimum memory usage. Our framework uses a merger of Genetic Algorithm (GA), parallel data distribution, and privacy-aware selective encryption techniques. The experiments and comparative analysis depict that our proposed framework outperforms others under consideration, in terms of execution time, memory usage and network throughput respectively.
Expression-tailored talking face generation with adaptive cross-modal weighting

The key of talking face generation is to synthesize the identity-preserving natural facial expressions with accurate audio-lip synchronization. To accomplish this, it requires to disentangle and fuse the latent features from multiple modalities, including the visual identity, facial expressions, and audio, etc. In this paper, we propose an end-to-end Expression-Tailored Generative Adversarial Network with Adaptive Cross-modal Weighting (ET-GAN-ACW). Different from previous talking face generation based on the identity image and audio, an expression video of arbitrary identity serves as the source in our system. On the one hand, multiple encoders are presented to disentangle the expression-tailored representation, audio-lip embedding, and face position localization in parallel. Additionally, instead of using a single image as the target identity, a multi-image identity encoder is proposed by exploring the different views of faces and merging them into a unified representation. These informative features from different modalities are then adaptively weighted and fused by the proposed Adaptive Cross-modal Weighting (ACW) mechanism. On the other hand, multiple discriminators are exploited to create the image-aware and video-aware realistic details, including a frame discriminator for the frame authenticity, and a spatial–temporal discriminator for the visual coherence of facial expression movements. Extensive quantitative evaluations on reconstruction error, identity preserving, expression retention, and audio-visual synchronization verify the superiority of our method. Qualitative results also demonstrate the effectiveness of our method in generating high-quality talking face videos.
Learned image compression with generalized octave convolution and cross-resolution parameter estimation

Recently, image compression approaches based on deep learning have gradually outperformed existing image compression standards including BPG and VVC intra coding. In particular, the application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utilized to effectively capture the spatial redundancy of the latent representations. However, the latent representations still contain some spatial correlations. In addition, these methods based on the context-adaptive entropy model cannot be accelerated in the decoding process by parallel computing devices, e.g. FPGA or GPU. To alleviate these limitations, we propose a learned multi-resolution image compression framework, which exploits the recently developed octave convolutions to factorize the latent representations into the high-resolution (HR) and low-resolution (LR) parts, similar to wavelet transform, which further improves the R-D performance. To speed up the decoding, our scheme does not use context-adaptive entropy model. Instead, we exploit an additional hyper layer including hyper encoder and hyper decoder to further remove the spatial redundancy of the latent representation. Moreover, the cross-resolution parameter estimation (CRPE) is introduced into the proposed framework to enhance the flow of information and further improve the rate-distortion performance. An additional information-fidelity loss is proposed to the total loss function to adjust the contribution of the LR part to the final bit stream. Experimental results show that our method separately reduces the decoding time by approximately 73.35 and 93.44 % compared with that of state-of-the-art learned image compression methods, and the R-D performance is still better than H.266/VVC(4:2:0) and some learning-based methods on both PSNR and MS-SSIM metrics across a wide bit rates.
Dual-granularity feature alignment for cross-modality person re-identification

RGB-infrared cross-modality person re-identification is a challenging image retrieval task due to the intra-class variations and cross-modality discrepancy. However, it is insufficient for existing methods to identify discriminative patterns with different granularities and align them across granularity and modalities in a synergistic manner. In this paper, we propose Dual-granularity Feature Alignment (DFA) approach for cross-modality re-ID, which accommodates dual-granularity feature extraction and cross-granularity feature alignment. More specifically, a feature extractor is introduced to generate discriminative patterns by dividing feature map into different granularities. Based on the above features, prototype learning is developed to assign different granularity features of the same identity to the same prototype and adaptively build the alignment across different granularities. To mine contextual relationships between training samples, a similarity inference is presented to enforce the similarity matrix closing to identity matrix. This allows model to find feature that conserves as much modality-shared information as possible while being least possible modality-specific information. Both modules work in a collaborative way to obtain the optimal intra-class compactness and inter-class separability for cross-modality image matching. Extensive experiments on several benchmarks demonstrate that our DAF surpasses the state-of-the-art methods. The code is available at https://github.com/PRIS-CV/DFA.
Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference

In power systems engineering, early faults detection, as well asa location in networks is a major challenge that results in damage and revenue to facilities and equipment, energy loss.The reason for the delay in detection is that most network operators rely on data/complaints supplied by customers without having a system in place to check and verify whether the information is misleading or veridical.This paper proposed the novel fault detection in power system operators for sub-station equipment by a convolutional neural network (CNN)for detecting and relegating blemish of the network by training voltage and power values by analyzing the circuit breakers of the network. Then, at that point, discrete wavelet change (DWT) has been coordinated with the preparation part to get qualities of voltage and current signs in numerous recurrence groups of the organization at the sub-stations and this will bean master arrangement of issue location. The fault locations have been done using a fuzzy interference-based fault location system (FI-FLS) of the network. By this fault detection and locator, the aberrant and mundane conditions of the networks has been relegated and located. The simulation results show feasibility, effectiveness, average precision. The fault located by this proposed method is line to ground, line to line and, phase fault predicted on the fault parameter values.The proposed approach attains 96 % average accuracy for the detection that shows the efficiency of the performance.
Drinking Gesture Detection Using Wrist-Worn IMU Sensors with Multi-Stage Temporal Convolutional Network in Free-Living Environments

Maintaining adequate hydration is important for health. Inadequate liquid intake can cause dehydration problems. Despite the increasing development of liquid intake monitoring, there are still open challenges in drinking detection under free-living conditions. This paper proposes an automatic liquid intake monitoring system comprised of wrist-worn Inertial Measurement Units (IMU s) to recognize drinking gesture in free-living environments. We build an end-to-end approach for drinking gesture detection by employing a novel multi-stage temporal convolutional network (MS-TCN). Two datasets are collected in this research, one contains 8.9 hours data from 13 participants in semi-controlled environments, the other one contains 45.2 hours data from 7 participants in free-living environments. The Leave-One-Subject-Out (LOSO) evaluation shows that this method achieves a segmental F1-score of 0.943 and 0.900 in the semi-controlled and free-living datasets, respectively. The results also indicate that our approach outperforms the convolutional neural network and long-short-term-memory network combined model (CNN-LSTM) on our datasets. The dataset used in this paper is available at https://github.com/Pituohai/drinking-gesture-dataset/. Clinical Relevance- This automatic liquid intake monitoring system can detect drinking gesture in daily life. It has the potential to be used to record the frequency of drinking water for at-risk elderly or patients in the hospital.
Addressing the Intra-class Mode Collapse Problem using Adaptive Input Image Normalization in GAN-based X-ray Images

Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the distribution of features present in the training imagery. Furthermore, the absence of diverse features in synthetic images can degrade the performance of machine learning classifiers. The mode collapse problem impacts Generative Adversarial Networks' capacity to generate diversified images. Mode collapse comes in two varieties: intra-class and inter-class. In this paper, the intra-class mode collapse problem is investigated, and its subsequent impact on the diversity of synthetic X-ray images is evaluated. This work contributes an empirical demonstration of the benefits of integrating the adaptive input-image normalization for the Deep Convolutional GAN to alleviate the intra-class mode collapse problem. Results demonstrate that the DCGAN with adaptive input-image normalization outperforms DCGAN with un-normalized X-ray images as evident by the superior diversity scores.
A critical review of formal analytical modeling for blockchain technology in production, operations, and supply chains: Harnessing progress for future potential

Blockchain technology use cases for supply chains—strategically and operationally—have been highlighted in practice and in research. In this respect, various applications of analytical models have been introduced to understand, analyze and make decisions related to blockchain phenomenon in production, operations, and supply chains. This work has received extensive—and some would argue unprecedentedly--rapid attention within the scholarly community. This early growth sets the stage for large-scale academic research and commercialization. Acknowledging this level of popularity, we provide a critical review of analytical models analyzing the current state-of-the-art research. This includes a synthesis of works that classify different research areas and general analytical model application within each research area. Bibliometric and network analysis tools help identify critical research contributions, key research areas, relationships among analytical models and research areas. Major findings include: (1) analytical models in this field are expanding rapidly; (2) the International Journal of Production Economics has been central to the analytical and production economic modeling research discourse; (3) ten research areas including antecedents, actions, scenarios and consequences are identified from a network analysis; and (4) the relationship between analytical models and research areas is evolving with insight into why and in which direction this work is likely to continue. We identify significant research gaps with great potential for scholarly advancement.
Tackling stain variability using CycleGAN-based stain augmentation

Considerable inter- and intra-laboratory stain variability exists in pathology, representing a challenge in development and application of deep learning (DL) approaches. Since tackling all sources of stain variability with manual annotation is not feasible, we here investigated and compared unsupervised DL approaches to reduce the consequences of stain variability in kidney pathology.We aimed to improve the applicability of a pretrained DL segmentation model to 3 external multi-centric cohorts with large stain variability. In contrast to the traditional approach of training generative adversarial networks (GAN) for stain normalization, we here propose to tackle stain variability by data augmentation. We augment the training data of the pretrained model by the stain variability using CycleGANs and then retrain the model on the stain-augmented dataset. We compared the performance of i/ the unmodified pretrained segmentation model with ii/ CycleGAN-based stain normalization, iii/ a feature-preserving modification to ii/ for improved normalization, and iv/ the proposed stain-augmented model.The proposed stain-augmented model showed highest mean segmentation accuracy in all external cohorts and maintained comparable performance on the training cohort. However, the increase in performance was only marginal compared to the pretrained model. CycleGAN-based stain normalization suffered from encoded imperceptible information into the normalizations that confused the pretrained model and thus resulted in slightly worse performance.Our findings suggest that stain variability can be tackled more effectively by augmenting data by it than by following the commonly used approach of normalizing the stain. However, the applicability of this approach providing only a rather slight performance increase has to be weighted against an additional carbon footprint.
Discriminative speaker embedding with serialized multi-layer multi-head attention

In this paper, a serialized multi-layer multi-head attention is proposed for extracting neural speaker embedding in text-independent speaker verification task. The majority of the recent approaches apply one attention layer to aggregate frame-level features. Inspired by the Transformer network, the proposed serialized attention contains a stack of self-attention layers. Unlike parallel multi-head attention, we propose to aggregate the attentive statistics in a serialized manner to generate the utterance-level embedding and it is propagated to the next layer by residual connection. We further propose an input-aware query for each utterance with the statistics pooling. To evaluate the quality of learned speaker embeddings, the proposed serialized attention mechanism is applied on two widely used neural speaker embedding architectures and validated on several benchmark datasets of various languages and acoustic conditions, including the VoxCeleb1, SITW, and CN-Celeb. Experimental results demonstrate the use of serialized attention can achieve better speaker verification performance.