Reviewerly/exHarmony huggingface dataset:
Dynamic Template Update Mechanism for Visual Object Tracking

Given the requirements for robust target classification and accurate target state estimation in visual tracking, SiamFC++ proposes a set of practical guidelines for designing high-performance general-purpose trackers by considering the special nature of visual tracking problems. Inspired by dynamic modules, We propose an empirical method for integrating a dynamic module into the image input, which is concatenated with the template module after feature maps are extracted by the backbone network. Since the position and shape of the object can change significantly within a video sequence, the added dynamic module can better focus on the target region of the feature map to obtain better similarity maps. Extensive experiments and comparisons demonstrate that our simple and effective method achieves reliable results on the benchmarks of LaSOT, TrackingNet, and GOT-10K and provides a significant speed advantage in real-time.
How slow is media stream: A survey of latency difference between media stream and text stream

Media data, including voice, pictures and videos, can offer a better sensory experience and more vivid information to users than text data. Supposing you are watching NBA finals on the spot and one of your friends is also watching that match through the internet. Then a beautiful dunk lit up the atmosphere of the whole court and you send this information to your friend. Did he see the dunk when he received the message? Probably not! That is the latency difference between real-time video and text data causing by transmission. Although the existing works measured the delay of different video live stream in various network environments and many works are devoted to the reducing the latency of video transmission. The transmission latency difference between video stream and text stream is still significant. In this paper, we conduct the first in-depth measurement of latency difference between real-time video stream and text stream on NBA games. We use the screenshot to record the time-board of both video data and text data at each sampling time. Then, the character recognition tool is used to write these data into excel file for evaluation. Finally, the latency difference is evaluated among different live source. Experiment results show that, the transmission latency difference between video stream and text stream is still significant which leads to the understanding that the transmission delay of streaming media needs to be further improved.
Image KeyPoints Detector Based On Transformer Of Wavelet Transform

Aiming at the problem of extracting keypoints from sparse feature scenes in computer vision, this paper presents a method based on deep learning. which can still detect the keypoints with repeatability and high matching score in the picture when the features are relatively rare. Compared with the traditional neural network supervised by Handcrafted keypoints, we design a self-supervised network structure, which can be trained only by the homologous matrix, and can generate keypoints and corresponding descriptors. We introduce such as wavelet transform, cell boundary drift and attention mechanism in Transformer to help us improve the repeatability of the interest point detector and descriptor matching performance. Our model compared with traditional Handcrafted detector such as ORB, SURF, BRISK, SIFT, and deep learning methods such as SuperPoint and UnsuperPoint, our method has greatly improved the comprehensive performance.
Research and comparison of lightweight U-Net based on GhostNets for medical and remote sensing images

U-Net is widely lightened to achieve fast semantic segmentation of medical images and remote sensing images. The cheap operation of the GhostNet series provides new lightening ideas. Currently, the GhostNet series has three versions, GhostNet, G-GhostNet, and GhostNetV2, each of which has its own advantages. In this paper, we realize the lightweight improvement of U-Net with the help of the GhostNet series to provide a reference for the lightweight research of U-Net. First, the backbone parts of GhostNet, G-GhostNet, and GhostNetV2 were used as the encoder embedding model of U-Net in turn, and the convolutional operation of U-Net is replaced by their unitary module. Second, the three models were trained and tested with medical public dataset and remote sensing image dataset to obtain various performance scores of the models. Finally, the scores were compared. The experimental results show that the G-GhostNet-based lightweight model is the most efficient in the remote sensing image segmentation task and achieves real-time segmentation. While in the medical imaging segmentation task, the GhostNet-based lightweight model is relatively faster and the segmentation accuracy is much higher than that of the G-GhostNet-based model, and the model is more efficient. GhostNetV2 is not dominant in both segmentation tasks.
Semantic segmentation of UAV remote sensing images based on improved U-Net

Semantic segmentation of unmanned aerial vehicle (UAV) remote sensing images has been widely used in various fields of remote sensing; however, manual visual interpretation methods are inefficient and highly dependent on ground investigation and a priori knowledge, which consume huge human and material resources. Therefore, we propose a deep convolutional neural network based on improved U-Net to solve the semantic segmentation problem of UAV remote sensing images. The network is a U-shaped structure with a symmetric encoder and decoder. We conducted experiments on the UAV remote sensing dataset Aeroscapes, and compared it with several other popular semantic segmentation networks, our method achieved significant improvement in segmentation accuracy.
A Joint Framework based on PU Learning and Active Learning for Object Detection

The problem of object detection is a critical issue in the field of image processing, and the quality of labels can significantly affect the performance of detectors. In recent years, many deep learning frameworks have achieved good detection results. However, in many practical scenarios of object detection, collecting complete annotation is a time-consuming and costly process. Therefore, how to use partially labeled samples to solve the object detection problem has attracted the attention of many researchers. We proposed a new joint training framework based on PU (Positive-Unlabeled) learning and active learning to solve the above problem, named the PU_AL learning method. This framework is based on the Faster RCNN two-stage detector, and we introduce the PU learning method to solve the problem of incomplete annotations, adapting the traditional semi-supervised learning problem to a PU learning problem. We also introduce the loss branch prediction method based on Active Learning to improve the selective labeling method of sample selection. Experiment results demonstrate that the proposed framework outperforms the baseline methods in varying degrees of missing annotation.
Efficient Byzantine Consensus Algorithm in IoT Scenarios

This article proposes a layered blockchain architecture based on partitioned IoT to reduce the scalability requirements of the Internet of Things for blockchain. In addition, in response to the problem of low performance of traditional Byzantine fault-tolerant consensus algorithms such as PBFT in IoT scenarios, a parallel block verification Byzantine consensus algorithm (PVBFT) is proposed based on a layered blockchain architecture based on partitioned IoT. This algorithm improves the VRF lottery algorithm to select consensus nodes quickly, replaces single chain storage structure with DAG structure, introduces coordination nodes, and fully utilizes the high concurrency of DAG. We have implemented multi-node collision-free block packaging and multi-block parallel verification, using aggregation signature to reduce communication costs during the Byzantine consensus process and effectively improve the throughput of the consensus algorithm. The experimental results show that compared with the PBFT consensus algorithm, PVBFT has lower communication overhead and higher performance.
Design and Implementation of 3D CNN Algorithm Based on FPGA

With the deepening of CNN algorithm network model, the effective implementation of 3D CNN algorithm has become a challenge. In this paper, a design architecture of 3D CNN algorithm based on software and hardware collaboration is proposed, and its feasibility is verified by simulation. A series of comparison experiments show that the proposed method reduces unit power consumption by 0.75um, and the computing time of 6-layer and 8-layer convolution is reduced by 8276ms and 161761ms, respectively, compared with CPU. Therefore, on the basis of making full use of resources and ensuring accuracy, the proposed method realizes the optimization of power consumption and running speed while ensuring accuracy.
Retrieval of small and occluded objects in road scenes based on improved YOLOv5 algorithm

The traffic volume in road traffic affects our travel and safety. Maintaining smooth traffic and real-time monitoring and unblocking of congested vehicles during peak hours is crucial for our daily lives. Most of the objects in existing traffic road scenes are occluded and mostly small objects. This paper proposes an improved YOLOv5 method for road scenes with certain occlusion and small objects and applies it to video object retrieval. Firstly, the SimAM attention mechanism is introduced in the YOLOv5 header to improve the detection accuracy of small objects without introducing additional parameters. In addition, using DIoU_ NMS replaces NMS as a predictive frame filtering algorithm to improve the problem of detecting object loss in the case of multiple overlapping objects. Then, the improved YOLOv5 model is introduced with structures such as Ghost and RepVGG to achieve the effect of a lightweight model. Finally, this article proposes to combine the improved YOLOv5 model with CLIP for video object retrieval, expanding the alignment ability between text description and the video frame where the object is located to the region level. The experimental results on the VisDrone2019 dataset show that the mAP0.5 of the improved YOLOv5 is 37.3%, which is 4% higher than the original YOLOv5. The lightweight model reduces the number of parameters by 43.5% and FLOPs by 51.7% while maintaining almost the same accuracy. The visualization results show that the combination of YOLOv5 and CLIP binding energy better retrieves the object in the road video.
A Hybrid Model for Random Drift Compensation of 3-axis MEMS Gyroscope

In this paper, a hybrid method of random drift compensation is proposed for 3-axis MEMS gyroscope based on auto regressive (AR) model and long short-term memory neural networks (LSTM). AR was used to model the linear portion of the random drift data, while the LSTM model was chosen to process the nonlinear data in the gyro. According to experimental findings, the hybrid model has better noise reduction after filtering than it did before, and it can successfully reduce drift error by up to 87%.
A Domain Generalization Model Based on Multilevel Domain Adversarial Invariant Representation Learning

Due to the influence of various factors such as cross-scene shooting height, shooting time, imaging angle of view, weather, etc., the performance of the defect detection model degrades in cross-scene situations. In this paper, we proposed a domain generalization model based on multilevel domain adversarial invariant representation learning, which included the designed defect space feature and channel feature enhancement modules. Among them, the defect space feature enhancement module uses the shallow feature invariance score to guide the model to extract cross-scene invariant representations, and at the same time, design and use the defect importance information in the shallow features to enhance the representation of defects in the shallow features. The channel feature enhancement module assigns learning weights to different channels of each sample according to the similarity between the sample and the whole, and the importance of different feature channels to defects, so as to better guide the model to extract cross-scene invariant representations. The experimental verification on the photovoltaic cell data set collected in outdoor natural scenes shows that, proposed model has stronger generalization ability than existed domain generalization methods.
Transformer-based Siamese Neural Network Face Verification Method

Face verification, as a key part of person intelligence tracing, aims to determine whether the same person is in two face photos, which can be approximately transformed into a binary classification problem. In this paper, the siamese neural network architecture is adopted as the overall structure of the method, and the traditional bacbone network generally uses a convolutional neural network, this paper will use Vision Transformer as the backbone network as the method. Compared with convolutional neural network, its multi-headed attention mechanism can better focus on the larger sensory field and extract face features, which can then better perform face verification, while Binary cross-entropy is used as the loss function. After the validation of the private dataset, the model can recognize faces well and has excellent validation effect, which is of great value for the field of human intelligence tracing.
Face micro-expression recognition algorithm based on ResNet depth model

Micro Expression (ME) is the subtle facial expressions that people show when they express their inner feelings. To address the problem that micro-expression recognition is difficult and less accurate due to the small number of samples and uneven distribution of different categories, we propose a model framework to improve the accuracy of micro-expression recognition. The peak frames containing more key expression information in the micro-expression video sequences are extracted; SE-ResNeXt-50, an improved residual network with SE module, is used to extract features from the peak frames of micro-expressions, where the SE module can better learn the key information in the features, and ResNeXt simplifies the structure by replacing the dense structure with the sparse structure through group convolution, which improves the recognition efficiency. The recognition efficiency is improved by replacing the dense structure with the sparse structure by group convolution. At the same time, the Focal Loss loss function can better solve the model performance problem caused by the imbalance of micro-expression data. Simulation experiments are conducted on the micro-expression dataset CASMEⅡ, and it is found that the improved residual network and peak frame improve the accuracy and F1 value of micro-expression recognition. The improved residual network and peak frame can reduce the effect of small data set, make the model have good fitting effect, and improve the performance of different categories, improve the recognition accuracy of micro-expressions, and have better recognition performance for micro-expression recognition.
AlignFuse: Multi-feature Alignment and Fusion for Medical Image Segmentation

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained.
A Novel Phase Unwrapping Method Based on a Quality Map and Local Polynomial Surface Fitting with Applications in MR Temperature Imaging

This study aims to develop a novel, fast, and robust 2D phase unwrapping method for MR temperature imaging. The proposed method performs fully automatic unwrapping using a pseudocorrelation map as a quality map, then performs the unwrapping operation following a noncontinuous path, and finally corrects the corrupted pixels with local polynomial surface fitting. The proposed method was evaluated on simulation data and in vivo brain data to test the performance and was compared with the PRELUDE algorithm. The proposed algorithm is demonstrated to be robust even in areas with substantial noise and can meet the requirements of real-time temperature imaging.
A Survey on Deep Learning Models to Detect Hate Speech and Bullying in Social Media

Any statement that is vituperative towards an individual or a group based on their traits like race, ethnicity, gender, sexual orientation, color, religion, nationality, or another attribute is described as hate speech. Hate speech and bullying, spreading uncontrolled might undermine society’s peace and harmony, becoming a societal issue. Especially when hate speech is used to hurt people or to hurt the respect of individuals, groups, or countries. This complicates the task since social media posts contain paralinguistic tools (e.g., emoticons and hash tags) and a lot of poor quality written text that does not follow grammatical norms. With the recent advancements in NLP, it is possible to analyze unstructured composite natural language content. The chapter first focuses on discussing various deep learning architectures such as DCNNs, Bi- LSTMs, Transformers and models like BERT and how they are applied in identifying hate speech in social media. The chapter examines the capacity of deep learning algorithms to capture hate speech on public media systematically. The chapter also reviews the accuracy of models on publicly available standard datasets. The findings of this study pave the way for more research into the discovery of spontaneous abusive conduct on social media in the future.
Techniques to Identify Image Objects Under Adverse Environmental Conditions: A Systematic Literature Review

For traffic and security surveillance, moving object detection and segmentation are critical. Detecting moving objects in dynamic environments is more difficult than it is in static environments. In this paper, all the research articles published between 2011 and 2022 in IEEE Xplore, ScienceDirect conferences, and various journals were referenced for a systematic review on identifying different objects from images/videos taken under adverse environmental conditions. We used different tags and keywords to search for papers on the topic under study. All the papers were studied, the proposed techniques were analyzed, and information was gathered. On the basis of this analysis, we present some future prospects for the area under study. We also present a survey of various techniques proposed by various researchers to detect moving objects under various environmental conditions over a period of time.
Automation Hosting

Choosing where your automation is going to run is a critical decision which affects the speed, cost, and availability for your backend processes.
Text Summarization for Big Data Analytics: A Comprehensive Review of GPT 2 and BERT Approaches

The goal of approaches to automatic text summarization is to construct summaries while extracting the essential information from one or more input texts. Large models could be trained thanks to the ability to examine text non-sequentially, which led to the Transformer becoming the most well-known NLP model. Big data and associated methodologies are frequently used to handle and alter these massive volumes of information. This chapter looks at large data methodologies and method such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer 2 (GPT 2) models for multi-document summarization. The Transformer, BERT and GPT and GPT 2 models in text summarization give very close results in terms of accuracy and they need to be compared to give a model that performs better. In this chapter, the two models have been compared and our results have shown that BERT performs better than GPT 2. This is found based on the results given by ROUGE metrics on a news article dataset containing 100 text files to summarize.
AI-Based Chatbot Agents as Drivers of Purchase Intentions: An Interdisciplinary Study

The area of e-marketing can benefit from the usage of digital technology like chatbots. This study aimed to determine the impact of chatbots on customers’ purchase intentions. An empirical study was carried out on the impact of chatbot agent’s informational support, Emotional Credibility, and trust on purchasing intentions. The data was collected through an online survey from 223 Delhi-NCR customers who use chatbots while making online purchases. PLS-SEM was used to analyze the data that was collected. The results of structural equation modeling (SEM) showed a significant impact on informational support, emotional credibility, and trust of chatbots on purchase intentions of customers. The results of the study can be used as guidance by marketers to achieve a competitive edge in the changing business environment. The findings of the present study will encourage marketers to use technologies such as chatbots and help customers to get information. The marketers are encouraged to utilize and monitor chatbots efficiently and effectively. This study intends to contribute to the field of e-marketing.
A Study of Cloud-Based Solution for Data Analytics

In this new age of cutting-edge computerized advancements, a gigantic measure of information is being produced quickly from varying backgrounds. All the industries dealing with large and complex datasets have been facing the need to deal with enormous information being generated/delivered by different sources. This humongous data exists in all forms, for example, structured, unstructured, and semi-structured, which are produced through various sources, such as IOT devices, sensors, social media data, imaging, and geographical data, from day-to-day trials and experiments. This humongous and distinguishable plethora of data accumulated using different data sources needs to be accumulated, studied, and analyzed through statistical tools to perform smarter predictions and analysis. With the introduction of big data technologies, cloud computing, and different types of data analytics technique, it now became easier to combine real-world data and data generated from scientific experiments to extract meaningful insights and use them in real-world scenarios. The cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP) can be used for data analytics in different industries. Cloud computing services from AWS and GCP are used for collecting, processing, storing, and mining of various types of data. This chapter studies the provisioning and usage of cloud-based architecture of AWS and GCP for building a data analytics platform.