Reviewerly/exHarmony huggingface dataset:
Sensor-Aided Calibration of Relative Extrinsic Parameters for Outdoor Stereo Vision Systems

Calibration of the stereo vision systems is a crucial step for precise 3D measurements. Restricted by the outdoors’ large field of view (FOV), the conventional method based on precise calibration boards is not suitable since the calibration process is time consuming and the calibration accuracy is not guaranteed. In this paper, we propose a calibration method for estimating the extrinsic parameters of the stereo vision system aided by an inclinometer and a range sensor. Through the parameters given by the sensors, the initial rotation angle of the extrinsic parameters and the translation vector are pre-established by solving a set of linear equations. The metric scale of the translation vector is determined by the baseline length provided by the range sensor or GNSS signals. Finally, the optimal extrinsic parameters of the stereo vision systems are obtained by nonlinear optimization of inverse depth parameterization. The most significant advantage of this method is that it enhances the capability of the stereo vision measurement in the outdoor environment, and can achieve fast and accurate calibration results. Both simulation and outdoor experiments have verified the feasibility and correctness of this method, and the relative error in the outdoor large FOV was less than 0.3%. It shows that this calibration method is a feasible solution for outdoor measurements with a large FOV and long working distance.
Comparative of Viola-Jones and YOLO v3 for Face Detection in Real time

This Face detection is considering one of the important topics for recognizing human, it is the first step before the face recognition process, it is considered one of the biggest challenges in the field of vision computer. In recent years Many algorithms for detection have appeared, which depend on extracting the features of the human face, and works continue to develop them to this day. This paper aims to make a comparison between two of the most commonly face detection methods, Viola Jones (V_J) and YOLO v3. This comparison is made to determine which of the two algorithms is being most useful when used to detect faces in digital video. These algorithms are used in many applications, including image classification, medical analysis of image, and objects detection in real time (especially in surveillance cameras). Both algorithms are applied to detect faces in the real time video. The experimental results of a sample consists of 20 video frames show that V_J algorithm consumes less time in comparison with YOLO v3 algorithm, but its results are less accurate, unlike the YOLO v3 algorithm, which is slower in detect face with high accurate rate. Index Terms— Viola jones, Yolo v3, V_J algorithm, Accuracy rate.
Opinion mining of online product reviews using a lexicon-based algorithm

Worldwide social media is a rich resource of user-generated data, which can help organisations to formulate their business strategies, and affect the process of decision making in product or service design and implementation. The focus of this paper is on the extraction and analysis of unstructured product reviews for training predictive models, which recognise a specific range of human affective states such as emotions, moods, opinions, or attitudes. Based on the textual and reactions analysis, the emotional reactions lexicon of English words is built from the product posts and comments, and a lexicon-based algorithm is used to predict user opinions on social media.
Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis

As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Speaker adaptation techniques provide a powerful solution to customise automatic speech recognition (ASR) systems for individual users. Practical application of unsupervised model-based speaker adaptation techniques to data intensive end-to-end ASR systems is hindered by the scarcity of speaker-level data and performance sensitivity to transcription errors. To address these issues, a set of compact and data efficient speaker-dependent (SD) parameter representations are used to facilitate both speaker adaptive training and test-time unsupervised speaker adaptation of state-of-the-art Conformer ASR systems. The sensitivity to supervision quality is reduced using a confidence score-based selection of the less erroneous subset of speaker-level adaptation data. Two lightweight confidence score estimation modules are proposed to produce more reliable confidence scores. The data sparsity issue, which is exacerbated by data selection, is addressed by modelling the SD parameter uncertainty using Bayesian learning. Experiments on the benchmark 300-hour Switchboard and the 233-hour AMI datasets suggest that the proposed confidence score-based adaptation schemes consistently outperformed the baseline speaker-independent (SI) Conformer model and conventional non-Bayesian, point estimate-based adaptation using no speaker data selection. Similar consistent performance improvements were retained after external Transformer and LSTM language model rescoring. In particular, on the 300-hour Switchboard corpus, statistically significant WER reductions of 1.0%, 1.3%, and 1.4% absolute (9.5%, 10.9%, and 11.3% relative) were obtained over the baseline SI Conformer on the NIST Hub5'00, RT02, and RT03 evaluation sets respectively. Similar WER reductions of 2.7% and 3.3% absolute (8.9% and 10.2% relative) were also obtained on the AMI development and evaluation sets.
A Data Security Model for Mobile Ad Hoc Network Using Linear Function Mayfly Advanced Encryption Standard

Mobile Ad Hoc network (MANET) is a connection of mobile nodes that are joined together to communicate and share information using a wireless link.Some of the MANET in use include mobile smart phones, laptops, personal digital assistant (PDAs), among others.However, MANET has been known for the major challenge of being vulnerable to malicious attacks within the network. One of the techniques which have been used by several research works is the cryptographic approach using advanced encryption technique (AES). AES has been found suitable in the MANET domain because it does not take much space in mobile nodes which are known for their limited space resources. But one of the challenges facing AES which has not been given much attention is the optimal generation of its secret keys. So, therefore, this research work presents a symmetric cryptography technique by developing a model for the optimal generation of secret keys in AES using the linear function mayfly AES (LFM-AES) algorithm. The developed model was simulated in MATLAB 2020 programming environment. LFM-AES was compared with mayfly-AES, particle swarm optimization AES (PSO-AES) using encryption time, computational time, encryption throughput, and mean square error. The simulation results showed that LFM-AES has lower encryption, computational, mean square error, and higher encryption throughput. Keywords-- MANET, Data Security, Key Management, LFM-AES, Mayfly-AES, PSO-AES, AES
ElDet: An Anchor-Free General Ellipse Object Detector

Ellipse detection is a fundamental task in object shape analysis. Under complex environments, the traditional image processing based approaches may under-perform due to the hand-crated features. Instead, CNN-based approaches are more robust and powerful. In this paper, we introduce an efficient anchor-free data-augmentation based general ellipse detector, termed ElDet. Different from existing CNN-based methods, our ElDet relies more on edge information which could excavate more shape information into learning. Specifically, we first develop an edge fusion module to composite an overall edge map which has more complete boundary and better continuity. The edge map is treated as augmentation input for our ElDet for ellipse regression. Secondly, three loss functions are tailored to our ElDet, which are angle loss, IoU loss, and binary mask prediction loss to jointly improve the ellipse detection performance. Moreover, we contribute a diverse ellipse dataset by collecting multiple classes of elliptical objects in real scenes. Extensive experiments show that the proposed ellipse detector is very competitive to state-of-the-art methods.
An online teaching process monitoring method of MOOC platform based on video recognition

In order to overcome the problem of low monitoring accuracy of traditional methods, a new online teaching process monitoring method based on video recognition for MOOC platform is proposed. The weight coefficient of online teaching process monitoring information of MOOC platform is calculated, and the monitoring information is reconstructed. On this basis, video recognition method is used to mine the online teaching process monitoring information of MOOC platform. Through the construction of monitoring model, the online teaching process monitoring of MOOC platform is realised. The experimental results show that the mean square error analysis results of the monitoring method based on video recognition verify that the effect of different gender students is different. The method has high correction coefficient, high monitoring accuracy and good application effect, which can effectively improve the effect of students' online learning.
Artificial Intelligence Models for Blockchain-Based Intelligent Networks Systems

Blockchain is a decentralized architecture with built-in security to improve the trust and integrity of transactions. Blockchain can facilitate authentication and authorization without utilizing any trusted authority. There is no single authority that governs how the rules will be applied. Anyone is free to join the public blockchain network. Bitcoin is an example of public blockchain. Consortium blockchain is considered as partly decentralized architecture that can be open or private. Hyperledger and R3CEV are the examples of consortium blockchain. In private blockchain, nodes are restricted and have strict authority management on data access. Keeping in view of different blockchain technologies, this chapter has proposed four different applications of blockchain technology. The first one is detecting product genuineness using blockchain. The second is an application of blockchain in citizen participation. The third one is implementation of security in cloud computing using blockchain. The fourth is e-voting and event registration using blockchain technology.
Reevaluating Contour Visualizations for Power Systems Data

Effective visual analytics tools are needed now more than ever as emerging energy systems data and models are rapidly growing in scale and complexity. We examined the suitability of colored contour maps to visually represent bus values in two different power system models: a dense 24k-bus distribution system and a 240-bus transmission system. In a quantitative analysis, we found that contour maps misrepresent power systems data, changing the statistical dispersion of the bus values, including the loss of extreme values. In a controlled empirical study with thirty professional power system research engineers, we found that these distortions significantly impact excursion identification tasks. Additionally, the engineers were less confident in their assessments using contour-based visualizations than glyph-based visualizations.
YOLOv5-based weapon detection systems with data augmentation

Closed-Circuit Television (CCTV) cameras in public places have become more prominent with the rising firearm-related criminal activities, such as robberies, open firing, threats at gunpoint, etc. Early detection of firearms in surveillance systems is crucial for security and safety concerns. In this paper, we present a You Only Look Once (YOLOv5)-based weapon detection system that detects different types of weapons such as rifles, pistols, knives, etc. The main objective of this work is to show the impact of data augmentation on different types of datasets and make a detailed comparative examination with the existing baseline study and other similar works in the literature. The results give new insights to consider for weapon detection systems and object detection, in general. A crisp taxonomy of the existing state-of-the-art and object detection trends over the past decades is also presented in the paper.
GLaLT: Global-Local Attention-Augmented Light Transformer for Scene Text Recognition

Recent years have witnessed the growing popularity of connectionist temporal classification (CTC) and attention mechanism in scene text recognition (STR). CTC-based methods consume less time with few computational burdens, while they are not as effective as attention-based methods. To retain computational efficiency and effectiveness, we propose the global–local attention-augmented light Transformer (GLaLT), which adopts a Transformer-based encoder–decoder structure to orchestrate CTC and attention mechanism. The encoder integrates the self-attention module with the convolution module to augment the attention, where the self-attention module pays more attention to capturing long-term global dependencies and the convolution module focuses on local context modeling. The decoder consists of two parallel modules: one is the Transformer-decoder-based attention module and the other is the CTC module. The first one is removed in the testing phase and can guide the second one to extract robust features in the training phase. Extensive experiments on standard benchmarks demonstrate that GLaLT achieves state-of-the-art performance for both regular and irregular STR. In terms of tradeoffs, the proposed GLaLT is at or near the frontiers for maximizing speed, accuracy, and computational efficiency at the same time.
A Blockchain-Based Wide-Area Agricultural Machinery Resource Scheduling System

<b>Highlights</b> <list list-type=bullet><list-item> The proposed system aims to reduce the high cost of wide-area agricultural production caused by the uneven distribution of agricultural machinery resources in diï¬erent regions. </list-item><list-item> The proposed system solves the current problem that differences in business models in different regions make it hard to establish a centralized dispatching platform. It can also improve the utilization rate of agricultural machinery and solve the trust problem between the participants. </list-item><list-item> We implement our system on Hyperledger Fabric that meets the needs of all farmers in a wide area for agricultural machinery resources. </list-item></list> <b>Abstract.</b> At present, there are mainly two forms of agricultural machinery scheduling in China, intra-region scheduling and wide-area scheduling. The former can only schedule agricultural machinery within a region, while the latter can schedule agricultural machinery among multiple regions in the global scope to solve the problem of uneven regional distribution of agricultural machinery resources. However, the different business models in different regions make it hard to establish a centralized scheduling platform. The agricultural machinery resources of individual farmers cannot be fully utilized, and the parties involved in the scheduling lack trust. To address these issues, this article proposes a blockchain-based wide-area agricultural machinery resource scheduling system and agricultural machinery scheduling optimization model. In this article, a smart contract based on hybrid algorithm is deployed on the blockchain. The information of agricultural machinery resources is stored in the interplanetary file system (IPFS), and the returned addresses and scheduling matching results are stored on the blockchain. Then, the proposed system implements an authenticated access mechanism, and only authorized users can join the system. In addition, the data on the blockchain cannot be altered, which ensures the security of the data. Finally, the Hyperledger Fabric blockchain is used as the experimental platform to conduct performance experiments in two towns in China. The results indicate that the proposed method can reduce the cost of wide-area agricultural production and improve the utilization rate of agricultural machinery resources. This article also puts forward suggestions on the quantity of agricultural machinery to be purchased.
Polarization Guided HDR Reconstruction via Pixel-Wise Depolarization

Taking photos with digital cameras often accompanies saturated pixels due to their limited dynamic range, and it is far too ill-posed to restore them. Capturing multiple low dynamic range images with bracketed exposures can make the problem less ill-posed, however, it is prone to ghosting artifacts caused by spatial misalignment among images. A polarization camera can capture four spatially-aligned and temporally-synchronized polarized images with different polarizer angles in a single shot, which can be used for ghost-free high dynamic range (HDR) reconstruction. However, real-world scenarios are still challenging since existing polarization-based HDR reconstruction methods treat all pixels in the same manner and only utilize the spatially-variant exposures of the polarized images (without fully exploiting the degree of polarization (DoP) and the angle of polarization (AoP) of the incoming light to the sensor, which encode abundant structural and contextual information of the scene) to handle the problem still in an ill-posed manner. In this paper, we propose a pixel-wise depolarization strategy to solve the polarization guided HDR reconstruction problem, by classifying the pixels based on their levels of ill-posedness in HDR reconstruction procedure and applying different solutions to different classes. To utilize the strategy with better generalization ability and higher robustness, we propose a network-physics-hybrid polarization-based HDR reconstruction pipeline along with a neural network tailored to it, fully exploiting the DoP and AoP. Experimental results show that our approach achieves state-of-the-art performance on both synthetic and real-world images.
Quality-aware massive content delivery in digital twin-enabled edge networks

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication. However, various multimedia applications possess huge differences in terms of object oriented (i.e., machine or user) and corresponding quality evaluation metric, which will significantly impact the design of encoding or decoding within content delivery strategy. To get over this dilemma, we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision (QoD) or Quality-of-Experience (QoE) for the guidance of content delivery. Then, in terms of machine-centric communication, a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment, which can achieve a balance among decision-making, delivered content, and encoding latency. Finally, in terms of user-centric communication, by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams, we develop a QoE-driven video enhancement scheme to supply high data fidelity. Numerical results demonstrate the remarkable performance improvement of massive content delivery.
Effects of the anthropomorphic image of intelligent customer service avatars on consumers' willingness to interact after service failures

Purpose Intelligent customer service has started replacing human employees in providing services to customers in numerous industries. Based on the expectancy disconfirmation theory, this study explores how different types of anthropomorphic avatar images of the intelligent customer service would affect consumer responses such as the willingness to interact, in the context of a service failure. The underlying mechanism and boundary conditions are also examined. Design/methodology/approach Two experimental studies were conducted to investigate the effect of the anthropomorphic image of intelligent customer service on consumers' willingness to interact and the potential role of consumer expectation and disappointment, following a service failure (Study 1). The moderating effect of anthropomorphic type was also explored (Study 2). Findings In the context of a customer service failure, an anthropomorphized intelligent customer service avatar that appeared competent (vs. warm) induced higher customer disappointment. However, if the anthropomorphic avatar had a cartoon-like appearance, the effect of avatar image perception (competent vs. warm) on consumers' willingness to interact diminishes. Originality/value This research enriches and expands the literature on interactive marketing and artificial intelligence and provides practical guidance for companies to design or choose avatar images for intelligent customer service.