Reviewerly/exHarmony huggingface dataset:
No-Reference Video Quality Assessment Using Local Structural and Quality-Aware Deep Features

Due to the growing demand for high-quality video services in 4G and 5G applications, measuring the quantitative quality of video services is expected to become a major vital task. The no-reference video quality assessment (NR-VQA) work published so far regresses computationally complex statistical transforms or convolutional neural network (CNN) features to predict a quality score. In this paper, we propose a novel NR-VQA scheme using systematic sampling of spatiotemporal planes (XY, XT, and YT) based on the high standard deviation (σ) of their high-frequency bands to represent distortion. Human visual system (HVS) is highly sensitive to structural information in visual scenes, and distortions disrupt the structural properties. The proposed scheme encodes two-level, three-dimensional structural video information using novel Local Spatiotemporal Tetra Patterns (LSTP) on the sampled highest σ planes from each block of planes. Besides, we extract quality-aware deep features from the second highest σ sampled video frames (XY-spatial) from each block using a fine-tuned CNN model. The extracted LSTP and deep quality-aware features of the two highest σ frames are average pooled and concatenated with the top hundred σ values of other frames to form video-level final features. Finally, the concatenated features are fed to a support vector regressor (SVR) to predict the perceptual quality scores of test videos. The proposed method is evaluated on ten publicly available standard exhaustive VQA databases containing synthetic, authentic, and mixed distortions. Comprehensive, robust, and extensive experiments indicate that the proposed model outperforms all the state-of-the-art VQA models and is consistent with human subjective assessment.
Incremental Dense Reconstruction From Monocular Video With Guided Sparse Feature Volume Fusion

Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently been shown to enable efficient and accurate incremental dense reconstruction without the need to first estimate depth, but they are not able to achieve as high of a resolution as depth-based methods due to the large memory consumption of high-resolution feature volumes. This letter proposes a real-time feature volume-based dense reconstruction method that predicts TSDF (Truncated Signed Distance Function) values from a novel sparsified deep feature volume, which is able to achieve higher resolutions than previous feature volume-based methods, and is favorable in large-scale outdoor scenarios where the majority of voxels are empty. An uncertainty-aware multi-view stereo (MVS) network is leveraged to infer initial voxel locations of the physical surface in a sparse feature volume. Then for refining the recovered 3D geometry, deep features are attentively aggregated from multiview images at potential surface locations, and temporally fused. Besides achieving higher resolutions than before, our method is shown to produce more complete reconstructions with finer detail in many cases. Extensive evaluations on both public and self-collected datasets demonstrate a very competitive real-time reconstruction result for our method compared to state-of-the-art reconstruction methods in both indoor and outdoor settings.
Large-Scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation

Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed texture mapping algorithm must be able to efficiently select views, fuse and map textures from these views to mesh models, at the same time, achieve consistent radiometry over the entire model. Existing approaches achieve efficiency either by limiting the number of images to one view per face, or simplifying global inferences to only achieve local color consistency. In this paper, we break this tie by proposing a novel and efficient texture mapping framework that allows the use of multiple views of texture per face, at the same time to achieve global color consistency. The proposed method leverages a loopy belief propagation algorithm to perform an efficient and global-level probabilistic inferences to rank candidate views per face, which enables face-level multi-view texture fusion and blending. The texture fusion algorithm, being non-parametric, brings another advantage over typical parametric post color correction methods, due to its improved robustness to non-linear illumination differences. The experiments on three different types of datasets (i.e. satellite dataset, unmanned-aerial vehicle dataset and close-range dataset) show that the proposed method has produced visually pleasant and texturally consistent results in all scenarios, with an added advantage of consuming less running time as compared to the state of the art methods, especially for large-scale dataset such as satellite-derived models.
Roth–Lempel NMDS Codes of Non-Elliptic-Curve Type

The defect of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$[n,k,d]$ </tex-math></inline-formula> linear code is defined as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s({\mathcal{ C}})=n-k+1-d$ </tex-math></inline-formula> . Codes with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s({\mathcal{ C}})=0$ </tex-math></inline-formula> are called maximum distance separable (MDS), while codes with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$s({\mathcal{ C}})=s({\mathcal{ C}}^{\perp})=1$ </tex-math></inline-formula> are called near maximum distance separable (NMDS). NMDS codes correspond to interesting objects in finite geometry and have nice applications in combinatorics and cryptography. There have been many constructions of NMDS codes, but most of them are focus on fixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> , except for constructions from elliptic curves. Roth and Lempel (IEEE Trans. Inf. Theory 1989) constructed a type of linear codes (referred as Roth-Lempel codes), and presented the necessary and sufficient conditions of Roth-Lempel code to be MDS. Especially, they pointed out that the resultant MDS codes is not linearly equivalent to Reed-Solomn codes. In this paper, the NMDS properties of Roth-Lempel codes will be analyzed. We also obtain the necessary and sufficient condition of Roth-Lempel codes to be NMDS, and further completely determine the weight distributions of Roth-Lempel codes with length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q+2$ </tex-math></inline-formula> and dimension <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\leq k\leq q$ </tex-math></inline-formula> . Besides, by analyzing the upper bound for the code lengths of elliptic curve MDS codes, we illustrate the linearly inequivalence of Roth-Lempel NMDS codes and elliptic curve NMDS codes when their corresponding code lengths exceed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4(q+2\sqrt {q}+1)/5+1$ </tex-math></inline-formula> .
Online Progressive Instance-Balanced Sampling for Weakly Supervised Vibration Damper Detection

The detection of vibration dampers is important for power systems. Deep learning-based damper detection needs massive annotations, which are labour-intensive and time-consuming. Therefore, weakly supervised object detection is considered. Part domination is the main problem in weakly supervised vibration damper detection. Current weakly supervised object detection methods neglect that overwhelming negative instances exist in each image during the training phase, which would mislead the training and make detection results stuck in the most discriminative parts of objects. To tackle this problem, an online progressive instance-balanced sampling (OPIS) algorithm based on hard sampling and soft sampling is proposed in this paper. The algorithm includes two modules: a progressive instance balance (PIB) module and a progressive instance reweighting (PIR) module. The PIB module, combining random sampling and IoU-balanced sampling, progressively mines hard negative instances while balancing positive instances and negative instances. The PIR module further utilizes classifier scores and IoUs of adjacent refinements to reweight the weights of positive instances to make the network focus on positive instances. Extensive experimental results on the vibration damper, PASCAL VOC 2007, and PASCAL VOC 2012 datasets demonstrate that the proposed method can significantly improve the baseline, which is also comparable to many existing methods. In addition, compared to the baseline, the proposed method requires no extra network parameters, and the supplementary training overheads are small.
Jointly Learning Structure for Human Pose Estimation using Convolutional Neural Networks

The estimation of human poses has received a great deal of attention in the study of human activity recognition and for many applications. However, it is a challenging problem due to complex background, appearance variations of the poses, self occlusion, object occlusions, low resolutions and illuminations of the images. To overcome the limitations and enhance the performance, a model is introduced for estimating the pose of human by taking advantage from the deep convolutional neural networks. This model is built on a parsing strategy called bottom-up that provides features for extracting skeletal keypoints and the keypoints association vector field using non-parametric description. The refined prediction from multiple stages causes further improvement of the localized keypoints accuracy. This proposed model is trained and tested on the benchmark MPII Human Pose dataset. The proposed model exceeds the current state-of-the-art algorithm in terms of accuracy. Moreover, it detects some occluded keypoints by incorporating occlusion network in feature representation process of the keyjoints resulting in 89.0% Mean Average Precision.
Prediction Approach of Breast Cancer using Dimensionality Reduction and Outlier Detection

Breast cancer has become an alarming issue throughout the world. Like other cancers, in breast cancer, the cell loses the ability to keep dividing in a controlled way and produces more cells similar to it and form tumors. Machine learning has a huge impact on prediction of diseases accurately. However, accuracy in predicting the disease also depends on its dataset. Our research work is to remove the outliers and also reduce the dimensionality of the dataset which will increase the accuracy, recall, and precision rate compared to existing models. In our study, we examine the impact of outliers and dimensionality reduction on the Wisconsin Diagnostic Breast Cancer Dataset, which is tested using three different machine learning classifiers. The results show that after the removal of outlier, the proposed technique is applied for dimensionality reduction using kernel-PCA then linear regression, Naive Bayes Classifier and decision tree classifier are applied and the highest accuracy are respectively (kernel cosine-logistic regression) 96.03%, (ker-nel cosine-Naive Bayes) 94.44%,(kernel sigmoid-decision tree) 93.65%. The two suggested strategies outperforms the unfiltered data in terms of accuracy. As a result, the efficiency of the breast cancer prediction is increased. We propose an approach where outliers are checked first for every features so that the feature with maximum outliers can be detected and outliers are removed. Then the dimensionality reduction is done.
Masked Face Recognition System using Extended VGG-19

The scientific community has observed several issues as a result of COVID-19, both directly and indirectly. The use of face mask for health protection is crucial in the current COVID-19 scenario. Besides, ensuring the security of all people, from individuals to the state system, financial resources, diverse establishments, government, and non-government entities, is an essential component of contemporary life. Face recognition system is one of the most widely used security technology in modern life. In the presence of face masks, the performance of the current face recognition systems is not satisfactory. In this paper, we investigate a flexible solution that could be employed to recognize masked faces effectively. To do this, we develop a unique dataset to recognize the masked face, consisting of a frontal and lateral face with a mask. We propose an extended VGG19 deep model to improve the accuracy of the masked face recognition system. Then, we compare the accuracy of the proposed framework to that of well-known deep learning techniques, such as the standard Convolutional Neural Network (CNN) and the original VGG19. The experimental results demonstrate that the proposed extended VGG19 outperforms the investigated approaches. Quantitatively, the proposed model recognizes the frontal face with the mask with high accuracy of 96%.
Blockchain

This chapter provides an exploration to the enormous potential of blockchain and its strategic benefits. The application of blockchain has the potential to revolutionize existing business models. Blockchain is a peer-to-peer model that can speed up processes resulting in robust tracking and reducing costs of transactions. It is essential for business professionals to understand the significance of the implications of this far-reaching technology on business activities. Consequently, it is imperative that business leaders comprehend aspects of the technology to be integrated in strategic initiatives. The purpose of this chapter is to inform readers about the strategic benefits of blockchain technology. The chapter explains blockchain in the context of business strategies while highlighting its applications. This chapter is structured to explain the idea of blockchain technology, expound on the importance of the technology's business applications, and illustrate how professionals can integrate this technology in business.
Applications of Augmented Reality in Medical Training

Augmented reality (AR) is amongst the largest skill fads in the world right away, and its sole purpose is developing a technology to get larger as a larger user base of AR-prepared smartphones and alternative devices in the world to make it to everyone around the world. AR is a part of Extended reality which is to grow our actual domain, including coatings of advanced information onto it. In contrast to a computer game (VR), AR doesn't produce the total simulated surroundings to exchange actually with a computer-generated one. It has been found that AR has many applications in medical training as well. Clinical understudies can enhance both their insight and abilities by exploiting stimulated and increased reality. Use instances of AR innovation incorporate: Making human body models that permit clinical understudies to learn life systems from top to bottom, giving additional preparation freedoms to clinical understudies with the assistance of reenactments, and conducting medical procedures on virtual patients. This paper presents the idea that a properly built AR application will induce in anyone who will use this technology these effects: • Causing trainees to notice the learning material and urge them to think about the substance by putting forward an unpredictable environment for learning. • Diminishing the authentic divergence between the setting where youngsters learn new data and the places of applications of what they have realized in this learning process.
L’immagine di Firenze e Bologna in Obrazy Italii di Pavel Muratov: riflessioni sulla traduzione del lessico artistico

This chapter focuses on Obrazy Italii, the best known work by Pavel Muratov, a Russian writer, journalist, critic and art historian. This work is a diary recounting the journey to Italy of the author at the beginning of the twentieth century; nowadays it is considered a classic text that provides a testimony on the way Italian art was perceived in Russia. Though it is well-known in Russia, this work has only recently received some attention in Italy following its recent translation. This chapter contains detailed and original analyses that compare Muratov’s translations with those found in Russian dictionaries written in the same period. These concern a selection of lemmas in art and architecture and the names of artists. This chapter shows that the language employed in this work cannot be defined as either specialised or standard, but as a mixture between the two.
Research on image enhancement algorithm for the monitoring system in coal mine hoist

As the mine hoist monitors video images with poor light, low brightness, heavy dust, and low contrast, the monitoring video images are not conducive to monitoring. They cannot meet the needs of applications. Based on actual video surveillance data, this paper proposes a dark channel prior (DCP) method integrated with a guided image filter video image enhancement algorithm. Firstly, we analyzed the characteristics of the mine hoist system’s video images. Then, the DCP technique was used to enhance the video images. A guided image filter algorithm was introduced to ensure that the video has more clarity and visual impact. Comparing the suggested method to the other four algorithms, it performed better both subjectively and objectively than the algorithms mentioned above. Experiments demonstrate that the proposed technique can successfully improve the entire clarity and contrast of video images while avoiding the over-enhancement of bright areas close to the light source, meeting the practical application requirements of video surveillance.
Federated deep learning for smart city edge-based applications

The growing quantities of data allow for advanced analysis. A prime example of it are smart city applications with forecasting urban traffic flow as a key application. However, data privacy becomes a real issue. This problem can be addressed by using federated learning trusted authority principle. In this paper, we investigate a novel federated deep learning approach to urban traffic flow forecasting that graph learning, and trusted authority mechanism. The road network is first pre-processed to eliminate the noise from the traffic data. Next, detecting anomalous features is performed to prune irrelevant edges and patterns. The generated graph is then utilized to learn a graph convolutional neural network for calculating the future city's traffic flow. We extensive evaluate our federated learning-based framework, where a case study on predicting the future traffic flows has been carried out using multiple datasets. We examine it with different baseline techniques as well. The findings show that the suggested framework greatly outperforms the baseline methods, particularly when the graph has a lot of nodes. Importantly, our approach is the first one that integrates trusted authority principle in federated learning and, by doing so, it is able to efficiently secure model data. Moreover, the average precision of the developed model reached 84%, while the baseline solutions did not exceed 77%.
Research on smart contract vulnerability detection method based on domain features of solidity contracts and attention mechanism

The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses.
Sensor-based Ambient Healthcare Architecture using Blockchain and Internet of Things

Increasing needs of the human race in the healthcare sector demands extensive use of technology and innovations. Healthcare is one dynamic area which grabs the technology for providing optimal and patient-centric treatments. Emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain provides broad spectrum of applications that caters the needs of healthcare. Ambience in healthcare can potentially enhances the monitoring procedures and reduces errors related to the clinical procedures. This paper proposes a novel healthcare architecture that incorporates ambient intelligence in the healthcare platform. Blockchain is used as the backbone for the system to provide secured data storage and exchange between different stakeholders in the network. A hardware prototype based on Raspberry Pi Model 4B is developed to which sensors to capture body vital parameters are interfaced. A high-end computing system is used to host the machine learning algorithms which can detect abnormalities in the critical data received from the patient body and the same is reported to the concerned clinicians. This system also performs blockchain mining. The developed system found to be superior in terms of reliability and delay when compared to the existing models. Finally, to better understand the performance of various popular algorithms, a comparative analysis is performed in terms of mean average error (MAE), mean square error (MSE) and root mean square error (RMS). The system is viable and can provide seamless integrated of any existing models.